A wireless emergency communication system for smart wards

Abstract

The present invention relates to the technical field of nursing staff allocation, and in particular to a wireless distress communication system for a smart ward. When a distress signal is received, the system obtains all relevant data information, including the nursing experience, location information, etc. of the nurse. By analyzing historical nursing data, the first matching factor (based on nursing experience) between each nurse and each patient is calculated; at the same time, the second matching factor (based on positional relationship) is calculated according to the movement trajectory of the nurse. The system also evaluates the cumulative workload and future predicted workload of the nurse to reflect their busyness and fatigue. In the distress matching module, the nursing experience, positional relationship, workload and tasks of the day of the nurse are comprehensively considered to screen the target nurse who best matches the patient to be rescued. This method combines multiple factors to achieve more flexible and accurate screening, ensuring that the selected nurse can reach the patient's location as quickly as possible and effectively solve the problem, thereby improving the efficiency and quality of medical care.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing staff allocation, and in particular to a wireless emergency communication system for a smart ward. Background Art

[0002] With the continuous advancement of medical technology and the widespread application of Internet of Things technology, the concept of smart wards has gradually emerged. Smart wards integrate various advanced communication devices and data analysis technologies to achieve real-time monitoring and intelligent management of patients' calls for help. However, in the call-for-help communication system of smart wards, how to efficiently and accurately match nurses and patients to ensure that patients can receive timely and professional care when they send out a distress signal is still an urgent problem to be solved.

[0003] The existing emergency call system is divided into all-call mode, responsible nursing mode, and custom mode. All of them transmit the information to the designated handheld mobile nursing terminal (PDA) by setting the incoming path of the emergency signal in advance. However, due to the complexity and diversity of nurses' daily work content, if the receiving terminal is specified in advance, there may be a situation where the emergency call times out and no response occurs. If the call is directed to all terminals, it will greatly interfere with the truly effective response, thus lacking flexibility and accuracy. Summary of the invention

[0004] In order to solve the technical problem that nurses have a lot of work to do every day, if the receiving terminal is specified in advance, there may be a situation where the call for help times out and there is no response, and if the call is directed to all terminals, it will interfere with the truly effective response, thereby lacking flexibility and accuracy, the purpose of the present invention is to provide a wireless call for help communication system for a smart ward, and the technical solution adopted is as follows:

[0005] The present invention proposes a wireless emergency communication system for a smart ward, the system comprising:

[0006] A data acquisition module is used to obtain the historical nursing data of each nurse for multiple days each time a distress signal occurs, wherein the historical nursing data includes the nursing time, nursing path and responsibility coefficient of each patient who has been cared for, and obtain the daily movement trajectory of each nurse and the historical daily distress call record of each patient; wherein the distress signal includes the historical distress signal and the current distress signal;

[0007] A matching factor judgment module is used to calculate the first matching factor of each nurse for each patient based on the historical nursing data of each nurse for all patients each time a distress signal occurs; and to determine the second matching factor of each nurse for each patient based on the daily movement trajectory of each nurse and the first matching factor of each nurse for each patient;

[0008] The nurse workload assessment module is used to calculate the historical cumulative workload of each nurse based on the nursing time, nursing path, and first matching factor and second matching factor of the corresponding patient in the historical nursing data of each nurse under the current distress signal; among all patients, screen the possible matching patients of each nurse based on the first matching factor between the patient and the nurse, and analyze the historical daily distress call records of the possible matching patients of each nurse to determine the predicted workload of each nurse;

[0009] The distress matching module is used to screen the target nurse who best matches the patient to be rescued from all nurses based on the second matching factor of each nurse for the patient to be rescued, the historical accumulated workload of each nurse, the predicted workload, and the nursing tasks of each nurse at that time under the current distress signal; and use the mobile terminal device of the target nurse as the receiving terminal of the current distress signal.

[0010] Furthermore, the method for obtaining the first matching factor includes:

[0011] When a distress signal occurs each time, among all patients and in the historical nursing data of each nurse for all days, the product of the nursing time for each patient and the responsibility coefficient is used as the single nursing experience value of each nurse for each patient;

[0012] Among all patients, the cumulative sum of all single nursing experience values ​​of each nurse for each patient is normalized to obtain the first matching factor of each nurse for each patient.

[0013] Furthermore, the method for obtaining the second matching factor includes:

[0014] When each distress signal occurs, the daily movement trajectory of each nurse is analyzed to obtain the response degree value of each nurse to each patient among all patients;

[0015] Among all patients, the product of the response degree value between each nurse and each patient and the first matching factor is normalized to obtain the value as the second matching factor for each nurse to each patient.

[0016] Furthermore, the method for obtaining the response degree value includes:

[0017] Every time a distress signal occurs, among all patients, the Manhattan distance between the position of each nurse and the position of each patient at the current moment is calculated as the distance factor;

[0018] Taking the position of each nurse at the current moment as the starting point and the position of each patient as the end point, we can get the nursing target vector between each nurse and each patient.

[0019] Take the current position of each nurse as the end point, and take the last position of each nurse in history that is different from the current position as the starting point on the daily action trajectory according to the time sequence, and obtain the movement direction vector of each nurse;

[0020] Calculate the cosine similarity between the movement direction vector of each nurse and the nursing goal vector between each nurse and each patient, and numerically adjust the cosine similarity to obtain the direction similarity factor between the movement direction of each nurse and the position of each patient;

[0021] According to the corresponding distance factor and directional similarity factor between each nurse and each patient, the response degree value between each nurse and each patient is obtained, and the distance factor is negatively correlated with the response degree value, and the directional similarity factor is positively correlated with the response degree value.

[0022] Furthermore, the method for obtaining the historical accumulated workload includes:

[0023] Under the current distress signal, the nursing path and nursing duration of each nurse for the patients cared for are integrated into the historical nursing data of each nurse on that day to determine the workload index of each nurse;

[0024] Normalize the difference between the first matching factor and the second matching factor of each nurse for each patient cared for on that day to obtain the additional workload coefficient of each nurse for each patient cared for on that day;

[0025] The cumulative value of each nurse's extra workload coefficient for all patients cared for on that day is taken as each nurse's extra workload factor;

[0026] The normalized value of the sum of each nurse's workload index and the additional workload factor is used as the historical cumulative workload of each nurse.

[0027] Furthermore, the method for obtaining the workload index includes:

[0028] Under the current distress signal, in the historical nursing data of each nurse, the nursing time is weightedly summed using the length of the nursing path of each nurse for the patients cared for that day to obtain the workload index of each nurse.

[0029] Furthermore, the method for obtaining the matching patient includes:

[0030] Under the current distress signal, for all patients, among all first matching factors corresponding to each patient, each patient is regarded as a possible matching patient corresponding to the nurse with the largest first matching factor.

[0031] Furthermore, the method for obtaining the predicted workload includes:

[0032] For any nurse, in the historical daily call records of each possible matching patient corresponding to the nurse, the sum of all nursing hours is used as the daily nursing hours of each possible matching patient corresponding to the nurse;

[0033] The accumulated values ​​of the daily nursing hours of all possible matching patients corresponding to the nurse are normalized to obtain the predicted workload of the nurse.

[0034] Furthermore, the method for screening the target nurses includes:

[0035] Under the current distress signal, the historical cumulative workload of each nurse is negatively correlated with the sum of the predicted workload and the normalized value is used as the idleness value of each nurse;

[0036] The product of each nurse's availability value and each nurse's second matching factor for the patients to be rescued is used as the first priority factor for each nurse to rescue the patients;

[0037] Determine the second priority factor for each nurse to treat the patient according to the nursing task of each nurse at that time;

[0038] The normalized value of the product of the first priority factor and the second priority factor of each nurse for the patient to be rescued is used as the matching degree value between each nurse and the patient to be rescued;

[0039] Among all the nurses, the nurse corresponding to the maximum matching degree value is selected as the target nurse who best matches the patient to be rescued.

[0040] Furthermore, the method for obtaining the second priority factor includes:

[0041] When the current distress signal occurs, if the nurse is currently caring for a patient, then in the historical daily distress call records of the patient being cared for, the average nursing time of all nursing processes is counted as the average nursing time of the nurse, and the difference between the average nursing time and the completed nursing time of the nursing process at the current moment is normalized to a value as the nursing time factor to be completed of the nurse;

[0042] If the nurse is not currently caring for a patient, the nurse's to-be-completed nursing time factor is the preset value;

[0043] The value after negative correlation mapping and normalization of each nurse's to-be-completed nursing time factor is used as the second priority factor for each nurse to rescue patients.

[0044] The present invention has the following beneficial effects:

[0045] When a distress signal is sent, when selecting the target terminal, or selecting the target nurse, it is necessary to ensure that the selected nurse can reach the patient's location as quickly as possible and solve the patient's problem as quickly as possible. Therefore, the nurse needs to have a better understanding of the patient's condition and be close to the patient's location; however, when caring for the patient, the nurse's physical fatigue is also accumulating, and whether it is in an idle state is also very important, so the nurse's workload assessment also needs to be taken into consideration, so in the data acquisition module, the present invention obtains all relevant data information. Then, each time a distress signal occurs, by analyzing each nurse's nursing experience for each patient, that is, based on historical nursing data, the first matching factor of each nurse and each patient is calculated; and based on each nurse's daily movement trajectory, reflecting the location information, the second matching factor of each nurse and each patient is calculated. Under the current distress signal, the cumulative workload of each nurse and the nurse's future predicted workload are determined, so as to provide a reference for the subsequent distress matching module. The cumulative workload is mainly calculated by the nursing time, nursing path and other data of the patient in the nurse's historical nursing data; the future predicted workload can be estimated by all the historical daily distress records of patients who may be matched to the nurse. Finally, in the distress matching module, under the current distress signal, a variety of factors are combined, including each nurse's historical cumulative workload, predicted workload, current nursing tasks, and each nurse's second matching factor for the patient to be rescued, to screen the target nurse who best matches the patient to be rescued among all the nurses, and use the target nurse's mobile terminal device as the receiving terminal for receiving the current distress signal. The determination of the target nurse and the receiving terminal takes into account the nurse's nursing experience, the positional relationship between the nurse and the patient to be rescued, the nurse's busyness and fatigue level, etc., so that the target nurse and the patient to be rescued can be more closely matched, achieving more flexible and more accurate screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a system block diagram of a wireless emergency communication system for a smart ward provided by an embodiment of the present invention.

[0048] Figure 2 is a method flow chart of a method for obtaining a second matching factor provided by an embodiment of the present invention;

[0049] Figure 3It is a method flow chart of a method for obtaining historical accumulated workload provided by an embodiment of the present invention;

[0050] Figure 4 is a flow chart of a target nurse screening process provided by an embodiment of the present invention;

[0051] Figure 5 It is a system structure diagram of a wireless emergency communication system for a smart ward provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of a wireless emergency call communication system for a smart ward proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] The following is a detailed description of a specific solution of a wireless emergency communication system for a smart ward provided by the present invention in conjunction with the accompanying drawings.

[0055] See also Figure 1 , which shows a system block diagram of a wireless distress call communication system for a smart ward provided by an embodiment of the present invention, the system includes: a data acquisition module 101, a matching factor judgment module 102, a nurse workload assessment module 103, and a distress call matching module 104.

[0056] The data acquisition module 101 is used to obtain the historical nursing data of each nurse for multiple days each time a distress signal occurs, wherein the historical nursing data includes the nursing time, nursing path and responsibility coefficient for each patient cared for, and obtain the daily movement trajectory of each nurse and the historical daily distress record of each patient; wherein the distress signal includes the historical distress signal and the current distress signal.

[0057] When the need for help is high, a nurse needs to deal with multiple patients, so receiving the distress signal is an extremely important prerequisite. The handheld mobile nursing terminal PDA can ensure that the nurse can receive the distress signal in real time. However, when the existing wireless call system based on the use of PDA is established, since the transmission of the distress signal to the PDA is preset, it may cause the distress timeout and no response, which lacks flexibility and accuracy.

[0058] In the embodiment of the present invention, by analyzing the distance between the patient and the nurse, the nursing experience of the nurse, and quantifying the workload of the nurse, the workload of the nurses is balanced, thereby ensuring the effectiveness of the communication of the distress signal. Therefore, first of all, in the data acquisition module, when each distress signal occurs, obtain the historical nursing data of each nurse for multiple days. The historical nursing data should include the nursing time, nursing path and responsibility coefficient for each patient who has been cared for; obtain the daily movement trajectory of each nurse and the historical daily distress call record of each patient. These data are usually stored in the hospital's electronic medical record system or nursing management system. When the nurse performs nursing work each time, the start time and end time of the nurse will be recorded to obtain the nursing time of each time (it is also possible to use the patient's bedside screen position as the center of the circle in the ward and a semicircle with a preset radius as the patient's nursing space. During each nursing process, the length of time the nurse's PDA is in the nursing space is counted to obtain the nursing time of each time). The responsibility coefficient is mainly used to indicate whether the nurse is the patient's main nursing person. If so, the responsibility coefficient is 1.2, if not, the responsibility coefficient is 1. The nurse's nursing path and the nurse's daily movement trajectory can be obtained in seconds through the built-in positioning system of the PDA. The historical daily distress call record of each patient records the number of times the patient calls for help every day and the length of time the patient is cared for each call for help. Among them, the distress signal should include historical distress signals and current distress signals. Acquiring multi-source data under historical distress signals can expand the data volume and provide more references for subsequent calculations.

[0059] It should be noted that the value of the responsibility coefficient can be adjusted according to the implementation scenario, and is not limited here, but it is necessary to ensure that the responsibility coefficient when the nurse is the patient's primary nursing person is greater than the responsibility coefficient when the nurse is not the patient's primary nursing person; the preset radius is set to two meters, and the specific range can be adjusted according to the implementation scenario, and is not limited here; the number of days for obtaining historical nursing data is set to 5 days, and includes historical nursing data on the day the distress signal occurs; the daily movement trajectory of each nurse is the historical daily movement trajectory on the day the distress signal occurs; each patient's historical daily distress record can choose the historical distress record on the day the distress signal occurs or the historical distress record on the day before the distress signal occurs, and is not limited here.

[0060] The collection and acquisition of various data in the embodiments of the present invention are all authorized by relevant users, and the process does not violate relevant laws and regulations and does not violate public order and good customs.

[0061] The matching factor judgment module 102 is used to calculate the first matching factor of each nurse for each patient based on the historical nursing data of each nurse for all patients each time a distress signal occurs; and to determine the second matching factor of each nurse for each patient based on the movement trajectory of each nurse and the first matching factor of each nurse for each patient.

[0062] When a distress signal is generated, when selecting the signal receiving terminal, that is, when selecting the target nurse, it is necessary to ensure that the nurse has a high degree of understanding of the patient and the time to reach the patient is the shortest. Therefore, every time a distress signal is generated, for all inpatients, the first matching factor of each nurse for each patient is calculated based on the historical nursing data of each nurse, which is used to reflect the degree of understanding of each nurse about the condition of each patient, so as to evaluate whether the nurse has the ability and experience to deal with the patient's problems when the patient sends a distress signal; based on the nurse's daily movement trajectory, this module further calculates the second matching factor between the nurse and the patient. The second matching factor is used to reflect the relationship between the nurse's current position and the patient's position, so as to evaluate the timeliness of the nurse's response to the patient's needs.

[0063] Preferably, in one embodiment of the present invention, the method for obtaining the first matching factor includes:

[0064] Every time a distress signal occurs, among all patients (here refers to all inpatients), the product of the nursing time for each patient and the responsibility coefficient in the historical nursing data of each nurse for all days will be used as the single nursing experience value of each nurse for each patient. The longer the nurse's nursing time for the patient, it means that the nurse has invested more time in the patient, and the nurse is the main person responsible for the patient's care, so the nurse will have a better understanding of the patient's condition, and the nursing experience value will be higher.

[0065] Then, among all patients, the cumulative sum of all single nursing experience values ​​of each nurse for each patient is normalized to obtain the first matching factor of each nurse for each patient. The larger the first matching factor, the more the nurse knows about the patient's condition and has more experience as reflected in the historical nursing data, and thus the higher the matching degree. Normalization is a technical means well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0066] It should be noted that, among all the inpatients, a certain patient may not appear in the historical nursing record of a certain nurse, so the nursing time and the responsibility coefficient are both set to 0, and finally the first matching factor between the patient and the nurse is 0.

[0067] The nurse's understanding of the patient's condition is not enough to fully represent whether the mobile terminal he / she carries is the most suitable receiving terminal for receiving distress signals. When the distance is too far, even if the nurse has a better understanding of the patient's condition, he / she may not be able to provide timely assistance. Therefore, in this case, the matching level of nurses who have a lower understanding but are closer to the patient can be appropriately improved. Therefore, based on the daily movement trajectory of each nurse and the first matching factor of each nurse for each patient, the second matching factor of each nurse for each patient is determined.

[0068] Preferably, in one embodiment of the present invention, the method for obtaining the second matching factor includes:

[0069] See also Figure 2 , which shows a method flow chart of a method for obtaining a second matching factor in one embodiment of the present invention, the method comprising the following steps:

[0070] Step S201: Every time a distress signal occurs, the daily movement trajectory of each nurse is analyzed to obtain the response level value of each nurse to each of all patients.

[0071] Manhattan distance is a distance measurement method commonly used in urban grid layout. In the embodiment of the present invention, it is applied to the internal environment of the hospital, so that every time a distress signal occurs, the Manhattan distance between the position of each nurse and the position of each patient at the current moment is calculated among all patients (here, all inpatients) as a distance factor. The larger the distance factor, the farther the actual distance between the nurse and the patient is, and the rescue may be delayed, so the response level value should be reduced.

[0072] At the same time, the embodiment of the present invention also considers whether the nurse's forward direction is consistent with the direction to the patient's position. If the consistency is higher, it can be regarded as more convenient, and the response degree value will be higher.

[0073] The position of each nurse at the current moment is taken as the starting point (the position can be obtained based on the positioning of the PDA carried by the nurse), and the position of each patient is taken as the end point (the positioning of the bedside screen of the patient's bed), and the nursing target vector between each nurse and each patient is obtained; the position of each nurse at the current moment is taken as the end point, and in accordance with the time sequence, on the daily movement trajectory of each nurse, the last position of each nurse in the historical moment that is different from the current moment is taken as the starting point to obtain the movement direction vector of each nurse; the nursing target vector indicates the direction characteristics of the nurse going to the patient's position, and the movement direction vector reflects the nurse's current forward direction. If the two directions are more consistent, it is considered that it will be more convenient for the nurse to go to the patient's position, and thus the response degree value will be improved.

[0074] Then, the cosine similarity between the movement direction vector of each nurse and the nursing goal vector between each nurse and each patient is calculated, and the cosine similarity is numerically adjusted to obtain the direction similarity factor between the movement direction of each nurse and the location of each patient. The formula model of the direction similarity factor includes:

[0075]

[0076] in, represents the directional similarity factor between each nurse's movement direction and each patient's location; Represents the cosine similarity between each nurse's movement direction vector and the care goal vector between each nurse and each patient.

[0077] In the formula model of the directional similarity factor, when the motion direction vector is more consistent with the nursing target vector, the cosine similarity is closer to 1, indicating that the nurse's forward direction is more consistent with the direction where the nurse goes to the patient's position; conversely, when the motion direction vector is less consistent with the nursing target vector, the cosine similarity is closer to -1, indicating that the nurse's forward direction is less consistent with the direction where the nurse goes to the patient's position; in order to facilitate subsequent calculations, the cosine similarity is numerically adjusted to obtain the directional similarity factor, so that The value range of is between 0 and 1. At this time, the closer the direction similarity factor is to 1, the more consistent the nurse's direction of travel is with the direction to the patient's location. Therefore, the nurse has a higher probability of responding to the patient's needs in a timely manner.

[0078] Finally, according to the distance factor and directional similarity factor corresponding to each nurse and each patient, the response degree value between each nurse and each patient is obtained. The formula model of the response degree value includes:

[0079]

[0080] in, It represents the responsiveness value between each nurse and each patient; represents the directional similarity factor between each nurse's movement direction and each patient's location; represents the distance factor between each nurse and each patient; Indicates the preset first parameter; Represents the normalization function.

[0081] In the formula model of the response degree value, based on the above analysis, it can be known that the larger the distance factor is, the farther the actual distance between the nurse and the patient is, and the response degree value should be reduced; the larger the direction similarity factor is, the more consistent the nurse's forward direction is with the direction to the patient's location, and thus the nurse has a higher probability of being able to respond to the patient's needs in a timely manner; therefore, the distance factor is negatively correlated with the response degree value, and the direction similarity factor is positively correlated with the response degree value. Therefore, in this embodiment of the present invention, the distance factor between each nurse and each patient is used as the denominator, and the direction similarity factor between each nurse's movement direction and the location of each patient is used as the numerator, so as to obtain the response degree value between each nurse and each patient, and the larger the response degree value is, the greater the probability that the nurse will respond to the patient's needs in a timely manner, and thus the matching degree will be correspondingly improved.

[0082] It should be noted that the first parameter is preset The role of is to prevent the denominator from being 0, and the value here can be 0.001. The specific value can be adjusted according to the implementation scenario and is not limited here. The calculation of Manhattan distance is a well-known technology and will not be described in detail here.

[0083] Step S202: The response level value of each nurse to each patient and the first matching factor are integrated to determine the second matching factor of each nurse to each patient.

[0084] Given that the larger the response degree value, the greater the probability that the nurse will respond to the patient's needs in a timely manner, and thus the matching degree will be correspondingly improved; the larger the first matching factor, the more the nurse understands the patient's condition better, has more experience, and has a higher matching degree, as reflected in the historical nursing data; therefore, among all patients, the product of the response degree value between each nurse and each patient and the first matching factor is normalized to the value obtained as the second matching factor for each nurse to each patient. At this time, the larger the second matching factor, the more the nurse understands the patient's condition better and can respond to the patient's needs in a timely manner. Wherein normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0085] The nurse workload assessment module 103 is used to calculate the historical cumulative workload of each nurse under the current distress signal based on the nursing time, nursing path, and the first matching factor and the second matching factor of each nurse's nursing for the corresponding patient on that day in the historical nursing data of each nurse; among all patients, the possible matching patients of each nurse are screened based on the first matching factor between the patient and the nurse, and the historical daily distress call records of each nurse's possible matching patients are analyzed to determine the predicted workload of each nurse.

[0086] In the daily work of nurses, when rescuing patients, the nurses' own physical fatigue is also accumulating. In order to make the workload more balanced and avoid medical accidents, when the current distress signal occurs, the current historical accumulated workload of each nurse can be determined according to the relevant data of each nurse's historical nursing data when caring for the patient and the first matching factor and the second matching factor of the patient when caring for the patient, which can be used to measure the fatigue level of the nurse when the current distress signal occurs; at the same time, the workload of the nurses in the future time period can also be predicted. In the matching factor judgment module, the first matching factor of each nurse for each patient when the current distress signal occurs can be calculated, so there is a first matching factor between each patient and each nurse. In an embodiment of the present invention, starting from the patient level, the nurse who is most likely to be assigned to him is determined. At this point, each nurse may correspond to one or more possible matching patients, and then based on the historical daily distress records of the possible matching patients corresponding to each nurse, the predicted workload of each nurse in the future time period is calculated to measure the possible busyness of each nurse.

[0087] Preferably, in one embodiment of the present invention, the method for obtaining the historical accumulated workload includes:

[0088] See also Figure 3 , which shows a method flow chart of a method for obtaining historical cumulative workload in one embodiment of the present invention, the method comprising the following steps:

[0089] Step S301: Under the current distress signal, the length of the nursing path and the nursing time of each nurse for the patients cared for are integrated into the historical nursing data of each nurse on that day to determine the workload index of each nurse.

[0090] When the current distress signal occurs, the product of the nursing path length and the nursing time of each nurse when caring for each patient already cared for on the day when the current distress signal occurs is calculated from the historical nursing data of each nurse, as the consumption value of each nurse caring for each patient already cared for on that day; the longer the nursing time, the longer the length of the nursing path for the nurse to reach the location of the cared patient, and the greater the consumption value, suggesting that the nurse has a heavier workload.

[0091] Then the sum of all consumption values ​​of each nurse on that day is taken as the workload index of each nurse. The larger the workload index is, the more workload the nurse has completed on the day when the current distress signal occurs.

[0092] Step S302: quantify the additional workload factor of each nurse on that day according to the first matching factor and the second matching factor of each nurse for each patient cared for on that day.

[0093] The difference between the first matching factor and the second matching factor of each nurse for each patient cared for on that day is normalized to obtain the extra workload coefficient of each nurse for each patient cared for on that day. The extra workload coefficient can indicate the proportion of the nurse's rescue of the patient due to the distance factor. The larger the extra workload coefficient is, the more it indicates that the nurse's rescue of the patient is not based on the nurse's understanding of the patient's condition. Therefore, in the embodiment of the present invention, it is regarded as the extra workload of the nurse. Since the difference here may be positive or negative, the normalization process can be adopted. function.

[0094] The cumulative value of the extra workload coefficient of each nurse for all patients cared for on that day is taken as the extra workload factor of each nurse. The larger the extra workload factor is, the more extra workload the nurse has completed on the day when the current distress signal occurs, and the more tired the nurse may be on that day.

[0095] Step S303: The workload index and the additional workload factor of each nurse on that day are integrated to obtain the historical cumulative workload of each nurse on that day.

[0096] Based on the analysis in the above steps, it can be known that the larger the workload index is, the more workload the nurse has completed on the day when the current distress signal occurs; the larger the additional workload factor is, the more additional workload the nurse has completed on the day when the current distress signal occurs; so the normalized value of the sum of the workload index and the additional workload factor of each nurse is used as the historical cumulative workload of each nurse. The larger the value of the historical cumulative workload is, the greater the workload burden of the nurse is on the day when the current distress signal occurs. Therefore, in order to avoid the occurrence of medical accidents caused by excessive fatigue of nurses, the workload of nurses should be reduced in the subsequent process, that is, the possibility of matching nurses with patients to be rescued should be reduced. Wherein normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0097] After obtaining the historical cumulative workload of each nurse when the current distress signal occurs, the predicted workload of each nurse in the future can be calculated, which can also be used as an indicator to measure the matching situation between nurses and patients to be rescued in the future.

[0098] Preferably, in one embodiment of the present invention, the method for obtaining the predicted workload includes:

[0099] When the current distress signal occurs, for all patients, here all inpatients, among all the first matching factors corresponding to each patient, each patient is regarded as the possible matching patient of the nurse corresponding to the largest first matching factor. At this time, each nurse may correspond to one or more possible matching patients, and the possible matching patient of a nurse can be regarded as the patient who is most likely to be assigned to the nurse after the patient sends a distress signal. Therefore, by analyzing the historical daily distress call records of each nurse's possible matching patients, the predicted workload of each nurse in the future is quantified. For example, there are two nurses, denoted as Nurse 1 and Nurse 2, and three patients, denoted as Patient A, Patient B, and Patient C. At this time, the first matching factor between Nurse 1 and Patient A is 0.5, the first matching factor between Nurse 1 and Patient B is 0.6, the first matching factor between Nurse 1 and Patient C is 0.7, the first matching factor between Nurse 2 and Patient A is 0.8, the first matching factor between Nurse 2 and Patient B is 0.65, and the first matching factor between Nurse 2 and Patient C is 0.58. Then Patient A should be regarded as a possible matching patient of Nurse 2, Patient B as a possible matching patient of Nurse 2, and Patient C as a possible matching patient of Nurse 1.

[0100] Then, for any nurse, in the historical daily call records for help of each possible matching patient corresponding to the nurse, the sum of all the nursing time is used as the daily nursing time of each possible matching patient corresponding to the nurse. The longer the daily nursing time, the greater the possible workload of the nurse in the future.

[0101] Finally, the accumulated values ​​of the daily nursing time of all possible matching patients corresponding to the nurse are normalized to obtain the predicted workload of the nurse. The larger the predicted workload, the more workload the nurse may have in the future. Therefore, it is also necessary to appropriately reduce the matching degree between the nurse and the patient to be rescued. Normalization is a technical means well known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0102] At this point, when the distress signal occurs on the day, the historical cumulative workload and predicted workload of each nurse can be calculated, which can be used as an indicator to measure the fatigue or busyness of each nurse and provide a reference for the subsequent distress matching module.

[0103] The distress matching module 104 is used to screen the target nurse who best matches the patient to be rescued from among all the nurses based on the second matching factor of each nurse for the patient to be rescued, the historical accumulated workload and predicted workload of each nurse, and the nursing tasks of each nurse at that time under the current distress signal; and use the mobile terminal device of the target nurse as the receiving terminal of the current distress signal.

[0104] When a distress signal is generated, the nurse who best matches the patient to be rescued can be selected based on the second matching factor of each nurse for the patient to be rescued and the busyness of each nurse. However, there is still a problem at this time. If the distress signal is sent to the nurse's PDA regardless of whether the nurse is in the process of rescuing a patient, it may also cause problems of untimely processing or response. Therefore, it is necessary to establish a reasonable mechanism. In an embodiment of the present invention, the nursing tasks of each nurse at the time of the distress signal are combined to screen the target nurse who best matches the patient to be rescued from all nurses. This can optimize resource allocation to a great extent, make the match between the target nurse and the patient to be rescued higher, and achieve more flexible and more accurate screening.

[0105] Preferably, in one embodiment of the present invention, the screening process of the target nurse includes:

[0106] See also Figure 4 , which shows a flow chart of a target nurse screening process in one embodiment of the present invention, the process includes the following steps:

[0107] Step S401: Under the current distress signal, the historical accumulated workload, predicted workload and the second matching factor of each nurse for rescuing patients are integrated to determine the first priority factor of each nurse for rescuing patients.

[0108] Under the current distress signal, consider the workload of each nurse, and calculate the sum of each nurse's historical cumulative workload and predicted workload. The larger the sum, the more workload the nurse has undertaken, which can be regarded as the busier the nurse is, and the more tired the nurse may be. In this case, the priority of the nurse as the target nurse for the patients to be rescued should be lowered. Therefore, the sum is negatively correlated and normalized to achieve logical relationship correction, thereby obtaining the idleness value of each nurse. At this time, the larger the idleness value, the less workload the nurse has undertaken, and the possibility of being selected as the target nurse for the patients to be rescued should increase. It should be noted that the negative correlation mapping and normalization here can be performed using the formula ,in, It represents an exponential function with the natural constant e as the base, and x represents the independent variable.

[0109] The second matching factor integrates the nursing experience of each nurse for the patients to be rescued and the positional relationship between each nurse and the patients to be rescued represented by the first matching factor. The larger the second matching factor of a nurse for the patients to be rescued, the better the nurse understands the condition of the patients to be rescued and can respond to the needs of the patients to be rescued in a timely manner, so the possibility of the nurse being selected as the target nurse for the patients to be rescued should be increased.

[0110] Therefore, the product of each nurse's availability value and each nurse's second matching factor for the patient to be rescued is used as the first priority factor for each nurse to rescue the patient. The larger the first priority factor, the higher the matching degree between the nurse and the patient to be rescued, that is, the higher the possibility of being selected as the target nurse for the patient to be rescued, and the higher the possibility of the nurse's PDA as the receiving terminal of the current distress signal.

[0111] Step S402: Determine the second priority factor for each nurse to rescue the patient based on the nursing task of each nurse at that time.

[0112] When the current distress signal occurs, if a nurse is currently caring for a patient, then in the historical daily distress records of the patient being cared for, the average nursing time of all nursing processes is counted as the mean nursing time of the nurse. The mean nursing time reflects the average time required for the historical nursing process of the patient being cared for by the nurse at the current moment. This value can be used to predict the time required for the nurse to complete the current nursing task. Specifically, the difference between the mean nursing time and the nursing time of the nursing process at the current moment is calculated. If the difference is positive and the larger it is, the more time is required to complete the current task, then the priority of the nurse to be selected as the target nurse should be lowered. On the contrary, if the difference is negative and the smaller it is, it can be regarded as the current task will be completed in the near future, then the priority of the nurse to be selected as the target nurse should be increased. Since the difference may be positive or negative, it is used The difference is normalized to obtain the nursing time factor of the nurse to be completed. The larger the nursing time factor is, the lower the priority of the nurse to be selected as the target nurse for the patient to be rescued. If a nurse is not caring for a patient at the current moment, the nursing time factor of the nurse to be completed is a preset value. In the embodiment of the present invention, since the value of the nursing time factor when the nurse is caring for a patient is greater than 0, the nursing time factor when the nurse is not caring for a patient is set to 0, that is, the preset value is 0.

[0113] Finally, the waiting time for nursing care of each nurse is negatively correlated and normalized to achieve logical relationship correction, thereby obtaining the second priority factor of each nurse for the patients to be rescued. The larger the second priority factor, the higher the match between the nurse and the patients to be rescued, that is, the higher the possibility of being selected as the target nurse for the patients to be rescued.

[0114] Step S403: The first priority factor and the second priority factor of each nurse for the patient to be rescued are integrated to determine the matching degree value between each nurse and the patient to be rescued, and based on the matching degree value, a target nurse who best matches the patient to be rescued is selected from all nurses.

[0115] Based on the above analysis, it can be known that the larger the first priority factor, the higher the matching degree between the nurse and the patient to be rescued; the larger the second priority factor, the higher the possibility that the nurse will be selected as the target nurse for the patient to be rescued; so the product of the first priority factor and the second priority factor of each nurse for the patient to be rescued is normalized as the matching degree value between each nurse and the patient to be rescued. The larger the matching degree value between a nurse and the patient to be rescued, the more the nurse understands the condition of the patient to be rescued and the closer the distance is. At the same time, the workload of the nurse is relatively low, so the nurse is more suitable as a nursing candidate for the patient to be rescued. Among them, normalization is a technical means well known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0116] Therefore, among all the nurses, the nurse with the maximum matching degree value is selected as the target nurse who best matches the patient to be rescued.

[0117] After determining the target nurse corresponding to the patient to be rescued who sends out the current distress signal, the mobile terminal device corresponding to the target nurse is used as the receiving terminal of the current distress signal.

[0118] In summary, when a distress signal is sent, when selecting a target terminal, or selecting a target nurse, it is necessary to ensure that the selected nurse can reach the patient's location as quickly as possible and solve the patient's problem as quickly as possible. Therefore, the nurse needs to have a better understanding of the patient's condition and be close to the patient's location; however, when caring for the patient, the nurse's physical fatigue is also accumulating, and whether it is in an idle state is also very important, so the nurse's workload assessment also needs to be taken into consideration, so in the data acquisition module, the embodiment of the present invention obtains all relevant data information. Then, each time a distress signal occurs, by analyzing each nurse's nursing experience for each patient, that is, based on historical nursing data, the first matching factor of each nurse and each patient is calculated; and based on each nurse's daily movement trajectory, reflecting the location information, the second matching factor of each nurse and each patient is calculated. Under the current distress signal, the cumulative workload of each nurse and the nurse's future predicted workload are determined, so as to provide a reference for the subsequent distress matching module. The cumulative workload is mainly calculated by the nursing time, nursing path and other data of the patient in the nurse's historical nursing data; the future predicted workload can be estimated by estimating the historical daily distress records of all patients who may be matched to the nurse. Finally, in the distress matching module, under the current distress signal, a variety of factors are combined, including each nurse's historical cumulative workload, predicted workload, current nursing tasks, and each nurse's second matching factor for the patient to be rescued, to screen the target nurse who best matches the patient to be rescued among all the nurses, and use the target nurse's mobile terminal device as the receiving terminal for receiving the current distress signal. The determination of the target nurse and the receiving terminal takes into account the nurse's nursing experience, the positional relationship between the nurse and the patient to be rescued, the nurse's busyness and fatigue level, etc., so that the target nurse and the patient to be rescued can be more closely matched, achieving more flexible and more accurate screening.

[0119] See also Figure 5 , which shows a system structure diagram of a wireless emergency call communication system for a smart ward provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502 and a communication interface 503, wherein the processor 500, the communication interface 503 and the memory 501 are connected via the bus 502; wherein the memory 501 may include a high-speed random access memory, the bus 502 may be an ISA bus, a PCI bus or an EISA bus, etc., and the processor 500 may be an integrated circuit chip with signal processing capabilities; the memory 501 stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps in each module of the wireless emergency call communication system for a smart ward are implemented.

[0120] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A wireless emergency communication system for a smart ward, characterized in that: The system includes: A data acquisition module is used to obtain the historical nursing data of each nurse for multiple days each time a distress signal occurs, wherein the historical nursing data includes the nursing time, nursing path and responsibility coefficient of each patient who has been cared for, and obtain the daily movement trajectory of each nurse and the historical daily distress call record of each patient; wherein the distress signal includes the historical distress signal and the current distress signal; A matching factor judgment module is used to calculate the first matching factor of each nurse for each patient based on the historical nursing data of each nurse for all patients each time a distress signal occurs; and to determine the second matching factor of each nurse for each patient based on the daily movement trajectory of each nurse and the first matching factor of each nurse for each patient; The nurse workload assessment module is used to calculate the historical cumulative workload of each nurse based on the nursing time, nursing path, and first matching factor and second matching factor of the corresponding patient in the historical nursing data of each nurse under the current distress signal; among all patients, screen the possible matching patients of each nurse based on the first matching factor between the patient and the nurse, and analyze the historical daily distress call records of the possible matching patients of each nurse to determine the predicted workload of each nurse; The distress matching module is used to screen the target nurse who best matches the patient to be rescued from all nurses based on the second matching factor of each nurse for the patient to be rescued, the historical accumulated workload of each nurse, the predicted workload, and the nursing tasks of each nurse at that time under the current distress signal; and use the mobile terminal device of the target nurse as the receiving terminal of the current distress signal.

2. According to the wireless emergency communication system of the smart ward of claim 1, it is characterized in that: The method for obtaining the first matching factor includes: When a distress signal occurs each time, among all patients and in the historical nursing data of each nurse for all days, the product of the nursing time for each patient and the responsibility coefficient is used as the single nursing experience value of each nurse for each patient; Among all patients, the cumulative sum of all single nursing experience values ​​of each nurse for each patient is normalized to obtain the first matching factor of each nurse for each patient.

3. The wireless emergency communication system for a smart ward according to claim 1, characterized in that: The method for obtaining the second matching factor includes: When each distress signal occurs, the daily movement trajectory of each nurse is analyzed to obtain the response degree value of each nurse to each patient among all patients; Among all patients, the product of the response degree value between each nurse and each patient and the first matching factor is normalized to obtain the value as the second matching factor for each nurse to each patient.

4. The wireless emergency communication system for a smart ward according to claim 3, characterized in that: The method for obtaining the response degree value includes: Every time a distress signal occurs, among all patients, the Manhattan distance between the position of each nurse and the position of each patient at the current moment is calculated as the distance factor; Taking the position of each nurse at the current moment as the starting point and the position of each patient as the end point, we can get the nursing target vector between each nurse and each patient. Take the current position of each nurse as the end point, and take the last position of each nurse in history that is different from the current position as the starting point on the daily action trajectory according to the time sequence, and obtain the movement direction vector of each nurse; Calculate the cosine similarity between the movement direction vector of each nurse and the nursing goal vector between each nurse and each patient, and numerically adjust the cosine similarity to obtain the direction similarity factor between the movement direction of each nurse and the position of each patient; According to the corresponding distance factor and directional similarity factor between each nurse and each patient, the response degree value between each nurse and each patient is obtained, and the distance factor is negatively correlated with the response degree value, and the directional similarity factor is positively correlated with the response degree value.

5. The wireless emergency communication system for a smart ward according to claim 1, characterized in that: The method for obtaining the historical accumulated workload includes: Under the current distress signal, the nursing path and nursing duration of each nurse for the patients cared for are integrated into the historical nursing data of each nurse on that day to determine the workload index of each nurse; Normalize the difference between the first matching factor and the second matching factor of each nurse for each patient cared for on that day to obtain the additional workload coefficient of each nurse for each patient cared for on that day; The cumulative value of each nurse's extra workload coefficient for all patients cared for on that day is taken as each nurse's extra workload factor; The normalized value of the sum of each nurse's workload index and the additional workload factor is used as the historical cumulative workload of each nurse.

6. The wireless emergency communication system for a smart ward according to claim 5, characterized in that: The method for obtaining the workload index includes: Under the current distress signal, in the historical nursing data of each nurse, the nursing time is weightedly summed using the length of the nursing path of each nurse for the patients cared for that day to obtain the workload index of each nurse.

7. The wireless emergency communication system for a smart ward according to claim 1, characterized in that: The method for obtaining the matching patient comprises: Under the current distress signal, for all patients, among all first matching factors corresponding to each patient, each patient is regarded as a possible matching patient corresponding to the nurse with the largest first matching factor.

8. The wireless emergency communication system for a smart ward according to claim 1, characterized in that: The method for obtaining the predicted workload includes: For any nurse, in the historical daily call records of each possible matching patient corresponding to the nurse, the sum of all nursing hours is used as the daily nursing hours of each possible matching patient corresponding to the nurse; The accumulated values ​​of the daily nursing hours of all possible matching patients corresponding to the nurse are normalized to obtain the predicted workload of the nurse.

9. The wireless emergency communication system for a smart ward according to claim 1, characterized in that: The screening method of the target nurses includes: Under the current distress signal, the historical cumulative workload of each nurse is negatively correlated with the sum of the predicted workload and the normalized value is used as the idleness value of each nurse; The product of each nurse's availability value and each nurse's second matching factor for the patients to be rescued is used as the first priority factor for each nurse to rescue the patients; Determine the second priority factor for each nurse to treat the patient according to the nursing task of each nurse at that time; The normalized value of the product of the first priority factor and the second priority factor of each nurse for the patient to be rescued is used as the matching degree value between each nurse and the patient to be rescued; Among all the nurses, the nurse corresponding to the maximum matching degree value is selected as the target nurse who best matches the patient to be rescued.

10. The wireless emergency communication system for a smart ward according to claim 9, characterized in that: The method for obtaining the second priority factor includes: When the current distress signal occurs, if the nurse is currently caring for a patient, then in the historical daily distress call records of the patient being cared for, the average nursing time of all nursing processes is counted as the average nursing time of the nurse, and the difference between the average nursing time and the completed nursing time of the nursing process at the current moment is normalized to a value as the nursing time factor to be completed of the nurse; If the nurse is not currently caring for a patient, the nurse's to-be-completed nursing time factor is the preset value; The value after negative correlation mapping and normalization of each nurse's to-be-completed nursing time factor is used as the second priority factor for each nurse to rescue patients.

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