A healthcare staff scheduling system and method based on predictive models

Abstract

The application relates to the technical field of medical staff scheduling, in particular to a medical staff scheduling system and method based on a prediction model, which comprises an information module, a scheduling module and a management module; the information module acquires patient and medical staff information of a current department; the scheduling module predicts short-term workload through a prediction model and schedules through a self-adaptive optimization algorithm; and the management module is used for receiving scheduling results and adjusting the same; the application statistically classifies patient data and medical staff data, predicts short-term workload according to the prediction model, thereby reasonably scheduling and guaranteeing the balance of medical staff and patient demands.

Description

Technical Field

[0001] This invention relates to the field of medical staff scheduling technology, specifically to a medical staff scheduling system and method based on a predictive model. Background Technology

[0002] The APN (Advanced Personnel Management) shift system is a commonly used model for medical staff scheduling. It divides employees into early, middle, and night shifts and is mainly applied to fields that require continuous operation, such as medical care, nursing, and security. This model achieves 24-hour service coverage through fixed shift times and optimizes human resource allocation by combining it with a hierarchical management structure. Its core advantages include reducing safety hazards during shift changes, strengthening staffing for middle and night shifts, ensuring work continuity, and achieving balanced workload for employees through shift rotation.

[0003] However, this scheduling model often relies on manual operation and experience-based judgment, which cannot flexibly cope with fluctuations in the workload of medical staff, emergencies, or changes in patient flow, resulting in low scheduling efficiency, uneven utilization of medical resources, and excessive burden on medical staff.

[0004] In view of this, we propose a medical staff scheduling system and method based on a predictive model. Summary of the Invention

[0005] The purpose of this invention is to provide a medical staff scheduling system and method based on a predictive model, so as to solve the problem mentioned in the background art that camera equipment is easily adjusted and tampered with.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A medical staff scheduling system based on a predictive model includes: an information module, a scheduling module, and a management module. The information module acquires patient and medical staff information for the current department and accesses the hospital's overall database to read this information. The scheduling module predicts short-term workload using a predictive model and performs scheduling through an adaptive optimization algorithm. The scheduling module imports current patient and medical staff data into the predictive model to predict the total short-term workload. After prediction, it schedules staff based on the adaptive optimization algorithm and previous shift data. When a large number of patients need to be admitted, the information module updates its internal information and reschedules to ensure the rational use of medical resources and reasonable work-rest arrangements for medical staff. The management module receives and adjusts the scheduling results to maximize the satisfaction of medical staff and improve their work motivation.

[0007] Preferably, the information module includes a patient unit, a medical staff unit, and a duty unit; the patient unit reads patient data for the department, accesses the hospital database to read all patient data for the current department, and classifies patients according to their severity; the medical staff unit reads existing medical staff data for the department, reads data on medical staff in the department, and classifies doctor and nurse data; the duty system adopts a mode of one doctor leading multiple nurses for duty; the duty unit reads the duty data of medical staff and performs... The duty unit is categorized into three levels based on the duty data of medical staff: Level 1, Level 2, and Level 3. The duty unit classifies medical staff according to their recent duty duration and frequency. In case of an emergency requiring rescheduling, staff with lower recent duty duration and frequency are prioritized. Level 1: Short duty duration and low duty frequency (i.e., those who have had few recent duty shifts); Level 2: Normal duty duration and normal duty frequency (i.e., those who have had regular duty shifts in recent days and are not currently on duty); Level 3: Staff currently on duty.

[0008] Preferably, the medical and nursing units are divided into an on-duty component and a leave component; the on-duty component reads the existing available medical and nursing staff in the department; the leave component reads the data of medical and nursing staff who are on leave and unable to work; the medical and nursing units also classify medical and nursing staff on leave and those working normally, thereby avoiding scheduling errors when doctors are on leave or away for training. In addition, the first-level personnel in this application are mostly those who have recently returned to work after leave. These medical and nursing staff have shorter shifts and have not been on duty recently, which facilitates the balancing of shift times in scheduling.

[0009] Preferably, the scheduling module includes a prediction unit, an adjustment unit, and a response unit. The prediction unit receives patient and medical staff data and imports it into a prediction model to predict short-term workload. The prediction unit obtains the current number of patients, severity of illness, and on-call data of medical staff in the department, and uses the prediction model to estimate short-term workload fluctuations. The adjustment unit formulates a scheduling strategy based on the prediction results and the classification of medical staff, and feeds back the strategy execution results to the management unit. When a short-term increase in workload is predicted, the adjustment unit quickly generates a Pareto optimal solution set based on an adaptive optimization algorithm to meet the scheduling needs of the increased short-term workload. The response unit sends on-call information to the corresponding medical staff according to the adjustment strategy and feeds back the response results of the medical staff to the adjustment unit for readjustment. The response unit receives the on-call schedule sent by the adjustment unit and then sends information to the medical staff on the on-call schedule to notify them, and sends the response status of the medical staff to the adjustment unit.

[0010] Preferably, the management module includes a receiving unit, a feedback unit, and a display unit. The receiving unit receives and executes feedback results from the adjustment unit. The adjustment unit sends information on medical staff who do not respond or refuse to work to the receiving unit. The management personnel communicate with the medical staff based on the data in the receiving unit, notify those who do not respond again, and review the reasons for refusal of those who choose to refuse to work. The feedback unit sends the execution results back to the adjustment unit for readjustment. The management personnel send the communication results to the adjustment unit through the feedback unit, and the adjustment unit readjusts the shift schedule based on the feedback results. The display unit displays the final shift schedule in a visual format for management personnel and medical staff to review.

[0011] A method for scheduling healthcare workers based on a predictive model includes the following steps: Step 1: The information module accesses the hospital database to obtain patient and medical staff data and classifies them. The information module accesses the hospital database to obtain patient data and medical staff data for the department, and classifies patients by condition registration and classification, and medical staff by classification based on recent shift duration and shift frequency to facilitate subsequent scheduling. Step 2: The scheduling module forecasts and adjusts workload based on patient and medical staff data; The scheduling module imports patient data, crisis level, and medical staff data into the prediction model. The prediction model then forecasts the workload in the short term and adjusts the scheduling based on the forecast results to meet the current workload demand. It also further adjusts the scheduling based on the medical staff's response to the schedule. Step 3: The management module processes the response and feeds the results back to the scheduling module for adjustment. The scheduling module sends non-response and scheduling cannot be scheduled temporarily to the management module. The administrator processes the information sent by the scheduling module and sends the processing result back to the scheduling module. The scheduling module then makes adjustments based on the feedback. Step 4: The scheduling module sends the final scheduling results to the management module; The scheduling module sends the final scheduling results to the management module and displays them visually for easy access by managers and medical staff.

[0012] Preferably, step 1 above further includes the following steps: Step 1.1: The patient unit connects to the hospital database to update the current department's patient data; The patient unit accesses the hospital database to obtain current departmental patient data and classifies patients according to their risk level, making it easier to determine the final number of on-call personnel required. Step 1.2: The medical and nursing unit accesses the hospital database to read the current department's medical and nursing data and classifies them as on-duty or on leave; The medical and nursing unit accesses the hospital database to read data on medical and nursing staff, and divides them into on-duty medical and nursing staff and those on leave. Among the on-duty medical and nursing staff, doctors and nurses are further classified. This system classifies shifts by grouping one doctor and multiple nurses into groups. Step 1.3: The duty unit reads and classifies the duty data of medical staff within the medical unit; The duty unit reads the duty information of on-duty medical staff and classifies them according to their most recent duty duration and frequency. Based on the classification results, medical staff with shorter recent duty duration and lower duty frequency are given priority to ensure a balance of duty duration and avoid excessively long duty time.

[0013] Preferably, step 2 above further includes the following steps: Step 2.1: The prediction unit reads patient data and on-duty medical staff data and combines them with the prediction model to perform calculations; The prediction unit reads the patient data and on-duty medical staff data that have been classified and imported into the prediction model to predict short-term workload. Step 2.2: The adjustment unit combines the prediction results and uses an adaptive optimization algorithm to schedule medical staff shifts step by step; The adjustment unit receives the prediction results, imports the medical and patient data, performs calculations through an adaptive optimization algorithm, thereby constructing the optimal shift schedule, and sends the shift data to the response unit; Step 2.3: The response unit confirms the notification according to the adjustment strategy and sends the feedback result to the adjustment unit; The response unit sends a notification to the medical staff on the schedule based on the scheduling data, reminding them of their schedule. The medical staff can choose to accept or refuse the schedule according to their own needs. If they refuse the schedule, they need to state the reasons. The response unit sends the acceptance and rejection results to the adjustment unit. At the same time, the results of no response within the specified time are also sent to the adjustment unit. Step 2.4: The adjustment unit sends the feedback result to the receiving unit; If the feedback results indicate that the current level of medical staff cannot meet the scheduling requirements, the adjustment unit will send the rejection and non-response data to the receiving unit for administrator processing.

[0014] Preferably, step 3 above further includes the following steps: Step 3.1: Management personnel communicate the feedback results through the receiving unit; Managers communicate with medical staff based on the data in the receiving unit. For those who do not respond, managers contact them again to make adjustments. For medical staff who refuse to be scheduled, the reasons are reviewed and communicated to handle the shift change. Step 3.2: The feedback unit sends the communication results to the adjustment unit; The management personnel will send the adjustment results to the adjustment unit through the feedback unit; Step 3.3: The adjustment unit readjusts based on the feedback results; The adjustment unit receives feedback results and makes adaptive adjustments based on the feedback results to determine a new shift schedule.

[0015] Preferably, step 3.1 above further includes the following steps: Step 3.1.1: The adjustment unit first schedules the first-level medical staff and sends the schedule through the response unit; The adjustment unit first selects the first-level medical staff for scheduling to balance the shift times among them and avoid the phenomenon of continuous shifts. The adjustment unit sends the proposed schedule to the response unit, and the response unit sends a notification to the scheduled staff. Step 3.1.2: If the response unit indicates that the first level is insufficient to meet the scheduling requirements, the feedback result is sent to the receiving unit. When medical staff choose to accept, refuse, or not respond, the response unit compiles the selection results of the medical staff and sends them to the adjustment unit. If the adjustment unit determines, based on the feedback results of the response unit, that the existing first-level medical staff cannot meet the scheduling requirements, the adjustment unit will send the data of the medical staff who refused or did not respond to the receiving unit. Step 3.1.3: The administrator communicates with the first-level medical staff who did not respond based on the information from the receiving unit; Managers communicate with medical staff based on the data in the receiving unit. For those who do not respond, managers contact them again to make adjustments. For medical staff who refuse to be scheduled, the reasons are reviewed and communicated to handle the shift change. Step 3.1.4: The adjustment unit confirms whether to select the next level of medical staff for scheduling based on the communication results, until the scheduling is successful; If the communication results allow the first level to meet the existing scheduling requirements, the scheduling is completed. If the feedback indicates that the first level cannot meet the existing scheduling requirements, a scheduling table is selected from the second level. If the second level cannot meet the requirements, the third level is selected, and so on, until the scheduling requirements can be met. If the scheduling requirements cannot be met in the end, the management personnel will forcibly designate medical staff and send the feedback to the adjustment unit to complete the scheduling.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. A medical staff scheduling system and method based on a predictive model. This invention classifies the duty levels of medical staff and balances the duty hours of each medical staff member, thereby ensuring the balance of medical staff and thus ensuring the satisfaction of medical staff.

[0017] 2. A medical staff scheduling system and method based on a predictive model. This invention statistically analyzes and classifies the number of patients, and then calculates the workload in a short period of time based on the predictive model, thereby making reasonable scheduling to ensure a balance between the needs of medical staff and patients.

[0018] 3. A medical staff scheduling system and method based on a predictive model. This invention avoids the phenomenon of continuous shifts for medical staff through a hierarchical scheduling strategy, thereby ensuring the physical condition of medical staff and providing better medical services. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall framework of the medical staff scheduling system of the present invention; Figure 2 This is a flowchart of the main body of the medical staff scheduling method of the present invention; Figure 3 This is a detailed flowchart of the medical staff scheduling method of the present invention.

[0020] In the picture: 1. Information Module; 11. Patient Unit; 12. Medical Staff Unit; 121. On-Duty Component; 122. Leave Component; 13. Shift Unit; 2. Scheduling module; 21. Forecasting unit; 22. Adjustment unit; 23. Response unit; 3. Management module; 31. Receiving unit; 32. Feedback unit; 33. Display unit. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The APN (Advanced Personnel Management) shift system is a commonly used model for medical staff scheduling. It divides employees into early, middle, and night shifts and is mainly applied to fields that require continuous operation, such as medical care, nursing, and security. This model achieves 24-hour service coverage through fixed shift times and optimizes human resource allocation by combining it with a hierarchical management structure. Its core advantages include reducing safety hazards during shift changes, strengthening staffing for middle and night shifts, ensuring work continuity, and achieving balanced workload for employees through shift rotation.

[0023] However, this scheduling model often relies on manual operation and experience-based judgment, which cannot flexibly cope with fluctuations in the workload of medical staff, emergencies, or changes in patient flow, resulting in low scheduling efficiency, uneven utilization of medical resources, and excessive burden on medical staff.

[0024] The present invention provides a technical solution: like Figures 1 to 3 As shown, a medical staff scheduling system and method based on a predictive model are presented: like Figure 1 As shown, a medical staff scheduling system based on a predictive model includes: an information module 1, a scheduling module 2, and a management module 3; the information module 1 acquires patient and medical staff information for the current department; the scheduling module 2 predicts short-term workload using a predictive model and schedules staff using an adaptive optimization algorithm; the management module 3 receives the scheduling results and makes adjustments. Specifically, Information Module 1 retrieves patient and medical staff information for the current department. Information Module 1 connects to the hospital's central database and reads the patient and medical staff information for the current department. Preferably, Information Module 1 periodically retrieves data from the hospital's central database for updates. Furthermore, whenever a new patient enters the department or a new medical staff member joins, the hospital's central database automatically sends the updated information to Information Module 1. Scheduling Module 2 predicts short-term workload using a predictive model and performs scheduling using an adaptive optimization algorithm. After each update by Information Module 1, Scheduling Module 2 updates the current patient data and the number of medical staff. The imported prediction model forecasts the total workload in the short term. After the forecast is completed, the adaptive optimization algorithm is used to schedule shifts based on the previous shift data of medical staff. When a large number of patients need to be admitted, the information module 1 will update the internal information and reschedule to ensure the rational use of medical resources and reasonable work and rest for medical staff. The management module 3 is used to receive and adjust the scheduling results to maximize the satisfaction of the scheduling wishes of medical staff, thereby improving their work enthusiasm.

[0025] In this embodiment, the information module 1 includes a patient unit 11, a medical staff unit 12, and a duty unit 13; the patient unit 11 reads the patient data of the department; the medical staff unit 12 reads the existing medical staff data of the department; the duty unit 13 is divided into a first level, a second level, and a third level according to the duty data of medical staff; Specifically, patient unit 11 reads patient data for the department, accesses the database to read all patient data for the current department, and classifies patients according to their severity. Medical and nursing unit 12 reads existing medical and nursing data for the department, including data on current medical and nursing staff, and categorizes doctor and nurse data. The scheduling system uses a model where one doctor leads multiple nurses for shift work. Duty unit 13 reads and classifies the duty data of medical and nursing staff, and then assigns a duty roster based on the severity of the illness. The medical staff on-duty data is divided into three levels: Level 1, Level 2, and Level 3. On-duty unit 13 classifies medical staff based on the most recent on-duty duration and frequency. When an emergency requires rescheduling, the medical staff with the lowest recent on-duty duration and frequency are rescheduled first. Level 1: short on-duty duration and low on-duty frequency, i.e., those who have been on duty less in the past few days; Level 2: normal on-duty duration and normal on-duty frequency, i.e., those who have been on duty normally in the past few days and are not on duty today; Level 3: those who are currently on duty today.

[0026] In this embodiment, the medical and nursing unit 12 consists of an on-duty component 121 and a leave component 122; the on-duty component 121 reads the existing medical and nursing staff available to work in the department; the leave component 122 reads the data of medical and nursing staff who are unable to work due to leave. Specifically, medical unit 12 also classifies medical staff on leave and those on regular duty to avoid scheduling errors when doctors are on leave or away for training. Furthermore, the first-level staff in this application are mostly those who have recently returned to work after leave. These medical staff have shorter shifts and have not been on duty recently, which makes it easier to balance the shifts.

[0027] In this embodiment, the scheduling module 2 includes a prediction unit 21, an adjustment unit 22, and a response unit 23. The prediction unit 21 receives patient and medical staff data and imports it into a prediction model to predict short-term workload. The adjustment unit 22 formulates a scheduling strategy based on the prediction results and the hierarchical classification of medical staff, and feeds back the strategy execution results to the management unit. The response unit 23 sends the duty information to the corresponding medical staff according to the adjustment strategy, and feeds back the response results of the medical staff to the adjustment unit 22 for readjustment. Specifically, the prediction unit 21 receives patient and medical staff data and imports it into a prediction model to predict short-term workload. The prediction unit 21 acquires data on the current number of patients, their severity, and the on-call status of medical staff in the department. Using the prediction model, it estimates short-term workload fluctuations. The prediction unit 21 constructs a multi-objective optimization model with the core objectives of minimizing patient safety risks, optimizing human resource costs, and maximizing medical staff satisfaction. The adjustment unit 22 formulates a scheduling strategy based on the prediction results and the classification of medical staff, and feeds back the strategy execution results to the management unit. When a short-term increase in workload is predicted, the adjustment unit 22 quickly generates a Pareto optimal solution set using an adaptive optimization algorithm to meet the scheduling needs of the increased short-term workload. The response unit 23 then adjusts the strategy accordingly. The duty roster is sent to the corresponding medical staff, and the response results of the medical staff are fed back to the adjustment unit 22 for readjustment. The response unit 23 receives the duty roster sent by the adjustment unit 22 and then sends information to the medical staff on the duty roster to notify them. The response status of the medical staff is also sent back to the adjustment unit 22. The medical staff can choose to respond or refuse according to the response unit 23. If they choose to refuse, they need to state a detailed reason and the adjustment unit 22 sends it to the management module 3 for review. If the medical staff does not respond within a unit of time, the feedback is also sent to the adjustment unit 22, and then the adjustment unit 22 sends it to the management module 3 for processing. If a hospital staff member refuses or does not respond multiple times, a forced shift will be scheduled if there is a short-term increase in workload.

[0028] In this embodiment, the management module 3 includes a receiving unit 31, a feedback unit 32, and a display unit 33; the receiving unit 31 is used to receive and execute the feedback results from the adjustment unit 22; the feedback unit 32 is used to feed back the execution results to the adjustment unit 22 for readjustment; and the display unit 33 is used to display the final scheduling results. Specifically, the receiving unit 31 is used to receive and execute the feedback results from the adjustment unit 22. The adjustment unit 22 sends information on medical staff who do not respond or refuse to be on duty to the receiving unit 31. The management personnel communicate with the medical staff based on the data in the receiving unit 31, notify the non-responding personnel again, and review the reasons for refusal of the selected personnel. The feedback unit 32 is used to feed back the execution results to the adjustment unit 22 for readjustment. The management personnel send the communication results to the adjustment unit 22 through the feedback unit 32, and the adjustment unit 22 readjusts the shift schedule based on the feedback results. The display unit 33 is used to display the final shift schedule results. The display unit 33 is used to present the final shift schedule in a visual form for management personnel and medical staff to review.

[0029] Figure 1This is a schematic diagram of the overall framework of the medical staff scheduling system of the present invention. The diagram details the composition and connection of each module. The information component reads patient and medical staff data from the current department by accessing the database, and classifies patient levels and medical staff on-duty status. Simultaneously, the duty unit 13 reads the duty data of medical staff in the area of ​​the medical staff unit 12, and classifies it according to recent duty status. After the information is read and processed, the prediction module imports the patient and medical staff data into a prediction model to predict the workload in the short term, and sends the prediction results to the adjustment unit 22. The adjustment unit 22 receives the prediction results and retrieves the patient data. The medical staff data and duty data are used to adjust the schedule using an adaptive algorithm. The response component sends notifications to medical staff according to the schedule and receives feedback from them. The response component sends the feedback information to the adjustment unit 22, which determines whether the scheduling requirements can be met. If not, it sends the information to the receiving module for management personnel to handle. After management personnel handle the feedback, the information is sent back to the adjustment module. The adjustment module creates a schedule based on the feedback results and displays it visually through the display unit 33.

[0030] like Figures 2 to 3 As shown, a method for scheduling medical staff based on a predictive model includes the following steps: Step 1: Information module 1 accesses the hospital database to obtain patient and medical staff data and classifies them accordingly; Step 2: Scheduling module 2 forecasts and adjusts workload based on patient and medical staff data; Step 3: The management module 3 processes the response results and feeds them back to the scheduling module 2 for adjustment. Step 4: The scheduling module 2 sends the final scheduling results to the management module 3; Specifically, Information Module 1 accesses the hospital's database to obtain patient and medical staff data for the department. It registers and classifies patients based on their condition and categorizes medical staff according to their recent shift duration and frequency, facilitating subsequent scheduling. Scheduling Module 2 imports patient data, crisis levels, and medical staff data into a predictive model. This model predicts short-term workload and adjusts the schedule based on the prediction results to meet current workload demands. It also adjusts the schedule based on medical staff responses. If the workload is normal or insufficient, the existing schedule remains unchanged; if the workload increases, more staff are added to maintain a balanced doctor-patient relationship and prevent staff shortages from causing patient shortages. Scheduling Module 2 sends unresponsive or temporarily unavailable scheduling responses to Management Module 3. The administrator processes the information sent by Scheduling Module 2 and sends the results back to Scheduling Module 2, which then adjusts the schedule based on the feedback. Finally, Scheduling Module 2 sends the final scheduling results to Management Module 3 and displays them visually for easy access by administrators and medical staff.

[0031] In this embodiment, step 1 above further includes the following step: Step 1.1: Patient Unit 11 accesses the hospital database to update the current department's patient data; Step 1.2: Medical and nursing unit 12 accesses the hospital database to read the current department's medical and nursing data and classifies them as on-duty or on leave; Step 1.3: Duty unit 13 reads and classifies the duty data of medical staff in medical unit 12; Specifically, the patient unit 11 accesses the hospital database to obtain current departmental patient data and classifies patients according to their risk level to facilitate the final determination of the required number of on-call personnel; the medical staff unit 12 accesses the hospital database to read medical staff data and divides medical staff into on-duty medical staff and off-duty medical staff, and further classifies doctors and nurses among the on-duty medical staff. This system classifies shifts by grouping one doctor and multiple nurses together; the on-call unit 13 reads the on-call information of on-duty medical staff and classifies them according to their most recent on-call duration and frequency. Based on the classification results, priority is given to selecting medical staff with shorter recent on-call durations and lower on-call frequencies to ensure a balance in on-call durations and avoid excessively long on-call times for medical staff.

[0032] In this embodiment, step 2 above further includes the following steps: Step 2.1: Prediction unit 21 reads patient data and on-duty medical staff data and combines them with the prediction model to perform calculations; Step 2.2: Adjustment unit 22 uses an adaptive optimization algorithm to schedule medical staff shifts step by step based on the prediction results; Step 2.3: Response unit 23 notifies and confirms according to the adjustment strategy and sends the feedback result to adjustment unit 22; Step 2.4: The adjustment unit 22 sends the feedback result to the receiving unit 31; Specifically, the prediction unit 21 reads the patient data and on-duty medical staff data completed at different levels, imports the data into the prediction model for short-term workload prediction; the adjustment unit 22 receives the prediction results, imports the medical staff data, and calculates the optimal shift schedule using an adaptive optimization algorithm, and then sends the shift schedule data to the response unit 23; the response unit 23 sends notifications to the medical staff on the shift schedule based on the shift schedule data, reminding them of their shift schedule. Medical staff can choose to accept or refuse the shift schedule according to their own needs. If they refuse the shift schedule, they need to state their reasons. The response unit 23 sends the acceptance and rejection results to the adjustment unit 22. At the same time, it also sends the results of no response within a specified time to the adjustment unit 22; if the feedback results indicate that the medical staff at the current level cannot meet the shift schedule, the adjustment unit 22 sends the rejection and non-response data to the receiving unit 31 for administrator processing.

[0033] In this embodiment, step 3 above further includes the following steps: Step 3.1: The management personnel communicate the feedback results through receiving unit 31; Step 3.2: Feedback unit 32 sends the communication results to adjustment unit 22; Step 3.3: Adjustment unit 22 readjusts based on the feedback results; Specifically, the management personnel communicate with the medical staff based on the data in the receiving unit 31. For those who do not respond, the management personnel will contact them again to make adjustments. For medical staff who refuse to be scheduled, the reasons will be reviewed and communicated to handle the shift change. The management personnel will send the adjustment results to the adjustment unit 22 through the feedback unit 32. The adjustment unit 22 will receive the feedback results and make adaptive adjustments based on the feedback results to determine a new shift schedule.

[0034] In this embodiment, step 3.1 above further includes the following steps: Step 3.1.1: The adjustment unit 22 first schedules the first-level medical staff and sends the schedule through the response unit 23; Step 3.1.2: If the response unit 23 indicates that the first level is insufficient to meet the scheduling requirements, the feedback result is sent to the receiving unit 31. Step 3.1.3: The management personnel communicate with the first-level medical staff who did not respond based on the information from receiving unit 31; Step 3.1.4: Adjustment unit 22 confirms whether to select the next level of medical staff for scheduling based on the communication results, until the scheduling is successful; Specifically, the adjustment unit 22 first selects the first-tier medical staff for shift scheduling to balance the shift durations among them and avoid continuous shifts. The adjustment unit 22 sends the proposed shift schedule to the response unit 23, which then sends a notification to the scheduled staff. The medical staff choose to accept, refuse, or not respond. The response unit 23 compiles the selection results and sends them back to the adjustment unit 22. If the adjustment unit 22 determines, based on the feedback from the response unit 23, that the existing first-tier medical staff cannot meet the shift scheduling requirements, the adjustment unit 22 sends the data of the refusing or non-responding medical staff to the receiving unit. Unit 31; Managers communicate with medical staff based on the data in receiving unit 31. For those who do not respond, managers contact them again for adjustment. For medical staff who refuse to be scheduled, the reasons are reviewed and communicated to handle the shift adjustment. If the communication results allow the first level to meet the existing scheduling needs, the scheduling is completed. If the feedback from the first level does not meet the existing scheduling needs, the scheduling table is selected from the second level. If the second level does not meet the needs, the third level is selected until the scheduling needs are met. If the scheduling needs are not met in the end, the manager forcibly designates medical staff and sends the information to the adjustment unit 22 through feedback unit 32 to complete the scheduling.

[0035] Figure 2 This is a flowchart illustrating the main process of the medical staff scheduling method of the present invention. The flowchart describes the main method used by the system. Information module 1 accesses the hospital database to acquire patient and medical staff data for the department, registers and classifies patients based on their condition, and categorizes medical staff according to their recent shift duration and frequency to facilitate subsequent scheduling. Scheduling module 2 imports patient data, crisis level, and medical staff data into a prediction model. The prediction model forecasts short-term workload and adjusts the scheduling based on the forecast results to meet current workload demands. It also monitors the medical staff's response to the scheduling. The results are adjusted again. If the workload is normal or too low, the existing schedule remains unchanged. If the workload increases, the number of staff on duty is increased to ensure a balance in the doctor-patient relationship and avoid a shortage of medical staff leading to a shortage of patients. The scheduling module 2 sends responses that are not responded to or cannot be scheduled to the management module 3. The administrator processes the information sent by the scheduling module 2 and sends the processing results back to the scheduling module 2. The scheduling module 2 adjusts again based on the feedback. The scheduling module 2 sends the final scheduling results to the management module 3 and displays them visually for easy access by managers and medical staff.

[0036] Figure 3This is a detailed flowchart of the medical staff scheduling method of the present invention; the patient unit 11 accesses the hospital database to obtain the current department's patient data and classifies the patients according to their risk level; the medical staff unit 12 accesses the hospital database to read the medical staff data and classifies the medical staff into on-duty medical staff and off-duty medical staff; the duty unit 13 reads the duty information of on-duty medical staff and classifies them according to their recent duty duration and duty frequency; the prediction unit 21 reads the classified patient data and on-duty medical staff data, imports the data into the prediction model for short-term workload prediction; the adjustment unit 22 receives the prediction results, imports the medical staff data, and adjusts the model through adaptive... The algorithm should be optimized to calculate and construct the optimal shift schedule, and the shift data should be sent to the response unit 23. The response unit 23 sends a notification to the medical staff on the shift schedule based on the shift data, reminding them of their shift schedule. The medical staff can choose to accept or refuse the shift according to their own needs. If they refuse the shift, they need to state the reasons. The response unit 23 sends the acceptance and rejection results to the adjustment unit 22. At the same time, the results of no response within the specified time are also sent to the adjustment unit 22. If the feedback results make it impossible for the medical staff at the current level to meet the shift results, the adjustment unit 22 sends the rejection and non-response data to the receiving unit 31 for the administrator to handle. The adjustment unit 22 first selects the first-tier medical staff for scheduling to balance the shift times among them and avoid continuous shifts. The adjustment unit 22 then sends the proposed schedule to the response unit 23, which sends a notification to the scheduled staff. The medical staff choose to accept, refuse, or not respond. The response unit 23 compiles the selection results and sends them back to the adjustment unit 22. If the adjustment unit 22 determines, based on the feedback from the response unit 23, that the existing first-tier medical staff cannot meet the scheduling requirements, the adjustment unit 22 sends the data of the refusing or unresponsive medical staff to the receiving unit 3. 1. Managers communicate with medical staff based on the data in receiving unit 31. For those who do not respond, managers contact them again for adjustment. For medical staff who refuse to be scheduled, their reasons are reviewed and communicated to handle shift changes. If the communication results allow the first level to meet the existing scheduling needs, the scheduling is completed. If the results show that the first level cannot meet the existing scheduling needs, a schedule is selected from the second level. If the second level cannot meet the needs, the schedule is selected from the third level until the scheduling needs can be met. If the scheduling needs cannot be met in the end, the manager will forcibly designate medical staff and send the schedule to the adjustment unit 22 through feedback unit 32 to complete the scheduling.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A medical staff scheduling system based on a predictive model, characterized in that, include: Information module (1), scheduling module (2), and management module (3); The information module (1) acquires the patient and medical staff information of the current department; The scheduling module (2) predicts short-term workload using a prediction model and schedules shifts using an adaptive optimization algorithm. The management module (3) is used to receive and adjust the scheduling results.

2. The medical staff scheduling system according to claim 1, characterized in that: The information module (1) includes a patient unit (11), a medical care unit (12), and a duty unit (13). The patient unit (11) reads the patient data of the department; The medical care unit (12) reads the existing medical care data of the department; The duty unit (13) is divided into a first level, a second level and a third level based on the duty data of medical staff.

3. The medical staff scheduling system according to claim 2, characterized in that: The medical care unit (12) consists of an on-duty component (121) and a leave component (122). The on-duty component (121) reads the existing available medical staff in the department; the leave component (122) reads the data of medical staff who are unable to work due to leave.

4. The medical staff scheduling system according to claim 1, characterized in that: The scheduling module (2) includes a prediction unit (21), an adjustment unit (22), and a response unit (23). The prediction unit (21) receives patient and medical staff data and imports it into the prediction model to predict short-term workload; The adjustment unit (22) formulates a scheduling strategy based on the prediction results and the classification of medical staff, and feeds back the strategy execution results to the management unit. The response unit (23) sends the duty information to the corresponding medical staff according to the adjustment strategy, and feeds back the response results of the medical staff to the adjustment unit (22) for readjustment.

5. The medical staff scheduling system according to claim 4, characterized in that: The management module (3) includes a receiving unit (31), a feedback unit (32), and a display unit (33). The receiving unit (31) is used to receive the feedback result from the adjustment unit (22) and execute it; The feedback unit (32) is used to feed back the execution result to the adjustment unit (22) for easy readjustment; The display unit (33) is used to display the final scheduling results.

6. A method for scheduling medical staff based on a predictive model, used in the scheduling of medical staff according to any one of claims 1-5, characterized in that, Includes the following steps: Step 1: Information module (1) Access the hospital database to obtain patient and medical staff data, and classify them; Step 2, Scheduling Module (2): Based on patient and medical staff data, predict and adjust work schedules; Step 3: The management module (3) processes the adjustment feedback and feeds the results back to the scheduling module (2) for adjustment; Step 4: The scheduling module (2) sends the final scheduling result to the management module (3).

7. The medical staff scheduling system according to claim 6, characterized in that: Step 1 above also includes the following steps: Step 1.1: The patient unit (11) accesses the hospital database to update the current department's patient data; Step 1.2: The medical and nursing unit (12) accesses the hospital database to read the current department's medical and nursing data and classifies them as on-duty or on leave; Step 1.3: The duty unit (13) reads and classifies the duty data of medical staff in the medical unit (12).

8. The method for scheduling medical staff according to claim 6, characterized in that: Step 2 above also includes the following steps: Step 2.1: The prediction unit (21) reads patient data and on-duty medical staff data and combines them with the prediction model to perform calculations; Step 2.2: The adjustment unit (22) uses an adaptive optimization algorithm to schedule medical staff in stages based on the prediction results; Step 2.3: The response unit (23) notifies and confirms the adjustment strategy and sends the feedback result to the adjustment unit (22); Step 2.4: The adjustment unit (22) sends the feedback result to the receiving unit (31).

9. The method for scheduling medical staff according to claim 6, characterized in that: Step 3 above also includes the following steps: Step 3.1: The management personnel communicate the feedback results through the receiving unit (31); Step 3.2: The feedback unit (32) sends the communication results to the adjustment unit (22); Step 3.3: Adjustment unit (22) readjusts according to the feedback results.

10. The method for scheduling medical staff according to claim 9, characterized in that: Step 3.1 above also includes the following steps: Step 3.1.1: The adjustment unit (22) first schedules the medical staff at the first level and sends the schedule through the response unit (23); Step 3.1.2: If the response unit (23) indicates that the first level is insufficient to meet the scheduling requirements, the feedback result is sent to the receiving unit (31). Step 3.1.3: The management personnel communicate with the first-level medical staff who did not respond based on the information from the receiving unit (31); Step 3.1.4: Adjustment unit (22) confirms whether to select the next level of medical staff for scheduling based on the communication results, until the scheduling is successful.

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