A patient monitoring reminder system

Abstract

The present application relates to a patient monitoring and reminding system, comprising at least: a reminding device configured to at least collect and process state data information of a patient, and provide corresponding reminders / guidance to the patient according to prompt instructions; a management system for pre-setting the prompt instructions, and receiving and storing the state data information collected by the reminding device, the system being capable of checking and revising the pre-set prompt instructions according to historical condition information and current condition information of the patient, and associating past prompt instructions corresponding to the state data information of the patient with planned prompt instructions, and providing feedback results to the patient and / or medical staff, so that the planned prompt instructions can be executed at least after the current checking and revising operation is confirmed.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring technology, and in particular to a patient monitoring and alerting system. Background Technology

[0002] CN108354738A discloses a medical timed reminder system, belonging to the field of medical devices. It includes a main monitoring terminal, installed in the medical staff's duty room, comprising several display lights, several timers, a first signal transceiver processor, and a buzzer. The display lights consist of several LEDs arranged side-by-side, each LED bearing a different label. The display lights and timers are grouped in a one-to-one correspondence and sequentially numbered. Each timer is connected to the first signal transceiver processor and the buzzer. It also includes several nursing terminals, installed at the monitored patient's location, each including a light button, a timer button, a second signal transceiver processor, and an audio player. This invention solves the problem of medical staff having to perform nursing work at fixed times and locations, which carries the risk of forgetting due to their heavy workload, and delays can lead to unnecessary consequences.

[0003] Before some special examinations or tests, doctors need to urge patients to prepare in advance, such as fasting, avoiding exercise and alcohol, or keeping warm; or some patients need someone to remind them of relevant matters during their rehabilitation treatment, such as explaining the medication schedule, so as to inform them of which medicine to take at the correct time, the corresponding dosage, and related precautions. However, current methods often involve nurses personally going to the patient's bedside to verbally inform them, or using pre-made written labels to remind them of relevant matters. This undoubtedly places a huge mental and physical burden on nursing staff. Furthermore, the sheer volume of daily work inevitably leads to errors, such as informing the wrong patient due to their individual circumstances, or confusing the priority of instructions and delaying the optimal notification time. Secondly, a nurse often needs to care for many patients simultaneously, inevitably leading to staffing difficulties, especially at night or in the early morning when the number of nurses on duty is even more limited. Therefore, it is difficult for a single nurse to quickly notify several patients simultaneously within a short period. On the other hand, the effectiveness of simple verbal or written reminders is also unsatisfactory, especially for elderly patients whose memory declines significantly with age. Without constant reminders or supervision, they may forget instructions or make mistakes, affecting subsequent examinations or treatments. All of these phenomena and problems could ultimately lead to more serious consequences. Therefore, there are still at least one or more aspects of the existing technology that need improvement.

[0004] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the present invention provides a patient monitoring and reminder system, which aims to solve at least one or more technical problems existing in the prior art.

[0006] To achieve the above objectives, the present invention provides a patient monitoring and reminder system, comprising at least: a reminder device configured to collect and process patient status data information and provide corresponding reminders / guidance to the patient according to prompting instructions; and a management device for pre-setting prompting instructions and receiving and storing status data information collected by the reminder device.

[0007] Preferably, the management device can check and correct the preset planned prompts based on the patient's historical and current medical information and by associating past prompts with planned prompts corresponding to the patient's status data. At the same time, the device provides feedback to the patient and / or medical staff so that the planned prompts can be executed at least after the current check and correction operation is confirmed.

[0008] Preferably, the management device can modify or replace at least a portion of the planned prompt instructions when at least a portion of the content in the planned prompt instructions does not meet the correlation and / or regularity between the planned prompt instructions and the past prompt instructions, based on the retrieval of past prompt instructions within a preset time period and the matching analysis results between them, and at least provide the modified and replaced feedback results to medical staff for confirmation.

[0009] Preferably, the management device is able to provide feedback reminders to patients and / or medical staff in advance based on the analysis and comparison results of the correlation between past reminder instructions and planned reminder instructions, and automatically generate new planned reminder instructions based on the correlation and / or regularity between multiple past reminder instructions, and at least provide the planned reminder instructions to medical staff for their confirmation.

[0010] Preferably, when medical staff preset new plan prompts and / or receive feedback reminders from the reminder system, the management device can automatically set and remind subsequent plan prompts based on the instructions currently input by the medical staff and the preset time period by adjusting the corresponding execution program.

[0011] Preferably, the management device can automatically set corresponding planned prompts by recognizing the content contained in the patient's medical record data at least when the medical record data is generated, and provide the planned prompts to medical staff so that they can modify the content of any of the planned prompts at least through a lookup table. Medical staff do not need to frequently set new prompts, thereby reducing the risk of errors caused by medical staff negligence or forgetfulness and the errors that may occur during frequent prompt setting.

[0012] Preferably, the reminder device includes at least: a control unit configured with multiple signal input terminals and output terminals; a data transmission unit connected to the control unit and connected to the management system via a network; a storage unit connected to the control unit for storing data information and program instructions uploaded to and / or issued by the management system; and an electronic medical record unit configured with a display module, which is at least capable of displaying patient status information stored in the management system and transmitted to the control unit by the communication module.

[0013] Preferably, the reminder device further includes at least a detection unit and an image acquisition unit, which work together to provide several status information related to the patient. The status information includes at least the patient's various physiological parameters, the patient's activity status in different spaces, and the environmental status of the space in which the patient is located.

[0014] Preferably, the management device can automatically generate and provide unplanned alert instructions to patients and medical staff based on the detection data of the detection unit, when at least one detection data exceeds the standard range value, by searching and matching related content in past and / or planned alert instructions. These unplanned alert instructions are then executed at least after confirmation by the medical staff. The generation time of the unplanned alert instruction depends on the difference between the detection data of the detection unit and the standard threshold range. This method further reduces the physical and mental burden on medical staff who need to pre-set multiple alert instructions, and also reduces the probability of unexpected risks caused by oversights in patient supervision due to unforeseen emergencies and the lack of corresponding alert instructions. Furthermore, medical staff only need simple confirmation or modification, eliminating the need for extensive instruction setup, thus improving the efficiency of medical staff supervision and management.

[0015] Preferably, based on the detection data of various environmental parameters in the patient's space and the changing trends of these parameters by the detection unit, medical staff can adjust the operating status of any device in advance to maintain the stability of various environmental parameters according to the standard values / thresholds corresponding to each environmental parameter and the analysis and calculation results of the control unit.

[0016] Preferably, the reminder device further includes an alert unit connected to the control unit, wherein the alert unit is configured to at least play a preset audio signal or related prompts, so as to provide the patient with warnings, reminders and / or guidance on related actions / behaviors through the alert unit. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system architecture of a preferred embodiment of the present invention;

[0018] Figure 2 This is a structural schematic diagram of a preferred embodiment of the present invention.

[0019] List of reference numerals

[0020] 10: Reminder device; 20: Management device; 101: Control unit; 102: Data transmission unit; 103: Electronic medical record unit; 104: Power supply unit; 105: Storage unit; 106: Detection unit; 107: Image acquisition unit; 108: Warning unit; 201: Platform database; 1021: Communication module; 1022: Identity authentication module; 1031: Display module. Detailed Implementation

[0021] The following is a detailed explanation with reference to the accompanying drawings.

[0022] This invention provides a patient monitoring and reminder system, which may include one of the following components: at least one reminder device 10 and a management device 20. Specifically, the management device 20 and the reminder device 10 are connected to each other via a network connection.

[0023] According to a preferred embodiment, the reminder device 10 can be configured on or used in conjunction with a hospital bed in a hospital, nursing home, or sanatorium. Preferably, at least some of the relevant functions of the reminder device 10 can be implemented through a smart playback / display device, or the reminder device 10 can also be a smart monitoring wristband. The management device 20 can be a PC terminal centrally configured at the nurses' station or a portable mobile device such as a tablet or smartphone, and can be configured with corresponding interface access programs such as apps, web browsers, or similar applications to implement relevant functions. In addition, third-party terminal devices can also be connected to the management device 20 and the reminder device 10 via a network, so that medical staff or other users can directly obtain patient-related status information from multiple channels. Preferably, the third-party terminal device can be a mobile device such as a mobile phone or tablet, or other portable multimedia device.

[0024] According to a preferred embodiment, the management device 20 is configured to at least receive patient status information related to the patient collected and uploaded by the reminder device 10, and to provide relevant nursing services to the patient and supervise and manage the patient by controlling the implementation of relevant functions of the reminder device 10. Further, the management device 20 is equipped with a platform database 201, which can be used to store medical record data related to the patient's personal identification information, relevant medical history / medication history / allergy history, personal physiological contraindications and nursing matters (such as medication arrangements, examination and testing arrangements, or surgical arrangements), as well as patient status information related to the patient collected and uploaded by the reminder device 10.

[0025] According to a preferred embodiment, nursing staff can control the operation of the reminder device 10 using the management device 20 based on patient-related status information and the treatment guidance of doctors or nurses. This allows the reminder device 10 to provide patients with reminders regarding several matters, such as taking medication on time, fasting before laboratory tests, or paying attention to diet and rest before surgery. Furthermore, nursing staff can at least make arrangements for relevant matters in advance based on the patient's actual condition, such as adjusting the type, frequency, and dosage of subsequent medications based on the patient's medication history; or scheduling related examinations, tests, surgeries, etc., within appropriate time periods based on the patient's current physical condition. Preferably, the patient-related status information includes, but is not limited to, the patient's various physiological parameters (e.g., blood pressure, heart rate, or body surface temperature), the patient's activity status in bed and different spaces (e.g., frequency of turning over in bed, frequency of getting out of bed, main activity areas in the space and their duration, or other patient-related behaviors that can be referenced), and the surrounding environmental conditions of the space (e.g., air quality, ambient humidity, or indoor brightness).

[0026] According to a preferred embodiment, the reminder device 10 may include a control unit 101, a data transmission unit 102, an electronic medical record unit 103, a power supply unit 104, and a storage unit 105. Specifically, the control unit 101 is connected to the other units via electrical and / or communication coupling, and the control unit 101 is equipped with multiple signal input terminals and output terminals for information transmission and interaction with the other units.

[0027] According to a preferred embodiment, the storage unit 105 has a data storage function, which can be used to synchronously store data uploaded to the platform database 201 and data instructions issued by the management device 20, and can synchronize and update the data in the storage unit 105 and the platform database 201 through the network.

[0028] According to a preferred embodiment, the power supply unit 104 may be directly connected to the mains power supply, or may be formed by a combination of several rechargeable batteries, configured to provide the power required for operation to the other functional units described above.

[0029] According to a preferred embodiment, the data transmission unit 102 may include a communication module 1021 and an authentication module 1022. Optionally, the communication module 102 uses a wired or wireless communication protocol, such as the Internet or Wi-Fi. The communication module 1021 and the authentication module 1022 are respectively connected to the control unit 101. Further, the communication module 1021 can be connected to the management device 20 via a network, and it is configured to at least transmit patient status information stored in the platform database 201 of the management device 20 to the electronic medical record unit 103. The authentication module 1022 is configured to at least be used to obtain data related to patient identity information, and the relevant data can be transmitted to the management device 20 for storage via a network. Preferably, the authentication module 1022 may be, for example, an RFID module.

[0030] According to a preferred embodiment, the electronic medical record unit 103 includes at least a display module 1031, i.e., a display screen, which is at least capable of displaying patient status information stored in the platform database 201 of the management device 20 and transmitted to the control unit 101 by the communication module 1021. This allows doctors or nurses to judge the patient's current physical condition and / or other related status information based on the data information on the display module 1031, and at the same time, to further understand the patient's current status based on information such as examination / treatment items, treatment records, etc. on the display module 1031.

[0031] According to a preferred embodiment, the reminder device 10 should further include at least a detection unit 106 and an image acquisition unit 107. The detection unit 106 and the image acquisition unit 107 are respectively connected to the control unit 101 of the reminder device 10, so that the control unit 101 controls the operation of the detection unit 106 and the image acquisition unit 107 and receives relevant detection data. The detection unit 106 and the image acquisition unit 107 are configured to work together to provide doctors or caregivers with multiple status information related to the patient, including but not limited to the aforementioned physiological parameters (e.g., blood pressure, heart rate, or body surface temperature), the patient's activity status in bed and in different spaces (e.g., frequency of turning over in bed, frequency of getting out of bed, main activity areas in the space and their duration, or other visually relevant behaviors related to the patient), and the surrounding environmental conditions of the space (e.g., air quality, ambient humidity, or indoor brightness).

[0032] According to a preferred embodiment, the detection unit 106 includes a plurality of sensor modules, including but not limited to heart rate sensors, blood pressure sensors, blood oxygen sensors, body temperature sensors or pulse sensors capable of collecting various physiological parameters of the patient, and / or humidity sensors, air quality sensors, brightness sensors or air pressure sensors for detecting the surrounding environmental conditions of the space where the patient is located, and / or fingerprint sensors for confirming the patient's identity information, as well as positioning sensors, weight / pressure sensors or acceleration sensors for detecting information related to the patient's physical behavior.

[0033] According to a preferred embodiment, the image acquisition unit 107 may be, for example, one or a combination of a camera, a video camera, or a still camera. Further, the image acquisition unit 107 is configured to at least capture images / videos of a patient lying in bed and / or moving within a certain space, so as to cooperate with the detection data of the detection unit 106 to enable medical staff to promptly understand the patient's condition. Furthermore, through the analysis and processing of the detection data of the detection unit 106 and the image acquisition data of the image acquisition unit 107 by the control unit 101, the patient can be monitored in real time, and potential accident risks can be predicted. Preferably, for example, based on the real-time detection data of the detection unit 106, the system can at least alert the patient and / or medical staff in ways including but not limited to sound, light, or text commands when a certain physiological parameter of a patient is outside the standard range or is about to be outside the standard range.

[0034] According to a preferred embodiment, the reminder device 10 may further include an alert unit 108, which is electrically or communicatively connected to the control unit 101. Preferably, the alert unit 108 may be a speaker or buzzer, configured to at least play audio signals preset by doctors or nurses via an external terminal device or real-time voice information input by doctors or nurses. Preferably, the alert unit 108 can provide warnings or reminders regarding patient-related actions / behaviors, and instruct or guide patients to perform actions including but not limited to taking medication, fasting, resting, exercising, and undergoing examinations and tests. Further, the alert unit 108 can be used in conjunction with the image acquisition unit 107, enabling doctors, nurses, or visiting family members to provide voice guidance to patients, and allowing the image acquisition unit 107 to perform functions such as monitoring and visiting patients. In addition, images / videos related to the patient captured by the image acquisition unit 107 can be transmitted to remote medical staff or patients' families via the network for them to view, making the hospital's supervision and management of patients transparent and open, and helping patients' families to understand the patient's physical condition in a timely manner.

[0035] Preferably, for example, based on the detection data of several sensors used to collect physiological parameters such as heart rate, blood pressure, blood oxygen, body temperature or pulse of patients, medical staff can promptly know the current physical condition of patients. Furthermore, based on the changes and trends of various physiological parameters in different time ranges, combined with the standard values / thresholds corresponding to each physiological parameter and the analysis and calculation results of the control unit 101, medical staff can predict potential risks and accidents of patients in advance. This enables medical staff to remind patients of relevant precautions through the reminder device 10 in an audible / visual manner and instruct them to make corresponding adjustments and / or adjust the relevant plans for subsequent examinations, treatments and rehabilitation of patients accordingly, so as to ensure the smooth progress of subsequent examinations, tests and / or rehabilitation treatments.

[0036] According to a preferred embodiment, based on the detection data of several sensors used to detect environmental parameters such as humidity, air quality, brightness, or air pressure in the patient's space, medical staff can promptly obtain the real-time environmental status of the patient's current space (e.g., ward, examination room, operating room, or isolation room). Furthermore, based on the changes and trends of various environmental parameters over different time ranges, combined with the standard values / thresholds corresponding to each environmental parameter and the analysis and calculation results of the control unit 101, medical staff can anticipate potential changes in the environmental status of the patient's space. This allows medical staff to maintain the stability of various environmental parameters by adjusting the operating status of other equipment in advance. For example, based on the detected values ​​and changing patterns of various environmental parameters, the operating modes of equipment such as ventilation equipment, disinfection equipment, or cleaning equipment in the ward can be set in advance. When the corresponding environmental parameters reach or are about to reach the standard values, the operating status of the equipment can be adjusted in advance to ensure a good environmental condition in the patient's space, thereby providing the patient with suitable activity space. At the same time, a good environment helps the patient's recovery.

[0037] According to a preferred embodiment, medical staff can also obtain timely information about a patient's actions / behaviors based on data from several sensors used to detect the patient's physical behavior, and take corresponding reminders or adjustments accordingly. For example, based on the detection data from the positioning sensor, the patient's daily movement trajectory and the corresponding dwell time in different areas can be obtained. This allows medical staff to deduce the patient's personal habits based on the data such as the movement trajectory, cycle, and frequency, and to verify these personal habits by combining the patient's behavioral information contained in the images / videos acquired by the image acquisition unit 107. This is because the patient's actions / behaviors are closely related to their personal emotions, the development of their condition, and the effectiveness of subsequent rehabilitation treatment. Specifically, for example, based on the patient's usual location, corresponding dwell time, and visible limb movements or facial expressions, medical staff can roughly understand or determine the patient's physiological or psychological state. For example, if a patient frequently goes to the restroom and stays for a relatively long time, they may have physiological or psychological discomfort. Therefore, medical staff can instruct or remind the patient and take corresponding measures before a major risk incident occurs. In addition, if a patient frequently turns over or gets out of bed, medical staff can generally know or determine that the patient may have physical or psychological discomfort. For some patients with language impairments or limited facial expression abilities, judging through physical behavior is more effective.

[0038] Furthermore, given the large number of patients a nurse needs to care for simultaneously, their workload is heavy. If they were to constantly monitor each patient's condition and selectively provide prompts / guidance or modify treatment plans based on that condition, other events could easily occur, causing medical staff to forget or confuse relevant instructions and / or modifications to treatment plans. This could lead to missed opportunities, resulting in errors in patient supervision and management, or even endangering patients' lives. Simultaneously, when medical staff are busy, they cannot promptly and effectively confirm whether patients have accurately received the correct instructions and whether they are strictly following them to complete the corresponding tasks or actions. Therefore, deep learning in the system can automatically generate prompts to provide intelligent reminders to patients and provide intelligent feedback on pre-set reminders from medical staff.

[0039] According to a preferred embodiment, the deep learning of the system can be implemented as follows: Based on the different individual circumstances of each patient, the treatment management plans adopted by medical staff and the instruction messages to be conveyed will also differ. For example, in the short term, the types of medications that each patient needs to take on a given day or at least for a certain period, as well as the corresponding frequency / cycle of medication, dosage, and / or related precautions, will be different; in the long term, the cycle / frequency of each patient's examinations or nursing treatments, and the corresponding precautions, will also be different. Therefore, medical staff need to pre-set multiple prompt messages / instructions for each patient at different time points with different specific instructions, and the corresponding reminder methods may also be different, so as to remind the patient or themselves to complete the relevant work in the instruction content in a pre-set reminder method for at least a part of the time before or at the corresponding time point.

[0040] Furthermore, given the large number of patients and the varying tasks they require to complete (whether instructed by the patients or performed by medical staff), medical staff may forget pre-set prompts, and patients may forget to execute them or even execute them incorrectly. If these prompts are not proactively reported and medical staff do not verify them, risk mitigation will be impossible. Therefore, the system can learn from a set of sample data to achieve intelligent reminders.

[0041] According to a preferred embodiment, medical staff can set reminder instructions for a patient at different dates and time points (e.g., at time A in the morning, time B at noon, and time C in the evening on Mondays, Wednesdays, etc.), and the content of each reminder instruction may also be different (e.g., fasting is required for at least a period of time before time A, medication b is required at time B, etc.). The specific reminder methods are also different (e.g., fasting can be reminded through the warning unit 108 based on a pre-input voice message from the medical staff, while medication can be reminded through ordinary sound / light methods, such as an alarm clock or alarm light). If the medical staff forgets to set or incorrectly sets the relevant reminder instructions, the system can at least analyze and verify the reminder instructions preset by the medical staff based on the patient's historical medical information, current medical information, and the information contained in the corresponding executed and / or to be executed reminder instructions.

[0042] For example, a medical professional might set the following instructions for a patient: Medication A needs to be taken 3 times a day, 4 hours apart, and 4 tablets per dose during a past period; Medication B needs to be taken 4 times a day, 2-3 hours apart, and 2 tablets per dose during a subsequent period; Medication B needs to be taken again during the current period, with the dosage changed to 1 tablet per dose, and after a certain period, the dosage will be 0.5 tablets per dose. Since the instructions differ across different periods, this can easily lead to forgetting or confusion. Therefore, the system should at least be able to check and correct the instructions based on the time periods preset by the medical professional.

[0043] Furthermore, the system can analyze the correctness of the prompts preset by medical staff based on sample data of prompts within a certain time period. For example, according to the preset instruction X that has been executed, the method of taking medication a is known. However, in the prompt instruction Y to be executed later, at least one of the following is different from the preset instruction X: the time of administration of medication a, the corresponding frequency cycle, or the dosage. In this case, the system can, on the one hand, remind the patient of the abnormality of prompt instruction Y through the reminder device 10 in the form of sound / light before prompt instruction Y is executed. On the other hand, it can also synchronously upload this abnormal information to the computer equipment at the nurse station or other portable multimedia equipment used for mobile rounds for medical staff to review and check, so that at least one of the patient and medical staff can be informed of the correctness of the prompts in a timely manner, thereby providing intelligent feedback on the preset instructions of medical staff. Secondly, when medical staff receive abnormal information about prompt instruction Y, they can confirm the correctness of the prompt instruction Y to be executed and modify or replace any information that does not conform to its instructions. On the other hand, the platform database 201 of the management device 20 stores a large number of prompt instructions. In some preferred embodiments, these prompt instructions are usually in the form of voice messages. In addition to the various matters instructed by medical staff, the specific content of the voice messages may also include patient-related information, such as a patient's name or a patient's corresponding bed number. Therefore, when medical staff preset a prompt instruction Z, the system can, for example, use voice recognition and, based on the retrieval of a large number of prompt instructions in the platform database 201, analyze the correctness of the instructions of prompt instruction Z and automatically correct it, while feeding back the results before and after correction to the medical staff. For example, if prompt instruction Z mentions "Zhang" or "bed number 003", and the system determines, based on analysis of past prompt instructions for "Zhang" and / or "bed number 003", that prompt instruction Z is incorrect, i.e., the patient or bed number corresponding to prompt instruction Z is incorrect, and / or the related matters corresponding to that patient or bed number are incorrect, based on the matching and association between prompt instruction Z and prompt instructions in the platform database 201, the system will modify and replace at least part of the content in prompt instruction Z and simultaneously feed back the modified and replaced results to medical staff for secondary confirmation. For example, for content after voice recognition or text recognition, the patient's name or bed number will be automatically replaced with existing voice or text messages, or for a specific patient, voice / text instructions regarding specific precautions will be automatically replaced with existing voice / text messages.

[0044] According to a preferred embodiment, based on the analysis and comparison of the patient's historical medical information, current medical information, and the information contained in the corresponding executed and / or to be executed prompt instructions, as well as the corresponding trends of change, the system can pre-set prompt instructions and / or pre-remind patients or medical staff. Specifically, based on the analysis of sample data of prompts within a certain time period, the system determines that a patient's frequency / cycle of taking medication A increases and / or the dosage decreases within a certain time period. Furthermore, the trend of increasing frequency / cycle or decreasing dosage may exhibit a certain regularity (e.g., an arithmetic sequence: 2 times / day, 3 times / day, ..., N+1 times / day; 4 tablets / time, 3 tablets / time, ..., N-1 tablets / time). The system can further generate new prompts based on the correlation between each prompt and according to a certain regularity. For example, if the frequency of taking medication A decreases once every week, and the corresponding dosage decreases by 0.5 tablets each time, the system can generate a new prompt based on the content that the frequency of taking medication A decreases by one compared to the previous cycle and the single dosage decreases by 0.5 tablets compared to the previous dose. This new prompt is then provided to medical personnel for confirmation. Preferably, a certain time period can be a time period specified by medical staff, such as one month; or it can be formed autonomously by the system based on the correlation or regularity between the specific contents contained in the prompt instructions. For example, if the trend of the change of drug A’s cycle, frequency and dosage is arithmetic reduction within a certain period of time, it can be defined as a time period. When the trend of at least one of them does not conform to the previous pattern, it can be assumed to enter the next time period.

[0045] According to a preferred embodiment, after medical staff preset a new prompt instruction, the system will analyze the specific content of the planned prompt instruction based on the retrieval of past prompt instructions in the platform database 201. Through deep learning, the system determines the correlation or regularity between past prompt instructions and the planned prompt instruction. When the correlation or regularity between the planned prompt instruction and past prompt instructions does not meet the corresponding trend, for example, as mentioned above, according to past prompt instructions, the frequency of medication A is reduced once every week, and the corresponding dosage is reduced by 0.5 tablets each time. When at least one of the cycle, frequency, or dosage of medication in the planned prompt instruction differs from the trend shown in past prompt instructions, the system will provide feedback to the medical staff to remind them of the correctness of the current preset prompt instruction and further correct any errors. Furthermore, when the content in the planned prompt instruction differs from the content in the past prompt instructions, the system will provide feedback to the medical staff to remind them of the correctness of the current preset prompt instruction and further correct any errors. When errors exist in the correlation between tasks, it may be due to adjustments made by medical staff to the configuration of multiple tasks such as examinations, medication, fasting, rest, and / or exercise based on the patient's physical condition, rather than a setting error. Therefore, medical staff should be able to adjust the settings of the corresponding execution procedures according to their specific arrangements when they preset new prompts and / or receive feedback reminders from the system. This allows the system to automatically set and remind users of subsequent prompts according to the new configuration. For example, after medical staff set new instructions, the system can be programmed to automatically set and remind users of subsequent prompts based on the current instructions and at a certain time period, without having to analyze the correlation between past prompts and the current prompts. This reduces the probability of errors in the system's multiple calculations and analysis, and further improves the accuracy of the system's automatic setting of relevant prompts.

[0046] According to a preferred embodiment, since each medical staff member needs to supervise and manage a large number of patients simultaneously, it is cumbersome to frequently set different prompts for each patient's actual situation. Even if the system automatically sets new planned prompts based on past prompts, medical staff still need to confirm multiple times before executing the corresponding prompts. However, preferably, in some preferred embodiments, the system can automatically configure prompts in the following way: In addition to basic information, medical history / diagnosis information, the patient's medical record data should also include the corresponding treatment or management plan. The treatment or management plan should include several pieces of information related to the patient's physical examination, medication, surgery, daily rest / exercise / diet, etc. For example, according to the medical record data, patient W has been on duty for one month from a specified time. The medication will be taken according to the following schedule: "3 times / day, 1 tablet / time, from 9:00 to 22:00, with an interval of at least 3 hours between doses, and a 1-day break after every 5 consecutive days of use." Therefore, when the patient's medical record data is generated, the system can automatically set corresponding planned prompts in text and / or voice by recognizing at least part of the content in the medical record data. The automatically set planned prompts will be provided to medical staff for review and verification in the form of data tables, thereby reducing the physiological and / or psychological burden caused by frequent operations by medical staff. At the same time, later, medical staff only need to modify the specific content of any planned prompt based on the patient's actual situation by looking up the table, without having to frequently set new prompts, thereby reducing the risk caused by medical staff's negligence or forgetfulness.

[0047] According to a preferred embodiment, when the system can automatically generate planned prompts by setting corresponding execution procedures, the system can generate new unplanned prompts more intelligently and effectively based on the following methods. Specifically, for example, a patient's various physiological parameters have certain normal range values. When a physiological parameter is outside the normal range, it may be necessary to take medication or undergo relevant examinations immediately to resolve the corresponding problem. For some more complex problems, due to professional limitations, it may be difficult for patients to make their own judgments. Therefore, they need to strictly follow the instructions of medical staff and cannot make decisions on their own. However, some changes may be instantaneous, and medical staff cannot predict all possible changes. Therefore, before some emergencies occur, if there are no prompts from medical staff, it is difficult for patients to make correct operations, which puts the patient's physical and mental health at risk. However, medical staff cannot pre-set corresponding prompts for all foreseeable or unforeseeable events. Preferably, in addition to planned prompts set by medical staff and automatically generated by the system based on the content of past prompts and patient medical records, for example, when the system detects that a patient's physiological parameter is about to exceed the standard range or has already exceeded the standard range based on the detection data of the detection unit 106, it can automatically generate a prompt related to the change by searching and matching the content related to the physiological parameter in past prompts and / or unexecuted planned prompts to overcome or eliminate the sudden change. Furthermore, the unplanned prompts generated in this way will be fed back to the patient and medical staff for confirmation, and will begin execution at least after confirmation by the medical staff. Preferably, the timing of the generation of unplanned prompts depends on the difference between the detection data of the detection unit 106 and the standard threshold range, that is, it is generated when a certain difference is met, and this difference can be set by the medical staff, because generally, the medical staff know the specific numerical range of controllable risks. This approach further reduces the physical and mental burden on medical staff who need to pre-set multiple prompts and instructions. It also lowers the probability of unexpected accidents caused by medical staff's inability to anticipate emergencies and thus fail to set corresponding prompts and instructions, leading to oversights in patient supervision and management. Furthermore, medical staff only need to confirm or modify the prompts and instructions without spending a lot of time setting them, thereby improving the efficiency of medical staff's supervision and management.

[0048] According to a preferred embodiment, when a prompt command set by medical staff is responded to / activated, the patient needs to reply to the corresponding prompt command so that the medical staff can confirm that they have received the relevant message. Furthermore, to further confirm whether the patient has strictly followed the instructions to complete the corresponding tasks after receiving the prompt command, the patient needs to provide secondary confirmation. Medical staff can verify whether the patient has followed the instructions to complete the corresponding tasks / behaviors through the detection data from the detection unit 106 and the image acquisition unit 107. Preferably, each patient confirmation and the medical staff's feedback to the system reminder can be considered as a check-in record for the medical staff during their work or on-call period. By combining these check-in records with the execution status and physical condition of multiple patients under their supervision, the performance of medical staff can be evaluated. Simultaneously, it allows patients' families to have a direct understanding of the hospital's supervision and management work.

[0049] According to a preferred embodiment, medical staff can supervise and manage multiple patients through the management device 20. Specifically, when a patient is hospitalized, they input their identity information through the identity authentication module 1022 of the reminder device 10 using their identity credentials (such as an ID card, admission permit, etc.). The reminder device 10 uploads the received identity information to the management device 20 via the network to confirm the patient's identity. After confirming the identity, the management device 20 generates an electronic medical record and simultaneously saves it in the platform database 201. The reminder device 10 can also download the electronic medical record synchronously via the network and store it in the storage unit 105 to achieve synchronous data updates. Furthermore, the management device 20 can connect to the systems of other medical-related units via the network to realize data transmission and / or information interaction between itself and the management device 20 and the reminder device 10.

[0050] According to a preferred embodiment, the reminder device 10 is connected to the management device 20 via a network. Therefore, the corresponding steps performed by the management device 20 can be transmitted to the reminder device 10 via the network. In some other preferred embodiments, the reminder device 10 can download and save the corresponding execution steps in advance to the data transmission unit 102 so that it can independently perform all its functions when it is not connected to the management device 20 via the network.

[0051] According to a preferred embodiment, in a specific ward, the various sensors included in the detection unit 106—whether they are heart rate sensors, blood pressure sensors, blood oxygen sensors, body temperature sensors, or pulse sensors capable of collecting various physiological parameters of the patient, or humidity sensors, air quality sensors, brightness sensors, or air pressure sensors used to detect the surrounding environmental conditions of the patient's space, or fingerprint sensors used to confirm the patient's identity, or positioning sensors, weight / pressure sensors, or acceleration sensors used to detect factors related to the patient's physical behavior—can all be roughly classified into categories A, B, C, and / or D sensors based on their corresponding power acquisition methods and data transmission methods. For example, category A sensors rely on ordinary primary batteries for power and transmit data wirelessly; category B sensors rely on power supplies or mains power and transmit data via wired connections; category C sensors rely on solar cells or rechargeable batteries for power and transmit data wirelessly; and category D sensors rely on multiple power supplies and are equipped with multiple data transmission channels. Sensor classifications are shown in the table below:

[0052] Class B sensors Power supply Wired data channel Class C sensor Solar power Wireless Data Channel Class D sensor Multiple power supply Multiple data channels

[0053] Furthermore, regardless of the data transmission mode or power supply method used by these sensors, they all need to be directly or indirectly connected to the corresponding alerting device 10 and management device 20. Therefore, during power outages, such as when there are significant voltage fluctuations (the effective value of the power supply voltage drops rapidly to 90%–10% of the rated value, mostly lasting 0.5–1.5 seconds), the detection behavior or data of some sensors will often produce unpredictable results. For wards where this system is deployed, this will inevitably generate a large number of feedback alerts and prompts from the system. These alerts and prompts will contain correct information, but also erroneous or interfering information. When the number of erroneous messages significantly exceeds the number of correct messages, medical staff are more likely to ignore all alerts or even shut down the system. This is a key reason why such equipment or systems are difficult to use frequently after deployment. Since the quality of the power grid is not under the hospital's control, a solution needs to be found within the system itself to eliminate the situation where the number of erroneous messages significantly exceeds the number of correct messages.

[0054] According to a preferred embodiment, in this system, although the data channels of various sensors are connected to different data collection devices such as the reminder device 10 of the present invention (and stored in their own storage units 105), the data from several reminder devices 10 configured in different areas, floors, and wards still need to be aggregated to the same data acquisition server, such as the management device 20 of the present invention. Since the reminder device 10 is usually configured to be online 24 hours a day, its required power is usually provided by the mains voltage. Generally, reminder devices 10 in the same ward are usually in the same power supply circuit, while reminder devices 10 on different floors are in different power supply circuits. When the power supply voltage fluctuates throughout the hospital building, a large number of reminder devices 10 on each floor will give abnormal signals within the same time interval (e.g., 50ms to 1s). Although these abnormal signals are different from each other, the management device 20 of the present invention receives a large number of requests from reminder devices 10 for pending reminder instructions. When the management device 20 of the present invention receives a large number of requests from reminder devices 10 for pending reminder instructions in a short period of time, it first regards these abnormal signals containing "requests for reminder instructions" as false reminder requests. To avoid false alarms but also to avoid missed alarms, the management system 20 of the present invention can filter out requests that are considered false alarms that require secondary screening based on reminders in the "past prompt instructions" that may endanger the patient's life (such as blood pressure, heart rate abnormalities, etc. that are considered to lead to the patient's accidental death).

[0055] Preferably, in the request set consisting of "requests considered false alerts", the management device 20, targeting "past alert instructions" that have shown life-threatening risks within a predetermined time period, categorizes the requesters of the relevant alert instructions into special priority alert targets. The management device 20 performs a third screening based on the location of these special priority alert targets. Reducing the target data to identify patients most likely to require nursing intervention is crucial because the system of this invention is not designed for special circumstances such as power outages or severe power fluctuations. Power outages causing shutdowns of life-sustaining equipment, such as oxygen supply interruptions or ventilator shutdowns, can also trigger widespread alarms. Prioritizing these alarms is therefore a critical task. In this case, for example, identifying a special priority alert list based on alert devices 10 that have shown "past alert instructions that have shown life-threatening risks" allows for the precise allocation of limited medical resources.

[0056] Preferably, when the management device 20 receives a large number of alert requests triggered by data collected from Class D sensors with multiple power supplies, and these alert devices 10 sending the alert requests are within the same power supply circuit, the management system 20 establishes a priority alert queue based on the number of alerts with life-threatening risks in the "past alert instructions". Class D sensors are often used in particularly important equipment, and due to their characteristics of multiple power supplies and multiple data channels, the management device 20 assigns them a higher priority for sending alert instructions. A large number of alerts in the same power supply circuit (i.e., the same ward) likely indicates a power failure, requiring a large number of medical personnel to intervene urgently. When these personnel enter the ward, they can determine the order of patient transfer or resuscitation based on the priority alert queue. Different alerts within the priority alert queue can be marked differently by their respective alert devices 10 so that medical personnel can visually detect them during emergency intervention.

[0057] Preferably, when the management device 20 receives a large number of alert requests triggered by data collected from battery-powered Class A sensors, and these alert devices 10 sending the alert requests are within the same power supply circuit, the management device 20 establishes a queue of potential priority alert targets based on the number of alerts with life-threatening risks in the "past alert instructions". The management device 20 distinguishes the alert devices 10 in the queue of potential priority alert targets according to the physical location corresponding to the power supply circuit, and establishes a sub-queue of the queue of potential priority alert targets within each power supply circuit. The management device 20 selects a queue of potential priority alerts from several queues of potential priority alerts based on alert requests from at least one other type of sensor, specifically based on the alert device 10 targeted by the alert request from the at least one other type of sensor, the routing information of the corresponding alert device 10, and the temporal correlation between the large number of alert requests from the at least one other type of sensor and the large number of alert requests from the Class A sensor. The management device 20 then selects routers equipped with alert devices 10 that issue the corresponding alert requests based on the routing information of these sub-queues of the potential priority alert queue containing the largest number of sub-queues. Based on the relevant routing information, the management device 20 determines the building, floor, or room with the most requests, and identifies the specific patient requiring medical personnel intervention based on the potential priority alert queue and the relevant sub-queues. Through the above-described method of the present invention, systemic faults in equipment, wiring, or power supply systems can be detected with minimal human effort. In the event of disasters such as thunderstorms, typhoons, or earthquakes, extremely limited medical personnel can be allocated to the most urgent areas and patients in a very short time. With the help of the above methods, it is also possible to identify power supply or power consumption equipment that may have short circuits, open circuits or other serious problems without the need for special equipment.

[0058] In this invention, since the number of battery-powered and wirelessly transmitting Class A sensors is the largest and the data is the most complex, the number of false alerts they generate is also the largest. However, it is precisely the data from Class A sensors that is a key link in improving the quality of care. The key to whether the system of this invention can be deployed in the long term and continuously improve the overall quality of care in the hospital is to distinguish between true and false data.

[0059] According to existing technology, when the management device 20 receives repeated prompt requests triggered by data collected from a battery-powered, wirelessly transmitting Class A sensor, medical staff or patients can manually assess the authenticity of the prompt requests on the reminder device 10. This freedom to assess authenticity often arises when patients wish to be outside of supervision, or when medical staff temporarily suppress excessive prompts and manually arrange treatment offline. Given the large amount of data involved, software systems similar to the system of this invention are difficult to deploy effectively in hospitals, and the data they collect cannot gain the full trust of hospital administrators. Among the data sent by a large number of Class A sensors, there is both interference data (such as abnormal data caused by power supply voltage fluctuations or sensor failures) and normal data collected within the same time period (such as data that has reached a corresponding threshold or the conditions for generating a related prompt instruction, thus sending a request to the management device 20). Therefore, it is crucial to quickly eliminate erroneous data from the large dataset, analyze the cause of each erroneous data and the specific location of the corresponding data sender, i.e., the location of the reminder device 10, as well as determine the authenticity / validity of the normal data collected, and configure different priority implementation instructions for the corresponding reminder device 10 for the data sender or instruction requester corresponding to the normal data collected.

[0060] According to a preferred embodiment, when the management device 20 receives a large number of alert requests triggered by data collected from battery-powered Class A sensors, the management device 20 determines the physical location of each alert device 10 based on the network routing information of each alert device 10 and the cabinet number corresponding to the router (a table showing the correspondence between cabinet numbers and wards exists). When a large number of alert requests originate from multiple alert devices 10 within the same power supply circuit, the management device 20 that receives these requests performs data analysis on at least one Class B, Class C, or Class D sensor powered by a power supply within that power supply circuit. Specifically, the management device 20 compares the time of data anomalies of multiple Class B, Class C, or Class D sensors within the same power supply circuit with the time of data anomalies of multiple Class A sensors, using the overlap of occurrence times as a filtering criterion for "false alerts". Preferably, the management device 20 compares the occurrence time of "loss" data anomalies in Class B, C, or D sensors within the same power supply circuit with the occurrence time of data anomalies in Class A sensors. Data anomalies in Class A sensors that overlap with the occurrence time of data loss in Class B sensors are filtered and included in a "pseudo-prompt list." This "pseudo-prompt list" is filtered by the management device 20 based on the degree of overlap with the occurrence time of data anomalies in Class C and / or D sensors within the same power supply circuit, extracting the item with the lowest time overlap. The corresponding prompt request from the reminder device 10 is then generated based on this item with the lowest time overlap. Thus, after receiving the relevant prompt, medical staff can, upon entering the relevant ward, identify patients requiring further visits. This patient information, including historical medical history, current medical history, and information related to past prompts, can be provided to the relevant medical staff in advance in an appropriate manner. Through the above design scheme of the present invention, when there are a large number of potential reminder requests in the same ward (i.e. the same power supply circuit), the management device 20 instructs medical staff to only view the patients corresponding to the reminder device 10 corresponding to the "item with the lowest time overlap". While avoiding batch reminders, it is also possible to determine the overall safety of the ward by viewing individual patients through instructions. Without significantly increasing the workload, reasonable reminders are given to medical staff in the form of patient priority ranking.

[0061] Preferably, when the management device 20 receives a large number of alert requests triggered by data collected by Class A, Class B, Class C, and Class D sensors within a predetermined time window, the management device 20 sets the patient monitoring and alert system status to offline and discards the data within that time window to prevent it from being included in the patient status data information used to activate past alert commands. The management device 20 jointly stores the time window and the building information corresponding to the power supply circuits involved in the alert requests, and archives this as feedback on power quality. After the patient monitoring and alert system status is set to offline, the alert device 10 and / or the management device 20 will notify the relevant nursing staff that the patient monitoring and alert system has entered a "non-working" state and requires manual intervention in the care of the relevant patients.

[0062] Preferably, when the management device 20 receives a large number of alert requests triggered by data collected from at least three types of sensors (Type A, Type B, Type C, and Type D) within a predetermined time window, the management device 20 will execute the operation of switching the system to a "non-working" state, and jointly store the time window and the building information corresponding to the power supply circuit involved in the alert requests, and archive it as feedback on time and building power supply quality. For power supply units, the power supply quality of a specific transformer circuit is difficult to measure accurately and objectively because the circuit often involves many loads, and its voltage fluctuations cannot be fully reflected at the power supply and distribution end, but rather appear at the user end. Large voltage fluctuations on-site are like a heart attack in humans; that is, the cause can only be found when it occurs. Therefore, large voltage fluctuations in wards or inpatient buildings also require 24-hour dynamic tracking, similar to a Holter monitor, to determine which large load's sudden intervention or disconnection caused the large voltage fluctuation, thereby directly eliminating related risks at the user end, such as the hospital. In particular, hospitals and their inpatient departments are often highly complex electrical users. It's common for old and new office buildings to be used together, and for old and new equipment to be online simultaneously. Furthermore, many high-value electrical devices and precision instruments are involved, making it impossible to verify and eliminate potential problems one by one. Before this invention, we often had to passively wait for problems to occur and then try to resolve them. However, with the widespread deployment of smart hospitals, by leveraging the aggregation of medical data, especially the cross-verification of data based on the reasonable classification of high-reliability medical sensors like those in this invention, we can quickly identify wards or buildings with frequent and significant voltage fluctuations in complex hospital environments within the aforementioned time window with numerous alert requests, without needing to add complex specialized equipment.

[0063] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and not intended to limit the scope of the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; terms such as "preferredly," "according to a preferred embodiment," or "optionally" indicate that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept.

Claims

1. A patient monitoring and alerting system, characterized in that, At least including: A reminder device (10) with a reminder function and a management device (20) with a preset reminder instruction function. The management device (20) can, at least based on the patient's historical and current medical information, modify and replace at least some of the content in the preset planned prompt instructions when at least some of the content does not meet the correlation and / or regularity between the content and the past prompt instructions corresponding to the patient's status data information. This is done by searching the past prompt instructions within a preset time period and analyzing the matching results between them and the preset planned prompt instructions. This allows for the verification and correction of the preset planned prompt instructions, and the feedback results are provided to the patient and / or medical staff, so that the preset planned prompt instructions can be executed at least after the current verification and correction operation is confirmed. The reminder device (10) further includes at least an image acquisition unit (107) and a detection unit (106). The detection unit (106) and the image acquisition unit (107) are used to provide several status information related to the patient. The status information includes at least the patient's various physiological parameters, the patient's activity status in different spaces, and the environmental status of the space where the patient is located. The management device (20) can automatically generate an unplanned prompt instruction related to the detection data based on the detection data of the detection unit (106) and when at least one detection data does not meet the standard range value, retrieve content related to the detection data from past prompt instructions and / or the preset planned prompt instructions, and match and associate the retrieved content with the detection data. The management device (20) provides the unplanned prompt instruction to the patient and medical staff, and starts execution at least after the medical staff confirms it. The generation time of the unplanned prompt instruction depends on the difference between the detection data of the detection unit (106) and the standard threshold range. Based on the detection data of various environmental parameters of the patient's space by the detection unit (106) and the changing trend of various environmental parameters, medical staff can adjust the operating status of any device in advance to maintain the stability of various environmental parameters according to the standard value / threshold corresponding to each environmental parameter and the analysis and calculation results of the control unit (101). When the prompts set by the medical staff are responded to, the patient needs to respond to the corresponding prompts so that the medical staff can confirm that they have received the relevant information. In order to further confirm whether the patient has followed the instructions and completed the corresponding tasks after receiving the prompts, the patient needs to make a second confirmation. The medical staff can verify whether the patient has followed the instructions and completed the corresponding tasks by using the detection data of the detection unit (106) and the image acquisition unit (107). The detection unit (106) includes several sensor modules, which are divided into Class A, Class B, Class C and / or Class D sensors according to their corresponding power acquisition method and data transmission method; wherein, the Class A sensors are battery powered and use a wireless data channel, the Class B sensors are power powered and use a wired data channel, the Class C sensors are solar powered and use a wireless data channel, and the Class D sensors are multi-channel powered and use multiple data channels; In the case where a large number of alert requests originate from multiple alert devices (10) within the same power supply circuit, the management device (20) that receives these requests performs data analysis on at least one Class B, Class C, or Class D sensor powered by the power supply within the power supply circuit. The management device (20) compares the time of data anomaly occurrence of multiple Class B, Class C, or Class D sensors within the same power supply circuit with the time of data anomaly occurrence of multiple Class A sensors, and uses the overlap of occurrence times as a filtering condition for "false alerts". Specifically, the management device (20) compares the time of data loss of Class B, Class C or Class D sensors within the same power supply circuit with the time of data loss of Class A sensors, and filters the data loss of Class A sensors that coincide with the time of data loss of Class B sensors into a pseudo-prompt list. The pseudo-prompt list is extracted by the management device (20) based on the degree of overlap with the time of data loss of Class C and / or Class D sensors within the same power supply circuit, and the item with the lowest time overlap is generated based on the item with the lowest time overlap to form a prompt request for the corresponding reminder device (10).

2. The reminder system according to claim 1, characterized in that, The management device (20) can, based on the analysis and comparison results of the correlation between past prompts and the preset planned prompts, provide feedback reminders to patients and / or medical staff in advance according to the relevant content in the preset planned prompts. And automatically generate new preset plan prompts based on the correlation and / or regularity between the multiple past prompts, and execute them through the reminder device (10) at least after the new preset plan prompts are confirmed.

3. The reminder system according to claim 2, characterized in that, When medical staff manually set new preset plan prompt instructions and / or when medical staff receive feedback reminders from the reminder system, the management device (20) can at least automatically set and remind subsequent preset plan prompt instructions based on the instructions input by the medical staff in adjusting the corresponding execution program and the preset time period.

4. The reminder system according to claim 3, characterized in that, The management device (20) can automatically set a preset plan prompt instruction corresponding to the medical record data by identifying the content contained in the medical record data when the patient's medical record data is generated, and provide the preset plan prompt instruction corresponding to the medical record data to the medical staff, so that the medical staff can at least change the content of any preset plan prompt instruction corresponding to the medical record data by means of a lookup table.

5. The reminder system according to claim 1, characterized in that, The reminder device (10) includes at least: A control unit (101) is configured with multiple signal input terminals and output terminals. A data transmission unit (102) connected to the control unit (101) is connected to the management device (20) via a network. A storage unit (105) connected to the control unit (101) is used to store data information and program instructions uploaded to and / or issued by the management device (20); The electronic medical record unit (103) is equipped with a display module (1031), which is at least capable of displaying patient status information stored in the management device (20) transmitted to the control unit (101) by the communication module (1021).

6. The reminder system according to claim 5, characterized in that, The reminder device (10) further includes an alarm unit (108), which is connected to the control unit (101). The warning unit (108) is configured to at least play preset audio signals or related prompts and instructions to provide patients with warnings, reminders and / or guidance on related actions / behaviors through the warning unit (108).

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