A medical intelligent operation management system

Abstract

The present invention relates to the field of medical management information technology, and specifically to a medical intelligence operation management system, wherein a patient demand management module obtains and records the condition information and demand information of hospitalized patients, and the demand information includes cleaning demand, diet demand and medication demand; a service scheduling module matches the recommended diet, recommended cleaning method and recommended medication information based on historical data according to the condition information, and generates a corresponding service sequence table; a synchronization module synchronizes the corresponding service sequence table to a relevant personnel notification terminal and generates a service plan, and the service plan includes a service path and a service time period; an emergency treatment module interrupts the service information when an abnormal situation occurs in the patient, and updates the service information based on the abnormal situation; a service quality feedback module, in which the patient scores the received service. The hospitalization needs of patients can be conveniently managed in combination with historical data, and conveniently synchronized to each participating user, reducing the burden on nurses and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of medical management information technology, and in particular to a medical intelligence operation and management system. Background Art

[0002] Healthcare operations management is a complex field that encompasses a variety of management activities and processes within healthcare organizations, designed to ensure the effective delivery of healthcare services, rational use of resources, and maximization of patient satisfaction.

[0003] Logistical support services play a vital role in the daily operation of hospitals. They not only affect the work efficiency of hospitals, but also directly affect the experience and satisfaction of patients and their families. Logistical support services mainly include cleaning, meal delivery and medication management. The main method is to set notification buttons in the wards and then notify nurses to go to the corresponding wards to obtain relevant needs. This greatly increases the management burden of nurses and reduces work efficiency when there are a large number of people. Summary of the invention

[0004] The purpose of the present invention is to provide a medical intelligent operation and management system, which aims to conveniently manage the patient's hospitalization needs in combination with historical data, and conveniently synchronize it to each participating user, thereby reducing the burden on nurses and improving management efficiency.

[0005] To achieve the above-mentioned purpose, the present invention provides a medical intelligent operation management system, including a patient demand management module, a service scheduling module, a synchronization module, an emergency treatment module and a service quality feedback module;

[0006] The patient demand management module is used to obtain and record the condition information and demand information of hospitalized patients, wherein the demand information includes cleaning requirements, dietary requirements and medication requirements;

[0007] The service scheduling module is used to match the recommended diet, recommended cleaning method and recommended medication information based on the historical data according to the condition information, and generate a corresponding service sequence table;

[0008] The synchronization module synchronizes the corresponding service sequence table to the relevant personnel notification terminal and generates a service plan, wherein the service plan includes a service path and a service time period;

[0009] The emergency treatment module is used to interrupt the service information when the patient has an abnormal condition, and update the service information based on the abnormal condition;

[0010] In the service quality feedback module, patients rate the services they receive.

[0011] Among them, the medical intelligent operation management system also includes a training module, and the training module is used to match corresponding training courses for logistics staff based on the scoring results.

[0012] Wherein, the patient demand management module includes a demand uploading unit, an analyzing unit, a matching unit, a confirming unit and a storage unit;

[0013] The demand uploading unit is used to upload the patient's demand information text segment;

[0014] The analysis unit is used to segment the demand information text segment based on cleaning, meal delivery and medication management to obtain classified text segments;

[0015] The matching unit is used to match corresponding demand keywords based on the classified text segments;

[0016] The confirmation unit is used to send the required keywords to the user side for confirmation;

[0017] The storage unit is used to store the demand keywords and corresponding classified text segments in the database after the user confirms the demand.

[0018] Wherein, the service scheduling module includes a condition acquisition unit, a data scheduling unit and a push unit;

[0019] The condition acquisition unit is used to extract condition information from the requirements submitted by the patient or medical staff;

[0020] The data scheduling unit is used to match the corresponding historical rehabilitation patient data based on the condition information and the personal vital sign information, wherein the historical rehabilitation patient data includes the patient's cleaning, meal delivery and medication management information;

[0021] The push unit is used to push cleaning, meal delivery and medication management information according to the patient's recovery time in historical records.

[0022] Wherein, the data scheduling unit includes a data acquisition subunit, a data processing subunit, a similarity calculation subunit and a judgment subunit;

[0023] The data acquisition subunit is used to extract detailed condition descriptions and personal vital signs data from the requirements submitted by patients or medical staff

[0024] The data processing subunit is used to standardize the description of the condition using a unified medical terminology library within the hospital;

[0025] The similarity calculation subunit is used to calculate the similarity score between the patient and the current patient in terms of condition and personal signs by using a cosine similarity method;

[0026] The judgment subunit is used to generate historical rehabilitation patient data only when the similarity score of historical cases exceeds this threshold, and select the top N to obtain the historical rehabilitation patient data.

[0027] The push unit includes a recovery time acquisition subunit, a comparison subunit and a push subunit.

[0028] The rehabilitation time acquisition subunit is used to acquire the rehabilitation time in historical rehabilitation patient data;

[0029] The comparison subunit is used to sort the recovery time according to length;

[0030] The push subunit is used to push the cleaning, diet and medication management information corresponding to the shortest recovery time.

[0031] Wherein, the synchronization module includes a service sequence table parsing unit, a path generation unit and a service time period calculation unit;

[0032] The service sequence table parsing unit is used to parse the service content based on the service sequence table;

[0033] The path generation unit is used to calculate the moving path using the shortest path algorithm based on the movable network nodes of the hospital;

[0034] The service time period calculation unit is used to calculate the service time period based on the moving time and the idle time period.

[0035] Among them, the emergency handling module includes an abnormality monitoring unit, an interruption unit and an emergency response unit; the abnormality monitoring unit is used to monitor the patient's vital signs data and issue an alarm when an abnormality occurs; the interruption unit is used to obtain alarm information and interrupt the current service sequence list; the emergency response unit is used to automatically start the corresponding emergency response procedure according to a pre-set emergency plan.

[0036] In a medical intelligent operation management system of the present invention, the patient demand management module collects and records the detailed condition information and specific needs of hospitalized patients, such as cleaning, diet and medication. Through electronic and structured data collection methods, not only the accuracy of information is improved, but also the subsequent data analysis and application are convenient. Based on the collected condition information and service needs, the service scheduling module intelligently matches the most suitable dietary advice, cleaning method and medication guidance for patients in combination with past historical data. It will generate a detailed service sequence list based on priority and service logic. This list will guide medical staff on how to provide various services to patients in an orderly manner, thereby improving overall nursing efficiency and service level. The synchronization module pushes the above-generated service sequence list to various terminal devices, such as nurse station computers, mobile nursing devices, etc. The emergency disposal module is used to monitor the patient's abnormal condition (such as changes in vital signs), and the system will immediately interrupt the current service process and update the service information according to the new health status. This helps to quickly adjust the nursing plan and ensure patient safety. The service quality feedback module is used for patients to rate their satisfaction after receiving the service, and these feedbacks will become an important basis for continuously improving service quality and evaluating performance. In addition, by analyzing these feedback data, potential problems were discovered to further optimize system functions and service processes. This medical smart operation management system not only realizes the effective management and care of inpatients from admission to discharge, but also greatly improves the efficiency of internal communication and collaboration in the hospital, and promotes the development of medical services towards intelligence and humanity. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be 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 any creative work.

[0038] Figure 1 It is a structural diagram of a medical intelligence operation and management system of the present invention.

[0039] Figure 2 It is a structural diagram of the patient demand management module of the present invention.

[0040] Figure 3 It is a structural diagram of the service scheduling module of the present invention.

[0041] Figure 4 It is a structural diagram of the data scheduling unit of the present invention.

[0042] Figure 5 It is a structural diagram of the push unit of the present invention.

[0043] Figure 6 It is a structural diagram of the synchronization module of the present invention.

[0044] Figure 7 It is a structural diagram of the emergency treatment module of the present invention.

[0045] Patient demand management module 101, service scheduling module 102, synchronization module 103, emergency treatment module 104, service quality feedback module 105, demand uploading unit 106, analysis unit 107, matching unit 108, confirmation unit 109, storage unit 110, condition acquisition unit 111, data scheduling unit 112, push unit 113, data acquisition subunit 114, data processing subunit 115, similarity calculation subunit 116, judgment subunit 117, recovery time acquisition subunit 118, comparison subunit 119, push subunit 120, service sequence table parsing unit 121, path generation unit 122, service time period calculation unit 123, abnormality monitoring unit 124, interruption unit 125, emergency response unit 126. DETAILED DESCRIPTION

[0046] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0048] See also Figure 1 to Figure 7The present invention provides a medical intelligent operation management system, including a patient demand management module 101, a service scheduling module 102, a synchronization module 103, an emergency treatment module 104 and a service quality feedback module 105; the patient demand management module 101 is used to obtain and record the condition information and demand information of hospitalized patients, and the demand information includes cleaning requirements, dietary requirements and medication requirements; the service scheduling module 102 is used to match the recommended diet, recommended cleaning method and recommended medication information based on historical data according to the condition information, and generate a corresponding service sequence table; the synchronization module 103 synchronizes the corresponding service sequence table to the relevant personnel notification terminal and generates a service plan, and the service plan includes a service path and a service time period; the emergency treatment module 104 is used to interrupt the service information when an abnormal situation occurs in the patient, and update the service information based on the abnormal situation; the service quality feedback module 105 is used for the patient to rate the service received.

[0049] In this embodiment, the patient demand management module 101 collects and records detailed information about the inpatient's condition and specific needs, such as cleaning, diet, and medication. The electronic and structured data collection method not only improves the accuracy of the information, but also facilitates subsequent data analysis and application. For example, a patient may have special dietary restrictions or a history of drug allergies, and this information will be accurately recorded to ensure that it is fully considered in subsequent service arrangements.

[0050] Based on the collected medical information and service needs, combined with past historical data, the service scheduling module 102 intelligently matches the most suitable dietary advice, cleaning methods and medication instructions for the patient. It will generate a detailed service sequence list based on priority and service logic. This list will guide medical staff on how to provide various services to patients in an orderly manner, thereby improving overall nursing efficiency and service level.

[0051] In order to ensure that all relevant personnel can obtain the latest service arrangements in a timely manner, the synchronization module 103 pushes the generated service sequence table to various terminal devices, such as the nurse station computer, mobile nursing equipment, etc. At the same time, it will also automatically generate a service plan containing specific service paths and service time periods, so that each link is clearly visible and easy to execute and supervise.

[0052] The emergency treatment module 104 is used to monitor the abnormal condition of the patient (such as changes in vital signs), and the system will immediately interrupt the current service process and update the service information according to the new health status. This helps to quickly adjust the nursing plan and ensure the safety of the patient.

[0053] The service quality feedback module 105 is used for patients to rate their satisfaction after receiving the service. These feedbacks will become an important basis for continuously improving service quality and evaluating performance. In addition, by analyzing these feedback data, potential problems are also found to further optimize system functions and service processes.

[0054] To sum up, this medical intelligent operation and management system not only realizes the effective management and care of inpatients from admission to discharge, but also greatly improves the efficiency of internal communication and collaboration in the hospital, and promotes the development of medical services towards intelligence and humanity.

[0055] The medical intelligence operation management system also includes a training module, which is used to match corresponding training courses for logistics staff based on the scoring results.

[0056] In this embodiment, the training module can intelligently analyze the service performance of each logistics staff member based on the scoring results collected in the service quality feedback module 105. For those employees who score low in specific areas (such as cleaning quality, meal preparation or medication management), the system will automatically match the corresponding training courses. For example, if a cleaning staff member receives multiple negative comments about incomplete room disinfection, then the employee will be recommended to participate in a more in-depth hospital infection control training course.

[0057] The patient demand management module 101 includes a demand uploading unit 106, an analyzing unit 107, a matching unit 108, a confirmation unit 109 and a storage unit 110; the demand uploading unit 106 is used to upload the patient's demand information text segment; the analyzing unit 107 is used to segment the demand information text segment based on cleaning, meal delivery and medication management to obtain classified text segments; the matching unit 108 is used to match the corresponding demand keywords based on the classified text segments; the confirmation unit 109 is used to send the demand keywords to the user side for confirmation; the storage unit 110 is used to store the demand keywords and the corresponding classified text segments in the database after the user side confirms the demand.

[0058] The demand uploading unit 106 receives text segments of patient demand information from different channels (such as ward nurse stations, mobile nursing equipment or patient self-service terminals). This information is entered by medical staff based on the patient's verbal description, or directly entered by the patient through an electronic device. The uploading unit is not limited to processing text information, but also accepts demand expressions in multimedia forms such as pictures and voices, and converts them into text segments in a unified format.

[0059] The analysis unit 107 performs in-depth analysis on the uploaded demand information text segment. It uses natural language processing (NLP) technology to segment the original text according to categories such as cleaning requirements, meal delivery requirements, and medication management requirements, thereby generating multiple classified text segments. For example, if a demand information includes both requirements for room cleaning frequency and special dietary preferences, the analysis unit 107 will segment it into two independent text segments corresponding to the cleaning and dietary categories. This classification helps to more accurately understand and respond to the diverse needs of patients.

[0060] Based on the above classified text segments, the matching unit 108 uses a predefined keyword library or a machine learning algorithm to automatically identify and extract keywords related to each demand category. For example, keywords such as "daily" and "disinfection" may be found in the cleaning demand text segment; and words such as "low salt" and "vegetarian" may be found in the meal delivery demand. In this way, the matching unit 108 can quickly locate the core terms that best represent the patient's specific needs and provide clear guidance for subsequent service arrangements.

[0061] The confirmation unit 109 is designed to ensure that all extracted demand keywords are accurate. It will send the matched keywords back to the user side (i.e. the patient who made the demand or his authorized agent) for their review and confirmation. This step is very important because it allows patients to have the opportunity to check whether the system has correctly understood their intentions and make adjustments if necessary. Only when the patient confirms that it is correct will the demand be considered as a valid input and enter the next processing flow.

[0062] The storage unit 110 securely stores the demand keywords and corresponding classified text segments confirmed by the user side into the central database of the system. This is not only for record keeping, but more importantly, to support subsequent service scheduling and service path planning. In addition, the stored data is also used for long-term trend analysis, service quality assessment, and development of personalized service solutions. Each demand item will be timestamped to track the time when the demand was generated and how it was met, and to facilitate future inquiries and audits.

[0063] The service scheduling module 102 includes a condition acquisition unit 111, a data scheduling unit 112 and a push unit 113; the condition acquisition unit 111 is used to extract condition information from the requirements submitted by patients or medical staff; the data scheduling unit 112 is used to match the corresponding historical rehabilitation patient data based on the condition information and personal vital signs information, and the historical rehabilitation patient data includes the patient's cleaning, meal delivery and medication management information; the push unit 113 is used to push the cleaning, meal delivery and medication management information according to the patient's rehabilitation time in the historical records.

[0064] The condition acquisition unit 111 extracts key condition information from the requirements submitted by patients or medical staff. These requirements are submitted in various ways, such as through electronic medical record systems, mobile nursing devices, or smart terminals in wards. The condition acquisition unit 111 uses natural language processing (NLP) technology to deeply analyze the text-based requirements, identify and extract important information such as disease names, symptom descriptions, and treatment progress. In addition, it also processes unstructured data in the form of images and audio to further enrich the source of condition information.

[0065] In addition to extracting information directly from the demand text, the condition acquisition unit 111 also integrates real-time data from other systems, such as personal vital signs information (body temperature, blood pressure, heart rate, etc.) uploaded by vital signs monitoring equipment. This makes the condition information more comprehensive and accurate, which helps to match services more accurately in the future.

[0066] Based on the condition information and personal vital signs information provided by the condition acquisition unit 111, the data scheduling unit 112 searches its internal huge historical rehabilitation patient database to find cases with similar symptom patterns. This database contains a large number of detailed records of recovered patients, especially their specific information on cleaning, meal delivery and medication management. Through machine learning algorithms, the data scheduling unit 112 identifies patients who have successfully recovered under similar conditions and extracts effective nursing and service strategies from them.

[0067] The data scheduling unit 112 is not limited to simple historical data matching, but can also adjust and optimize the recommended service content according to the unique situation of the current patient. For example, if a patient has special dietary restrictions or a history of drug allergies, the system will take these factors into special consideration in the recommended meal delivery and medication management plan to ensure that the services provided are both safe and effective.

[0068] Once the most suitable service plan is determined, the push unit 113 will gradually push the corresponding cleaning, meal delivery and medication management information to the relevant medical staff according to the timeline of the patient's recovery in the historical records. This means that every day after the patient is admitted to the hospital, the system will automatically send the service reminders required for the day according to the preset schedule, including specific cleaning requirements, meal types, and drug dosages. This timeline-based service push mechanism not only improves work efficiency, but also ensures the continuity and consistency of services.

[0069] The push unit 113 is highly flexible and can dynamically adjust the push content according to the patient's real-time condition and the doctor's new instructions. For example, if the patient's condition suddenly changes, the system will immediately update the service information and give priority to the emergency treatment measures. At the same time, the push unit 113 also supports two-way communication, allowing medical staff to feedback the execution status and service effect at any time, so that the system can continuously optimize the service process.

[0070] The data scheduling unit 112 includes a data acquisition subunit 114, a data processing subunit 115, a similarity calculation subunit 116 and a judgment subunit 117; the data acquisition subunit 114 is used to extract detailed descriptions of the condition and personal vital signs data from the requirements submitted by patients or medical staff; the data processing subunit 115 is used to standardize the condition description using a unified medical terminology library within the hospital; the similarity calculation subunit 116 is used to calculate the similarity score between it and the current patient in terms of condition and personal vital signs using the cosine similarity method; the judgment subunit 117 is used to generate historical rehabilitation patient data only when the similarity score of the historical case exceeds this threshold, and select the top N to obtain the historical rehabilitation patient data.

[0071] The main task of the data acquisition subunit 114 is to extract detailed descriptions of the condition and personal vital signs data from the requirements submitted by patients or medical staff. These requirements exist in various forms, including but not limited to text descriptions, images, audio recordings, etc. The data acquisition subunit 114 uses natural language processing (NLP) technology to parse the text content, identify and extract key information such as disease name, symptom description, treatment progress, etc. At the same time, it can also integrate real-time data from vital signs monitoring equipment, such as temperature, blood pressure, heart rate and other personal vital signs information, to ensure that the acquired data is both comprehensive and accurate.

[0072] The data processing subunit 115 uses the hospital's internal unified medical terminology library to standardize all condition descriptions. This means that whether it is a doctor's handwritten note or a patient's verbal description, after this processing step, it will be converted into a format that conforms to standard medical terminology. This standardization not only improves the consistency and accuracy of the data, but also facilitates subsequent algorithm processing and model training.

[0073] The similarity calculation subunit 116 uses the cosine similarity method to quantify the similarity between the current patient and the historical recovered patients. Specifically, it calculates the cosine value of the vector angle between the two based on the standardized condition description and personal physical sign data. The closer this value is to 1, the more similar the two cases are; conversely, the less similar they are. In this way, the similarity calculation subunit 116 can objectively evaluate the correlation between different cases and provide a scientific basis for subsequent selection.

[0074] Considering that different features may have different effects on the condition, the similarity calculation subunit 116 also dynamically adjusts the weight of each feature based on expert knowledge or machine learning models. For example, in some cases, a specific physical sign change (such as blood sugar level) may be more important than a general symptom description, and therefore a higher weight is given when calculating the similarity.

[0075] The judgment subunit 117 sets a threshold value of the similarity score. Only when the similarity score of the historical case exceeds this threshold value, the corresponding historical rehabilitation patient data will be generated. This threshold value is flexibly adjusted according to different disease types and clinical needs to ensure that the selected cases are indeed of reference value.

[0076] Once the qualified historical cases are determined, the judgment subunit 117 selects the top N cases with the highest similarity scores as the final historical rehabilitation patient data. N here is configured according to the actual application scenario, for example, more reference cases are selected in the case of complex diseases, while fewer but more representative cases are selected in the case of common diseases.

[0077] The push unit 113 includes a recovery time acquisition subunit 118, a comparison subunit 119 and a push subunit 120. The recovery time acquisition subunit 118 is used to obtain the recovery time in the historical recovery patient data; the comparison subunit 119 is used to sort the recovery time according to length; the push subunit 120 is used to push the cleaning, diet and medication management information corresponding to the shortest recovery time.

[0078] The recovery time acquisition subunit 118 extracts the specific recovery time from the historical recovery patient data. These data come from the hospital's internal case database, covering a large number of detailed records of successfully recovered patients. Each recovery data not only includes specific information such as the patient's cleaning needs, diet delivery and medication management, but also specifically records the time span from admission to complete recovery.

[0079] The comparison subunit 119 sorts all qualified historical rehabilitation patient data according to the length of rehabilitation time. This step is based on the data provided by the rehabilitation time acquisition subunit 118, and adopts an efficient sorting algorithm (such as quick sort or merge sort) to ensure that the sorting of large-scale data can be completed in a short time.

[0080] Based on the sorting results generated by the comparison subunit 119, the push subunit 120 selects the top N cases with the shortest recovery time and extracts the corresponding cleaning, diet and medication management information from them. The "shortest" here does not mean the shortest in an absolute sense, but shortening the recovery time as much as possible while meeting certain treatment effects. In this way, the push subunit 120 can provide a fast and effective recovery path reference for the current patient.

[0081] The push subunit 120 is highly flexible and can dynamically adjust the push content according to the patient's real-time condition and the doctor's new instructions. For example, if the patient's condition suddenly changes, the system will immediately update the service information and give priority to the emergency treatment measures. At the same time, the push subunit 120 also supports two-way communication, allowing medical staff to feedback the execution status and service effect at any time, so that the system can continuously optimize the service process.

[0082] The synchronization module 103 includes a service sequence table parsing unit 121, a path generating unit 122 and a service time period calculating unit 123; the service sequence table parsing unit 121 is used to parse the service content based on the service sequence table; the path generating unit 122 is used to calculate the mobile path based on the movable network nodes of the hospital using the shortest path algorithm; the service time period calculating unit 123 is used to calculate the service time period based on the moving time and the idle time period.

[0083] The service sequence table parsing unit 121 performs detailed parsing of the service sequence table generated by the service scheduling module 102. The sequence table contains a series of care and service items customized for each patient, such as cleaning, meal delivery, medication management, rehabilitation training, etc. The parsing unit uses natural language processing (NLP) technology and structured data processing methods to convert unstructured service descriptions into a format that is easy for computers to understand and process.

[0084] During the parsing process, the service sequence table parsing unit 121 will also sort the tasks according to the service priorities stipulated within the hospital. For example, emergency medical treatment tasks will be automatically placed at the top, while daily cleaning tasks may be scheduled in relatively idle time periods. This priority-based task division helps ensure that important services can be performed in a timely manner.

[0085] In addition to analyzing the specific service content, the analysis unit also identifies the resources required for each service, including the required equipment, staffing, and special conditions (such as sterile environment). This information is crucial for subsequent route planning and service time scheduling.

[0086] The path generation unit 122 first needs to analyze the distribution of movable network nodes in the hospital. These nodes are fixed facilities such as nurse stations, pharmacies, and examination rooms, as well as mobile devices such as mobile nursing vehicles and robot assistants. By modeling the location and connectivity of these nodes, the path generation unit 122 constructs a complete internal hospital traffic map.

[0087] Based on the above traffic map, the path generation unit 122 uses the shortest path algorithm (such as Dijkstra algorithm or A* search algorithm) to calculate the moving path. This algorithm not only takes into account the physical distance, but also comprehensively considers various factors in the actual walking route, such as corridor width, traffic flow, elevator waiting time, etc. In this way, the path generation unit 122 can plan the optimal travel route for medical staff and mobile devices, thereby improving work efficiency and reducing unnecessary waiting time.

[0088] One of the tasks of the service time period calculation unit 123 is to estimate the travel time required for medical staff or mobile devices to reach another node from one node based on the travel path provided by the path generation unit 122. This step needs to take into account the traffic conditions within the hospital in different time periods, such as the difference between the peak hours on weekdays and the night shift period.

[0089] In order to maximize resource utilization, the service time period calculation unit 123 also analyzes the idle time periods in each area of ​​the hospital. By connecting with the hospital's calendar and other systems (such as the reservation management system), it finds out which time periods are relatively idle and arranges non-urgent service items in these time periods as much as possible. This can avoid congestion during peak hours and ensure that service quality is not affected.

[0090] Finally, the service time period calculation unit 123 will comprehensively consider the travel time and idle time period to determine the most appropriate service time period for each service. This time period not only meets the requirements of service order and priority, but also takes into account the workload of medical staff and the actual needs of service recipients. For example, when arranging services for a critically ill patient who needs special care, the calculation unit may choose a time period that does not disturb other patients and allows medical staff to concentrate on providing high-quality services.

[0091] The emergency handling module 104 includes an abnormality monitoring unit 124, an interruption unit 125 and an emergency response unit 126; the abnormality monitoring unit 124 is used to monitor the patient's vital signs data and issue an alarm when an abnormality occurs; the interruption unit 125 is used to obtain alarm information and interrupt the current service sequence list; the emergency response unit 126 is used to automatically start the corresponding emergency response procedure according to a pre-set emergency plan.

[0092] The main task of the abnormal monitoring unit 124 is to monitor the patient's vital signs data in real time. These data are obtained in a variety of ways, such as from bedside monitoring equipment (such as electrocardiographs, blood oxygen saturation meters), wearable health devices (such as smart bracelets, smart watches) and vital signs information (body temperature, blood pressure, heart rate, etc.) uploaded by mobile nursing devices. All these data will be continuously collected and transmitted to the central server for analysis. In order to improve the accuracy and reliability of monitoring, the abnormal monitoring unit 124 adopts multi-source data fusion technology to comprehensively analyze data from different devices. In addition, it also applies advanced machine learning algorithms and early warning models constructed by expert systems to identify potential abnormal situations. For example, if a patient's blood pressure suddenly rises and is accompanied by an increase in heart rate, the system will determine whether this is an abnormality based on a preset threshold and decide whether to issue an alarm.

[0093] The interruption unit 125 receives the alarm information sent by the abnormality monitoring unit 124. First, it will perform a preliminary verification of the alarm information to ensure the authenticity of the alarm. This step is completed by comparing with historical data or consulting other monitoring systems to avoid unnecessary interruptions caused by false alarms.

[0094] Once the alarm is confirmed to be true, the interruption unit 125 will immediately take action to interrupt the currently executed service sequence list. This means that all non-urgent service items (such as routine cleaning, meal delivery, etc.) will be suspended, giving priority to medical staff to focus on emergency situations. In this way, the impact on the patient's treatment process is minimized and precious time is gained for emergency treatment.

[0095] In addition to interrupting existing services, the interruption unit 125 also reallocates necessary resources based on actual conditions. For example, it mobilizes backup nurses to provide support at the scene of the incident, or adjusts the drug preparation process in the pharmacy to meet emergency needs. This flexible resource management helps improve the ability to respond to emergencies.

[0096] At the heart of Emergency Response Unit 126 is a database of emergency plans. These plans cover a variety of emergency situations that the hospital may encounter, such as heart attacks, allergic reactions, breathing difficulties, etc. The plan library not only contains detailed treatment steps, but also includes a list of human and material resources required and a division of responsibilities. More importantly, the unit regularly updates the plan content based on the latest medical guidelines and technological developments to ensure that it is always in the best condition.

[0097] After receiving the alarm information transmitted by the interruption unit 125, the emergency response unit 126 will automatically select the most appropriate emergency plan according to the pre-set rules and immediately start the corresponding emergency response procedure. This process is fully automated and does not require human intervention, thereby greatly shortening the response time. For example, in the case of detecting cardiac arrest, the system will automatically call the emergency team and guide them to go to the scene with the necessary rescue equipment.

[0098] Emergency Response Unit 126 is highly flexible and can dynamically adjust response strategies based on actual conditions on site. For example, if a patient's condition is found to be more serious than expected, the system will promptly adjust the emergency plan and add more support measures. In addition, Emergency Response Unit 126 also supports two-way communication, allowing on-site medical staff to provide feedback on treatment progress and service results at any time, so that the system can continuously optimize the response process.

[0099] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art understands that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A medical intelligence operation management system, characterized in that: It includes patient demand management module, service scheduling module, synchronization module, emergency treatment module and service quality feedback module; The patient demand management module is used to obtain and record the condition information and demand information of hospitalized patients, wherein the demand information includes cleaning requirements, dietary requirements and medication requirements; The service scheduling module is used to match the recommended diet, recommended cleaning method and recommended medication information based on the historical data according to the condition information, and generate a corresponding service sequence table; The synchronization module synchronizes the corresponding service sequence table to the relevant personnel notification terminal and generates a service plan, wherein the service plan includes a service path and a service time period; The emergency treatment module is used to interrupt the service information when the patient has an abnormal condition, and update the service information based on the abnormal condition; In the service quality feedback module, patients rate the services they receive.

2. A medical intelligence operation management system as claimed in claim 1, characterized in that: The medical intelligence operation management system also includes a training module, which is used to match corresponding training courses for logistics staff based on the scoring results.

3. A medical intelligence operation management system as claimed in claim 2, characterized in that: The patient demand management module includes a demand uploading unit, an analyzing unit, a matching unit, a confirming unit and a storage unit; The demand uploading unit is used to upload the patient's demand information text segment; The analysis unit is used to segment the demand information text segment based on cleaning, meal delivery and medication management to obtain classified text segments; The matching unit is used to match corresponding demand keywords based on the classified text segments; The confirmation unit is used to send the required keywords to the user side for confirmation; The storage unit is used to store the demand keywords and corresponding classified text segments in the database after the user confirms the demand.

4. A medical intelligence operation management system as claimed in claim 3, characterized in that: The service scheduling module includes a condition acquisition unit, a data scheduling unit and a push unit; The condition acquisition unit is used to extract condition information from the requirements submitted by the patient or medical staff; The data scheduling unit is used to match the corresponding historical rehabilitation patient data based on the condition information and the personal vital sign information, wherein the historical rehabilitation patient data includes the patient's cleaning, meal delivery and medication management information; The push unit is used to push cleaning, meal delivery and medication management information according to the patient's recovery time in historical records.

5. A medical intelligence operation management system as claimed in claim 4, characterized in that: The data scheduling unit includes a data acquisition subunit, a data processing subunit, a similarity calculation subunit and a judgment subunit; The data acquisition subunit is used to extract detailed condition descriptions and personal vital signs data from the requirements submitted by patients or medical staff The data processing subunit is used to standardize the description of the condition using a unified medical terminology library within the hospital; The similarity calculation subunit is used to calculate the similarity score between the patient and the current patient in terms of condition and personal signs by using a cosine similarity method; The judgment subunit is used to generate historical rehabilitation patient data only when the similarity score of historical cases exceeds this threshold, and select the top N to obtain the historical rehabilitation patient data.

6. A medical intelligence operation management system as claimed in claim 5, characterized in that: The push unit includes a recovery time acquisition subunit, a comparison subunit and a push subunit. The rehabilitation time acquisition subunit is used to acquire the rehabilitation time in historical rehabilitation patient data; The comparison subunit is used to sort the recovery time according to length; The push subunit is used to push the cleaning, diet and medication management information corresponding to the shortest recovery time.

7. A medical intelligence operation management system as claimed in claim 6, characterized in that: The synchronization module includes a service sequence table parsing unit, a path generation unit, and a service time period calculation unit; The service sequence table parsing unit is used to parse the service content based on the service sequence table; The path generation unit is used to calculate the moving path using the shortest path algorithm based on the movable network nodes of the hospital; The service time period calculation unit is used to calculate the service time period based on the moving time and the idle time period.

8. A medical intelligence operation management system as claimed in claim 7, characterized in that: The emergency handling module includes an abnormality monitoring unit, an interruption unit and an emergency response unit; the abnormality monitoring unit is used to monitor the patient's vital signs data and issue an alarm when an abnormality occurs; the interruption unit is used to obtain alarm information and interrupt the current service sequence list; the emergency response unit is used to automatically start the corresponding emergency response procedure according to a pre-set emergency plan.

Images