Patient monitoring method and system

Abstract

A method for processing wireless sensor data at a server is provided, the method comprising: receiving sensor data from a plurality of wireless sensors; determining a device strategy associated with the received sensor data; retrieving and activating at least one handler for the received sensor data, the at least one handler comprising a driver configured to process the sensor data into parameterized data; and converting the received data into parameterized data.A method for generating normalized component risk scores based on measurements of one or more physiological parameters of a patient is provided, the method comprising: receiving sensor data; measuring the physiological parameters from the sensor data; determining normalized component risk scores based on the physiological parameters; and outputting the normalized component risk scores.

Description

[Technical Field]

[0001] The present disclosure relates generally to the collection and remote processing of data from one or more wireless sensors. More particularly, the present invention relates to the real-time collection, monitoring, and display of patient data. [Background technology]

[0002] In today's healthcare environment, patients can be in a variety of health states, either from their own illnesses or from controlled surgical interventions. In many situations, patients are monitored to either assess their health status or to identify early indicators of deteriorating health status and the need for changes in treatment. Most patients with unstable health conditions exhibit some form of respiratory failure or altered respiratory function.

[0003] Outside of specialized care settings, such as ICUs and cardiothoracic care, current techniques for monitoring patient health, and in particular, monitoring for respiratory failure or changes in respiratory function, rely on manual observation and estimation (e.g., for respiratory rate, manually counting breaths) or on the use of sensors attached to the patient that monitor one or more physiological parameters of the patient. Sensors such as pulse oximeters or heart rate sensors are commonly used. Manual monitoring is only occasionally preferred, while verification of sensor data is only intermittent.

[0004] In a healthcare environment, a patient's medical records are typically kept as paper charts at the patient's bedside. Typically, to keep track of a patient's monitoring, the healthcare provider makes notes at regular intervals on the patient's chart detailing instantaneous sensor readings and comparing these to previous readings over time. This places significant constraints on the nurse's or other healthcare provider's time.

[0005] The increasing use of electronic medical records ensures that information about the care received by individuals between healthcare providers is accurate and up-to-date. To provide patients with optimal, coordinated, and most cost-effective healthcare, healthcare providers need rapid access to their patients' up-to-date medical histories. definition

[0006] Patient - an individual being treated for an illness or disease or whose health or health status is otherwise being monitored; the patient may typically be in a hospital setting, or alternatively, may be in the home or elderly care setting. Summary of the Invention

[0007] In a first aspect, the present disclosure may be broadly described as a method of generating a component risk score based on measurements of one or more physiological parameters of a patient, the method comprising receiving sensor data, measuring the patient's physiological parameters from the sensor data, determining a component risk score based on the physiological parameters, and outputting the component risk score.

[0008] Optionally, the sensor data is received across one or more advertising channels of the short-range wireless communication component.

[0009] Optionally, determining the component risk score includes associating at least one of the at least one physiological parameter with a corresponding risk band of a plurality of risk bands, each risk band of the plurality of risk bands having a respective first upper threshold and a first lower threshold, and the associated at least one physiological parameter falling between the first upper threshold and the first lower threshold of the corresponding risk band.

[0010] Optionally, the component risk scores correspond to clinical risk indices. Optionally, the clinical risk indices correspond to early warning scores.

[0011] Optionally, the physiological parameters include any one or more of respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, carbon dioxide level.

[0012] Optionally, the supplemental oxygen is measured by a sensor on and / or integrated into the breathing apparatus.

[0013] Optionally, the risk band corresponds to a predetermined early warning score value.

[0014] Optionally, the plurality of risk bands comprises a first risk band and a second risk band.

[0015] Optionally, the size of the first risk band is different from the size of the second risk band.

[0016] Optionally, the size of the risk band is defined by the difference between its first upper threshold and its first lower threshold.

[0017] Optionally, the physiological parameter is one of temperature, body temperature, blood pressure, respiratory rate, heart rate, carbon dioxide level, and SpO2 (blood oxygen saturation).

[0018] Optionally, the physiological parameters and component risk scores are scored in a time series.

[0019] Optionally, the physiological parameters and component risk scores are updated in real time.

[0020] Optionally, the component risk scores are recalculated at regular time intervals.

[0021] Optionally, the component risk scores are recalculated only when new sensor data is received and updated physiological parameters are measured.

[0022] Optionally, the method further includes measuring a second physiological parameter from the sensor data, measuring a second component risk score based on the second physiological parameter, outputting the second component risk score, and measuring an overall component risk score based on the first and second component risk scores.

[0023] Optionally, determining the overall component risk score includes selecting the higher of the first and second component risk scores as the overall component risk score.

[0024] Optionally, determining the overall component risk score comprises averaging the first and second component risk scores.

[0025] Optionally, determining the overall component risk score comprises calculating a weighted average of the first and second component risk scores, the weighting being based on a predetermined set of rules.

[0026] Optionally, determining the total component risk score comprises summing the first and second component risk scores and comparing the summed value to a predetermined threshold.

[0027] Optionally, the method further comprises displaying the component risk scores in a first view of the display screen, the component risk scores being displayed together with a patient identifier, the patient identifier corresponding to a patient associated with the sensor data and the physiological parameters derived from the sensor data.

[0028] Optionally, the patient identifier comprises any one or more of a patient name, a patient ID, a location identifier, and a bed identifier.

[0029] Optionally, the component risk scores are displayed in the form of a gauge, the gauge being divided according to a second upper threshold and a second lower threshold of a plurality of risk bands.

[0030] Optionally, the gauge displays a rolling average of the component risk scores.

[0031] Optionally, displaying the component risk scores further comprises displaying a graphical timeline showing current and historical values ​​of the component risk scores.

[0032] Optionally, the method further comprises displaying the physiological parameter in a first view on a display screen.

[0033] Optionally, displaying the physiological parameter further comprises displaying a graphical timeline showing current and historical values ​​of the physiological parameter.

[0034] Optionally, the graphic timeline is updated in real time as updated values ​​are measured.

[0035] Optionally, the method further comprises displaying the second physiological parameter, displaying the second component risk score, and displaying the total component risk score.

[0036] Optionally, the display of the first and second physiological parameters is arranged based on their associated first and second component risk scores.

[0037] Optionally, the display of the first and second physiological parameters is arranged according to the relative contribution of the first and second physiological parameters to the overall component risk score.

[0038] Optionally, the method further comprises receiving, by the respiratory assistance device, parameters of a therapy being provided to the patient and / or usage data indicative of the patient's use of the respiratory assistance device, and displaying the therapy parameters and / or usage data on a display screen.

[0039] Optionally, therapy parameters and / or usage data are displayed in chronological order and updated in real time.

[0040] Optionally, the method further comprises displaying one or more patient identifiers in a second view of the display screen, each associated with a patient and a component risk score, the one or more patient identifiers being arranged on the display screen based on the relative values ​​of their associated component risk scores.

[0041] Optionally, the one or more patient identifiers are grouped into regions of the display, each region being associated with one risk band of the plurality of risk bands.

[0042] Optionally, the size and / or relative position of the one or more patient identifiers within a region of the display is based on one or more of a rate of change and a direction of change of the corresponding component risk scores.

[0043] Optionally, the location of one or more patient identifiers is updated in real time when the measurements of the associated component risk scores are updated.

[0044] Optionally, the one or more patient identifiers are present in the form of tiles.

[0045] Optionally, the one or more patient identifiers each include a risk indicator.

[0046] Optionally, the risk indicator comprises a numerical value indicating the patient's risk level based on their component risk scores and associated risk bands.

[0047] Optionally, the risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

[0048] Optionally, the number is a rolling average.

[0049] Optionally, the risk indicator further comprises a risk trend indicator configured to change its state depending on whether the patient's risk level increases, decreases, or remains the same.

[0050] Optionally, the risk trend indicators are color coded.

[0051] Optionally, the one or more patient identifiers further comprise an indication of one or more physiological parameters associated with the patient, the selection and placement of the one or more physiological parameters being based on their contribution to the component risk scores.

[0052] Optionally, the display of one or more physiological parameters is updated in real time.

[0053] Optionally, the display of the one or more physiological parameters includes a rolling average of the one or more physiological parameters.

[0054] Optionally, the one or more patient identifiers each include one or more of the following elements: a patient identification number, a patient room number, a patient bed number, a patient bed location, a patient name, and an indicator of the time that has expired since the clinician last interacted with the patient.

[0055] Optionally, the presence, size, and / or display parameters of one or more elements are based on one or more of the magnitude, rate of change, and direction of change of the component risk scores.

[0056] Optionally, the presence, size, and / or display parameters of the one or more elements are based on one or more of: an amount of time since a patient identifier moves from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score; and an estimated amount of time until a patient identifier moves from one area of ​​the display associated with the first risk band to another area of ​​the display associated with the second risk band as a result of a change in the respective component risk score.

[0057] Optionally, the second risk band is a higher risk band than the first risk band.

[0058] Optionally, the estimated amount of time is provided by analyzing trends in historical values ​​of the component risk scores.

[0059] Optionally, one or more patient identifiers are grouped into regions of the display, each region being associated with a discharge risk as measured at least in part by a component risk score.

[0060] Optionally, the size and / or relative position of the one or more patient identifiers within a region of the display is based on one or more of a rate of change and a direction of change of the corresponding component risk scores.

[0061] Optionally, the location of one or more patient identifiers is updated in real time when the measurements of the associated component risk scores are updated.

[0062] Optionally, the one or more patient identifiers are present in the form of tiles.

[0063] Optionally, the one or more patient identifiers each include a discharge risk indicator.

[0064] Optionally, the discharge risk indicator comprises a numerical value indicating the patient's discharge risk level based on their component risk scores.

[0065] Optionally, the discharge risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

[0066] Optionally, the number is a rolling average.

[0067] Optionally, the discharge risk indicator further comprises a discharge risk trend indicator configured to change its state depending on whether the patient's discharge risk level increases, decreases, or remains the same.

[0068] Optionally, the discharge risk trend indicators are color coded.

[0069] Optionally, the one or more patient identifiers further comprise an indication of one or more physiological parameters associated with the patient, the selection and placement of the one or more physiological parameters being based on their contribution to the component risk scores.

[0070] Optionally, the display of one or more physiological parameters is updated in real time.

[0071] Optionally, the display of the one or more physiological parameters includes a rolling average of the one or more physiological parameters.

[0072] Optionally, the one or more patient identifiers each include one or more of the following elements: a patient identification number, a patient room number, a patient bed number, a patient name, and an indicator of the time that has expired since the clinician last interacted with the patient.

[0073] Optionally, the presence, size, and / or display parameters of one or more elements are based on one or more of the magnitude of the component risk score, the discharge risk score, and / or the rate and direction of change of the component risk score.

[0074] Optionally, the presence, size, and / or display parameters of the one or more elements are based on one or more of: an amount of time since a patient identifier moves from one area of ​​the display associated with a first discharge risk band to another area of ​​the display associated with a second discharge risk band as a result of a change in the corresponding component discharge risk score; and an estimated amount of time until a patient identifier moves from one area of ​​the display associated with the first discharge risk band to another area of ​​the display associated with the second discharge risk band as a result of a change in the corresponding component discharge risk score.

[0075] Optionally, the second risk band is a lower risk band than the first risk band.

[0076] Optionally, the estimated amount of time is provided by analyzing trends in historical values ​​of the component risk scores.

[0077] Optionally, the discharge risk score depends on one or more of patient demographics, treatment characteristics, and / or socioeconomic factors. Optionally, the patient demographics include any one or more of age, weight, ethnicity, and sex. Optionally, the treatment characteristics include any one or more of disease, diagnosis, treatment applied, treatment device used, and time during treatment. Optionally, the socioeconomic factors include any one or more of the patient's support network and home environment.

[0078] Optionally, discharge risk is measured by any one or more of a decision tree, a rules engine, or a flowchart.

[0079] Optionally, the display is configurable to switch between displaying discharge risk and displaying component risk scores.

[0080] Optionally, the method comprises sending the alert to a display.

[0081] Optionally, the method includes displaying summarized physiological parameters for multiple time periods.

[0082] Optionally, the summary includes any one or more of a selected value, an average time, a maximum value, a minimum value, an arithmetic mean value, an upper quartile value, a lower quartile value, and a median value.

[0083] Optionally, the plurality of time periods comprises an integer number of hours, days, weeks, and / or months.

[0084] Optionally, each physiological parameter is displayed on an independent axis.

[0085] Optionally, the summarized physiological parameters are displayed in the form of a chart.

[0086] Optionally, the chart comprises any one or more of a radar chart, an area chart, a lollipop chart, a radial column chart, a star chart.

[0087] Optionally, the chart includes at least two periods.

[0088] Optionally, the chart displays a risk category corresponding to the physiological parameter.

[0089] Optionally, the chart displays data interpolated between adjacent time periods.

[0090] Optionally, the chart summarizes data from archived time series data.

[0091] In a further aspect, the present disclosure may be generally said to consist in a patient monitoring system configured to implement the above-described methods.

[0092] Optionally, the patient monitoring system includes one or more sensors and a processing server.

[0093] Optionally, the component risk scores are normalized component risk scores and / or include normalizing the component risk scores.

[0094] Optionally, determining the normalized component risk score includes looking at a scaling or min-max normalization of the physiological parameter within a first threshold of the risk band to provide a measure of the location of the physiological parameter within the first threshold of the risk band.

[0095] Optionally, determining the normalized component risk scores includes standardizing the size of each risk band to a preferred size W within the plurality of risk bands, each risk band having a second upper threshold and a second lower threshold separated by W.

[0096] Optionally, the normalized component risk score is measured by the following formula: JPEG2025539068000002.jpg17170In one aspect, the disclosure may be broadly described as a method for monitoring patients with a plurality of health sensors to identify patients experiencing deteriorating health, the method including receiving sensor data based on measurements of individual patients from a plurality of sensors across one or more advertising channels of a short-range wireless communication component, measuring physiological parameters of the individual patients from the sensor data, measuring a health status based on the physiological parameters, and outputting the health status.

[0097] Optionally, the method of the above aspect may optionally include any of the steps or features of the preceding or following aspect in which the health state is a component risk score.

[0098] In one aspect, the present disclosure may be broadly described as a method of operating a sensor system to monitor the health of a patient, the method including receiving sensor data from a plurality of sensors through one or more advertising channels of a short-range wireless communication component; measuring one or more physiological parameters of the patient based on the sensor data from the plurality of sensors; measuring a health status based on the physiological parameters; and outputting the health status.

[0099] Optionally, each of the plurality of sensors is associated with or attachable to an item of furniture to which it is attached. Optionally, the furniture includes one or more of a bed and a chair. Optionally, the processing system is operable to identify a patient ID by measuring a patient ID associated with the furniture. Optionally, the plurality of sensors includes an under-mattress sensor. Optionally, the sensor includes an underaction sensor. Optionally, the sensor is a non-contact sensor for respiratory rate and / or heart rate. Optionally, the hub receives transmission data from the plurality of advertising channels.

[0100] Optionally, the method of the above aspect may optionally include any of the steps or features of the preceding or following aspect in which the health state is a component risk score.

[0101] In one aspect, the present disclosure may be broadly described as a method of operating a sensor system to monitor the health of a patient, the method including receiving sensor data indicative of one or more physiological parameters of the patient from a plurality of sensors through one or more advertising channels of a short-range wireless communication component; detecting that the patient's health parameters are above or below a predetermined threshold based on the sensor data; and outputting an indication of the health parameters to a healthcare provider for causing the healthcare provider to treat the patient.

[0102] Optionally, detecting that the patient's health parameter is above or below a predetermined threshold includes comparing two or more different physiological parameters, each physiological parameter being a physiological parameter obtained from sensor data from a plurality of sensors.

[0103] Optionally, detecting that the patient's health parameter is above or below a predetermined threshold includes measuring one or more physiological parameters from the sensor data, measuring the patient's health parameter based on the one or more physiological parameters, and comparing the health parameter to a predetermined threshold.

[0104] Optionally, the method of the above aspect may optionally include any of the steps or features of the preceding or following aspect in which the health state is a component risk score.

[0105] In a further aspect, the disclosure may generally reside in a method of operating a hub for a system for monitoring the health of a patient, the method including: receiving a first data packet from a first sensor about the hub through a first advertising channel of a short-range communications component, the first data packet including sensor data from the first sensor related to a first physiological measurement of the patient; transmitting the first data packet to a server about the hub through a network communications component; receiving a second data packet from a second sensor about the hub through a second advertising channel of the short-range communications component, the second data packet including sensor data from the second sensor related to a second physiological measurement of the patient; and transmitting the second data packet to the server through the network communications component.

[0106] Optionally, the first advertising channel is the same as the second advertising channel.

[0107] Optionally, the first advertising channel is different from the second advertising channel.

[0108] Optionally, further comprising at least partially processing the first data packet before transmitting the first data packet to the server.

[0109] Optionally, further comprising formatting the first data packet before sending the first data packet to the server.

[0110] Optionally, further comprising stamping a timestamp onto the first data packet.

[0111] Optionally, further comprising forwarding the raw data and / or the unprocessed first sensor data in the first data packet to a server.

[0112] Optionally, the first physiological parameter is the same as the second physiological parameter, and the method further comprises combining the first data packet and the second data packet before transmitting the first data packet and the second data packet to the server.

[0113] Optionally, the network communication component is a wireless internet component.

[0114] Optionally, the first data packet is received in a continuous stream of data packets from the first sensor over the first advertising channel.

[0115] Optionally, the first data packet and the second data packet are transmitted to the server simultaneously.

[0116] Optionally, further comprising receiving a third data packet from a third sensor around the hub through a third advertising channel of the short-range communication component, the third data packet including sensor data from the third sensor related to a third physiological measurement of the patient.

[0117] Optionally, the first data packet includes a patient identifier and the second data packet includes a patient identifier.

[0118] Optionally, the method of the above aspect may optionally include any of the steps or features of the preceding or following aspect in which the health state is a component risk score.

[0119] In a further aspect, the disclosure may be broadly described as a hub for a system for monitoring the health of a patient, the hub comprising: a network communications component; a processor; and memory storing instructions that, when executed by the processor, cause the processor to: receive a first data packet from a first sensor about the hub through a first advertising channel of the short-range communications component, the first data packet including sensor data from the first sensor related to a first physiological measurement of the patient; transmit the first data packet through the network communications component to a server about the hub; receive a second data packet from a second sensor about the hub through a second advertising channel of the short-range communications component, the second data packet including sensor data from the second sensor related to a second physiological measurement of the patient; and transmit the second data packet to the server through the network communications component.

[0120] In a further aspect, the present disclosure may be broadly described as a system for monitoring the health of a patient, the system including: a central hub comprising a short-range communication component having a plurality of advertising communication channels; a first sensor about the central hub, the first sensor configured to obtain measurements of a first physiological parameter of the patient and to transmit first data packets to the central hub through a first advertising channel of the plurality of advertising communication channels, each of the first data packets including data related to the measurement of the first physiological parameter; and a second sensor about the central hub and the first sensor, the second sensor configured to obtain measurements of a second physiological parameter of the patient and to transmit second data packets to the central hub through a second advertising channel of the plurality of advertising communication channels, each of the second data packets including data related to the measurement of the second physiological parameter.

[0121] Optionally, the above two aspects may optionally include any of the following system steps or features, where the health state is a component risk score.

[0122] In a further aspect, the present disclosure may be broadly described as a patient management system including a data management server in communication with a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients, the data management server configured to receive sensor data from the plurality of sensors over a network, determine a component risk score for each of the plurality of patients from the sensor data, assign the plurality of patients to a plurality of groups based at least in part on the component risk scores and a plurality of rules, and cause a graphical user interface on a display to display one or more patient identifiers in a first display mode, each patient identifier being associated with one of the plurality of patients and their component risk scores, wherein the one or more patient identifiers are arranged on the display screen based on the values ​​of their associated component risk scores.

[0123] Optionally, the sensor data is received across one or more advertising channels of the short-range wireless communication component.

[0124] Optionally, the component risk scores are calculated according to the method steps described above and / or below.

[0125] Optionally, the component risk scores correspond to clinical risk indices.

[0126] Optionally, the physiological parameters include any one or more of respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, carbon dioxide level.

[0127] Optionally, one or more patient identifiers are grouped into regions of the display, each region associated with upper and lower thresholds for a component risk score, said thresholds defining a plurality of risk bands.

[0128] Optionally, the size and / or relative position of the one or more patient identifiers within a region of the display is based on one or more of a rate of change and a direction of change of the corresponding component risk scores.

[0129] Optionally, the location of one or more patient identifiers is updated in real time when the measurements of the associated component risk scores are updated.

[0130] Optionally, the one or more patient identifiers are present in the form of tiles.

[0131] Optionally, the one or more patient identifiers each include a risk indicator.

[0132] Optionally, the risk indicator comprises a numerical value indicating the patient's risk level based on their component risk scores and associated risk bands.

[0133] Optionally, the risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

[0134] Optionally, the number is a rolling average.

[0135] Optionally, the risk indicator further comprises a risk trend indicator configured to change its state depending on whether the patient's risk level increases, decreases, or remains the same.

[0136] Optionally, the risk trend indicators are color coded.

[0137] Optionally, one or more patient identifiers are grouped into regions of the display, each region associated with upper and lower thresholds for discharge risk, the discharge risk depending, at least in part, on the component risk scores.

[0138] Optionally, the size and / or relative position of the one or more patient identifiers within a region of the display is based on one or more of a rate of change and a direction of change of the corresponding component risk scores.

[0139] Optionally, the location of one or more patient identifiers is updated in real time when the measurements of the associated component risk scores are updated.

[0140] Optionally, the one or more patient identifiers are present in the form of tiles.

[0141] Optionally, the one or more patient identifiers each include a discharge risk indicator.

[0142] Optionally, the discharge risk indicator comprises a numerical value indicating the patient's discharge risk level based on their component risk scores.

[0143] Optionally, the discharge risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

[0144] Optionally, the number is a rolling average.

[0145] Optionally, the discharge risk indicator further comprises a risk trend indicator configured to change its state depending on whether the patient's risk level increases, decreases, or remains the same.

[0146] Optionally, the discharge risk trend indicators are color coded.

[0147] Optionally, the one or more patient identifiers further comprise an indication of one or more physiological parameters associated with the patient, the selection and placement of the one or more physiological parameters being based on their contribution to the component risk scores.

[0148] Optionally, the display of one or more physiological parameters is updated in real time.

[0149] Optionally, the display of the one or more physiological parameters includes a rolling average of the one or more physiological parameters.

[0150] Optionally, the one or more patient identifiers each include one or more of the following elements: a patient identification number, a patient room number, a patient bed number, a patient name, and an indicator of the time that has expired since the clinician last interacted with the patient.

[0151] Optionally, the presence, size, and display parameters of one or more elements are based on one or more of the magnitude, rate of change, and direction of change of the component risk scores.

[0152] Optionally, the presence, size, and / or display parameters of the one or more elements are based on one or more of: an amount of time since a patient identifier moves from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score; and an estimated amount of time until a patient identifier moves from one area of ​​the display associated with the first risk band to another area of ​​the display associated with the second risk band as a result of a change in the respective component risk score.

[0153] Optionally, the second risk band is a higher risk band than the first risk band.

[0154] Optionally, the estimated amount of time is provided by analyzing trends in historical values ​​of the component risk scores.

[0155] Optionally, the sensor data is received from a respiratory assistance device that includes or is in communication with multiple sensors.

[0156] Optionally, the component risk scores are normalized component risk scores.

[0157] In a further aspect, the present disclosure may be broadly described as a method of patient management including receiving, over a network, sensor data from a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients; determining a component risk score for each of the plurality of patients; assigning the plurality of patients to a plurality of groups based, at least in part, on the component risk scores and the plurality of rules; and causing a graphical user interface on a display to display, in a first display mode, one or more patient identifiers, each associated with one of the plurality of patients and their component risk scores, wherein the one or more patient identifiers are arranged on the display screen based on the relative values ​​of their associated component risk scores.

[0158] Optionally, the sensor data is received across one or more advertising channels of the short-range wireless communication component.

[0159] Optionally, the component risk scores may be calculated by any one or more of the method steps described above or below, and / or the system may be used as described above or below.

[0160] Optionally, assigning the plurality of patients to the plurality of groups includes reassigning patients from a first group having an associated risk band to a second group having a second associated risk band.

[0161] Optionally, the method further comprises generating an alert condition if the second associated risk band is associated with a higher clinical risk than the first risk band.

[0162] Optionally, the alert condition triggers a notification to a hospital monitoring system.

[0163] Optionally, the alert condition triggers a notification to a secondary display.

[0164] Optionally, the secondary display is a portable computing device. Optionally, the portable computing device is associated with a clinician responsible for the reassigned patient.

[0165] Optionally, the component risk scores correspond to clinical risk indices.

[0166] Optionally, the physiological parameters include any one or more of respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, carbon dioxide level.

[0167] Optionally, the component risk scores are normalized component risk scores.

[0168] In a further aspect, the present disclosure may be broadly described as a method of patient management including receiving, over a network, sensor data from a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients; determining a component risk score for each of the plurality of patients; selecting a patient from the plurality of patients based on an assigned user of the system; assigning the selected patient to a plurality of groups based, at least in part, on the component risk scores and the plurality of rules; and causing a graphical user interface on a display in a first display mode to display one or more patient identifiers in the graphical user interface, each associated with a selected patient.

[0169] Optionally, the component risk scores may be calculated by any one or more of the method steps described above or below, and / or the system may be used as described above or below.

[0170] Optionally, one or more patient identifiers are arranged on the display screen based on the relative values ​​of their associated component risk scores.

[0171] Optionally, assigning the plurality of patients to the plurality of groups includes reassigning patients from a first group having an associated risk band to a second group having a second associated risk band.

[0172] Optionally, the method further comprises generating an alert condition if the second associated risk band is associated with a higher clinical risk than the first risk band.

[0173] Optionally, the alert condition triggers a notification to the assigned user.

[0174] Optionally, the alert condition triggers a notification to the assigned user.

[0175] Optionally, selecting a patient from a plurality of patients is further based on an assigned user and / or display location.

[0176] Optionally, the assigned user is a clinician. Optionally, the patient selection is based on the patients for which the assigned user is responsible.

[0177] Optionally, the method includes assigning the selected patient to a plurality of groups based at least in part on the component risk scores and the plurality of rules, and causing the second display to display the graphical user interface in a second display mode one or more patient identifiers, each patient identifier being associated with a plurality of patients.

[0178] Optionally, the component risk scores are normalized component risk scores.

[0179] In a further aspect, the present disclosure may be broadly described as a method for processing wireless sensor data at a server, the method including the steps of receiving sensor data from a plurality of wireless sensors; determining a device type associated with the received sensor data; retrieving and activating at least one handler for the received sensor data, the at least one handler comprising a driver configured to process the sensor data into parameterized data; and converting the received data into parameterized data.

[0180] Optionally, the device type is determined by characteristics of the received sensor data and / or a device ID coded in the received sensor data.

[0181] Optionally, at least one handler corresponds to a measured device type.

[0182] Optionally, the sensor data comprises a continuous stream of data packets.

[0183] Optionally, the method further comprises storing the parameterized data.

[0184] Optionally, the parameterized data is stored in time series.

[0185] Optionally, the method further comprises measuring the event based on the stored parameterized data.

[0186] Optionally, the parameterized data is sensor independent and the measured data from many of the multiple wireless sensors is compatible.

[0187] Optionally, the sensor data from the plurality of wireless sensors is received from a wireless router, which forwards the received data to a server.

[0188] Optionally, the wireless router forwards the received data to a server in real time.

[0189] Optionally, the sensor data is an advertising packet of a short-range wireless sensor, optionally a Bluetooth™ wireless sensor.

[0190] Optionally, the step of activating the handler comprises determining whether a handler exists for the received sensor data, and if not, instantiating the handler for the received data.

[0191] Optionally, the step of measuring the handler comprises identifying one or more characteristics of the structure of the sensor data.

[0192] Optionally, measuring the handler includes identifying one or more characteristics of a portion of the sensor data.

[0193] Optionally, the portion of the sensor data includes an advertising portion.

[0194] Optionally, identifying the one or more characteristics includes comparing the structure of the sensor data to a plurality of known structures.

[0195] Optionally, the method further comprises deactivating the handler if no additional sensor data is received within a period of time.

[0196] Optionally, the method further comprises measuring a patient associated with the device.

[0197] Optionally, the method further comprises measuring an item of equipment associated with the device.

[0198] Optionally, the furnishings include one or more of a bed and a chair.

[0199] Optionally, the method further includes measuring a patient associated with the equipment. Optionally, the method further includes measuring a location associated with the equipment. Optionally, measuring the location includes reading a detector associated with a known location. Optionally, the detector comprises an RFID device. Optionally, the location includes an area within a hospital community room. Optionally, the wireless router comprises a sensor for sensing the detector. Optionally, the sensor is an RFID reader. Optionally, the detector has a limited range. Optionally, the sensor comprises an under-mattress sensor. Optionally, the sensor includes an underaction sensor. Optionally, the sensor is a non-contact sensor for respiratory rate and / or heart rate.

[0200] Optionally, the method further comprises determining a clinician associated with at least one of the device, the location, and / or the equipment. Optionally, the method further comprises updating the clinician associated with the device, the location.

[0201] Optionally, the method further comprises associating a wireless router with the fixture.

[0202] Optionally, the method further comprises the step of associating the parameterized data with the measured patient.

[0203] Optionally, the received sensor data is associated with a timestamp.

[0204] Optionally, the received sensor data is associated with a timestamp at the router or server.

[0205] Optionally, each data packet of received sensor data is associated with a timestamp.

[0206] Optionally, the method further comprises associating a timestamp with the received sensor data.

[0207] Optionally, each of the handlers operates within a container, and multiple containers are created to handle sensor data from multiple wireless sensors.

[0208] Optionally, the method further comprises a controller, the controller configured to monitor the handler and the received sensor data and adjust available processing resources.

[0209] Optionally, the received sensor data is stored in a cache (bus) prior to the step of determining the device type, storage allowing allocation of processing capacity.

[0210] Optionally, the database is a time series database.

[0211] Optionally, the method further comprises obtaining a trusted assessment for each of the plurality of wireless sensors.

[0212] Optionally, obtaining a trusted assessment includes any one or more of: measuring a trusted assessment from the sensor data; obtaining a trusted assessment from a handler; and obtaining a trusted assessment from a server. Optionally, the method further includes selecting between readings from the plurality of wireless sensors based on the trusted assessment.

[0213] Optionally, the method further comprises comparing sensor data from a plurality of sensors. Optionally, the method further comprises calibrating sensors with relatively low confidence ratings based on the sensors with relatively high confidence ratings. Optionally, the method further comprises determining accuracy limits of the sensors based on the confidence ratings.

[0214] Optionally, the method includes calculating a component risk score based on the parameterized data. Optionally, the component risk score may be calculated by any one or more of the method steps described above or below and / or the system may be used as described above or below.

[0215] In a further aspect, the present disclosure may be broadly described as a method of processing wireless sensor data at a server, the method including receiving at least one sensor datum from a first sensor of a plurality of wireless sensors; identifying a first patient associated with the at least one sensor datum; deriving a first parameter from the at least one sensor datum; and adding the first parameter from the at least one sensor datum to a medical record of the first patient in a time series record.

[0216] Optionally, the method further includes receiving at least one sensor data from a second sensor of the plurality of wireless sensors; identifying a second patient associated with the at least one sensor data; deriving a second parameter from the at least one sensor data; and adding the first parameter from the at least two sensor data to the second patient's medical record in a time series record.

[0217] Optionally, the first patient and the second patient are the same patient.

[0218] Optionally, the plurality of wireless sensors includes one or more of a respiration rate sensor, a heart rate sensor, an SpO2 sensor, and a temperature sensor.

[0219] Optionally, the server receives the at least one sensor data in real time.

[0220] Optionally, identifying the first patient comprises identifying an item of furniture associated with the at least one sensor data, and identifying a patient associated with the furniture. Optionally, the furniture is a bed or a chair.

[0221] In a further aspect, the present disclosure may be broadly described as a method of collecting data from a plurality of wireless sensors, the method including the steps of associating each of the plurality of wireless sensors with a patient; receiving, at a server, sensor data from one of the plurality of wireless sensors; identifying a patient associated with the sensor data; processing the sensor data to identify at least one parameterized data; and associating the at least one parameterized data with the patient.

[0222] Optionally, the patient is associated with the sensor by scanning technology.

[0223] Optionally, the method further comprises storing the parameterized data in the patient's electronic medical record, the electronic measurement record comprising a time series database.

[0224] Optionally, the sensor data is received in real time.

[0225] Optionally, the plurality of wireless sensors includes one or more of a respiration rate sensor, a heart rate sensor, an SpO2 sensor, and a temperature sensor.

[0226] Optionally, identifying the patient comprises identifying an item of equipment associated with the at least one sensor data, and identifying the patient associated with the equipment. Optionally, the equipment is a bed or a chair.

[0227] Optionally, the component method may use a system as described above or below and / or include any of the method steps described above and / or below.

[0228] In a further aspect, the present disclosure may be broadly described as a method of processing wireless sensor data at a server, the method including receiving sensor data from one of a plurality of wireless sensors, measuring device characteristics of the received data, processing the received data into parameterized data based on the device characteristics, and storing the parameterized data.

[0229] Optionally, the method further comprises determining a patient ID associated with the received data and associating the parameterized data with the patient ID. Optionally, determining the patient ID comprises determining a bed ID or chair ID associated with the wireless sensor data and determining a patient ID associated with the bed ID or chair ID.

[0230] Optionally, one of the plurality of wireless sensors includes one or more of a temperature sensor, a respiration rate sensor, a heart rate sensor, an SpO2 sensor, a carbon dioxide sensor, and a blood pressure sensor.

[0231] Optionally, the device characteristic is the type of sensor or the manufacturer of the sensor.

[0232] Optionally, the method further comprises determining a driver for interpreting the sensor data.

[0233] Optionally, the driver is determined based on device characteristics.

[0234] Optionally, the sensor data is by short-range wireless communication and the server receives the sensor data by long-range wireless communication.

[0235] Optionally, the short-range wireless communication is Bluetooth™ and / or the long-range wireless communication is TCP / IP.

[0236] Optionally, the method further comprises temporarily storing the received sensor data before measuring the device characteristic.

[0237] Optionally, the method further comprises temporarily storing the processed data before storing the parameterized data.

[0238] Optionally, the temporary store comprises a bus or a cache.

[0239] Optionally, the sensor data is received in real time.

[0240] Optionally, the plurality of wireless sensors comprises one or more of a respiration rate sensor, a temperature sensor, a heart rate sensor, an SpO2 sensor, a carbon dioxide sensor, and a blood pressure sensor.

[0241] Optionally, the method includes calculating a component risk score based on the parameterized data. Optionally, the component risk score may be calculated by any one or more of the method steps described above or below and / or the system may be used as described above or below.

[0242] In a further aspect, the present disclosure may be broadly described as a processing system for processing measurements from a plurality of sensors into an electronic patient record having an associated patient ID, the processing system including: a processing server in data communication with a hub operable to receive transmissions from the plurality of sensors; and one or more databases for storing the plurality of electronic patient records on the processing server, the processing system operable to receive sensor data from one of the plurality of sensors through the hub, identify the sensor that transmitted the sensor data, identify a patient ID associated with the sensor, measure at least one parameterized data from the sensor data, and store the parameterized data in at least one of the plurality of electronic patient records.

[0243] Optionally, the system further comprises a medical device for providing therapy to a patient associated with the patient ID, and the processing system is further configured to receive, through the hub, one or more therapy parameters from the medical device, identify a patient ID associated with the medical device and / or the therapy parameters, and store the received therapy parameters in at least one of the plurality of electronic patient records.

[0244] Optionally, the therapy parameters include one or more of flow rate, humidity, and O2 fraction of a gas stream for delivery to the patient.

[0245] Optionally, the plurality of sensors includes wearable sensors attachable to the patient.

[0246] Optionally, the server receives the sensor data from the hub in real time.

[0247] Optionally, the plurality of sensors comprises one or more of a respiration rate sensor, a temperature sensor, a heart rate sensor, an SpO2 sensor, a carbon dioxide sensor, and a blood pressure sensor.

[0248] Optionally, each of the plurality of sensors is associated with or attachable to an item of furniture to which it is attached. Optionally, the furniture includes one or more of a bed and a chair. Optionally, the processing system is operable to identify a patient ID by measuring a patient ID associated with the furniture. Optionally, the hub comprises a sensor for detecting the detector. Optionally, the sensor is an RFID reader. Optionally, the sensor has a limited range.

[0249] Optionally, the sensor comprises an under-mattress sensor. Optionally, the sensor includes an underaction sensor. Optionally, the sensor is a non-contact sensor for respiratory rate and / or heart rate. Optionally, the hub receives transmission data from multiple advertising channels.

[0250] Optionally, the method includes calculating a component risk score based on the parameterized data. Optionally, the component risk score may be calculated by any one or more of the method steps described above or below and / or the system may be used as described above or below.

[0251] In a further aspect, the present disclosure may be broadly described as a system for processing patient measurements comprising: a plurality of wireless sensors; one or more databases for storing a plurality of electronic patient records for the plurality of wireless sensors, each patient record being associated with a patient ID; a hub configured to receive wireless transmissions from the plurality of wireless sensors; and a processing server operable to receive, through the hub, the wireless transmission from one of the plurality of wireless sensors, identify the sensor that transmitted the wireless transmission, identify a patient ID associated with the sensor, measure at least one parameterized data from the wireless transmission, and store the parameterized data in at least one of the plurality of electronic patient records.

[0252] Optionally, the system further comprises a medical device for providing therapy to a patient associated with the patient ID, and the hub is further configured to receive one or more therapy parameters from the medical device, and the processing server is further operable to identify the patient ID associated with the medical device and / or the therapy parameters and store the received therapy parameters in at least one of the plurality of electronic patient records.

[0253] Optionally, the therapy parameters include one or more of flow rate, humidity, and O2 fraction of a gas stream for delivery to the patient.

[0254] Optionally, the processing server is further operable to retrieve and activate at least one handler, the at least one handler comprising a driver configured to process the wireless transmission into at least one parameterized data.

[0255] Optionally, at least one handler corresponds to the sensor that transmitted the wireless transmission.

[0256] Optionally, the sensor that transmitted the wireless transmission is identified by a characteristic of the wireless transmission and / or a device ID coded in the wireless transmission.

[0257] Optionally, the wireless transmission comprises a continuous stream of data packets.

[0258] Optionally, the parameterized data is stored in time series.

[0259] Optionally, the processing server is further operable to measure events based on the parameterized data.

[0260] Optionally, the hub forwards the wireless transmissions to a server in real time.

[0261] Optionally, the wireless transmission is an advertising packet for a short-range wireless sensor, optionally a Bluetooth™ wireless sensor.

[0262] Optionally, the processing server receives the sensor data from the hub in real time.

[0263] Optionally, the plurality of wireless sensors includes one or more of a respiration rate sensor, a heart rate sensor, an SpO2 sensor, a carbon dioxide sensor, and a blood pressure sensor.

[0264] Optionally, the processing server is further operable to determine whether a handler is present for a received wireless transmission, and if not, to instantiate a handler for the received wireless transmission.

[0265] Optionally, the processing server is further operable to identify one or more characteristics of the structure of the received wireless transmission.

[0266] Optionally, the processing server is further operable to identify one or more characteristics of a portion of the received wireless transmission.

[0267] Optionally, the portion of the received wireless transmission includes an advertising portion.

[0268] Optionally, the processing server is further operable to compare the structure of the received wireless transmission with a plurality of known structures.

[0269] Optionally, the processing server is further operable to deactivate the handler if no additional wireless transmissions are received within a period of time.

[0270] Optionally, each of the plurality of sensors is associated with or attachable to an item of furniture to which it is attached. Optionally, the furniture includes one or more of a bed and a chair. Optionally, the processing system is operable to identify a patient ID by measuring a patient ID associated with the item of furniture. Optionally, the item of furniture is a furniture ID. Optionally, the hub comprises a sensor for detecting the detector. Optionally, the sensor is an RFID reader. Optionally, the sensor has a limited range.

[0271] Optionally, the plurality of sensors includes an under-mattress sensor. Optionally, the sensor includes an underaction sensor. Optionally, the sensor is a non-contact sensor for respiratory rate and / or heart rate. Optionally, the hub receives transmission data from a plurality of advertising channels.

[0272] In a further aspect, the present disclosure may be broadly said to consist in a wireless sensor configured to transmit on at least one advertising channel or wireless communication system, the wireless communication system comprising a first frequency bandwidth advertising channel and a second frequency bandwidth data channel, the wireless sensor configured to sense a value and broadcast the sensed value on the at least one advertising channel.

[0273] Optionally, the system further comprises one or more of a respiration rate sensor, a temperature sensor, a heart rate sensor, an SpO2 sensor, a carbon dioxide sensor, and a blood pressure sensor.

[0274] In a further aspect, the present disclosure may be broadly stated to be a method of transmitting sensor measurements from a wireless sensor having at least one advertising channel or mode and at least one data channel or mode, the method including transmitting sensed measurements in the at least one advertising channel or mode.

[0275] Optionally, encrypting the sensed measurements using ChaCha encoding or Diffie-Hellman encoding. Optionally, the sensed measurements are transmitted as part of an advertising packet, the advertising packet including the sensed measurements and a device specific portion.

[0276] Optionally, the sensed measurements are transmitted as a continuous stream of data packets.

[0277] Optionally, the method includes calculating a component risk score based on the parameterized data. Optionally, the component risk score may be calculated by any one or more of the method steps described above or below and / or the system may be used as described above or below.

[0278] In a further aspect, the present disclosure may be broadly described as a system for processing patient measurements comprising: a plurality of wireless sensors having at least one advertising channel or mode and at least one data channel or mode, the wireless sensors configured to transmit sensor data in the at least one advertising channel or mode; a hub configured to receive sensor data from the plurality of wireless sensors in the at least one advertising channel or mode; and a processing server operable to receive sensor data from one of the plurality of wireless sensors through the hub, identify the sensor that transmitted the sensor data, measure at least one parameterized data from the sensor, and store the parameterized data.

[0279] Optionally, any of the above-described systems and / or methods further comprise displaying the parameterized data on a display screen in a first view, the parameterized data being displayed together with a corresponding patient identifier.

[0280] Optionally, the patient identifier comprises any one or more of a patient name, a patient ID, a location identifier, and a bed identifier.

[0281] Optionally, two or more patient identifiers and associated parameterized data are displayed in the first view.

[0282] Optionally, the patient identifier displayed depends on one or more of a registered user of the display, a location of the display, and / or a registered location of the display. Optionally, the registered location of the display includes a hospital room or a shared room.

[0283] Optionally, upon selection of a patient identifier, a second view is displayed on the display screen, the second view including current and historical values ​​of the parameterized data for the corresponding patient displayed in chronological order.

[0284] Optionally, the second view further comprises a component risk score indicator characterizing the patient's risk of deterioration based at least in part on the obtainable parameterized data.

[0285] Optionally, the component risk scores correspond to clinical risk indices and / or early warning scores.

[0286] Optionally, the component risk score indicators are provided in the form of gauges, which are segmented according to thresholds of corresponding parameterized data.

[0287] Optionally, the threshold corresponds to an integer value of the component risk score.

[0288] Optionally, any of the above-described systems further includes a display system configured to display the parameterized data on a display screen in a first view, the parameterized data being displayed together with a corresponding patient identifier.

[0289] Optionally, the patient identifier comprises any one or more of a patient name, a patient ID, a location identifier, and a bed identifier.

[0290] Optionally, two or more patient identifiers and associated parameterized data are displayed in the first view.

[0291] Optionally, the system is configured to receive a selection of a patient identifier and, upon detection of the selection, display a second view on the display screen, the second view including current and historical values ​​of the parameterized data for the corresponding patient displayed in chronological order.

[0292] Optionally, the display screen is configured to display, as part of the second view, a component risk score indicator characterizing the patient's risk of deterioration based at least in part on the obtainable parameterized data.

[0293] Optionally, the component risk score indicator indicates the value of one or more parameterized data relative to one or more thresholds.

[0294] Optionally, one or more thresholds are customizable by a user or clinician.

[0295] Optionally, an alarm condition is set depending on the value of one or more parameterized data relative to one or more thresholds.

[0296] Optionally, the component risk score indicators are provided in the form of gauges, which are segmented according to thresholds of corresponding parameterized data.

[0297] Optionally, the threshold corresponds to an integer value of the component risk score.

[0298] Optionally, the component risk scores are normalized component risk scores.

[0299] In a further aspect, the present disclosure may be broadly said to be an electronic device comprising a touch display, one or more processors, a memory, and one or more programs, the one or more programs stored in the memory and configured to be executable by the one or more processors, the programs including instructions for performing the steps of any of the methods described above.

[0300] In a further aspect, the present disclosure may be generally referred to as being in a bed comprising a hub operable to receive transmissions from a plurality of sensors associated with the bed and / or a patient associated with the bed, the hub configured to transmit measurements from the plurality of sensors to a processing system.

[0301] Optionally, the hub forwards the raw sensor data to a processing system. Optionally, the hub comprises a position sensor configured to sense a position of the bed. Optionally, the position sensor comprises an RFID reader configured to read the RFID sensor.

[0302] Optionally, the bed is a hospital bed.

[0303] Optionally, the processing system is configured to process the measurements into an electronic patient record. Optionally, the processing system comprises a processing server in data communication with one or more databases for storing a plurality of electronic patient records in said processing server, the processing system operable to: identify, through the hub, the sensor that received and transmitted the sensor data, identify a patient ID associated with the sensor, measure at least one parameterized data from the sensor data, and store the parameterized data in at least one of the plurality of electronic patient records.

[0304] Optionally, the bed comprises one or more sensors.

[0305] Optionally, the one or more sensors include a non-contact sensor for sensing a physiological parameter.

[0306] Optionally, the one or more sensors include an under-mattress sensor configured to sense a physiological parameter.

[0307] Optionally, the physiological parameters include any one or more of respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, carbon dioxide level.

[0308] Optionally, the patient parameters include respiratory rate and heart rate.

[0309] In a further aspect, the present disclosure may be broadly referred to as a patient monitoring system comprising: a data management server in communication with a hub, the hub wirelessly connected to a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients, the data management server configured to receive sensor data from the plurality of sensors through the hub, at least one of the plurality of sensors comprising at least one non-contact sensor configured to measure at least one of a respiratory rate and a heart rate, the hub configured to transmit the sensor data to the data management server, and the data management server configured to convert the received sensor data into parameterized data.

[0310] Optionally, one or more of the plurality of sensors are associated with and / or coupled to an item of equipment. Optionally, the item of equipment is a bed or a chair. Optionally, the item of equipment is associated with a patient. Optionally, a patient ID is used.

[0311] Optionally, the one or more sensors include an under-mattress sensor.

[0312] Optionally, the patient management system is for use in one or more of a hospital or a home environment.Optionally, the patient management system is for use in comprehensive care in a hospital.

[0313] Optionally, the system is configured to allow connection of one or more additional sensors to the hub and data management server.

[0314] Optionally, the data management system is configured to calculate component risk scores based at least in part on the parameterized data.

[0315] Optionally, the one or more sensors include any one or more of a respiration rate sensor and a heart rate sensor.

[0316] Optionally, the one or more sensors include multiple types of sensors, each type of sensor configured to sense a different physiological parameter.

[0317] Optionally, the sensors include any one or more of the following: respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, carbon dioxide level.

[0318] Optionally, the component method may use a system as described above or below and / or include any of the method steps described above and / or below.

[0319] Features from one or more aspects or configurations may be combined with features from one or more other aspects or configurations.

[0320] As used herein, the term "(s)" following a noun refers to the plural and / or singular form of that noun.

[0321] As used herein, the term "and / or" means "and" or "or," or, where the context allows, both.

[0322] The term "comprises" as used herein means "consisting at least in part of." When interpreting each statement herein that includes the term "comprises," features other than those preceded by the term may also be present. The related terms "comprises" and "comprises" may be interpreted in the same manner.

[0323] Reference to a range of numbers disclosed herein (e.g., 1 to 10) is intended to incorporate reference to all rational numbers within that range (e.g., 1, 1.1, 2, 3, 3.9, 4, 5, 6, 6.5, 7, 8, 9, and 10), and also to any range of rational numbers within that range (e.g., 2 to 8, 1.5 to 5.5, and 3.1 to 4.7), such that any subrange of any range explicitly disclosed herein is expressly disclosed herein. These are merely examples of what is specifically intended, and all possible combinations of numerical values ​​between the recited lower and upper limits should be considered to be expressly stated in the same manner in this application.

[0324] The present disclosure may also be generally referred to as consisting in the parts, elements, and features referred to individually or collectively in the specification of this application, and in any or all combinations of any two or more of said parts, elements, or features.

[0325] Where specific integers that have known equivalents in the art to which this disclosure pertains are referred to herein, such known equivalents are deemed to be incorporated herein as if they were individually set forth.

[0326] The present disclosure has been set forth above and also contemplates the following constructions, which are given by way of example only. [Brief explanation of the drawings]

[0327] Certain aspects and modifications thereof will become apparent to those skilled in the art from the detailed description herein, which refers to the following figures.

[0328] [Figure 1] 1 illustrates a patient monitoring system 10 according to one aspect of the present disclosure. [Figure 2] This shows a bed detector system located in one room of the facility. [Figure 3] 1 illustrates a pre-processing server portion of a patient monitoring system 10 according to one embodiment of the present disclosure. [Figure 4] 1 illustrates a post-processing server portion of a patient monitoring system 10 according to one aspect of the present disclosure. [Figure 5] 1 is a flowchart illustrating a method implemented in a router according to one aspect of the present disclosure. [Figure 6] 1 is a flowchart illustrating a method according to one aspect of the present disclosure. [Figure 7] 1 is a flowchart illustrating a method according to one aspect of the present disclosure. [Figure 8] 1 is a flowchart illustrating a data collection method according to one aspect of the present disclosure. [Figure 9] 1 shows elements of a graphical user interface provided on the screen of a display system. [Figure 10] 1 shows elements of a graphical user interface provided on the screen of a display system. [Figure 11] From 2021, an exemplary acquired warning score matrix from the Adult Early Warning Scoring System used in New Zealand is shown. [Figure 12] From 2021, an exemplary acquired warning score matrix from the Adult Early Warning Scoring System used in New Zealand is shown. [Figure 13] 1 shows elements of a graphical user interface provided on the screen of a display system. [Figure 14] 1 shows a schematic diagram of how component risk scores and clinical risk indices may be based on patient data. [Figure 15] 1 shows exemplary early warning score categories along with corresponding ranges of normalized clinical risk indices. [Figure 16] 1 shows a patient view of monitored patient data and component risk scores. [Figure 17] 1 shows a patient view of data readouts for multiple live patients with component risk scores. [Figure 18] Data for living patients across time periods are presented. [Figure 19] 1 shows radar plots of patient data at different time points. [Figure 20] 1 shows a group view of multiple patients being monitored, each represented by a tile. [Figure 21] 21 shows an example tile from FIG. 20. [Figure 22] Shows sections from group view, tiles adjusted for emphasis. [Figure 23] Shows sections from group view, tiles adjusted for emphasis. [Figure 24] 1 shows a section of the group view where the display of tiles is adjusted based on risk band. [Figure 25] Shows a group view of multiple patients being considered for discharge, each patient represented by a tile. DETAILED DESCRIPTION OF THE INVENTION

[0329] Although certain aspects and examples are disclosed herein, the subject matter of the present invention extends to other alternative aspects and / or uses of the specifically disclosed aspects, as well as modifications and equivalents thereof. Accordingly, the claims or aspects appended to the present invention are not limited to any of the specific aspects described herein. For example, in any method or process disclosed herein, the acts or operations of the method or process may be performed in any suitable order and are not necessarily limited to any particular disclosed order. While various operations may be described as multiple separate operations, i.e., in a manner that may be helpful in understanding a particular embodiment, the order of description should not be construed to imply that these operations are order-dependent. Furthermore, some structures described herein may be embodied as integrated components or as separate components. For purposes of comparing various embodiments, certain aspects and advantages of these embodiments are described. Not all such aspects or advantages are necessarily achieved by any particular embodiment. Thus, for example, various aspects may be implemented in a manner that achieves or optimizes one advantage or group of advantages taught herein without necessarily achieving other aspects or advantages that may also be taught or suggested herein.

[0330] It should be emphasized that numerous variations and modifications can be made to the aspects described herein, and it should be understood that these elements may be present in other acceptable embodiments. All such modifications and variations are intended to be included within the scope of this disclosure and are intended to be protected by the following claims. Furthermore, nothing in the foregoing disclosure is intended to imply that any particular component, feature, or processing step is required or essential. In some aspects, the processes or method steps described herein are implemented by the disclosed systems. In some aspects, the systems described herein implement one or more of the disclosed methods and processes. System Overview

[0331] 1 illustrates a system for monitoring one or more patients in a hospital or home environment. The patient location may be referred to as a facility covering either a hospital, medical field, or home. Aspects of the present disclosure form at least a portion of the system. Briefly, the system provides for the collection of patient data from multiple sensors 20 and the subsequent forwarding of that data to one or more processing servers 40 through intermediate routers and / or hubs 30. The terms hub and router may be used interchangeably to refer to devices configured to at least receive wireless communications from the sensors and forward the communications to a server.

[0332] The sensor 20 may include a sensor worn by the patient, attached to the patient bed 1 and / or to furniture 22, such as a non-contact sensor. The sensor 20, 22 may be attached to or part of a treatment device 21. For example, the sensor may be attached to or part of a respiratory support or respiratory treatment device. For example, the respiratory support or treatment device may be a high-flow treatment device (e.g., Airvo™ 2 or Airvo™ 3), a respiratory humidifier, or a THRIVE™ treatment device in anesthesia or sedation. The system may also communicate with surgical humidifiers or other gas delivery or gas conditioning devices used in surgery or anesthesia or sedation procedures. The sensor may include multiple sensors of different types configured to measure different physiological parameters. In certain embodiments, the system is configured to monitor a patient's respiratory health, which is often indicative of other health issues / diseases. Furthermore, respiratory distress or failure is often the trigger that transfers patients to intensive care, which is resource intensive and may result in lower quality patient outcomes compared to earlier treatment of the health problem / disease.

[0333] Conventional systems rely on self-reporting by patients, healthcare providers who take periodic vital sign measurements, and / or non-relatively collected sensor information. However, patients often do not recognize that their condition is deteriorating until after significant or increased levels of care are required. Similarly, periodic vital sign measurements provide only a small, snapshot of a patient's condition, and concerns may not arise until after the patient requires significant or increased levels of care. Furthermore, asymmetric collection of information from sensors (e.g., blood pressure sensors, heart monitors, temperature sensors, respiratory monitors, etc.) can limit the availability of information from any one sensor (e.g., available only to the provider in the room) and / or can provide an incomplete picture of the patient's health status (e.g., when the provider cannot view information from multiple sensors at once).

[0334] Notably, conventional sensors all output data in different formats and / or with different user interfaces for display on different devices local to the patient. Each device may have a proprietary format or communication protocol, requiring independent monitoring and limiting the ability to combine measured data. Furthermore, the number of sensors in a facility necessitates comprehensive collection and processing of potentially interfering signals. Each of these technical deficiencies can force healthcare providers to respond only to critical needs, reducing the quality of care a patient receives and / or negatively impacting patient outcomes. For example, delaying care until critical or escalated care is required can negatively impact a patient's ability to recover from a health condition / disease. Furthermore, escalated care is resource-intensive, diverting healthcare provider attention and / or available space within a healthcare facility from other patients. In some cases, the system and / or method provides an open architecture that allows sensors to be easily added or removed. In some cases, the system and / or method also allows hospitals to apply their own monitoring procedures and / or customize system procedures, monitoring procedures, and / or output displays. The open architecture may allow the monitoring procedures to be implemented in a simple and centralized manner, e.g., raw sensor data is processed on the server, so that only the server processes and / or handlers need to be adjusted or customized to change the system and / or method.

[0335] In some cases, to overcome these technical deficiencies in conventional systems, the present systems and methods can help healthcare providers detect early deterioration in a patient's respiratory health, thereby enabling rapid and timely intervention by a supervising clinician. For example, the present systems and methods disclosed herein can use multiple sensors 20 to continuously (or near-continuously) monitor various vital signs (e.g., respiratory health, cardiac health, etc.). Data from multiple sensors 20 can help detect respiratory deterioration early (e.g., as soon as a change in respiratory health occurs), reducing (or eliminating) the need for patient self-reporting and significantly supplementing periodic vital sign measurements. That is, early identification can enable healthcare providers to initiate treatment for respiratory distress / disorder significantly sooner, thereby helping to prevent the patient from needing to escalate to acute (or other, higher level) care. Thus, the present systems and methods disclosed herein are expected to help improve patient outcomes and reduce resource demands associated with treating patients, thereby allowing healthcare providers to have more bandwidth and / or resources to treat other patients.

[0336] Furthermore, as discussed in more detail below, the disclosed systems and methods may enable efficient collection, storage, processing, and / or analysis of data from multiple sensors 20 in a central location. For example, the systems and methods disclosed herein may use multiple sensors 20 to collect live data about a patient and / or may use a router 30 to collect live data from multiple sensors 20. More specifically, in some cases, the multiple sensors 20 and the router 30 may communicate using advertising channels of short-range wireless communication (e.g., Bluetooth™). By communicating data using advertising channels, any number of sensors may communicate with the router 30 without requiring dedicated channels, dedicated connections, and / or dedicated data formats. As a result, the systems and methods disclosed herein enable a greater number and / or a greater variety of sensors to communicate with the router 30. Additionally or alternatively, because the router 30 and the multiple sensors 20 do not need to maintain constant connections with each other, the router 30 and each of the multiple sensors 20 require less energy. In some cases, the use of advertising signals or packets, optionally coded, used to transmit sensor data reduces the power consumption and / or bandwidth consumption of the sensor and / or hub, meaning the sensor and / or hub uses less power or requires a smaller power source to operate, making the system easier to use as it requires less charging.

[0337] Additionally or alternatively, additional sensors can be added ad hoc to customize monitoring for a particular patient's healthcare needs without disrupting the network and / or requiring updates to accommodate the additional sensors. After router 30 receives data from multiple sensors, it forwards the data to processing server 40. In one aspect, router 30 passes the raw data directly to processing server 40 without caching or performing any other processes on the data. This allows the data to be forwarded (and ultimately displayed) in real time or near real time, providing a better-resolution view of the patient's health status. Processing server 40 processes the raw sensor data to generate parameterized data, which is then stored and added to the patient's electronic medical file and, optionally, displayed on a screen as part of the patient monitoring hardware, either in a hospital or home environment.

[0338] Thus, the systems and methods described herein are expected to reduce the resource requirements for accommodating multiple sensors 20. Furthermore, in contrast to conventional systems, the systems and methods described herein allow data from a wide variety of sensors to be collected, processed, displayed, and / or analyzed in a central location. A detailed description of the system's components and operation

[0339] The construction and operation of aspects of the system will now be described in further detail.

[0340] In one aspect, the plurality of sensors 20 is configured to monitor a patient and generate sensor data indicative of the patient's condition. The plurality of sensors 20 may be referred to as health sensors because they measure physiological parameters of the patient that are indicative of the patient's health. The parameters may indicate improvement or deterioration of the patient's health.

[0341] In one aspect, the plurality of sensors includes one or more of a respiration rate sensor, a temperature sensor, a heart rate sensor, and an SpO2 sensor provided in the form of one or more wearable sensors attachable to the patient; note that respiratory distress is often a key indicator of a patient's deteriorating health. In a further aspect, the plurality of sensors 20 may include one or more sensors provided in or under an item of furniture, such as under the mattress of the patient's hospital bed or chair. These sensors may be capable of measuring one or more physiological parameters of the patient, such as respiration rate and heart rate. For example, a ballistocardiograph may measure heart rate and / or respiration rate without contacting the patient's body. Those skilled in the art will appreciate that any suitable sensors may be incorporated into the plurality of sensors 20 configured to operate as described below.

[0342] An advantage of using one or more sensors under the mattress of a patient's bed is that it improves patient comfort by avoiding the need to wear a sensor for the parameter being measured in an otherwise appropriate location. Furthermore, healthcare providers save time by not having to secure any corresponding sensors to the patient, avoiding the risk that healthcare providers will forget to secure a sensor or that the sensor will accidentally become dislodged / move from the patient. Sensors located under the mattress can also be more hygienic and do not need to be replaced or cleaned between different patients occupying the bed. Non-contact sensors attached to an item of furniture also reduce measurement errors associated with sensor movement and insufficient patient attachment to the sensor. In some cases, non-contact sensors also improve patient medication guidance because the sensor does not require patient input and is simply part of the item of furniture. This means that no patient attachment or connection to the patient is required other than associating the patient with, for example, a bed or bed position.

[0343] In conventional systems, an insufficient sensor can throw off a healthcare provider's interpretation of data, forcing the healthcare provider to take multiple measurements using the same sensor or rely on an inaccurate sensor during periodic vital sign measurements. To overcome this deficiency in one aspect of the current technology, the multiple sensors 20 can include two or more sensors sensing the same parameter, and the system 10 is configured to average values ​​to determine the measured parameter or select data from the highest quality data (e.g., lowest noise signal). In another aspect, the system 10 can prioritize measurements from selected and / or trusted sensors. Sensor accuracy and reliability can vary significantly, for example, between manufacturers and models, even when the underlying technology is similar. In other cases, different sensors 20 will provide greater levels of accuracy based on different technology, tolerances, or accuracy levels (e.g., electrocardiograms are generally more accurate than pulse oximeters). By prioritizing more accurate sensors, the more accurate data available improves the performance of the system 10.

[0344] In one aspect, the system 10 obtains a rating for each sensor 20 (e.g., an rating of the sensor's accuracy or the reliability of the sensor's value). The rating can be provided, for example, in a signal from the sensor 20, obtained by a processing server (e.g., by a sensor handler), or obtained from a database. The rating system generates a hierarchy of sensors 20 according to the available sensors and their respective ratings. The sensor with the highest score can then be used to measure the most accurate sensor value. In cases where multiple sensors are available, the system 10 can also monitor the accuracy of less accurate sensors. This monitoring can be used to adjust less accurate sensors if or when the most accurate sensor becomes disconnected, for example, to introduce an offset or to set accuracy boundaries on the sensor data. In one aspect, accuracy boundaries (either determined in comparison to other sensors or provided in system 10) can be interpreted by the system to determine when to trigger and alarm or set risk bands based on the accuracy of the sensor.

[0345] In one aspect, the system uses the distance to the wireless sensor or the strength of the wireless sensor to determine whether to process the sensor, which allows the system to ignore sensors that are located too far away, reducing the processing load.

[0346] The plurality of sensors 20 are configured to transmit sensor data to the router 30. In one aspect, the sensor data is in the form of raw, or otherwise unprocessed, data from the sensors. In the case where one or more of the plurality of sensors 20 are analog, the sensor data can be in the form of an analog signal, or alternatively, a digital value representing the analog signal.

[0347] As described above, in embodiments of the present technology, data transmission from the plurality of sensors 20 to the router 30 is provided via short-range wireless transmission, such as NFC (Near Field Communication), RFID (Radio Frequency Identification), Zigbee™, or any other suitable means. In the illustrated embodiment, data transmission is accomplished via Bluetooth™, in which sensor data is encoded into advertising data packets (e.g., suitable for an advertising channel in a short-range wireless protocol). Typically, advertising data packets are used to handshake or agree on a communication protocol between two devices, rather than for sensor data transmission in the present disclosure. Thus, sensor data can be read by the router 30 without the need to pair the router 30 with each of the plurality of sensors 20. Thus, the router 30 does not need to maintain a separate connection with each of the plurality of sensors 20. This allows the system 10 to work with any type of sensor with a minimal configuration, including sensors that may otherwise be unsuitable or impossible to pair with a conventional router 30 or processing server 40. This further enables the system 10 to work with many more sensors than would be possible if the router 30 had to fully pair with each sensor of the plurality of sensors 20. Additionally, individual sensors do not necessarily need to maintain a connection with the router 30, which results in improved battery life.

[0348] The transmission of sensor data between multiple sensors 20 and router 30 can therefore be considered connectionless, allowing sensors to be added or removed from the system without any of the complexities associated with handshaking or handoff. In one aspect, the advertising packet includes a Bluetooth™ MAX address / UUID unique to each device / sensor, along with a payload portion containing the sensor data. In a further aspect, this payload portion includes additional information, such as the make and model of the sensor device or other metadata. In some cases, only the advertising portion of the wireless connection is used, thereby reducing the time, bandwidth, and / or processing required to transmit the sensor data.

[0349] In a further aspect, a wireless hub receives transmissions on at least one advertising channel. The wireless hub's wireless communication system has a first frequency bandwidth configured to receive advertising channels from multiple sensors and a second frequency bandwidth data channel configured to receive data. Data transmission typically begins after a handshake is performed on the advertising channel. However, the wireless hub can be configured to monitor the advertising channels and receive sensed values ​​on at least one advertising channel. Receiving sensed values ​​on the advertising channels reduces the time required to connect devices, allowing more devices to be connected and transmitted to the server. The hub can optionally decode the sensed readings using ChaCha encoding or Diffie-Hellman encoding. The hub can measure sensed measurement and device-specific portions of the data on the advertising channel or forward them directly to the server. Optionally, the sensed measurements are transmitted as a continuous stream of data packets.

[0350] In one embodiment, the sensor data is transmitted as a continuous stream of data. In another embodiment, the sensor data is transmitted at fixed intervals. In an alternative embodiment, the sensor data is coded and transmitted as soon as new and / or updated data is generated by the sensors 20 so that bandwidth is not consumed by unchanged sensor readings. In a further embodiment, transmission of the sensor data is facilitated by pull notifications from the router 30. In a preferred embodiment, transmission of the sensor data is performed in real time. In one embodiment, the sensor data is transmitted at intervals set by individual sensors of the plurality of sensors 20 and unknown to the processing server 40, yet allowing transmission of sensor data at any time. In a further embodiment, the system 10 is optimized for data transmission at 1 Hz or less, but may be capable of handling higher frequency data, for example, at 10 Hz.

[0351] In one embodiment, the plurality of sensors 20 are programmed to operate using a data protocol / data standard such that sensor data is output according to a Bluetooth™ specific format.

[0352] In one embodiment, the router 30 includes a controller, an antenna, and a power source and is configured to receive sensor data from multiple sensors 20 and forward the data to the processing server 40. In one embodiment, the sensor data is time-stamped upon receipt and before transmission to the processing server 40. By receiving the sensor data in Bluetooth™ advertising data packets and passing the received data to the server 40 for processing, the router 30 can operate multiple sensors with minimal configuration and processing requirements. The advertising data packets can be received on a common advertising channel or from multiple advertising channels, which may depend on the structure or protocol of the short-range wireless communication device used. The router 30 formats or otherwise processes the data packets before forwarding them to the server 30. For example, the router 30 can time-stamp, decode the signal, and / or package the signal with other incoming signals.

[0353] The methods and mechanisms of transmission from the plurality of sensors 20 to the router 30 and from the router 30 to the processing server 40 need not be the same, i.e., the router functions as a gateway. For example, in the illustrated embodiment, the router 30 exports data received from the plurality of sensors 20 to the processing server 40 via IP communication protocol. In an alternative embodiment, the router 30 exports the received sensor data to a local server via Ethernet.

[0354] In one embodiment, router 30 is provided by a Cassia™ X2000, X1000, E1000 Bluetooth™ Enterprise Bluetooth™ router, or similar device.

[0355] In the illustrated embodiment, the processing server 40 comprises a dispatcher 60 , one or more handlers 70 , internal storage 80 , a time series database 90 , a processor 100 , and an event handler 110 .

[0356] In one aspect, sensor data is time-stamped and cached upon receipt by the processing server 40. This time-stamping of received sensor data by the processing server 40 allows the system 10 to handle irregular real-time data streams without the need for interpolation or time averaging. Additionally, the server 40 may utilize a single central clock, which synchronizes received data across multiple sensors 20 and reduces time errors in the data. The time-stamps on the received sensor data are carried through the processing server 40 and applied by the processing server 40 to parameterized data outputs to provide a time series indicative of the patient's condition. The use of a cache allows for asynchronous receipt and processing of received sensor data from multiple sensors 20; for example, temporary bandwidth limitations affecting the transmission of sensor data from the router 30 need not interrupt the processing of data already received and cached at the processing server 40.

[0357] The sensor data is then passed to a dispatcher 60, which is configured to determine the source device and / or device type (i.e., the type of sensor from which the sensor data was generated). In one aspect, this determination is performed by identifying one or more characteristics of at least a portion of the received sensor data, such as a Bluetooth™ MAC address / UUID or Bluetooth™ class. In one aspect, the dispatcher 60 performs pattern matching on the sensor data packets received from each individual sensor. In an alternative aspect, the source device is identified by a device ID coded into the sensor data generated by the particular device. In one aspect, device identification is performed by the router 30, and the device ID is transmitted to the processing server 40 along with the sensor data.

[0358] After identifying the source device, the dispatcher 60 retrieves the driver 71 and activates the corresponding handler 70. If a handler 70 does not exist for the sensor data from the identified source device, the dispatcher 60 is operable to instantiate a handler 70. In one aspect, the processing server 40 can retrieve the handler 70 and driver 71 corresponding to any possible device ID from an external source, which can be continuously updated / maintained as new sensors become available. In a further aspect, the server 40 can include a library of various driver definitions. Furthermore, the server 40 can build drivers based on pre-stored data types and / or multiple sensors 20 are programmed to transmit standard data structures / types. The server 40 can build custom drivers based on the received sensor data and data library.

[0359] The dispatcher 60 then forwards the sensor data to activated handlers 70, which comprise drivers 71 that operate to process the sensor data into parameterized data. The handler / driver architecture described above allows the processing server 40 to process sensor data from any conceivable brand, create and model sensors, and also adapts the processing server 40 to work with any new sensors that are added to the system after initial setup. Furthermore, because the sensor data is processed centrally by the processing server 40, there is no need to update router or sensor software to adapt to changes in the system 10.

[0360] In one embodiment, the parameterized data corresponds to one or more of a patient's physiological parameters, such as respiratory rate, temperature, carbon dioxide level, heart rate, and SpO2.

[0361] Once parameterized, the data is sensor independent, i.e., has values ​​and units / dimensions according to specific parameters, and no factor related to the specific brand or model of the original sensor is necessary to interpret the parameterized data. In one aspect, the dispatcher 60 deactivates the handler 70 if no additional corresponding sensor data is received for a predetermined time period, or as soon as the transmission of sensor data resolves.

[0362] In one embodiment, each handler 70 runs in an independent container 72 on the processing server 40, allowing for better resource control as well as parallel processing of sensor data from different individual sensors by different handlers 70. In an alternative embodiment, each container 72 contains one or more handlers 70, up to a configurable limit.

[0363] As sensor data is received from additional sensors of the plurality of sensors 20, additional handlers 70 and containers 72 can be instantiated.

[0364] In one embodiment, the driver 71 is accessed directly by the dispatcher 60 without the use of a dedicated handler 70 .

[0365] The parameterized data is cached and subsequently stored by the processing server 40 in storage 80. In the illustrated embodiment, the parameterized data is stored in a dedicated database 90 in time series, where the time series is a sequence of timestamp and parameterized value pairs, with a separate time series for each physiological parameter of parameterized data being provided for each patient ID. The pre-stored cache allows for asynchronous processing of sensor data and storage of generated parameterized data.

[0366] The parameterized data is then added to the patient's electronic medical record. The parameterized data is further passed to an event handler 110, which can generate an alert and / or trigger an action if the parameterized data falls below or exceeds a predetermined threshold for a predetermined time period. The parameterized data rising above or below a predetermined threshold can determine whether the system outputs an indication of the parameterized data (or, for example, a component risk score) to a healthcare provider or clinician or to a display. This can ensure appropriate care is provided to the patient, or that an alarm or automated care is provided.

[0367] In one embodiment, the processing server 40 is also used to register a patient for each of the plurality of sensors 20 using a patient registration device 41 that carries the patient and associates the device IDs of the sensors in the plurality of sensors 20 with the patient ID of the patient. In one embodiment, the patient's personal information is also stored, albeit separately from the patient ID, so that the patient ID can be shared and / or searched without violating the patient's privacy. In one embodiment, these device ID-patient ID pairs are stored in a database at the processing server 40. In an alternative embodiment, the patient registration device 41 is hosted outside of the processing server 40, and the processing server 40 has access to a database 45 in which patient IDs and device IDs are stored.

[0368] In an alternative embodiment, or in combination with the previous embodiment, the processing server 40 also includes a hospital bed and uses a bed registration service that associates the device ID of each sensor in the plurality of sensors 20 with the bed ID of the bed and is used to register the hospital bed with each of the plurality of sensors 20. In one embodiment, these device ID-bed ID pairs are stored in a database on the processing server 40. In an alternative embodiment, the bed registration service is hosted outside of the processing server 40, and the processing server 40 accesses a database 45 in which the bed IDs and device IDs are stored. In one embodiment, this functions such that the bed registration service functions similarly to the patient registration service described herein. Although beds are described, the registration process can be associated with other hospital furniture, including chairs.

[0369] Associating sensors with hospital beds can reduce tracking complexity within the system. In some facilities, such as hospitals, clinicians identify patients through their hospital beds and / or the location of the hospital bed within a particular room or community room. By associating sensors with hospital beds, clinicians can be directly alerted to the location of patients of interest when their attention is needed.

[0370] In one aspect, each bed can identify its location within the hospital. The identification process can include detecting a location marker associated with the location within the hospital. The location marker can be a short-range wireless device. In some cases, a passive short-range wireless device is used so that the location marker does not require power. For example, an RFID tag is a passive device that can be read by the hub. For example, each potential bed location can have an associated RFID tag. A router 30 on the bed can detect the RFID tag when it is at the bed location. This detection allows the system to detect an association between the bed and the bed location. The patient can then be associated with the bed.

[0371] When a bed is moved around the hospital, the bed location can automatically update the location of the bed, and therefore the patient. The bed location can also update other record data or associations. For example, the location can be associated with a clinician. When moving a bed to a new location changes the clinician (e.g., nurse or doctor), the system can update the clinician responsible for the bed to the clinician at the new location. For example, when a bed is moved between shared rooms, the responsible clinician and / or shared room processes can be automatically updated.

[0372] In one aspect, a hospital bed has a loading process. The loading process can include associating a patient with the bed. The loading process can associate any sensors already associated with the patient with the bed. The loading process can associate any sensors already associated with the bed with the patient. Whether a sensor is associated with the bed or patient may depend on whether the sensor is attached to and / or connected to the bed or patient. For example, a sensor attached to the bed frame and / or mattress can be associated with the bed, while a sensor that monitors heart rate and is attached to the patient's body can be associated with the patient (which can then be associated with the bed). In some cases, staff can select sensors to associate with the patient and / or bed. In some cases, sensors can be associated with a location. In some cases, the loading process can also be used to associate sensors with the bed separately or together with the bed ID process.

[0373] In at least one embodiment where one or more sensors are located on the patient's bed and are not directly attached to the patient assigned to that bed, the system is configured to verify the patient's presence in the bed before processing / accepting any data from the sensor(s). Patient presence can be detected by a pressure sensor, a PIR sensor, and / or by detecting the presence of an RFID tag embedded in the patient's ID bracelet. The RFID tag can be assigned to the bed and / or patient as part of the onboarding process. Bed sensors may include non-contact sensors for sensing physiological parameters. For example, an under-mattress sensor can be used. The bed sensors can measure any one or more of the following: respiratory rate, SpO2 (blood oxygen saturation), supplemental oxygen, temperature, body temperature, heart rate and systolic blood pressure, and carbon dioxide levels. In cases where non-contact sensors are unavailable, cost-prohibitive, or an alternative is desired, contact sensors can be used. For example, a contact SpO2 sensor can be used.

[0374] In one aspect, the bed sensors include a respiration rate sensor and a heart rate sensor. For example, a ballistocardiographic sensor can be used to measure both heart rate and respiration rate without contacting the patient. The use of heart rate and respiration rate typically provides an accurate component risk score and allows the system to quickly identify patients at potential risk without the need for contact sensors, since the patient simply needs to be in bed and / or chair. Sensor data associated with the bed can be transmitted to a processing server through a hub (which can also be associated with the bed). Advantageously, the bed provides a central location for the hub and allows for the connection of additional sensors as needed or as the patient's condition progresses. Optionally, a hemoglobin composition sensor can be used to provide a further indicator of the patient's health. Optionally, a hemoglobin composition sensor can be used to provide a further indicator of the patient's health. The data can then be processed by the processing server and reported or displayed as described herein.

[0375] In a positional embodiment, the respiratory rate and heart rate are sensed by one or more non-contact sensors. The sensor may be a single sensor that senses both respiratory rate and heart rate, or separate sensors can be used. Respiratory rate is an early indicator of a patient's health. Because respiratory rate is derived from a normal range, it is an indicator of deteriorating respiratory health, which is often a precursor to other, more commonly diagnosed conditions. For example, if a patient has the flu, their respiratory rate and heart rate will increase above normal (i.e., resting respiratory rate and heart rate are greater than the normal range) before any fever, or runny nose or cough develops. Therefore, changes in respiratory rate and heart rate can be a good indicator that a patient needs some kind of intervention, and providing this with non-contact sensing attached to an item of equipment ensures that potential conditions are identified quickly and early, allowing for reporting to a clinician and improving patient outcomes by, for example, initiating high-flow therapy. Under-mattress sensing also reduces the opportunity for human error or machine overlooking a signal because it is automatic and provides an objective indicator when patient presence is detected. Using an objective indicator ensures complete monitoring of all patients, including when declining health is not apparent to the clinician.

[0376] Associating sensors with hospital beds may also simplify hardware installation. For example, hospital beds typically have a power source to which the router 30 can be connected. Similarly, hospital beds may have sensors attached to them. If permanently or substantially permanently attached, the sensors are permanently associated with the bed and inaccurate reporting can be avoided. Because the router 30 can be attached directly to the bed, the router 30 can be easily maintained or updated when the bed is not in use.

[0377] In one embodiment, patient personal information is also stored, albeit separately from the bed ID, allowing patient information to be shared and / or searched without violating patient privacy.

[0378] FIG. 2 shows six hospital beds (labeled 1 through 6) in a room, such as a hospital community room. A locator 8, such as an RFID tag, is located behind each bed. Each bed includes or is attached to a router 30, which can be fixed to the bed frame, for example. The router 30 can be located under the bed, attached to the head of the bed, or otherwise connected. The hospital beds may be mobile. For example, the hospital beds may be on wheels so that the patient can move the bed around the facility. As each bed moves into position, or at other times, the locators 8 and router 30 communicate to determine the bed's location. For example, as bed 1 moves into position, bed 1 communicates with the neighboring locator 8 to determine its location within the room or facility. Communication is optionally wireless, such as when the locator system 8 is an RFID device that is read by the router 30, but wired or other physical connections can be used.

[0379] In some cases, communications have a limited range. The limited range can avoid interference or confusion between adjacent beds. For example, communications can have a transmission distance of less than 2 meters, less than 1 meter, or less than 0.5 meters. In some cases, routers 30 of adjacent beds, or beds in the same room or shared ward, can interact to measure the relative positions of the beds within the room. This can be advantageous when Locators 8 are not present or operational. For example, the router 30 of Bed 2 can obtain distance measurements for Bed 1 and Bed 4 based on these (or additional readings) and triangulate their positions. Distance measurements can be calculated by signal strength or angle of arrival. In some cases, the routers 30 can have GPS or other location sensors configured to measure their positions and can reference a hospital or building map to determine their positions. Other Locators 8 are possible, for example, geofences, Bluetooth™, or reference point(s) can be organized for each room or location.

[0380] FIG. 3 illustrates an embodiment in which, in addition to receiving sensor data from multiple sensors 20, router 30 is also configured to receive data regarding therapy being provided to a patient by one or more medical devices 200. In one embodiment, medical device 200 is a respiratory treatment device or respiratory assistance device. In one embodiment, the respiratory treatment device can provide respiratory treatment or assistance and can be a high-flow therapy device or ventilator, or a humidifier configured to provide one or more of these. The respiratory device can be a respiratory humidifier that can condition gas from a ventilator before providing the gas to the patient. CPAP (Continuous Positive Airway Pressure), NHF (Nasal High Flow), or bilevel therapy. The respiratory treatment / assist device can be a multi-mode therapy device configured to provide both high-flow therapy and bilevel therapy. The respiratory device includes a flow generator, a humidifier, tubing, and a patient interface. The respiratory treatment device includes sensors for recording various therapy parameters, such as flow, pressure, temperature, and humidity. The device may also transmit usage information and / or data measured by on-board sensors, such as SpO2, respiratory rate, and heart rate, measured either directly or indirectly by other physiological parameters or indicators monitored by the medical device 200 itself. In one embodiment, one or more of the sensors 20 are provided as part of the medical device 200. Those skilled in the art will appreciate that the data processing method disclosed herein, given its functionality as described below, is independent of and tolerant of the exact and specific characteristics of the medical device 200. In one embodiment, one or more medical devices 200 include a Bluetooth™ communication module connectable to the processing server 40 through the router 30. In one embodiment, the router 30 can identify the type, make, and / or model of the medical device 200 from the transmitted therapy data. In one embodiment, the router 30 identifies the medical device 200 from the structure of the therapy data. In an alternative embodiment, the device ID of the medical device 200 is coded into the therapy data itself.

[0381] In one aspect, the sensors can include a hemoglobin composition sensor and an under-mattress sensor that can sense respiratory rate and / or heart rate. The combination of these two sensors can provide an early indicator of a patient's declining health.

[0382] In one embodiment, the therapy data comprises one or more parameters related to the flow rate, humidity level, dew point, duration of use, pressure rate or pressure delivery, or O2 fraction of the gas flow for delivery to the patient, or any other suitable or desired data. In one embodiment, the therapy data is broadcast to the router 30, where it is time-stamped before being delivered to the processing server 40. In one embodiment, clinician-configurable therapy data can be broadcast. In an alternative embodiment, the therapy data is time-stamped upon receipt by the processing server 40. In one embodiment, the therapy data is encoded within advertising data packets for Bluetooth™ transmission by the router 30 for collection, which then transmits the therapy data to the processing server 40 via an IP protocol. In one embodiment, the therapy device is configured to transmit the therapy data to the hub through the advertising packets. The therapy device can be equipped with a short-range wireless interface, such as a Bluetooth™ interface. The therapy device can incorporate the therapy data into advertising packets transmitted by the therapy device.

[0383] The processing server 40 is operable to process received therapy data in the same manner as the received sensor data described above, i.e., by activating a handler 70 corresponding to the identified medical device or by creating a handler 70 if one does not already exist with the appropriate driver 71. This allows new medical devices to be added to the system without having to reprogram component parts of the system to cause changes in the hardware; rather, the processing server only needs to access and / or create the handlers 70 and drivers 71 corresponding to the new medical device. Similarly, one or more medical devices 200 can be removed from the system, and their corresponding handlers can be disconnected or otherwise deactivated so that the processing server 40 continues to function efficiently.

[0384] This treatment data, along with the corresponding processed sensor data related to the patient's physiological parameters, can then be added to the patient's electronic medical file in chronological order, providing a comprehensive and continuous treatment history of the patient along with the corresponding physiological parameters.

[0385] This temporal pairing of parameterized data describing the patient's condition with therapy parameters describing the therapeutic conditions existing at the time the sensor data was generated provides the reader of the electronic medical record with an indication of both the effectiveness of the therapy being applied and the patient's compliance with the prescribed treatment regimen. In one aspect, this efficacy and compliance data is parameterized and stored with the patient's corresponding therapy and physiological parameters.

[0386] FIG. 4 illustrates a portion of system 10 that receives parameterized data from processing server 40. As shown, the parameterized data can be forwarded to a sensor data engine 300, otherwise referred to as a data processing engine or patient data management engine, which can be either part of system 10 or external to system 10. The sensor data engine 300 can update a copy of the patient's electronic medical record, run it through a data analysis or prediction engine (once any personal information about the patient, if any, has been shredded) for research and forecasting / trend analysis, generate patient-specific alerts or notifications, and / or automatically generate patient forms and records, if necessary. In one aspect, the parameterized data is transmitted to a display system 310, present in either a hospital or home environment, to visually display the parameterized data.

[0387] FIG. 5 illustrates the process performed by the router 30. In step S110, the router scans the radio frequency band for any transmissions. In step S120, a determination is made if any transmissions, e.g., transmissions of sensor data from one or more of the plurality of sensors 20, are incoming. In one embodiment, the router 30 is configured to scan for any known sensors registered for the patient. In a further embodiment, the router 30 may discover newly added sensors at this stage. If a transmission is detected, the router 30 checks for an active connection to the processing server 40 in step S130. If a connection is not available, the router 30 continues to scan and cache the sensor data locally. Once an active connection is established, the router 30 communicates the data transmission to the processing server 40.

[0388] FIG. 6 illustrates a process performed by the dispatcher 60 of the processing server 40. In step S210, a transmission associated with a router 30 is received. In one embodiment, the transmission is passed directly to the dispatcher 60. In an alternative embodiment, the transmission from the router 30 is cached and provided to the dispatcher 60 depending on available processing resources. In step S220, the dispatcher 60 identifies the device that generated the received sensor data and checks for an active handler 70 corresponding to the identified device. If a corresponding handler 70 is located, the sensor data is forwarded to the handler 70. Alternatively, if no active handler 70 is found, the dispatcher 60 determines whether the identified device is compatible with the system 10. If compatibility is detected, the device information is logged and no further action is taken. On the other hand, if the device is deemed compatible, the dispatcher 60 creates a handler instance with the correct driver 71 for the measured device in step S240.

[0389] FIG. 7 illustrates further processing implemented in the dispatcher 60 of the processing server 40. After receiving sensor data from multiple sensors 20 through the router 30 and identifying the specific device or type of device that generated the sensor data, the handler 70 is instantiated with the correct driver 71 in step S310. In step S320, the patient registration device 41 and associated database 45 are contacted to discover whether the measured device ID is associated with a patient ID. If the device is not assigned to a patient ID, the device ID is associated with the patient ID registered with the router 30 to which the sensor data was communicated. If the device ID is discovered to be associated with a patient ID other than the one registered with the router 30, the device ID is reassigned. In step S330, a counter begins waiting a predetermined length of time during which sensor data is expected to be received. If sensor data is not communicated from the router 30 during this time, a timeout count is initiated and gradually incremented. If a timeout condition is reached before any sensor data is received, the handler instantiates itself in step S370. In one aspect, the handler terminates immediately after processing the transmission of the sensor data so that system resources are not idle during transmission. Once the sensor data is received, a timeout counter is reset in step S340, after which a timestamp corresponding to the time of receipt is created in step S350. In step S360, the transmitted sensor data is parsed by the driver. The parameterized data, patient ID, device ID, and timestamp are then placed in a cache ready to be added to the database 90, and the process returns to step S330 to wait for more sensor data. If monitoring information is included in the sensor data, the handler 70 returns to step S330. In one aspect, the handler 70 can take corrective action according to the driver 71, for example, attempting to switch to another driver 71 if available. If the sensor data cannot be understood by the handler 70, an error condition is logged and the handler self-terminates upon proceeding to step S370.

[0390] In one aspect, the process can be adapted for use with a bed ID. A bed registration service can be contacted at or before S320 to determine whether a device ID is associated with a bed IR and / or a patient ID. The system can then verify whether the bed ID is associated with a patient ID. In this, or by a separate process, the device ID is linked to the patient ID through the bed ID.

[0391] 8 illustrates the process implemented in database 90 of processing server 40 for storing parameterized data in time series. In step S410, database 90 receives either new parameterized data output by handler 70 or a request for patient data already stored in database 90. In the former case, a check is made in step S430 to determine whether a time series exists in database 90 for the sensor, parameter, and / or patient that is the subject of the request. If no series exists, the newly received data is appended to the existing series. Alternatively, a new series is created and the new data is added as the first entry.

[0392] If there is a request for patient data, a check is made in step S420 as to whether the patient identified in the request is included in database 90. If not, a corresponding error message is generated. Alternatively, a further check is made in step S440 to determine whether a time series exists in database 90 for the patient and / or any particular parameters that are the subject of the request. A negative result returns an error message in response to the request. If a time series is identified, the series is queried in step S450 to determine whether measurements exist for any particular time ranges identified in the request, and the requested result is either returned in answer to the initial query or an error is generated stating that no corresponding data is available. Display System 310

[0393] Aspects relating to the display of parameterized data will now be described in more detail. It has been discovered that displaying the data as described below allows for simpler and easier monitoring by the patient, user, and / or clinician. The data is further collected in real time and displayed substantially in real time, thereby enabling real-time monitoring and real-time alarms by the clinician. Here, "real-time" and "substantially real-time" are used to refer to data being processed and displayed immediately as soon as it is generated, transmitted, and received. Thus, "real-time" depends on the sampling rate of the sensor(s), the transmission rate of the raw data, the latency in processing the raw data into parameterized data, and the refresh rate of the display itself.

[0394] As described above, the parameterized data can be output as a time series associated with a particular patient ID and / or bed ID and can be stored as part of the patient's electronic medical record either in real time, periodically, or when specifically requested. This parameterized data can also be displayed through the display system 310, allowing the patient and clinician to visually monitor the patient's condition and interact with the available data.

[0395] In one embodiment, the display system 310 comprises a screen and one or more user input means. The user input means may take the form of a graphical user interface 400 operated by one or more of an integrated touchscreen interface, a mouse and keyboard, and / or other physical control means. Additional physical controls, such as buttons, switches, and knobs, may also be provided around the periphery of the screen to allow a user, such as a clinician, to interact with the displayed information. Those skilled in the art will appreciate that any suitable form of user input means may be utilized. As shown in FIG. 9 , the screen displays the graphical user interface 400 with a dashboard containing actively monitored patients organized into groups, where an overview of the current status of patients in each group can be inspected by the clinician through a so-called “group view” 410. This “group view” may provide a curated view of health data points for each patient in the group, including the most recently received values ​​of certain parameterized data, along with patient name, ID, and / or bed number, and, where appropriate, an indication of each patient's location within the hospital / community room. Specific parameterized data is displayed in the "Group View," which can include one or more or all of the physiological parameters being monitored, such as respiratory rate, heart rate, and SpO2. The specific selection of parameters displayed can be customized by the clinician, can be set at the organization or facility level, or can reflect the system by default. The order in which patients are displayed in the "Group View" can be alphabetical or numerical (according to either patient name or ID), or can alternatively be measured by a score, such as an early warning score, associated with each patient. Patients with higher scores can appear higher in the list or more prominently. Such scoring systems are described in more detail below.

[0396] An individual "Patient View" 420 is also accessible for each actively monitored patient and can be examined directly or by selecting a single patient entry from the "Group View" described above. As shown in FIG. 10 , in this "Patient View," the patient name and ID are again displayed. Bed ID and location may also be displayed. The name and sensor ID of each sensor associated with the patient are also displayed, along with associated parameterized data received from each corresponding sensor. In one aspect, sensor information and associated parameterized data corresponding to each sensor are automatically added to the patient view 420 upon detection by the sensor 20 forming part of the system and receipt of the corresponding data by the relay 30 or processing server 40. Conversely, when a sensor is disconnected or stops transmitting data, the corresponding display element can be removed from the patient view. Alternatively, a warning or notification can indicate the loss of data, allowing for reconnection if a sensor loss is unavoidable.

[0397] If parameterized data is received or derived from one or more medical devices 200 (such as the respiratory treatment devices described above), the device name and ID are displayed along with the associated parameters, similar to those described above. In one aspect, the most recent value received for each physiological parameter is displayed in a tile located near each sensor detail and / or medical device detail. If a sensor or medical device provides data for multiple parameters, a separate tile is displayed for each parameter. One or more time series graphs can also be displayed, displaying current and historical values ​​of parameters monitored by sensors and / or medical devices connected to the patient. A separate graph can be provided for each parameter (each graph having a common timescale), or time series for two or more parameters, whether from sensor data or a medical device, can be overlaid on the same graph. The size of the period displayed on the graph can be adjusted by selecting from a list of preset values ​​(e.g., 5, 10, 15, 30 minutes, or an integer number of days, weeks, or months) or by sliding and interacting with a scale, dial, or other control element forming part of user interface 400, thereby allowing any desired time window to be selected. The portion of the time series displayed on the graph at any one time is represented by a graphical timeline 430. In the illustrated embodiment, a highlighted area represents the period viewable on the graph. The length of the period displayed on the graph can be increased or decreased by interacting with graphical timeline 410, changing the size of the highlighted area. Similarly, the highlighted area can be shifted along the timeline to move the display position forward or backward in time, thereby allowing historical data to be viewed at higher resolution.

[0398] In a further embodiment, the display system 310 also includes indicators 440, which can form part of either or both of the "group view" and "patient view" described above. A separate indicator 440 can be provided for each monitored and displayed physiological parameter and can incorporate the parameter's most recent value, or alternatively, a rolling average over a preset or user-selected time period. In the embodiment shown in FIG. 10, the indicators are provided in the form of a radial gauge surrounding the parameter value. The gauge is segmented according to parameter value thresholds, with a lower threshold on the left and a higher threshold on the right. A pointer 441 is also provided, the position of which represents the current parameter value, which is displayed in the center of the gauge.

[0399] In one embodiment, the gauge segments are measured and colored according to the patient's associated condition. For example, a gauge corresponding to a patient's heart rate may have a green segment for values ​​between a lower threshold of 40 bpm and an upper threshold of 160 bpm, which is typically considered healthy. These thresholds may also be set to predetermined values ​​associated with the facility and / or country in which the system 10 is used. In one embodiment, the system 10 is provided with a look-up table of such predetermined values ​​and automatically uses them when the location is entered and / or detected. Thresholds may also be manually defined or selected by a technician during initial system setup, or may be edited by a clinician or user according to their specific preferences. In one embodiment, thresholds may be set by the facility, for example, based on protocols used at the facility. In certain embodiments, the gauge may be segmented and appropriately colored according to parameter thresholds of a scoring system, such as an early warning scoring system.

[0400] Alternatively, or in addition, gauges are provided to reflect component risk scores. A component risk score can be a single number reflecting a patient's risk of deterioration. A component risk score can be referred to as and / or represent a patient's health parameter, and vice versa, and these terms are understood interchangeably herein. In some cases, a component risk score includes at least a subset of measurements required for a clinical risk index, such as a composite early warning score (EWS). A clinical risk index serves to measure a patient's risk of health deterioration by quantifying the combined impact of risks indicated by individual physiological parameters. A clinical risk index can be referred to as a patient's health parameter and / or represent a patient's health status, and vice versa, and these terms are understood interchangeably herein. A clinical risk index provides a comprehensive view of a patient's condition at a given point in time. However, in many cases, a clinical risk index includes quantitative assessments and / or assessments performed by a clinician. Furthermore, a clinical risk index is also useful only when all measurements are available, usually for a specific combination of multiple physiological parameters. A component risk score uses multiple sensed physiological parameters to estimate a patient's risk of deterioration or to generate a numerical value reflecting the patient's risk of deterioration, or to estimate a clinical risk index. The component risk scores and / or health parameters may use a single physiological parameter with the greatest risk level to determine the component risk score. In some cases, a component risk score is calculated for each physiological parameter, and the calculated values ​​are then compared to determine a final component risk score, e.g., based on the maximum value.

[0401] Exemplary clinical risk indices include the Early Warning Score (EWS), the NHS National Early Warning Score (NEWS), and the Clinical Risk Index for Infants (CRIB). These indices quantify the combined impact of multiple physiological parameters in a patient. Processing a clinical risk index involves collecting parameter measurements, categorizing the risk of each individual parameter, and assessing the combined impact of that risk for an individual patient. However, if one or more parameters are missing, the combined impact cannot be calculated due to the specificity of the missing values ​​and index requirements, and / or the inability to calculate a risk index. For example, some risk indices require a clinician to verify the patient's condition, for example, to measure consciousness. If this verification is unavailable or cannot be measured in real time, an official risk index cannot be calculated. In this case, a component risk score can be used instead. A component risk score measures the risk posed by each measurable factor (or, if measurements are available, the risk). Thus, a component risk score attempts to predict a risk index based on available measurements. In some cases, this is equivalent because the component risk scores can use the maximum risk score of any one of the measured parameters (which is often equivalent to the risk index process). In some cases, multiple risk indices, or risk scores, can be calculated in parallel. This can be advantageous when different indices or scores target different potential issues or categories of risk.

[0402] Calculating and displaying a patient's component risk score in real time allows clinicians to assess the patient and alter treatment before the patient deteriorates too severely. The score can be derived from one or more physiological parameters, such as systolic blood pressure, heart rate, oxygen saturation, respiratory rate, and temperature. An index can add additional physiological parameters, such as level of consciousness. The score can be calculated according to an average-weighting system, in which each component physiological parameter is assigned points according to its degree of physiological abnormality (i.e., deviation from expected normal values). In some cases, the aggregate score reflects the maximum risk component. The maximum risk component represents the physiological parameter with the greatest risk to the patient. The maximum risk component is calculated by calculating and comparing individual risk components for each physiological parameter. This can be simplified by normalizing the individual risk components before comparison. This is an advantage of component risk scores, as it eliminates the need to have all of the risk index data before calculating the risk score. Components can be normalized to the score scale. For example, if respiratory rate (in yellow) and temperature (in red) are monitored, the aggregate score will be red (or is a calculated score in the red region). Normalization can help compare component risk score measurements to each other to measure maximum individuality. Alternatively, raw scores can be compared to known relationships or scaling between scores to accommodate potentially different units of individual risk scores.

[0403] 11 and 12 show an exemplary clinical risk index framework in the form of an early warning score matrix from the Adult Early Warning Scoring System used in New Zealand beginning in 2021, including color-coded zones associated with each index or score value, and specifying risk bands or risk categories and associated ranges for component physiological parameters. A component risk score can include any one or more component physiological parameters. In some cases, a component risk score can include any one or more of the component physiological parameters that are measurable or sensible. Alternative clinical risk index and scoring frameworks can be used. Alternative component risk scores can be developed from these frameworks. The frameworks used may vary from country to country or region to region. In one aspect, gauges are compartmentalized according to these score associations and color-coded accordingly. In use, the particular clinical risk index framework, component risk scores, and / or early warning score system employed may also vary by patient, facility, or location, and separate systems (and thresholds) are contemplated for use in maternal and pediatric applications.

[0404] In one embodiment, each measured parameter of the component risk score is compared to one or more thresholds, where the value of the measured parameter relative to the threshold is shown on the display. These thresholds can be related to the early warning score matrix shown in FIG. 11, where each measured parameter is compared to a corresponding threshold defined in the early warning score framework and assigned to a corresponding risk band or category (white, yellow, orange, red, and blue in the illustrated example). In particular, each parameter can have multiple corresponding thresholds representing various (i.e., worsening or improving) conditions of the patient. For example, a measured respiratory rate from a patient wearing a sensor, under-bed sensor, or medical device is measured in real time or substantially in real time. The measured respiratory rate is compared to various thresholds of the early warning framework. The measured respiratory rate is classified based on the comparison to the thresholds. In one particular scenario, if the respiratory rate is less than 5 breaths per minute, the patient is classified as being in the blue risk band or category, indicating that they require urgent attention / resuscitation. Similarly, if the respiratory rate is between 5 and 8, the patient is classified as being in the red risk band or category. Exemplary thresholds for the early warning framework are defined in the table shown in Figure 11. SpO2 (or any other measured parameters, alone or in some combination) can also be used to establish patient status within the early warning framework. The same or similar thresholds and / or measurements can also be used for the component risk scores, or these can be adapted from other clinical risk indices.

[0405] The system further allows for custom thresholds to be defined by a user, technician, or clinician. It is contemplated that individual hospitals can set custom thresholds and define custom early warning frameworks or component risk scores. For example, component risk scores may depend on commonly available sensors or measurements. Alternatively, thresholds can be customized for each patient. Aspects of the present disclosure provide for customization of both the inputs and outputs of the system, advantageously allowing hospitals or clinicians to provide the required reporting and / or monitoring as defined by regulations or hospital protocols. This can be centrally managed due to the open nature of the system and the flexibility of the system architecture in storing and displaying patient health parameters, patient data, or the appearance of one or more community rooms.

[0406] Based on the thresholds for the patient and the measured component risk scores, appropriate actions can be taken, as defined (for example) in the table of FIG. 12. Additionally or alternatively, actions can include generating an alarm in an appropriate hospital alarm system or automatically sending a message to a clinician containing data regarding the particular measured, exceeded parameter(s) and threshold(s). These actions can also include recording each instance (and associated metadata) where a measured patient parameter is outside a particular threshold. Such instances can be added to the patient's electronic medical record. Thus, if a parameter falls below a threshold, increases above a threshold, or is outside a threshold range, the system can identify when this occurred and for which parameter and report, as appropriate. In cases where a patient ID or bed ID, including one associated with a location and then associated with a clinician, is associated with a clinician, an alarm can be sent directly to the associated clinician. This reduces the number of alarms or alerts for each clinician. For example, in some cases where an alarm is not responded to or rejected within a certain period of time, an alarm or alert can be sent to a second clinician or to a hospital alarm system. Alarms or alerts can be customized for the emergency situation or condition that triggered the alarm. For example, a graph of a physiological parameter can be included in the alarm or a component risk score can be shown.

[0407] Although indicator 440 is shown as a radial gauge, those skilled in the art will appreciate that any suitable graphical representation may be used, such as a linear gauge, a bullet graph, or a pie gauge.

[0408] 13 shows a version of the patient view 420 described above, according to an embodiment in which a patient is receiving therapy from a medical device 200 (such as the respiratory therapy device described above). Here, the display includes a graphical representation (e.g., in the form of a time series graph) of therapy settings over time, including an indication of the operating mode of the therapy device and usage data, along with corresponding measured physiological parameters and indicators 440. This allows a clinician to observe the effectiveness of the therapy and intervention by reviewing the patient's measured physiological parameters and any subsequent changes. It is contemplated that providing access to a patient's therapy history, particularly along a shared timeline over which physiological parameters were monitored, can be useful for establishing a baseline of how the patient is likely to respond to therapy settings based on previously observed responses.

[0409] In one aspect, the system addresses the challenge of handling sensor data from multiple sensors. While more patient information is useful for clinicians to make decisions about actions or treatments, it does not directly identify the patient's problem when provided by multiple independent sensors. By combining sensor outputs with real-time displays and presenting them to focus on patients of greatest concern, the system provides improved insight into patient health. Component Risk Score

[0410] As mentioned above, there are many assessment frameworks that are used in clinical settings to aggregate various inputs and categorize a patient's risk of deterioration. Often, a combination of physiological and subjective measurements is used to derive an output, such as a score or risk band / category, that can inform care decisions.

[0411] The Early Warning Score (EWS) framework described above is one example of a clinical risk index that is widely used in hospitals. For each metric or contributing physiological parameter, a score or clinical risk index is determined based on where the value of the metric or parameter falls between predefined thresholds. Based on this score, patients are placed into five color categories or risk bands (e.g., white, yellow, orange, red, and blue) associated with the framework that represent increasing risk of poor health. The illustrated example corresponds to the Early Warning Score framework described above. It is contemplated that any particular color or risk band can be used.

[0412] Traditional clinical practice is to take "instantaneous snapshots" of a patient's condition by taking measurements of their physiological parameters and calculating their clinical risk index and corresponding risk band at regular intervals. However, when reviewing a chart of a patient's risk of deterioration over time, the resulting chart resembles a series of steps as the patient's clinical risk index changes and changes sufficiently to cause a shift in risk band / color category. When a patient remains in a single risk band for an extended period of time, risk progression is not apparent until they change between bands. However, the physiological parameters that contribute to the clinical risk index may fluctuate within the boundaries of the current band / category. These fluctuations are invisible to clinicians due to the large range of physiological parameters that correspond to a single risk band / category. This challenge is common to most clinical risk index frameworks.

[0413] A further challenge arises from the fact that for many clinical risk indices, the range of values ​​for the various physiological parameters corresponding to each risk band or category may differ, making it difficult to compare the relative risk presented by each contributing parameter to the overall risk associated with the patient.

[0414] In one aspect of the present disclosure, component risk scores are adapted from clinical risk indices. FIG. 14 illustrates how aspects of clinical risk indices can be incorporated into component risk scores. A component risk score can incorporate one or more clinical risk index measurements or measurements related to or based on clinical risk index measurements. By using only available measurements and scaling the score to the maximum or minimum combination of available measurements, a component risk score can continuously estimate a clinical risk index. In some cases, as shown in FIG. 14, clinician-reported values, such as consciousness, are not included in the component risk score. In some cases, this is because such reported values ​​cannot be measured in real time or require external evaluation. Alternatively, a component risk score can include both measurement data and clinically reported values ​​and / or patient demographics (e.g., age, weight, height, and length of stay). In one aspect of the present disclosure, the intercategorical range of parameter values ​​is normalized for each contributing measurement of the component risk score. This increases the granularity of risk information available to clinicians, allowing them to directly compare the relative risks presented by various measurements using integral values. In some cases, normalized values ​​allow the system to rank the risk scores for each contributing measurement within a risk band. However, it is possible to instead use raw values ​​and / or have risk bands defined by raw values. This allows tracking patient trends within a single risk band or category, for example, with a linear scale. In some cases, the scale is non-linear, highlighting changes in areas of greatest concern. For example, the scale can be non-linear to highlight changes for high-risk patients, one step above low-risk patients who show less variation.

[0415] The use of component risk scores can be particularly useful because they allow tracking of patient risk in finer increments than the risk band or category they fall into, allowing clinicians to track a patient's overall risk more granularly within each band (rather than simply between bands), making patient progression more visible.

[0416] FIG. 15 shows an exemplary component risk score (in this case, referring to the early warning score described above) along with the corresponding normalized component risk score generated according to one embodiment. The normalization process for the component risk score or early warning score framework can involve dividing the five risk categories or bands into 10 discrete values, so that patients can be graded on a 50-point risk scale ranging from white (0) to blue (40+) across the risk bands. As shown in FIG. 11, for each measurement type (i.e., contributing physiological parameter), the range of values ​​corresponding to each category or risk band may vary. Therefore, min-max normalization is used to ensure that the output normalized clinical risk index always reflects the percentage distance to the next risk band and from the previous risk band.

[0417] In one embodiment, the normalized component risk score is calculated according to the following formula: JPEG2025539068000003.jpg18170 where the measured value is the value of a physiological parameter (e.g., the patient's respiratory rate) obtained from sensor data. The lower risk threshold is the parameter value at the edge of a risk band that marks a move to a lower risk band. Conversely, the upper risk threshold is the limit at the edge of an adjacent higher risk band. The lower risk score is the lower limit of the new normalized component risk score for that risk band. The number of scalers is equal to the normalized component risk score range applied to each risk band (i.e., 10 in this example, but the number of scalers can be varied to any number depending on the desired point scale of the normalized component risk scores).

[0418] In a working example where a single physiological parameter (e.g., respiratory rate) is monitored, a patient breathing 6 breaths per minute would fall into the red risk band of the early warning score framework (as shown in FIG. 11). According to FIG. 11, this would result in a lower risk threshold of 8 and an upper risk threshold of 5. As can be seen in FIG. 15, the lower risk score for the red risk band is 30. Applying these numbers to the above formula results in a normalized component risk score of 37, where the red risk band corresponds to any normalized component risk score that falls within the range of 30-37. Thus, it is immediately apparent that this patient occupies a relatively high-risk position within their current risk band and may receive treatment priority over other patients who are lower risk but would otherwise be in the same risk band.

[0419] If the patient's respiratory rate changes to 5 or 8, a traditional early warning score framework would only indicate the patient would remain in the red risk band with a score of 3, but the normalized component risk score would change from 37, indicating the patient is at higher or lower risk within the red risk band. In this way, the normalized clinical risk index is an improvement over the non-normalized index not just because increased granularity is provided as to the patient's location within each risk band, but also because the normalized component risk scores provide an indication of how (and when) a patient is likely to move from one risk band to another as they change (and at what rate) toward the risk band thresholds.

[0420] In further aspects, individual component risk scores or normalized component risk scores (hereinafter, use of component risk scores will be understood to also or alternatively refer to normalized component risk scores, where possible) can be calculated for each of multiple physiological parameters, including, but not limited to, respiratory rate, heart rate, SpO2, blood pressure, body temperature, carbon dioxide level, and temperature. The calculated individual component risk scores are then used to calculate an overall, or combined, component risk score. In one aspect, the combined component risk score is set to the maximum value of the individual component risk scores. In further aspects, the combined component risk score is determined by averaging the values ​​of the individual component risk scores. In some cases, component risk scores can account for differences in measurements, for example, weighted averages or normalized component risk scores can be used. In an alternative aspect, the combined component risk score is determined by a weighted average of the individual component risk scores, where the weighting is based on a predetermined rule set that may optionally depend on the framework or number used and / or the particular combination of physiological parameters being measured. The overall component risk score can also be set by a clinician according to their preferences. In still further embodiments, the overall component risk score is determined by summing two or more of the individual component risk scores and comparing the sum to a predetermined threshold, which again may depend on the scoring framework, the number and / or the particular combination of physiological parameters being measured or set by the clinician.

[0421] 16 shows a patient view 420 according to one embodiment. A graphical display of the monitored parameterized data and corresponding sensor information is provided along with the component risk scores (normalized in this case) displayed over time. This can be an individual component risk score derived from a single physiological parameter, or an overall component risk score derived from multiple physiological parameters as described above.

[0422] The time series graph can be accompanied by indicators 440 as described above. In one aspect, visual indications of risk bands associated with various thresholds of component risk scores extend across the time axis of the graph. Figure 16 shows this in horizontal grayscale bars, but these can be color-coded to correspond to the component risk scores.

[0423] In one aspect, the time series graph is updated in real time as new data is received from the sensors and more recent values ​​of the component risk scores are calculated. Indicators 440 can provide a rolling average of the component risk scores over a preset or user-selected time period. Alternatively, indicators 440 may be updated only when data is received that results in a change in the displayed value of the component risk score. Thus, the clinician is presented with a continuous view of the patient's risk over time, which contributes to improvement rather than the incremental jumps in risk bands that would otherwise be displayed without the component risk scores.

[0424] FIG. 17 illustrates a patient view according to a further embodiment in which component risk scores are displayed along with the live patient data described above in connection with FIG. 10 , thereby allowing a clinician to visually compare trends in the component risk scores with the constituent health indicators. In embodiments in which an overall component risk score is calculated from two or more component physiological parameters, the order of the graphs on the screen can be such that the data associated with each physiological parameter is presented in descending order of its contribution to the overall component risk score, i.e., physiological parameters that indicate the patient is at higher risk can be located higher on the screen or otherwise in a more prominent position. This allows a clinician to visually compare trends in the component risk scores with the constituent health indicators.

[0425] In a further embodiment, treatment details or parameters of the treatment being administered to the patient may also be displayed as described above in connection with FIG.

[0426] In one aspect, the patient view can show changes to live patient data over multiple time periods. For example, FIG. 18 shows patient data for three time periods, although more or fewer time periods can be used. For each time period, average data can be used to represent changes to the patient data over that time period. FIG. 18 shows three one-week time periods 500. For each time period 500, an average value is calculated. The median 501, upper quartile 502, and lower quartile 503 are shown. This consolidates patient changes over longer time periods into the patient view. This distills the time series data stored by the system into metrics on which the patient's health depends. As shown, interpolation can be used to connect the time periods to create a visual connection between them. The interpolation is shown as a straight line, but an estimated curve or alternative interpolation can be used. The patient view can include risk categories 506, shown in this case as horizontal bands. This helps the patient view show changes in patient data compared to expected performance.

[0427] In some cases, the time periods may be longer or shorter. For example, daily periods, hourly periods, four-hourly periods, monthly periods, or integer days, weeks, or months. The number of periods may vary, for example, 4 or 7 (representing days of the week, respectively) representing weeks in a month. Patient views can be switched to see different measurement data and / or multiple data plots can be displayed in each view. Patient views can have a list of preset values ​​(e.g., 5, 10, 15, 30 minutes, or integer days, weeks, or months). Using a time series database allows the system to quickly display time period views and / or update them in real time as data is quickly retrieved from the time series database. A time series database is a database optimized for storing and functioning time series with associated pairs of period(s) and value(s). This allows for the generation of profiles or curves based on the data. Each data point in the database is associated with a timestamp. In some cases, the time series database does not store data indefinitely or reduces the amount of old data stored. This can be achieved, for example, by downsampling or deletion. The time series database can store a predetermined period, or window, of data (e.g., last year, last month, last week). Time stamps can be used as key indices to improve processing of the data. In some cases, the system ensures that each sensor measurement has an accurate time stamp for entry into the time series database. One example of a time series database is InfluxDB™.

[0428] FIG. 19 shows an alternative patient view of patient data over different time periods. Radial graphs are shown to represent five patient data values ​​(in this case, respiration rate, temperature, heart rate, SpO2, and blood pressure). Values ​​are shown for two time periods 601, 602. These can be values ​​at specific times or can be minimum, maximum, and / or average values ​​over the time period. The radial graphs allow the values ​​of each measured value to be compared across time periods. For example, a clinician can note that heart rate has increased significantly while blood pressure has remained relatively constant. Radial graphs can be used to show the clinician the changes in a series of characteristics over time without the clinician having to review each individual measurement and / or the entire time series. Again, a time series database can quickly generate the data needed for these graphs to store the data in time series.

[0429] In some cases, alternatives to radial graphs can be used, such as area charts, lollipop charts, radial column charts, and star charts. In each case, multiple variables can be displayed in a single view, showing measurements from multiple times or periods (or period averages). In some cases, risk categories 606 can be shown on the chart, allowing measured data to be compared to, for example, potential risk. Although two periods are shown, more or fewer periods can be used, for example, because time series information is available and animations can show changes over time. Period charts can also be used to measure sensor accuracy or performance, since inaccurate readings would be noticeable when reviewed by a clinician.

[0430] 20 illustrates a group view in accordance with a further aspect. As shown, the display screen is divided horizontally into multiple sections 1000, each representing a particular risk band in the component risk score being utilized. In the illustrated embodiment, the screen is divided into five sections 1000, corresponding to the white, yellow, orange, red, and blue risk bands / categories of the early warning score framework.

[0431] Each patient of a group of monitored patients is represented in group view by a tile 1100 displayed in section 1000 of the display according to the component risk score and risk band within which it falls. The tile can reference bed ID and / or location, allowing a clinician to quickly locate the patient in addition to or on behalf of the patient. As a patient's component risk score changes, the display updates, causing the tile to be repositioned within its section and eventually moved to another section corresponding to the new risk band (if the score changes sufficiently). In one aspect, the location of the tile within a section can be driven by a time average of the component risk scores, where the average is calculated over a period predetermined or set by the clinician, so that the patient tile does not flicker between sections of the display. In a further aspect, the movement of a patient tile from one section to another can be governed by one or more predetermined rules in addition to the time average of the component risk scores, for example, if the index value indicates that the patient falls into a different risk band for a certain number of times within a predetermined period. In some cases, the system can exhibit hysteresis to slow movement between sections. For example, the boundary between compartments may not be a single number (e.g., 30), but instead can be a larger number when entering a compartment (e.g., 32) and a smaller number when leaving a compartment (e.g., 28). In this way, a patient cannot move between compartments as quickly, and monitoring can continue, for example, until the patient has completely left the high-risk compartment.

[0432] A group view can be a multi-communal room view. A multi-communal room view allows patients to be monitored simultaneously across multiple rooms and / or communal rooms. A multi-communal room view allows clinicians to have a clear overview of all patients across a facility or hospital, or across home care locations on a single display. In some cases, a multi-communal room view can be customized to allow clinicians to see a subset of patients across one or more facilities. For example, a clinician may want to see the patients they are responsible for, which may be spread across multiple locations. A multi-communal room view provides the ability to see all of these patients in real time and view the most important information or highest-risk clients in the most detail.

[0433] Each tile contains key identifying information, such as the patient's name, ID number, room, and / or bed assignment. Additional health information may be displayed, including, but not limited to, the overall component risk score associated with the patient, the most recent values ​​of the physiological parameters that most strongly contribute to the overall component risk score, any other contributing physiological parameters being actively monitored, and an indicator 1200 of parameters associated with any therapy the patient is receiving. Any such therapy parameters are not used in calculating the component risk scores, yet provide context to clinicians monitoring patients in this view. This allows clinicians to directly compare metrics that drive health changes between patients shown on a single screen. Exemplary tiles are shown in FIG. 21.

[0434] In one aspect, the indicator 1200 of the overall component risk score is color-coded according to the trend in its value. That is, if the most recent consecutive calculation of the component risk score indicates a downward trend in patient risk (within a risk band or between adjacent risk bands), the indicator 1200 can be color-coded green. Conversely, a trend indicating an increasing risk may result in the indicator being color-coded red. Patients with stable indices, i.e., scores that show no substantial trend in either direction, may be color-coded gray or not color-coded. Trends in component risk scores can also be measured by an increase or decrease in the rolling average of the component risk score; in either case, the patient tile is updated and made more prominent in the group view. It is contemplated that alternative visual trend indicators can be used, including the size and font used to display the current value of the component risk score or the color of the tile's associated graphical element.

[0435] In one aspect, the data contained in the patient tile (or equivalently, the bed tile), as well as how it is displayed (e.g., layout, font size, color, typeface), is dynamically updated in real time or near real time based on one or more of the magnitude of the patient's component risk score, the trend of the component risk score, the time since updated data about the patient has been received, or the time since the patient was last seen by a clinician. The display of the tile itself (e.g., size, background color, outline) can also be updated in real time or near real time based on one or more of the above factors.

[0436] 22 and 23 show group view sections associated with particular risk bands, along with patient tiles within each section. As shown, patients with component risk scores trending in one direction or another are given priority in displaying tiles within their section. In some embodiments, tiles for patients whose sections have recently changed from one risk band to another are also given priority in displaying tiles. In one embodiment, this involves expanding the patient tiles and positioning them toward the top of their corresponding section. Any expanded tiles within a section can be organized by gradient of change in component risk score, its magnitude, or some combination of both factors. In certain embodiments, the expanded tiles contain more useful information for the clinician, such as an expanded set of vital measurements corresponding to physiological parameters being actively monitored for that patient. In this way, patients most likely to move between sections (or risk bands) are highlighted to the clinician over patients more likely to maintain their current risk level.

[0437] Patients with stable component risk scores or with scores below a certain threshold that have a risk band may have their tiles minimized and displayed at the bottom of the corresponding display pane. In one aspect, these minimized tiles contain the same data as provided in the expanded tiles, albeit reduced in size and emphasized, with patient data still available to the clinician but without sacrificing more prominent display of patient tiles representing patients more likely to require attention. If any of these patients' component risk scores become unstable, their tiles can be expanded and repositioned toward the top of the pane. This allows a clinician or observer of the system to gauge at a glance which patients in their care are at greatest risk without the need for a control and menu interface.

[0438] In one embodiment, the appearance of patient tiles may change depending on the compartment they are displayed in, i.e., the risk band the patient falls in. Figure 24 shows an embodiment in which patient tiles in the blue compartment (or highest risk band) include a larger, bold font.

[0439] In one aspect, the size of each section may vary depending on the relative proportion of patient tiles displayed in that section. For example, when a large number of patient tiles fit into the red section, the sizes of the white, yellow, orange, and blue sections may be reduced to ensure that all patient tiles in the red section are simultaneously visible on the display screen at the appropriate resolution and size. In an alternative aspect, tiles for patients in sections corresponding to lower risk bands may be reduced in size to accommodate the display of all patient tiles in sections corresponding to upper risk bands. These reduced tiles may show less information than would otherwise be the case, such as fewer vital metrics. In a further aspect, any sections without patient tiles may be minimized altogether or removed from the display. The group view described above improves current clinical practice by streamlining risk assessment. Patients are arranged on the screen based on their risk level, health trends, and relative to other patients in the group. Patients needing clinician attention are most visible and can be seen first on the dashboard. Furthermore, clinicians can view all of the above information on the patient tile, plus the risk level associated with each patient in their care, on a single screen, without having to access a computer mouse or touchscreen access menu or interface. This allows the most relevant information to be presented to clinicians from the vast amount of data and information within the patient management system. Furthermore, the live, real-time nature of the data used to measure patient physiological parameters and component risk scores means that the patient tile is constantly updating and moving around the screen accordingly. This means that the display can ensure that clinicians focus on the most pressing patients and / or tasks. Similarly, the use of optionally normalized component risk scores reduces the amount of space required to display information.

[0440] In one aspect, the display of patient tiles (or the way they are displayed) can be dynamically adjusted depending on the characteristics of the display. For example, a display can have registered users, such as clinicians. The display can then update to show and / or highlight and / or enlarge only the tiles for which the nurse is responsible. In this way, the clinician's attention is directed to the patients (or beds in a room or shared room) for which they are responsible, and a limited display area focuses on or shows only data for these patients. Alternatively, a characteristic can be the location of the display. For example, the display can show and / or highlight and / or enlarge tiles that are in the same room as the display. In some cases, the display is within RFID range of a bed detector system (such as the RFID system described above).

[0441] In one aspect, the display is mounted in a location such as a room or a shared room. The display is configured to show tiles for each patient in the room or shared room. The display can be configured to determine from the system which patients are in the room or shared room and display only those patients present. The display of each tile can be adjusted as described above based on the component risk score and / or how close the clinician is to the patient. For example, if a clinician is standing next to a patient, the display can show substantially only that patient. This allows the display to clearly indicate to staff the current situation at a particular location. Tiles can be enlarged and / or highlighted as described above to indicate specific areas of concern. Discharge System

[0442] Aspects relating to the display of parameterized data have been described with reference to monitoring a patient during care by a clinician. However, additional views of the parameterized data can be used for other purposes. In some cases, the display can switch between alternative views depending on clinical requirements. For example, a first view for monitoring the patient's health and a second view for considering whether the patient can be removed or discharged. The alternative views can be based on component risk scores (or risk indices). The component risk scores can be the same for each view (e.g., in different representations), or alternative component risk scores can exist. The alternative views can be based on alternative metrics. In this example, consider a discharge score. The discharge score is an indicator of the patient's risk of being discharged. The discharge score can consist of or include the component risk scores. The discharge score can use adjusted component risk scores compared to patient monitoring or use different measured characteristics to better represent the patient's discharge risk.

[0443] FIG. 25 illustrates a group view in accordance with a further embodiment. This embodiment is configured, for example, to allow a clinician to identify patients ready for discharge. However, alternative features or combinations of features can be used to create and display patient bands. As shown, the display screen is divided horizontally into multiple sections 2000, each representing a particular risk band for the patient's discharge. In the illustrated example, the screen is divided into five sections 2000, each corresponding to an exemplary risk band associated with the patient's discharge. As described with respect to component risk scores, the discharge score can be based on or incorporate component scores and / or one or more values ​​measured in real time or near real time. Customization of the discharge view advantageously allows a hospital or clinician to provide required reporting and / or monitoring prior to discharge, as set by regulations or hospital protocol. This can be centrally managed due to the open nature of the system and the flexibility of the system architecture in storing and displaying patient health parameters, patient data, or the appearance of one or more shared rooms.

[0444] Of multiple patients being monitored, each patient is represented in group view by a tile 2100 displayed in section 2000 of the display according to the discharge score and risk band within which they fall. As a patient's discharge score changes, the display is updated, causing the tile to be repositioned within its section and eventually moved to another section corresponding to the new risk band (if the score has changed sufficiently). In one aspect, the location of the tile in a section can be driven by a time average of the discharge score, where the average is calculated over a clinician-preferred or clinician-set period, so that the patient tile does not flicker between sections of the display. In a further aspect, the movement of a patient tile from one section to another can be governed by one or more predetermined rules in addition to the time average of the discharge score, e.g., if the score value indicates that the patient falls into a different risk band for a certain number of times within a predetermined period. For example, if a patient appears to have the lowest discharge risk but was only recently placed in this category or is periodically removed from this category, they can be placed in an upper risk category until their discharge score improves.

[0445] Each tile is displayed containing key identifying information, such as the patient's name, ID number, and room or bed assignment. Additional health information may be displayed, including, but not limited to, an indicator 1200 of the patient's associated overall discharge score, the most recent value of the physiological parameter that most strongly contributes to the overall discharge score (e.g., the greatest risk component potentially limiting discharge), any other contributing physiological parameters being actively monitored, and parameters associated with any therapy the patient is receiving. Any such therapy parameters are not used in the discharge score calculation, yet provide context to clinicians considering patient discharge in this view. This allows clinicians to directly compare metrics that guide health changes between patients shown on a single screen.

[0446] The discharge score can also, or alternatively, use additional algorithms or measurements to determine the risk category associated with a patient's discharge. For example, algorithms such as decision trees, rule engines, or flow charts can be used to determine the patient's category. Characteristics can include the patient's measured trends, patient demographics (e.g., age, weight, ethnicity, sex), or treatment characteristics (disease, diagnosis, treatment applied, treatment device used, time on treatment). The patient's measured trends can include the discharge score, a portion thereof, or physiological measurement data collected using a real-time patient monitoring system. In some cases, treatment characteristics can include initial diagnosis; diagnostic records from the patient's stay at the facility; observations by caregivers or clinicians; and / or comorbidities and past medical history. In some cases, socioeconomic factors, such as the patient's support network and / or home environment, can also be considered. Patient, treatment, and / or socioeconomic data can be entered into the system by the clinician, the patient, or can be entered from an electronic medical record or patient medical record. Additional patient, treatment, and socioeconomic characteristics can also be included as available.

[0447] In one aspect, the discharge component risk score attempts to consider at least the patient's current condition and the patient's predicted future condition. Patient demographics may be useful in examining future risk of readmission and / or risk of deterioration outside of the care facility. Predictions of expected discharge can use data on historical patient performance (using the current system or from corresponding data).

[0448] In one aspect, the system is configurable to allow the discharge score or discharge risk level to be adjustable at or within each facility, allowing each facility to configure the desired discharge process to suit their practice. The system allows for adjustment of any one or more of the inputs, weightings, and categorizations.

[0449] Using additional displays, tiles and tile features such as those shown in FIGS. 21, 22, and 23 can be used with a focus on the discharge score. In one aspect, the indicator 1200 of the overall discharge score is color-coded according to the trend in its value. That is, if the most recent consecutive calculation of the discharge score indicates a downward trend in the patient's discharge risk (within the risk band or between adjacent risk bands), the indicator 1200 can be color-coded green. Conversely, a trend indicating an increasing risk may result in the indicator being color-coded red. Patients with stable indices, i.e., those not showing substantial trends in either direction, may be color-coded gray or not color-coded. The coloring can depend on the risk level; a stable low risk can be colored green to also suggest appropriate discharge. The trend in the discharge score can also be measured by an increase or decrease in the rolling average of the discharge score; in either case, the patient tile is updated and made more prominent in the group view. It is contemplated that alternative visual trend indicators may be used, including the size and font used to display the current value of the discharge score or the color of the associated graphical element of the tile.

[0450] In one aspect, the data contained for a patient, as well as how it is displayed (e.g., layout, font size, color, typeface), is dynamically updated in real time or near real time based on one or more of the magnitude of the patient's discharge score, the trend of the discharge score, the time since updated data about the patient was received, or the time since the patient was last seen by a clinician. The display of the tile itself (e.g., size, background color, outline) can also be updated in real time or near real time based on one or more of the above factors. Although described as a discharge score, in some aspects the discharge score relates to a surrogate indicator or requirement, such as any one or more of patient monitoring, discharge, or a change in therapy. In some cases, the system can perform tasks based on the displayed data. For example, the system can identify or alert patients who are potentially ready for discharge. For example, if a patient indicates that they are ready for discharge or is potentially ready for discharge, the system can prepare a discharge form. The discharge form can be provided to the clinician electronically, for example, via a link on the display. The clinician can perform steps to complete the discharge decision after receiving the form. An indicator on the display can show that the form or other material is ready. In one aspect, the display can determine whether to show a discharge view or a monitoring view based on patient risk. For example, if the patient is in a high-risk band, the display can only show a monitoring view.

[0451] In one aspect, the system includes a secondary display or device, which can provide alerts or alarms to a portable computing device, such as a tablet or cell phone. Alerts to the secondary device can be limited to a subset of possible alerts. For example, alerts can be sent to the secondary device only at certain risk levels or based on a subset of patients (e.g., a clinician's direct care). In one aspect, alerts can also flash or be announced on the primary display. Using a secondary display to receive targeted alerts reduces the number of alarms or alerts for each clinician. For example, in some cases where an alarm is not responded to or rejected within a certain period of time, the alarm or alert can be sent to a second clinician or to a hospital alarm system. Alarms or alerts can be customized for emergency situations or conditions that trigger the alarm. For example, an alarm can include a graph of a physiological parameter or show a component risk score.

[0452] In one aspect, the display only shows a selection of patients in the discharge view. For example, the display can show only the lowest band or the lowest two bands. This reduces the complexity of what is displayed on the screen and allows for a focus on patients who are most likely to be discharged. This reduces the complexity of what is displayed on the screen and allows for a focus on patients who are most likely to be discharged. The use of the discharge view allows for a large amount of information to be clearly displayed on the display by directing the clinician's attention to the most relevant or most urgent information.

[0453] In healthcare environments, the time available for healthcare providers to monitor patients is limited (either by directly interacting with data provided by sensors / medical devices or by reviewing the patient's updated medical records). Conventional practice is to review data output by patient sensors / medical devices at regular intervals, such as every four hours. One or more of the above-described aspects can advantageously provide an efficient system for efficiently and continuously collecting, processing, and evaluating data from multiple patient sensors and / or medical devices and automatically updating corresponding electronic medical records and display screens in real time. This allows for remote, continuous, and immediate automated surveillance of a patient's condition, enabling early detection of any deterioration in the patient's health or condition.

[0454] Thus, without increasing the workload required of the healthcare professionals themselves, but rather by utilizing sensors and / or medical devices to continuously monitor and report on a patient's condition in real time, and by providing a system that can dynamically receive and process data from such sensors and / or medical devices, the healthcare professional's response to a sudden deterioration in a patient's condition can be dramatically improved. In particular, the real-time aspect provides the healthcare professional with greater visibility and certainty, allowing for earlier intervention if a patient's condition worsens. Furthermore, prompt identification of patients showing signs of clinical deterioration can result in improved patient outcomes in care. This is typically expressed as a reduced length of care, such as admission to an intensive care unit (ICU), or a reduced acuity of care.

[0455] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, or any combination thereof. When implemented in software, firmware, middleware, or microcode, program code or code segments to perform the necessary tasks can be stored in a machine-readable medium such as a storage medium or other storage(s). A processor can perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment can be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, augmentations, parameters, or memory contents. Information, augmentations, parameters, data, etc. can be passed, forwarded, or transmitted via any suitable means, including memory sharing, message passing, token passing, network transmission, etc.

[0456] In the foregoing, a storage medium may represent one or more devices for storing data, including read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and / or other machine-readable media for storing information. The terms "machine-readable medium" and "computer-readable medium" include, but are not limited to, portable or permanent storage devices, optical storage devices, and / or various other media that can store, contain, or carry instruction(s) and / or data, including non-transitory media.

[0457] The various illustrative logic blocks, modules, circuits, elements, and / or components described in connection with the embodiments disclosed herein may be implemented or embodied by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic component, individual gate or transistor logic, individual hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, circuit, and / or state machine. A processor may also be implemented as a combination of computing components, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration.

[0458] The methods or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a processor-executable software module, or a combination thereof, in the form of a processing unit, programming instructions, or other instructions, and may be contained in a single device or distributed across multiple devices. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. The storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor.

[0459] One or more of the components and functions shown may be rearranged and / or combined into a single component or embodied in several components without departing from this disclosure. Additional elements or components may also be added without departing from this disclosure. Furthermore, the features described herein may be implemented in software, firmware, hardware, and / or any combination thereof.

[0460] In its various aspects, the disclosure may be embodied in a computer-implemented process, a machine (such as an electronic device, or general-purpose computer, or other apparatus providing a platform on which a computer program can be executed), a process performed by such a machine, or an article of manufacture, including a computer program product or digital information product having a computer-readable storage medium containing computer program instructions or computer-readable data stored thereon, as well as processes and machines for making and using such articles of manufacture.

Claims

1. 1. A patient management system, comprising:

1. A data management server in communication with a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients, the data management server comprising: receiving sensor data from the plurality of sensors over a network; determining a component risk score for each of the plurality of patients from the sensor data; assigning each of the plurality of patients to one of a plurality of groups based, at least in part, on the component risk scores and a plurality of rules; a data management server configured to cause a display to display a graphical user interface in a first display mode one or more patient identifiers, each associated with one patient of the plurality of patients and their component risk scores; A patient management system wherein the one or more patient identifiers are arranged on the display screen based on the values ​​of their associated component risk scores.

2. The patient management system of claim 1 , wherein the sensor data is received across one or more advertising channels of a short-range wireless communication component.

3. 3. The patient management system of claim 1, wherein the one or more patient identifiers are grouped into regions of the display, each region associated with upper and lower thresholds for the component risk scores, the thresholds defining a plurality of risk bands.

4. 4. The patient management system of claim 3, wherein the size and / or relative position of the one or more patient identifiers within an area of ​​the display is based on one or more of a rate of change and a direction of change of the corresponding component risk score.

5. The patient management system of claim 3 or 4, wherein the disposition of the one or more patient identifiers is updated in real time when the measurement of the associated component risk score is updated.

6. The patient management system of any one of claims 1 to 5, wherein the one or more patient identifiers are present in the form of tiles.

7. The patient management system of any preceding claim, wherein the one or more patient identifiers each include a risk indicator.

8. The patient management system of claim 7 , wherein the risk indicator comprises a numerical value indicating a patient's risk level based on at least one of their component risk scores and associated risk bands.

9. The patient management system of claim 8 , wherein the risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

10. The patient management system of claim 8 or 9, wherein the value is a rolling average.

11. 11. The patient management system of claim 8, wherein the risk indicator further comprises a risk trend indicator configured to change state depending on whether the risk level of the patient is increasing, decreasing, or staying the same.

12. The patient management system of claim 11 , wherein the risk trend indicator is color coded.

13. the one or more patient identifiers further comprise an indication of one or more physiological parameters associated with the patient; The patient management system of any one of claims 1 to 12, wherein the selection and placement of the one or more physiological parameters is based on their contribution to the clinical risk indicator.

14. The patient management system of claim 13 , wherein the display of the one or more physiological parameters is updated in real time.

15. 15. The patient management system of claim 13 or 14, wherein the display of the one or more physiological parameters comprises a rolling average of the one or more physiological parameters.

16. 16. The patient management system of claim 1, wherein the one or more patient identifiers each include one or more of the following elements: a patient identification number, a patient room number, a patient bed number, a patient name, and an indication of the time that has expired since a clinician last interacted with the patient.

17. 17. The patient management system of claim 16, wherein the presence, size, and display parameters of the one or more elements are based on one or more of a magnitude, a rate of change, and a direction of change of the component risk scores.

18. The presence, size, and / or display parameters of said one or more elements are: the amount of time since a patient identifier moves from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score; and 18. The method of claim 16, wherein the method is based on one or more of an estimated amount of time for a patient identifier to move from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score.

19. 20. The patient management system of claim 18, wherein the second risk band is a higher risk band than the first risk band.

20. 20. The patient management system of claim 18 or 19, wherein the estimated amount of time is provided by analyzing trends in historical values ​​of the component risk scores.

21. The patient management system of any preceding claim, wherein the sensor data is received from a respiratory assistance device comprising or in communication with the plurality of sensors.

22. The patient management system of any one of claims 1 to 21, wherein the component risk scores are normalized component risk scores.

23. 1. A patient management method comprising: receiving, over a network, sensor data from a plurality of sensors configured to measure one or more physiological parameters of a plurality of patients; determining a component risk score for each of the plurality of patients; assigning each of a plurality of patients to one of a plurality of groups based, at least in part, on the component risk scores and a plurality of rules; causing a display to display a graphical user interface in a first display mode one or more patient identifiers, each associated with one patient of the plurality of patients and their component risk scores; The method wherein the one or more patient identifiers are arranged within groups of the plurality of groups on the display screen based on the relative values ​​of their associated component risk scores.

24. 24. The method of claim 23, wherein the sensor data is received across one or more advertising channels of a short-range wireless communication protocol.

25. assigning the plurality of patients to a plurality of groups includes reassigning patients from a first group having an associated risk band to a second group having a second associated risk band; 25. The method of claim 23 or 24, wherein the method further comprises generating an alert condition if the second associated risk band is associated with a higher clinical risk than the first risk band.

26. 26. The method of claim 25, wherein the alert condition triggers a notification to a hospital monitoring system.

27. 1. A method for generating normalized component risk scores based on measurements of one or more physiological parameters of a patient, the method comprising: receiving sensor data; measuring at least one physiological parameter of the patient from the sensor data; determining a component risk score based on the at least one physiological parameter; and outputting the component risk scores.

28. 28. The method of any one of claims 27, wherein the sensor data is received over one or more advertising channels of a short-range wireless communication protocol.

29. 29. The method of claim 27 or 28, wherein determining the component risk score comprises associating at least one of the at least one physiological parameter with a corresponding risk band of a plurality of risk bands, each risk band of the plurality of risk bands having a respective first upper threshold and a first lower threshold, and wherein the associated at least one physiological parameter falls between the first upper threshold and the first lower threshold of the corresponding risk band.

30. 30. The method of any of claims 29, wherein the risk band corresponds to a predetermined early warning score value.

31. The method of any one of claims 29 to 30, wherein the plurality of risk bands comprises a first risk band and a second risk band.

32. 32. The method of claim 31, wherein the size of the first risk band is different from the size of the second risk band.

33. 33. The method of claim 32, wherein the size of the risk band is defined by the difference between its first upper threshold and its first lower threshold.

34. 34. The method of any one of claims 27 to 33, wherein the physiological parameter is one of body temperature, blood pressure, respiratory rate, heart rate, carbon dioxide level, and SpO2.

35. The method of any one of claims 27 to 34, wherein the physiological parameters and component risk scores are scored in a time series.

36. The method of any one of claims 27 to 35, wherein the physiological parameters and component risk scores are updated in real time.

37. The method of any one of claims 27 to 36, wherein the component risk scores are recalculated at regular time intervals.

38. The method of any one of claims 27 to 37, wherein the component risk scores are recalculated only when new sensor data is received and updated physiological parameters are measured.

39. measuring a second physiological parameter from the sensor data; determining a second component risk score based on the second physiological parameter; outputting the second component risk score; and determining an overall component risk score based on said first and second component risk scores.

40. 40. The method of claim 39, wherein determining the overall component risk score comprises selecting the higher of the first and second component risk scores as the overall component risk score.

41. 40. The method of claim 39, wherein determining the overall component risk score comprises averaging the first and second component risk scores.

42. 40. The method of claim 39, wherein determining the overall component risk score comprises calculating a weighted average of the first and second component risk scores, the weighting being based on a predetermined set of rules.

43. 40. The method of claim 39, wherein determining the overall component risk score comprises summing the first and second component risk scores and comparing the summed value to a predetermined threshold.

44. 44. The method of claims 27-43, further comprising displaying the component risk scores in a first view on a display screen, the component risk scores being displayed along with the patient identifier, the patient identifier corresponding to a patient associated with the sensor data and the physiological parameter derived from the sensor data.

45. 45. The method of claim 44, wherein the component risk scores are displayed in the form of a gauge, the gauge being divided according to the second upper threshold and second lower threshold of the plurality of risk bands.

46. 46. ​​The method of claim 45, wherein the gauge displays a rolling average of the component risk scores.

47. 47. The method of any one of claims 44-46, wherein displaying the component risk scores further comprises displaying a graphical timeline showing current and historical values ​​of the component risk scores.

48. The method of any one of claims 44 to 47, further comprising displaying the physiological parameter in the first view on a display screen.

49. 49. The method of claim 48, wherein displaying the physiological parameter further comprises displaying a graphical timeline showing current and historical values ​​of the physiological parameter.

50. 50. The method of claim 49, wherein the graphical timeline is updated in real time as updated values ​​are measured.

51. displaying the second physiological parameter; displaying the second component risk score; 40. The method of any one of the preceding claims when dependent on claim 39, further comprising the step of: displaying the combined component risk score.

52. 52. The method of claim 51, wherein the display of the first and second physiological parameters is arranged based on their associated first and second component risk scores.

53. 52. The method of claim 51, wherein the representation of the first and second physiological parameters is arranged according to the relative contribution of the first and second physiological parameters to the overall component risk score.

54. receiving, by a respiratory assistance device, parameters of a therapy being provided to said patient and / or usage data indicative of said patient's use of said respiratory assistance device; A method according to any one of claims 27 to 53, further comprising the step of: displaying said therapy parameters and / or usage data on said display screen.

55. 55. The method of claim 54, wherein the therapy parameters and / or usage data are displayed in chronological order and updated in real time.

56. displaying one or more patient identifiers in a second view of the display screen, each associated with a patient and a component risk score; 56. The method of any one of claims 44 to 55, wherein the one or more patient identifiers are arranged on the display screen based on the relative values ​​of their associated component risk scores.

57. 57. The method of claim 56, wherein the one or more patient identifiers are grouped into regions of the display, each region being associated with one of the plurality of risk bands.

58. 58. The method of claim 57, wherein the size and / or relative position of the one or more patient identifiers within the area of ​​the display is based on one or more of a rate of change and a direction of change of the corresponding component risk score.

59. 60. The method of claim 58, wherein the disposition of the one or more patient identifiers is updated in real time as the measurement of the associated component risk score is updated.

60. 60. The method of any one of claims 56 to 59, wherein the one or more patient identifiers are in the form of tiles.

61. 61. The method of claim 60, wherein the one or more patient identifiers each include a risk indicator.

62. 62. The method of claim 61, wherein the risk indicator comprises a numerical value indicating the risk level of a patient based on their component risk scores and the associated risk band.

63. 63. The method of any of claims 62, wherein the value is a rolling average.

64. 64. A method according to any one of claims 61 to 63, wherein the risk indicator is updated in real time as updated values ​​of the component risk scores are measured.

65. 65. The method of any one of claims 61 to 64, wherein the risk indicator further comprises a risk trend indicator configured to change state depending on whether the risk level of the patient is increasing, decreasing, or staying the same.

66. 66. The method of claim 65, wherein the risk propensity indicator is color coded.

67. the one or more patient identifiers further comprise an indication of one or more physiological parameters associated with the patient; 67. The method of any one of claims 56 to 66, wherein the selection and placement of the one or more physiological parameters is based on their contribution to the component risk scores.

68. 68. The method of claim 67, wherein the display of the one or more physiological parameters is updated in real time.

69. 69. The method of claim 68, wherein the display of the one or more physiological parameters comprises a rolling average of the one or more physiological parameters.

70. 70. The method of any one of claims 56-69, wherein the one or more patient identifiers each include one or more of the following elements: a patient identification number, a patient room number, a patient bed number, a patient name, and an indication of the time that has expired since a clinician last interacted with the patient.

71. 71. The method of claim 70, wherein the presence, size, and / or display parameters of the one or more elements are based on one or more of the magnitude, rate of change, and direction of change of the component risk scores.

72. The presence, size, and / or display parameters of said one or more elements are: the amount of time since a patient identifier moves from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score; and 71. The method of claim 70, based on one or more of an estimated amount of time it takes for a patient identifier to move from one area of ​​the display associated with a first risk band to another area of ​​the display associated with a second risk band as a result of a change in the respective component risk score.

73. 73. The method of claim 72, wherein the second risk band is a higher risk band than the first risk band.

74. 74. The method of any one of claims 72 and 73, wherein the estimated amount of time is provided by analyzing trends in historical values ​​of the component risk scores.

75. 75. The method of any one of claims 27 to 74, comprising normalizing the component risk scores.

76. 76. The method of claim 75, wherein determining the component risk score comprises looking at a scaling or min-max normalization of the physiological parameter within the first threshold of the risk band to provide a measure of the location of the physiological parameter within the first threshold of the risk band.

77. 77. The method of any one of claims 75-76, wherein determining the normalized component risk scores comprises normalizing the size of each risk band within the plurality of risk bands to a preferred size W, each normalized risk band having a second upper threshold and a second lower threshold separated by W.

78. the normalized component risk scores are:

79. A patient monitoring system comprising: being configured to perform a method according to any one of claims 27 to 78.

80. one or more sensors; 53. The patient monitoring system of claim 52, comprising: a processing server.

81. 1. A method for monitoring a patient with a plurality of health sensors for identifying a patient whose health is deteriorating, the method comprising: receiving sensor data based on individual patient measurements from the plurality of sensors across one or more advertising channels of a short-range wireless communication component; measuring physiological parameters of the individual patient from the sensor data; measuring a health parameter based on said physiological parameter; and outputting the health parameter.

82. 1. A method of operating a sensor system for monitoring the health of a patient, the method comprising: receiving sensor data from a plurality of sensors through one or more advertising channels of the short-range wireless communication component; measuring two or more physiological parameters of the patient based on the sensor data from the plurality of sensors; determining a health status based on said physiological parameters; and outputting the health status.

83. 1. A method of operating a sensor system for monitoring the health of a patient, the method comprising: receiving, via one or more advertising channels of a short-range wireless communication component, sensor data indicative of one or more physiological parameters of the patient from a plurality of sensors; Detecting whether a health parameter of the patient is above or below a predetermined threshold based on the sensor data; and outputting to a healthcare provider an indication of the health parameter for allowing the healthcare provider to treat the patient.

84. 84. The method of claim 82 or 83, wherein detecting that the health parameter of the patient is above or below the predetermined threshold comprises comparing different physiological parameters, each physiological parameter obtained from sensor data from the plurality of sensors.

85. Detecting the health parameter of the patient being above or below the predetermined threshold may include: measuring the one or more physiological parameters from the sensor data; determining the health parameter of the patient based on the one or more physiological parameters; and comparing the health parameter to the predetermined threshold.

86. 1. A method of operating a hub for a system for monitoring the health of a patient, said method comprising: receiving a first data packet from a first sensor about the hub over a first advertising channel of a short-range communication component, the first data packet including sensor data from the first sensor related to a first physiological measurement of the patient; transmitting the first data packet through a network communication component to a server around the hub; receiving a second data packet from a second sensor about the hub through a second advertising channel of the short-range communications component, the second data packet including sensor data from the second sensor related to a second physiological measurement of the patient; transmitting the second data packet to the server through the network communication component.

87. 87. The method of claim 86, wherein the first advertising channel is the same as the second advertising channel.

88. 87. The method of claim 86, wherein the first advertising channel is different from the second advertising channel.

89. 89. The method of any one of claims 86 to 88, further comprising at least partially processing the first data packet before sending the first data packet to the server.

90. 90. The method of any one of claims 86 to 89, further comprising formatting the first data packet before sending the first data packet to the server.

91. The method of any one of claims 86 to 90, further comprising stamping the first data packet with a timestamp.

92. A method according to any one of claims 86 to 88, further comprising forwarding the raw and / or unprocessed first sensor data in the first data packet to the server.

93. 93. The method of any one of claims 86 to 92, wherein the first physiological parameter is the same as the second physiological parameter, and the method further comprises combining the first data packet and the second data packet before transmitting the first data packet and the second data packet to the server.

94. The method of any one of claims 86 to 93, wherein the network communication component is a wireless internet component.

95. A method according to any one of claims 96 to 94, wherein the first data packets are received in a continuous stream of data packets from the first sensor over the first advertising channel.

96. A method according to any one of claims 86 to 95, wherein the first data packet and the second data packet are sent to the server simultaneously.

97. 97. The method of any one of claims 86 to 96, further comprising receiving a third data packet over a third advertising channel of the short-range communications component from a third sensor about the hub, the third data packet including sensor data from the third sensor related to a third physiological measurement of the patient.

98. 98. The method of any one of claims 86 to 97, wherein the first data packet includes a patient identifier and the second data packet includes the patient identifier.

99. 1. A hub for a system for monitoring the health of a patient, said hub comprising: a short-range communication component; a network communication component; a processor; When executed by the processor, the processor: receiving a first data packet from a first sensor about the hub over a first advertising channel of the short-range communications component, the first data packet including sensor data from the first sensor related to a first physiological measurement of the patient; transmitting the first data packet through the network communication component to the server around the hub; receiving a second data packet from a second sensor about the hub through a second advertising channel of the short-range communications component, the second data packet including sensor data from the second sensor related to a second physiological measurement of the patient; transmitting the second data packet to the server through the network communication component.

100. 1. A system for monitoring the health of a patient, said system comprising: a central hub, said central hub comprising a short-range communication component comprising a plurality of advertising communication channels; a first sensor about the central hub, the first sensor configured to obtain a measurement of a first physiological parameter of the patient and to transmit first data packets to the central hub over a first advertising channel of the plurality of advertising communication channels, each of the first data packets including data related to the measurement of the first physiological parameter; a second sensor surrounding the central hub and the first sensor, the second sensor configured to obtain a measurement of a second physiological parameter of the patient and to transmit second data packets to the central hub through a second advertising channel of the plurality of advertising communication channels, each of the second data packets including data related to the measurement of the second physiological parameter.

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