Sepsis monitoring

Abstract

The invention relates to a system for monitoring sepsis in a medical facility. The system calculates a sepsis score for the patient using physiological parameter data captured by one or more sensors. The system generates a sepsis score trend based on the sepsis score, and generates a predicted sepsis trend based on the sepsis score and the treatment administered to the patient. According to the system, the sepsis trend is displayed and predicted on the basis of the sepsis score trend in an overlapping manner. The system may also display a profile including sepsis states and treatment states for a plurality of patients within the medical facility.

Description

[0001] This application was filed as a PCT international patent application on October 4, 2023, claiming the benefits and priority of the U.S. Provisional Patent Application filed on October 14, 2022, with application number US63 / 379,482, the disclosure of which is incorporated herein by reference in its entirety. Background Art

[0002] Sepsis is a life-threatening condition that occurs when the body's response to an infection causes damage to its own tissues and organs. Sepsis occurs when pathogens are released into the bloodstream, causing inflammation throughout the body.

[0003] In the early stages, some symptoms of sepsis (such as fever, increased heart rate, and increased respiratory rate) are similar to those of other diseases, making it difficult to distinguish sepsis from other illnesses. Early sepsis is often reversible with antibiotics, fluids, and other supportive medical interventions, so being able to detect the condition in its early stages is critical. However, the risk of death increases significantly over time. Summary of the invention

[0004] In general, the present disclosure relates to monitoring sepsis in medical facilities. Various aspects described in the present disclosure include, but are not limited to, the following aspects.

[0005] One aspect relates to a system for monitoring sepsis in a medical facility, the system comprising: at least one processing device; and a memory device storing instructions that, when executed by the at least one processing device, cause the at least one processing device to: calculate a sepsis score for a patient using physiological parameter data captured by one or more sensors; generate a sepsis score trend based on the sepsis score; generate a predicted sepsis trend based on the sepsis score and a treatment administered to the patient; and display the predicted sepsis trend overlaid on the sepsis score trend.

[0006] Another aspect relates to a method for monitoring sepsis in a medical facility, the method comprising: calculating a sepsis score for a patient using physiological parameter data; generating a sepsis score trend based on the sepsis score; generating a predicted sepsis trend based on the sepsis score and a treatment administered to the patient; and displaying the predicted sepsis trend overlaid on the sepsis score trend.

[0007] Yet another aspect relates to a system for monitoring sepsis in a medical facility, the system comprising: at least one processing device; and a storage device storing instructions, which when executed by the at least one processing device cause the at least one processing device to: obtain a sepsis status from a plurality of monitoring devices, each of the plurality of monitoring devices monitoring a patient in the medical facility; obtain a treatment status from an electronic medical record system, each treatment status being associated with a patient monitored by a monitoring device; and display a user interface providing an overview of the sepsis status and treatment status of each patient monitored by the monitoring device. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following drawings constitute part of this application, illustrate the technology described herein, and are not intended to limit the scope of protection of the present invention in any form.

[0009] Figure 1 An example of a sepsis monitoring system is schematically shown.

[0010] Figure 2 Schematically shows Figure 1 An example of a monitoring device in the illustrated system, the monitoring device is shown communicatively coupled to one or more sensors.

[0011] Figure 3 Shows Figure 2 An example of a user interface presented on a display device of a monitoring device is shown.

[0012] Figure 4 Shows Figure 2 Another example of a user interface presented on a display device of a monitoring device is shown.

[0013] Figure 5 Schematically shows Figure 2 An example of a sepsis monitoring method performed on the monitoring device is shown.

[0014] Figure 6 Shows Figure 1 An example of a user interface presented on a status board display in the illustrated system.

[0015] Figure 7 Shows Figure 1 Another example of a user interface presented on a status board display device in the illustrated system.

[0016] Figure 8 Schematically shows Figure 1 An example of a sepsis monitoring method implemented on a status board in the system shown.

[0017] Fig. 9 Shows Figure 1 Another example of a user interface presented on a status board display device in the illustrated system. DETAILED DESCRIPTION

[0018] Figure 1 An example of a sepsis monitoring system 100 is schematically shown. The system 100 may be implemented in a medical facility, such as a hospital, surgical center, nursing home, long-term care facility, or similar type of facility. In some cases, the system 100 is implemented within a specific department, unit, or floor of a medical facility.

[0019] The system 100 includes one or more monitoring devices 200a-200n connected to a network 600. Each of the one or more monitoring devices 200a-200n acquires physiological parameter data from patients admitted to a medical facility. The physiological parameter data acquired by the one or more monitoring devices 200a-200n is used to calculate a sepsis score for each patient admitted to the medical facility. In addition, a trend of the sepsis score of each patient over time is generated and displayed on each monitoring device 200a-200n. Figure 2 An example of a monitoring device is shown and described in detail below.

[0020] Another example Figure 1 As shown, an Electronic Medical Record (EMR) system 300 (or Electronic Health Record (EHR) system) is connected to a network 600. The EMR system 300 stores physiological parameter data acquired by one or more monitoring devices 200a-200n in an electronic medical record 302 (or Electronic Health Record) of each patient admitted to the medical facility. The EMR system 300 communicates with one or more monitoring devices 200a-200n via the network 600.

[0021] The system 100 also includes a health information system 400 that communicates with one or more monitoring devices 200a-200n and the EMR system 300 via a network 600. The health information system 400 centrally manages patient data to facilitate communication and coordination between healthcare providers within a medical facility. For example, the health information system 400 can collect and organize patient data received via the network 600 from various devices and systems within a medical facility, such as one or more monitoring devices 200a-200n and the EMR system 300.

[0022] The health information system 400 communicates the patient data to one or more additional devices 700 within the medical facility, such as one or more workstations 702, mobile devices 704, and status boards 706. The health information system 400 may communicate the patient data to the one or more workstations 702, mobile devices 704, and status boards 706 via a network 600. Alternatively, the health information system 400 may communicate the patient data to the one or more workstations 702, mobile devices 704, and status boards 706 using a different network, and / or may communicate the patient data directly to the one or more workstations 702, mobile devices 704, and status boards 706 without using a network 600.

[0023] By way of example, one or more workstations 702 are part of a nurse's station within a medical facility where nurses and other healthcare providers work and communicate with each other when they are not working directly with patients. One or more mobile devices 704 may include smart phones, tablets, and other portable computing devices carried by nurses and other healthcare providers during their shifts within a medical facility. One or more status boards 706 are monitors mounted to a wall or otherwise positioned within an area of ​​a medical facility to centrally display patient data for all patients admitted to a particular department, unit, or floor within the medical facility.

[0024] The network 600 may include any type of wired or wireless communication or any combination thereof. Examples of wireless communications include broadband cellular network connections, such as 4G or 5G. In some examples, wireless connections may be implemented using Wi-Fi, ultra-wideband (UWB), Bluetooth, radio frequency identification (RFID), and similar types of wireless connections. Wired connections may be established via Ethernet and other standardized computer network technologies. In some examples, the network 600 includes the Internet. In some examples, the network 600 is a local area network (LAN).

[0025] Figure 2 An example of a monitoring device 200 is schematically shown, which is communicatively coupled to one or more sensors 102a-102n for acquiring physiological parameter data of a patient. The physiological parameter data captured by the one or more sensors 102a-102n may include, but are not limited to, respiratory rate, blood pressure, heart rate, blood oxygen saturation (SpO2), and body temperature. As described in detail below, the physiological parameter data is used to calculate a sepsis score of the patient, and sepsis monitoring is performed by trending the sepsis score over time.

[0026] like Figure 2As shown, the monitoring device 200 includes a computing device 202 having a processing device 204 and a storage device 206. The processing device 204 is, for example, a processing unit such as a central processing unit (CPU), and in some cases may include one or more CPUs. In some examples, the processing device 204 may include one or more digital signal processors, field programmable gate arrays, or other electronic circuits.

[0027] The storage device 206 is operable to store data and instructions for execution by the processing device 204 . Figure 2 In some examples, the storage device 206 stores a sepsis scoring algorithm 208 for calculating a sepsis score based on physiological parameter data received from one or more sensors 102a-102n. In some examples, the sepsis scoring algorithm 208 calculates a quick sequential organ failure assessment (qSOFA) score based on three factors: (1) low blood pressure (e.g., systolic blood pressure ≤ 100 mmHg); (2) high respiratory rate (≥ 22 times / minute); (3) changes in mental status (e.g., Glasgow Coma Scale < 15). Each factor is scored as 0 or 1 based on whether its condition is met, and these factors are added together to calculate the qSOFA score.

[0028] As another example, the sepsis scoring algorithm 208 calculates a systemic inflammatory response syndrome (SIRS) score based on four factors: (1) body temperature greater than 100.4 degrees Fahrenheit (38 degrees Celsius) or less than 96.8 degrees Fahrenheit (36 degrees Celsius); (2) heart rate greater than 90 beats per minute; (3) respiratory rate greater than 20 breaths per minute or CO2 partial pressure less than 32 mmHg; (4) white blood cell (WBC) count greater than 12,000. Each factor is scored as 0 or 1 based on whether its condition is met, and these factors are added together to calculate the SIRS score.

[0029] In another example, the sepsis scoring algorithm 208 calculates a custom score defined by a medical facility such as a hospital, surgical center, nursing home, long-term care facility, or similar type of facility, or a department, unit, or floor within a medical facility. The custom score may be based on a combination of factors such as blood pressure, respiratory rate, mental state, body temperature, heart rate, white blood cell count, etc.

[0030] Another example Figure 2As shown, the storage device 206 stores a sepsis prediction algorithm 209 for calculating a predicted sepsis score based on the patient's sepsis score and the treatment implemented on the patient. For example, the sepsis prediction algorithm 209 can predict changes in various factors used to calculate the sepsis score based on the treatment implemented on the patient. For example, the sepsis prediction algorithm 209 can predict changes in one or more factors such as blood pressure, respiratory rate, psychological state, body temperature, heart rate, white blood cell count, etc. Then, the changes in these factors can be used to calculate the predicted sepsis score.

[0031] The storage device 206 also stores an alarm generation algorithm 210 that generates alarms, alerts, and / or notifications based on the sepsis score calculated by the sepsis scoring algorithm 208 or the predicted sepsis score calculated by the sepsis prediction algorithm 209 .

[0032] Storage device 206 includes computer-readable media, which may include any media accessible to processing device 204. Examples of computer-readable media include computer-readable storage media, including volatile and non-volatile, removable and non-removable media implemented in any device configured to store information, such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media may include, but are not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory, and other storage technologies, including any media that can be used to store information accessible to monitoring device 200. Computer-readable storage media are non-transitory computer-readable program storage media.

[0033] Other examples of computer-readable media include computer-readable communication media that embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal (such as a carrier wave or other transport mechanism), and includes any information delivery media. The term "modulated data signal" refers to a signal that sets or changes one or more signal characteristics in a manner that encodes information within the signal. For example, computer-readable communication media include wired media (such as a wired network or a direct wired connection) and wireless media (such as acoustic, radio frequency, infrared, and other wireless media). Any combination of the above is within the scope of computer-readable media.

[0034] The monitoring device 200 includes a sensor interface 212 that communicates with one or more sensors 102a-102n. The sensor interface 212 may include both a wired interface and a wireless interface. For example, one or more sensors 102a-102n may be wirelessly connected to the sensor interface 212 via Wi-Fi, ultra-wideband (UWB), Bluetooth, and similar types of wireless connections. Alternatively, one or more sensors 102a-102n may be connected to the monitoring device 200 via a wired connection plugged into the sensor interface 212.

[0035] like Figure 2 As shown, the monitoring device 200 includes a display device 214 that operates to display a user interface 216. In some examples, the display device 214 is a touch screen, so that the display device 214 operates as both a display device and a user input device. The monitoring device 200 may also support physical buttons that operate to receive input from a healthcare provider to control the operation of the monitoring device and enter data.

[0036] Figure 3 An example of a user interface 216a displayed on the display device 214 of the monitoring device 200 is shown. Similar user interfaces may be displayed on one or more workstations 702, mobile devices 704, and status boards 706. The user interface 216a enhances sepsis monitoring performed in the monitoring device 200 by providing an output that is intuitive and easy for the healthcare provider to interpret so as to quickly understand the sepsis status of the patient being monitored by the monitoring device 200, thereby allowing the healthcare provider to expedite action and improve patient care and outcomes.

[0037] The user interface 216a includes a sepsis score trend 218. The sepsis score trend 218 is generated from a sepsis score calculated by the sepsis score algorithm 208 using as input the physiological parameter data received from one or more sensors 102a-102n. Figure 2 In the example of , the sepsis score algorithm 208 is stored on the storage device 206 of the monitoring device 200 so as to calculate the sepsis score on the monitoring device 200. Alternatively, the sepsis score algorithm 208 may be stored on the memory of the health information system 400 so as to calculate the sepsis score on the health information system 400 and communicate the sepsis score to the monitoring device 200 via the network 600.

[0038] In some examples, the sepsis score algorithm 208 uses artificial intelligence and / or machine learning algorithms to calculate a sepsis score based on physiological parameter data received from one or more sensors 102a-102n. In other examples, the monitoring device 200 transmits the physiological parameter data received from the one or more sensors 102a-102n to the health information system 400 via the network 600, the health information system 400 executes the artificial intelligence and / or machine learning algorithms to calculate the sepsis score based on the physiological parameter data, and the health information system 400 transmits the sepsis score to the monitoring device 200 via the network 600. By executing the artificial intelligence and / or machine learning algorithms on the health information system 400, the processing power requirements of the computing device 202 are reduced, thereby improving the efficiency of using the computing device 202 of the monitoring device 200.

[0039] In some examples, the sepsis score and the sepsis score trend 218 are transmitted to the EMR system 300 via the network 600 and stored in the patient's electronic medical record 302. In such examples, any one of the one or more monitoring devices 200a-200n, the workstation 702, the mobile device 704, and the status board 706 can obtain the sepsis score and the sepsis score trend 218 by accessing the patient's electronic medical record 302.

[0040] like Figure 3 As shown, the sepsis score trend 218 includes visual markers 220 that represent trend events. For example, a first visual marker 220a identifies the onset of sepsis (e.g., when a patient is first identified as having sepsis) on the sepsis score trend 218. A second visual marker 220b identifies the start of sepsis treatment on the sepsis score trend 218. Additional visual markers may be displayed on the sepsis score trend 218 to identify additional clinical events.

[0041] Additionally, the sepsis score trend 218 can have a different visual appearance before and after the visual marker 220. For example, the sepsis score trend 218 can be drawn in different colors (including dark or light shades) and / or patterns to indicate the before and after changes in the visual marker 220. Figure 3 In the example of FIG. 2 , before the first visual marker 220 a identifies the onset of sepsis, the sepsis score trend 218 is plotted in a first color (eg, gray), and after the first visual marker 220 a identifies the onset of sepsis, the sepsis score trend 218 is plotted in a second color (eg, blue).

[0042] like Figure 3 As shown, the user interface 216 a also provides one or more message boxes 222 , each of which includes a clinically relevant message describing a clinical event on the sepsis score trend 218 . Figure 3 , the first message box 222a includes the message “Time since sepsis onset: 2 hours 31 minutes”. The second message box 222b includes the message “Treatment started”. The message boxes 222 provide further context for a healthcare provider to interpret the sepsis score trend 218 when viewing the user interface 216a on the monitoring device 200.

[0043] The sepsis score trend 218 provides an output that is easy for the healthcare provider to interpret to verify whether the treatment implemented on the patient has improved the patient's sepsis score. For example, when the second visual mark 220b indicates that the treatment has started, the sepsis score trend 218 decreases (e.g., the sepsis score is decreasing), which verifies that the treatment implemented on the patient is effective, so there is no need to change the treatment plan. Otherwise, when the second visual mark 220b indicates that the treatment has started, the sepsis score trend 218 increases (e.g., the sepsis score is increasing), which indicates that a change in the treatment plan is needed to improve the patient's sepsis condition.

[0044] In some cases, the monitoring device 200 generates an alarm when the second visual marker 220b indicates that the sepsis score trend 218 increases after the start of treatment, that is, the patient's condition worsens. For example, when the monitoring device 200 detects that the sepsis score trend 218 increases after the start of treatment, the alarm generation algorithm 210 generates an alarm or an alert. In other examples, when the monitoring device 200 detects that the sepsis score trend 218 does not decrease within a predetermined period of time after the start of treatment, the alarm generation algorithm 210 generates an alarm or an alert. In other examples, when the monitoring device 200 detects that the sepsis score trend 218 does not improve (e.g., decrease) after 30 minutes, the alarm generation algorithm 210 generates an alarm or an alert.

[0045] Figure 4 Another example of a user interface 216b presented on the display device 214 of the monitoring device 200 is shown. Similar user interfaces may be displayed on one or more workstations 702, mobile devices 704, and status boards 706. The user interface 216b enhances sepsis monitoring performed in the monitoring device 200 by providing an output that is intuitive and easy for the healthcare provider to interpret so as to quickly understand the sepsis status of the patient being monitored by the monitoring device 200, thereby allowing the healthcare provider to expedite action and improve patient care and outcomes.

[0046] Figure 4 The user interface 216b in Figure 3 The illustrated user interface 216a shares many similar elements. For example, the user interface 216b includes a sepsis score trend 218, visual markers 220a, 220b, and a message box 222. In addition, the user interface 216b includes a predicted sepsis trend 224 displayed with the sepsis score trend 218. The predicted sepsis trend 224 is displayed superimposed on the sepsis score trend 218 to visually indicate the difference between the patient's actual sepsis score and the patient's predicted sepsis score after treatment is provided to the patient (e.g., after the second visual marker 220b).

[0047] The predicted sepsis trend 224 is plotted differently from the sepsis score trend 218 to emphasize and / or highlight the difference between the two trends. For example, the predicted sepsis trend 224 may be plotted in a different color (e.g., red) than the sepsis score trend 218. Additionally and / or alternatively, the predicted sepsis trend 224 may be plotted in a different pattern (e.g., a dashed line) than the sepsis score trend 218. Other examples may be used with respect to the different presentations of the predicted sepsis trend 224 and the sepsis score trend 218, which are merely illustrative examples.

[0048] The predicted sepsis trend 224 is calculated by the sepsis prediction algorithm 209, which uses the patient's sepsis score and the treatment implemented on the patient as input to predict the patient's future sepsis score for a period of time after the start of treatment. In some examples, the sepsis prediction algorithm 209 is trained using patient historical data, and the historical patient data includes physiological parameter data of other patients with similar sepsis scores (these patients received the same treatment as the patient monitored by the monitoring device 200). In some examples, the sepsis prediction algorithm 209 is an artificial intelligence and / or machine learning algorithm.

[0049] like Figure 2 As shown in the example of , the sepsis prediction algorithm 209 is stored on the storage device 206 of the monitoring device 200 so as to calculate the predicted sepsis trend 224 on the monitoring device 200. Alternatively, the sepsis prediction algorithm 209 may be stored on the memory of the health information system 400 so as to calculate the sepsis trend 224 on the health information system 400, which then communicates the sepsis trend 224 via the network 600 for display on the monitoring device 200.

[0050] As described above, in some examples, the sepsis prediction algorithm 209 utilizes an artificial intelligence and / or machine learning algorithm to calculate the predicted sepsis trend 224. In such examples, the health information system 400 may execute the artificial intelligence and / or machine learning algorithm to calculate the predicted sepsis trend 224, and the health information system 400 transmits the predicted sepsis trend 224 via the network 600 for display on the monitoring device 200. By executing the artificial intelligence and / or machine learning algorithm on the health information system 400 to calculate the predicted sepsis trend 224, the processing power requirements of the computing device 202 are reduced, thereby improving the efficiency of the use of the computing device 202 of the monitoring device 200.

[0051] Figure 4In the illustrative example shown, the predicted sepsis trend 224 after the second visual marker 220b (marking the start of treatment) is downward, which is expected because the treatment administered to the patient should improve the patient's condition and reduce the sepsis score. Figure 4 In the example shown, the sepsis score trend 218 is trending upward. This provides a visual output to the healthcare provider that the sepsis score trend 218 does not match the predicted sepsis trend 224, and therefore the treatment being administered to the patient is not working as intended. In this particular example, the sepsis score trend 218 indicates that the treatment being administered to the patient is not working and the patient's condition is actually getting worse. Therefore, when the sepsis score trend 218 is displayed together with the predicted sepsis trend 224, the sepsis score trend 218 provides an output that is easily interpreted by the healthcare provider to verify whether the treatment being administered to the patient is working as intended.

[0052] In other examples, a visual marker 220 is added to the sepsis score trend 218 when a new treatment is administered to the patient. For example, a second treatment may be administered to the patient when a first treatment is not effective in reducing the patient's sepsis score. In such examples, a visual marker 220 is added to the sepsis score trend 218 to indicate when the first treatment was initiated, and another visual marker 220 is added to the sepsis score trend 218 to indicate when the second treatment was initiated. A visual marker 220 may be added to the sepsis score trend 218 each time a new treatment is administered to the patient.

[0053] In other examples, each time a new treatment is administered to a patient, the predicted sepsis trend 224 is dynamically updated. For example, when a first treatment cannot effectively reduce the patient's sepsis score, and a second treatment is administered to the patient to improve the patient's condition, the predicted sepsis trend 224 is updated based on the second treatment administered to the patient. Therefore, when a new treatment is administered to the patient, the predicted sepsis trend 224 can be continuously updated.

[0054] Figure 5 A sepsis monitoring method 500 is schematically shown. In some examples, the method 500 is performed on the monitoring device 200.

[0055] like Figure 5 As shown, method 500 includes operation 502, calculating a sepsis score for a patient. In operation 502, the sepsis score is calculated by sepsis score algorithm 208, which uses physiological parameter data obtained from one or more sensors 102a-102b as input. In some examples, in operation 502, an artificial intelligence and / or machine learning algorithm is used to calculate the sepsis score. In some examples, in operation 502, the sepsis score is calculated at a predetermined interval (e.g., every 30 minutes).

[0056] The method 500 may include an operation 504 to determine whether the patient suffers from sepsis based on the sepsis score calculated in operation 502. For example, operation 504 may include determining whether the sepsis score exceeds a threshold for determining whether the patient suffers from sepsis. When it is determined that the patient does not suffer from sepsis (i.e., "no" in operation 504), the method 500 may return to operation 502 and continue to calculate the sepsis score. When it is determined that the patient suffers from sepsis (i.e., "yes" in operation 504), the method 500 may continue to operation 506 to generate the sepsis score trend 218. In some alternative examples, operation 504 is optional and is not included in the method 500.

[0057] Operation 506 includes generating a sepsis score trend 218 using the sepsis score calculated in operation 502. In some examples, operation 506 may include drawing a line for the sepsis score when the sepsis score is calculated at a predetermined interval (e.g., every 30 minutes). In other examples, the sepsis score is calculated continuously in operation 502, so that operation 506 does not include drawing a line for the sepsis score. Operation 506 may include adding a visual marker 220, such as a first visual marker 220a, to identify on the sepsis score trend 218 when it is detected in operation 504 that the patient has sepsis; and a second visual marker 220b to identify on the sepsis score trend 218 when treatment is being administered to the patient. Furthermore, operation 506 may include adding one or more message boxes 222 to provide clinically relevant information along with the sepsis score trend 218, such as indicating the amount of time since it was detected in operation 504 that the patient has sepsis.

[0058] Next, method 500 includes an operation 508 of generating a predicted sepsis trend 224 based on the patient's sepsis score and the treatment administered to the patient. In operation 508, predicted sepsis trend 224 is calculated by sepsis prediction algorithm 209. In some examples, sepsis prediction algorithm 209 is an artificial intelligence and / or machine learning algorithm trained using patient historical data.

[0059] Next, the method 500 includes an operation 510 of superimposing the predicted sepsis trend 224 generated in operation 508 on the sepsis score trend 218 generated in operation 506. After completing operation 510, the user interface 216 (e.g., Figure 4 In the user interface shown in FIG. 1 , a predicted sepsis trend 224 is provided as an overlay display on the sepsis score trend 218 .

[0060] Figure 6An example of a user interface 710a presented on a display 708 of a status board 706 is shown. Similar user interfaces may also be displayed on one or more monitoring devices 200a-200n, workstations 702, and mobile devices 702. The user interface 710a uses data received from one or more monitoring devices 200a-200n and the EMR system 300 via the network 600 to summarize the sepsis status of patients admitted to a medical facility or a specific department, unit, or floor within a medical facility. The user interface 710a displays the sepsis status of each patient and / or a certain number of high-risk sepsis patients, so that the user interface 710a can be used to determine how to allocate resources (including staffing) of a medical facility or a department, unit, or floor within a medical facility based on the sepsis status of the patient monitored by the monitoring device 200a-200n. In addition, the user interface 710 can be used to determine which patients have received appropriate sepsis treatment based on data received from the EMR system 300.

[0061] review Figure 1 , the status board 706 is communicatively connected to one or more monitoring devices 200a-200n via the network 600. Therefore, Figure 6 The status board 706 shown may receive the sepsis status of the patient monitored by one or more monitoring devices 200a-200n in near real time via the network 600. The status board 706 may receive the sepsis score calculated by the one or more monitoring devices 200a-200n using the sepsis scoring algorithm 208 via the network 600. Alternatively, the status board 706 may receive the sepsis status stored in the electronic medical record 302 of the patient in the EMR system 300 via the network 600. Other examples of receiving the sepsis status are possible.

[0062] Another example Figure 1 As shown, status board 706 is communicatively coupled to the EMR system via network 600. Thus, based on data received from EMR system 300, status board 706 may receive, via network 600, sepsis treatments being administered to patients admitted to a department, unit, or floor within a medical facility.

[0063] In some examples, the status board 706 receives sepsis status and sepsis treatments from one or more monitoring devices 200a-200n and the EMR system 300 using Health Level 7 (HL7) data communications. In other examples, the sepsis status and treatments can be automatically populated on the status board 706 without user input.

[0064] Now refer to Figure 6The user interface 710a includes a list of patients admitted to a medical institution or a department, unit or floor within the medical institution (e.g., patient 1, patient 2, ... patient N). For each patient in the patient list, an overview 711a is provided, including at least a sepsis status 712 and a sepsis treatment status 714.

[0065] Sepsis status 712 is displayed in different ways to visually distinguish the severity of the patient's sepsis status. For example, sepsis status 712 can be color-coded to visually distinguish the severity of the patient's sepsis status. Figure 6 In the example of FIG. 710 , the sepsis status 712a of patient 1 may be drawn in yellow to indicate a moderate sepsis status, the sepsis status 712b of patient 2 may be drawn in green to indicate that sepsis is not detected, and the sepsis status 712n of patient 1 may be drawn in red to indicate a severe sepsis status. There may also be other examples of visually distinguishing sepsis statuses in the user interface 710a.

[0066] The sepsis treatment status 714 may also be displayed in different ways to distinguish whether sepsis treatment has been implemented for each patient in the user interface 710a. For example, the sepsis treatment status 714 may be color-coded to intuitively distinguish whether sepsis treatment has been implemented for each patient. Figure 6 , the sepsis treatment status 714a of patient 1 may be drawn in red to indicate that treatment has not been applied to patient 1 (who is in a moderate sepsis state), and the sepsis treatment status 714n of patient N may be drawn in green to indicate that treatment has been applied to patient N (who is in a severe sepsis state). The sepsis treatment status 714b of patient 2 may be drawn in green because the sepsis status 712b of patient 2 is marked as not having sepsis, so no treatment is required. There may be other examples of intuitively distinguishing sepsis treatment statuses.

[0067] Figure 7 Another example of a user interface 710b presented on the display 708 of the status board 706 is shown. Similar user interfaces may also be displayed on one or more workstations 702 and one or more mobile devices 704. As with the user interface 710a described above, Figure 7 The illustrated user interface 710b uses data received from one or more monitoring devices 200a-200n and the EMR system 300 via the network 600 to provide a high-level view of the sepsis status of patients admitted to a medical facility or a specific department, unit, or floor within a medical facility. The user interface 710b can be used to determine how resources within a medical facility or a department, unit, or floor within a medical facility should be allocated based on the sepsis status of patients monitored by one or more monitoring devices 200a-200n. Furthermore, the user interface 710b can be used to determine which patients received appropriate sepsis treatment based on data received from the EMR system 300.

[0068] like Figure 7 As shown, the user interface 710b includes a first column 716 that lists patients admitted to the medical facility or a specific department, unit, or floor within the medical facility. The user interface 710b also includes an overview 711b that includes a second column 718 that lists the sepsis score of each patient listed in the first column 716; and a third column 720 that lists the sepsis treatment status of each patient listed in the first column 716. Figure 7 In the example of FIG. 7 , the second column 718 provides that the sepsis score of patient 1 is 3 (e.g., moderate sepsis state), and the third column 720 provides that patient 1 has not received sepsis treatment. The second column 718 also provides that the sepsis score of patient 2 is 0 (e.g., sepsis has not occurred), and the third column 720 provides that patient 2 has not received sepsis treatment, because patient 2 does not have sepsis and does not need treatment. The second column 718 also provides that the sepsis score of patient 3 is 5 (e.g., moderate sepsis state), and the third column 720 provides that patient 3 has received sepsis treatment.

[0069] The sepsis score values ​​included in the second column 718 of the user interface 710b may be displayed in different ways to visually indicate the severity of the sepsis score of each patient admitted to the medical facility or a specific department, unit, or floor within the medical facility. For example, the sepsis score values ​​may be color-coded. Figure 7 In the example of FIG. 710b, a sepsis score of 3 for patient 1 may be drawn in yellow to indicate a moderate sepsis score, a sepsis score of 0 for patient 2 may be drawn in green to indicate that sepsis is not present, and a sepsis score of 5 for patient 3 may be drawn in red to indicate a severe sepsis score. There may be other examples of visually distinguishing sepsis scores in the user interface 710b.

[0070] Similarly, the sepsis treatment status included in the third column 720 of the user interface 710b can be displayed in different ways to intuitively indicate whether sepsis treatment has been implemented for patients admitted to the medical facility or a specific department, unit or floor within the medical facility. For example, the sepsis treatment status can be color-coded. Figure 7 In the example of FIG. 710b, the sepsis treatment status of patient 1 may be colored red to indicate that treatment has not been administered to patient 1. The sepsis treatment status of patient 3 may be colored green to indicate that treatment has been administered to patient 3. The sepsis treatment status of patient 2 is not colored because the sepsis score of patient 2 indicates that sepsis has not developed and therefore treatment is not required. There may be other examples of visually distinguishing sepsis scores in the user interface 710b.

[0071] As described above, the user interfaces 710a, 710b may also be displayed on one or more mobile devices 704. In some examples, one or more mobile devices 704 are equipped with a clinical task management system, such as the system described in U.S. Patent Application No. 16 / 674,735, which is incorporated herein by reference in its entirety. When a clinical task management system is installed on one or more mobile devices 704, communication between healthcare providers within a healthcare facility is facilitated. Communication in a clinical task management system installed on one or more mobile devices 704 enables healthcare providers to respond to a request. Figure 6 and Figure 7 The illustrated user interfaces 710a, 710b include treatment statuses for managing sepsis treatment.

[0072] Figure 8 An example of a sepsis monitoring method 800 is schematically shown. In some examples, the method 800 is performed on the status board 706.

[0073] The method 800 includes an operation 802 of obtaining a sepsis status of each patient admitted to a medical facility or a department, unit, or floor within a medical facility. Operation 802 may include obtaining a sepsis score calculated by one or more monitoring devices 200a-200n using a sepsis scoring algorithm 208. Operation 802 may include obtaining the sepsis status from one or more monitoring devices 200a-200n via a network 600. In some examples, operation 802 includes obtaining the sepsis status from one or more monitoring devices 200a-200n using HL7 data communications.

[0074] Next, method 800 includes operation 804 of obtaining a sepsis treatment status of a patient admitted to a medical facility or a department, unit, or floor within a medical facility. Operation 804 may include obtaining the sepsis treatment status from EMR system 300 via network 600. In some examples, operation 804 includes obtaining the sepsis treatment status from EMR system 300 using HL7 data communication.

[0075] Next, method 800 includes operation 806, displaying sepsis profiles in a user interface, each sepsis profile including sepsis status and sepsis treatment status of patients admitted to a medical facility or a department, unit, or floor within a medical facility. In some examples, operation 806 includes displaying Figure 6 The overview 711a provided in the user interface 710a is shown. In some examples, operation 806 includes displaying Figure 7 An overview 711b is provided in the user interface 710b.

[0076] Fig. 9Another example of a user interface 710c presented on a display 708 of a status board 706 is shown. Similar user interfaces may also be displayed on one or more workstations 702 and one or more mobile devices 704. As with the user interfaces 710a, 710b described above, the user interface 710c uses data received from one or more monitoring devices 200a-200n and the EMR system 300 via the network 600 to provide a high-level view of the sepsis status of patients admitted to a medical facility or a specific department, unit, or floor within a medical facility. The user interface 710c can be used to determine how resources within a medical facility or a department, unit, or floor within a medical facility should be allocated based on the sepsis status of patients monitored by one or more monitoring devices 200a-200n. Furthermore, the user interface 710c can be used to determine which patients received appropriate sepsis treatment based on data received from the EMR system 300.

[0077] like Fig. 9 As shown, the user interface 710c lists all patients admitted to the medical facility or a specific department, unit, or floor within the medical facility in a first column 902. For each patient, the first column may include the patient's name, patient identification (ID) number, and / or the patient's ward / bed number. There may also be other types of information used to identify the patients listed in the first column 902. Furthermore, in some examples, the first column may include less information to identify each patient admitted to the medical facility.

[0078] The user interface 710c also includes a second column 904 identifying a sepsis score for each patient listed in the first column 902. According to the above example, the sepsis score is obtained from one or more monitoring devices 200a-200n via the network 600, and the one or more monitoring devices 200a-200n use the physiological parameter data captured by the one or more sensors 102a-102n as input to the sepsis score algorithm 208 to calculate the sepsis score as a numerical value representing the severity of the patient's sepsis condition.

[0079] The user interface 710c includes a third column 906 that provides relevant physiological parameters used to calculate a sepsis score for each patient identified in the second column 904. The third column 906 may be used to identify which physiological parameters cause the patient's sepsis score to be above normal.

[0080] The user interface 710c includes a fourth column 908 including a sepsis score trend 218 for each patient listed in the first column 902. As described above, the sepsis score trend 218 is generated based on the sepsis score calculated for each patient over a period of time. In addition, the sepsis score trend 218 can be displayed on one or more monitoring devices 200a-200n monitoring the patients listed in the first column 902.

[0081] The user interface 710c includes a fifth column 910 including a sepsis treatment status for each patient listed in the first column 902. For example, the fifth column 910 may indicate whether the sepsis treatment for each patient is complete or incomplete. In an example where the patient's sepsis score is low and thus the patient is not at risk for sepsis, the patient does not require sepsis treatment, and thus the fifth column 910 may indicate that the patient's sepsis treatment status is complete. There may be other examples of sepsis treatment statuses.

[0082] The above embodiments are provided only in an illustrative manner and should not be interpreted as limiting in any way. Various modifications may be made to the above embodiments without departing from the true spirit and scope of protection of the present disclosure.

Claims

1. A system for monitoring sepsis in a medical facility, the system comprising: at least one processing device; and A memory device storing instructions, which when executed by the at least one processing device cause the at least one processing device to: calculating a sepsis score for the patient using physiological parameter data captured by the one or more sensors; generating a sepsis score trend based on the sepsis score; generating a predicted sepsis trend based on the sepsis score and treatment administered to the patient; as well as The predicted sepsis trend is displayed superimposed on the sepsis score trend.

2. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: Visual markers identifying clinical events were added to the sepsis score trends.

3. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: adding a first visual marker identifying the onset of sepsis on the sepsis score trend; as well as A second visual marker identifying the start of treatment was added to the sepsis score trend.

4. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: One or more message boxes each describing a clinical event are added to the sepsis score trend.

5. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: Based on the sepsis score, it is determined whether the patient suffers from sepsis.

6. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: When the sepsis score trend increases after treatment initiation, or when the sepsis score trend does not decrease within a predetermined period of time after treatment initiation, an alarm is generated.

7. The system according to claim 1, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: An overview is displayed including a sepsis status and a treatment status for each of a plurality of patients within the medical facility.

8. The system according to claim 7, wherein: The sepsis status is displayed in different ways to indicate the severity of the sepsis status of each patient, and the treatment status is displayed in different ways to indicate whether treatment has been administered to each patient.

9. A method for monitoring sepsis in a medical facility, the method comprising: Physiological parameter data were used to calculate the patient's sepsis score; generating a sepsis score trend based on the sepsis score; generating a predicted sepsis trend based on the sepsis score and treatment administered to the patient; as well as The predicted sepsis trend is displayed superimposed on the sepsis score trend.

10. The method according to claim 9, further comprising: Visual markers identifying clinical events were added to the sepsis score trends.

11. The method according to claim 9, further comprising: adding a first visual marker identifying the onset of sepsis on the sepsis score trend; as well as A second visual marker identifying the start of treatment was added to the sepsis score trend.

12. The method according to claim 9, further comprising: Add a message box describing the clinical event on the sepsis score trend.

13. The method according to claim 9, further comprising: When the sepsis score trend increases after treatment initiation, or when the sepsis score trend does not decrease within a predetermined period of time after treatment initiation, an alarm is generated.

14. The method according to claim 9, further comprising: An overview is displayed having a sepsis status and a treatment status for each of a plurality of patients within the medical facility.

15. The method according to claim 14, wherein: The sepsis status is displayed in different ways to indicate the severity of the sepsis status of each patient, and the treatment status is displayed in different ways to indicate whether sepsis treatment has been administered to each patient.

16. A system for monitoring sepsis in a medical facility, the system comprising: at least one processing device; and A memory device storing instructions, which when executed by the at least one processing device cause the at least one processing device to: obtaining a sepsis status from a plurality of monitoring devices, each of the plurality of monitoring devices monitoring a patient within a medical facility; obtaining treatment status from an electronic medical record system, each treatment status being associated with a patient monitored by the monitoring device; as well as A user interface is displayed providing an overview of the sepsis status and treatment status of each patient monitored by the monitoring device.

17. The system of claim 16, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: displaying the sepsis status in different ways on the user interface to indicate the severity of the sepsis status of each patient; as well as The treatment status is displayed in different ways on the user interface to indicate whether sepsis treatment has been implemented for each patient.

18. The system of claim 16, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: Using the physiological parameter data to calculate a sepsis score for the patient; and Based on the sepsis score, a sepsis score trend was generated for each patient.

19. The system of claim 18, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: generating a predicted sepsis trend for each patient based on the sepsis score and treatment administered to each patient; as well as The predicted sepsis trend is displayed superimposed on the sepsis score trend for each patient.

20. The system of claim 16, wherein: When the instructions are executed by the at least one processing device, the at least one processing device further causes the at least one processing device to: When the sepsis score trend for each patient increases after the start of treatment, or when the sepsis score trend for each patient does not decrease within a predetermined period of time after the start of treatment, an alarm is generated.

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