Clinical scheduling simulation method and system for nursing human resource configuration and medium

Abstract

The invention relates to the technical field of data processing, and provides a clinical scheduling simulation method and system for nursing human resource allocation and a medium, and the method comprises the steps: obtaining a structured nursing task model and clinical scheduling parameters; after a simulation time window and a preset clinical scene are set, a structured nursing task is generated based on the clinical scheduling parameters through a structured nursing task model; through a simulation task allocation mechanism and in combination with a fatigue evolution mechanism, allocation of structured nursing tasks is carried out on each nurse; generating simulation path information of the nursing personnel in the nursing area based on the spatial topological graph; and in the simulation operation process, responding to the situation that the current simulation time reaches the starting time point of the target structured nursing task, simulating the nursing personnel to execute the target structured nursing task according to the simulation path information, and outputting a scheduling simulation evaluation index. According to the invention, the dynamic simulation and quantitative evaluation of the scheduling strategy at the task level can be realized.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a clinical scheduling simulation method, system and medium for nursing human resource allocation. Background Technology

[0002] Clinical nursing scheduling is a core task in hospital nursing management, and its scientific nature directly affects nursing quality, staff workload balance, and the ability to respond to emergencies.

[0003] Current clinical nursing scheduling methods mainly include four types: manual experience-based scheduling, shift rotation scheduling, mathematical modeling scheduling, and information system-assisted scheduling. Although the aforementioned scheduling methods have made some progress in generating schedules, they lack quantitative evaluation and feedback mechanisms for the quality of the generated schedules. Summary of the Invention

[0004] This application provides a clinical scheduling simulation method, system, and medium for nursing human resource allocation. By coupling task-level simulation path modeling and fatigue evolution mechanism, it supports the simulation operation and performance evaluation of different scheduling strategies in various clinical scenarios, realizes dynamic simulation and quantitative evaluation of scheduling strategies at the task level, and improves the scientificity, visualization, and practicality of scheduling strategies.

[0005] In one aspect, this application provides a clinical scheduling simulation method for nursing human resource allocation, the method comprising:

[0006] Obtain a structured nursing task model and clinical scheduling parameters; the clinical scheduling parameters include nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules;

[0007] After setting the simulation time window and preset clinical scenarios, structured nursing tasks are generated based on the clinical scheduling parameters through the structured nursing task model; wherein, the nursing human resources, nursing task data, nursing ward layout structure and scheduling rules are different in different clinical scenarios;

[0008] The structured nursing tasks are assigned to each nursing staff by simulating a task allocation mechanism and combining it with a fatigue evolution mechanism.

[0009] Construct a spatial topology map, and generate simulation path information for the nursing staff in the nursing ward based on the spatial topology map;

[0010] The simulation will run according to the time step set within the simulation time window.

[0011] During the simulation, when the current simulation time reaches the start time of the target structured nursing task, the nursing staff will simulate the execution of the target structured nursing task according to the simulation path information, and the scheduling simulation evaluation index will be output.

[0012] On the other hand, this application provides a clinical scheduling simulation system for nursing human resource allocation, the system including an input configuration module, a task generation module, a task allocation module, a path planning module, a time advancement module, a task execution module, and an indicator output module;

[0013] The input configuration module is used to obtain the structured nursing task model and clinical scheduling parameters; the clinical scheduling parameters include nursing human resources, nursing task data, nursing ward layout structure and scheduling rules;

[0014] The task generation module is used to generate structured nursing tasks based on the clinical scheduling parameters through the structured nursing task model after setting the simulation time window and preset clinical scenarios; wherein, the nursing human resources, nursing task data, nursing ward layout structure and scheduling rules are different in different clinical scenarios.

[0015] The task allocation module is used to allocate the structured nursing tasks to each nursing staff by simulating a task allocation mechanism and combining it with a fatigue evolution mechanism.

[0016] The path planning module is used to construct a spatial topology map and generate simulated path information of the nursing staff in the nursing ward based on the spatial topology map;

[0017] The time advancement module is used to perform simulation operation according to the time step set within the simulation time window;

[0018] The task execution module is used to respond to the start time point of the target structured nursing task when the current simulation time reaches the simulation time during the simulation operation, and simulate the nursing staff to execute the target structured nursing task according to the simulation path information.

[0019] The indicator output module is used to output the simulation evaluation indicators for the scheduling.

[0020] In another aspect, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements any of the clinical scheduling simulation methods for nursing human resource allocation described in the present invention.

[0021] In another aspect, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the clinical scheduling simulation methods for nursing human resource allocation described in the present invention.

[0022] In another aspect, this application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the clinical scheduling simulation method for nursing human resource allocation described in the foregoing aspects.

[0023] This application provides a clinical scheduling simulation method, system, and medium for nursing human resource allocation. By acquiring a structured nursing task model and clinical scheduling parameters, and after setting a simulation time window and preset clinical scenarios, the system generates structured nursing tasks based on the clinical scheduling parameters using the structured nursing task model. Through a simulated task allocation mechanism combined with a fatigue evolution mechanism, it assigns structured nursing tasks to various nursing staff. Furthermore, it can generate simulation path information for nursing staff within the nursing ward based on a constructed spatial topology map. The simulation runs according to the time steps set within the simulation time window, responding to the start time of the target structured nursing task at the current simulation time. The simulated nursing staff execute the target structured nursing task according to the simulation path information, outputting scheduling simulation evaluation indicators. By coupling task-level simulation path modeling and fatigue evolution mechanisms, it supports the simulation operation and performance evaluation of different scheduling strategies in various clinical scenarios, achieving dynamic simulation and quantitative evaluation of scheduling strategies at the task level, and improving the scientific rigor, visualization, and practicality of scheduling strategies. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the architecture of a clinical scheduling simulation system for nursing human resource allocation provided in an embodiment of this application;

[0025] Figure 2 This is a flowchart illustrating the steps of a clinical scheduling simulation method for nursing human resource allocation provided in an embodiment of this application.

[0026] Figure 3 This is a schematic diagram of the clinical scheduling simulation method provided in the embodiments of this application;

[0027] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application;

[0028] Figure 5 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Clinical nursing scheduling is a core task in hospital nursing management, and its scientific nature directly affects nursing quality, staff workload balance, and the ability to respond to emergencies.

[0031] Current clinical nursing scheduling methods mainly include four categories: manual experience-based scheduling, shift rotation scheduling, mathematical modeling scheduling, and information system-assisted scheduling. Manual experience-based scheduling relies heavily on nurses' personal experience and subjective judgment for shift arrangements. Widely used in traditional management, it offers advantages in terms of humanization and flexibility. However, this method heavily depends on the manager's personal experience, lacks quantitative basis, and is not standardized. Rotational scheduling, based on fixed rotation rules, offers some fairness in personnel allocation, but often ignores differences in task intensity, patient needs, and individual nurses' abilities, easily leading to staff shortages during peak periods. Mathematical modeling scheduling, mostly based on integer programming and heuristic algorithms, generates optimal solutions satisfying multiple constraints, offering advantages in automation. However, scheduling results are often based on idealized assumptions and lack practical task simulation and evaluation capabilities. Information system-assisted scheduling integrates Human Resource Information Systems (HRIS) with electronic scheduling platforms, supporting rapid generation and publication of scheduling templates and achieving basic information management. However, most systems are limited to schedule creation and data visualization, lacking support for task-level dynamic simulation and scheduling quality feedback.

[0032] As can be seen from the above, firstly, most methods focus only on generating the schedule, ignoring the task completion during the scheduling process. They fail to assess whether the allocated manpower is sufficient to complete all nursing tasks, leading to problems such as task delays and backlogs in actual work. Secondly, current scheduling systems generally lack simulation capabilities, failing to conduct drills and evaluations based on dynamic clinical scenarios such as patient changes, emergencies, or peak rescue periods, making scheduling plans inadequate to cope with complex and ever-changing clinical realities. Thirdly, the lack of multi-dimensional evaluation indicators for nursing safety and manpower utilization makes it impossible to compare the advantages and disadvantages of different scheduling plans, thus failing to provide managers with quantitative optimization suggestions. In conclusion, although the above scheduling methods have made some progress in generating schedules, they lack a quantitative evaluation and feedback mechanism for the quality of the generated schedules.

[0033] This application embodiment integrates elements such as task generation, path time modeling, nurses' capabilities, and fatigue threshold settings, and couples task-level simulation path modeling with fatigue evolution mechanisms. It can dynamically evaluate the actual effects of scheduling schemes on task completion rate, response time, and resource load in simulated real nursing scenarios. It supports the simulation operation and performance evaluation of different scheduling strategies in various clinical scenarios, realizes dynamic simulation and quantitative evaluation of scheduling strategies at the task level, and improves the scientificity, visualization, and practicality of scheduling strategies.

[0034] To address the lack of quality assessment and optimization criteria in existing scheduling methods, this application proposes a clinical scheduling simulation system that integrates nursing task modeling, scheduling simulation, and multi-scenario extrapolation analysis.

[0035] Reference Figure 1 This document illustrates a schematic diagram of the architecture of a clinical scheduling simulation system for nursing human resource allocation provided in an embodiment of this application. The provided clinical scheduling simulation system is a modular system, which may include an input configuration module M1, a task generation module M2, a task allocation module M3, a path planning module M4, a task execution module M5, a fatigue assessment module M6, an emergency simulation module M7, a time progression module M8, an indicator output module M9, and a scene control module M10.

[0036] Specifically, the input configuration module M1 is used to obtain the structured nursing task model and clinical scheduling parameters. The clinical scheduling parameters are obtained based on the initial settings and can be mainly used to construct the simulation basic data structure required for the structured nursing task model. For example, the clinical scheduling parameters may include nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules, etc., and this application embodiment does not limit them.

[0037] The task generation module M2 is used to generate structured nursing tasks based on clinical scheduling parameters through a structured nursing task model after setting the simulation time window and preset clinical scenarios. Specifically, it can generate daily task flows based on nursing levels. Nursing tasks can be standardized and databased according to nursing task category, execution frequency, task priority, average time, etc., to form a task database. It should be noted that the structured nursing tasks support scenario-based parameter settings. Different clinical scenarios can be, for example, night shifts, holidays, peak hours, etc. The nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules are different in different clinical scenarios, and this application embodiment does not impose any limitations on this.

[0038] The task allocation module M3 is used to allocate structured nursing tasks to various nursing staff by simulating a task allocation mechanism and combining it with a fatigue evolution mechanism. For example, the task allocation mechanism may include a responsibility group task assignment mechanism, a team leader collaboration mechanism, and a floating shift mechanism, supporting priority and dynamic scoring scheduling. Specifically, it can comprehensively consider factors such as the nursing staff's job suitability, current workload, and spatial accessibility of the task location to dynamically allocate and reschedule tasks.

[0039] The path planning module M4 is used to construct a spatial topology map and generate simulated path information for nursing staff in the nursing ward based on the spatial topology map. The simulated path information includes the target movement path and the access time. The access time is used to indicate the total time spent on the target movement path through the channel, which can realize the simulation of the movement path and time spent by nursing staff in the nursing ward.

[0040] The task execution module M5 is used to respond to the start time of the target structured nursing task when the current simulation time reaches the start time point during the simulation operation. It simulates nursing staff to execute the target structured nursing task according to the simulation path information, and can synchronously record the entire process information of the task from start, response, journey, execution to completion / interruption, for subsequent behavior tracing and performance analysis.

[0041] The fatigue assessment module M6 is used to evaluate the fatigue index and workload distribution of nursing staff in real time during simulation. Specifically, it can dynamically calculate the nursing staff fatigue index FI, taking into account shift weights and task intensity, and supporting fatigue early warning and scheduling rationality analysis. Optionally, the fatigue index (FI) can be obtained based on the total time spent on the target structured nursing tasks performed by nursing staff each day and the shift fatigue coefficient of nursing staff. The shift fatigue coefficient is used to reflect the aggravating effect of different shift categories on nursing staff fatigue, and the fatigue index FI is used to indicate the fatigue level of nursing staff and provide fatigue exceeding warning, so as to affect task allocation and path speed adjustment in real time during simulation, realizing dynamic feedback of the fatigue evolution mechanism.

[0042] The emergency simulation module M7 is used to set the types of emergencies and the probability of occurrence of each type of emergency. It simulates emergencies such as falls, rescue, and catheter dislodgement, and dynamically generates emergency tasks according to the patient's care level and the probability of occurrence of the event.

[0043] The time progression module M8 is used to run the simulation according to the time step set within the simulation time window, such as advancing the simulation process by minute, linking task scheduling, execution status and indicator recording, to simulate a complete shift cycle.

[0044] The indicator output module M9 is used to output the scheduling simulation evaluation indicators after each shift cycle. These scheduling simulation evaluation indicators can be key performance indicators, such as multi-dimensional performance indicators including task completion rate, emergency response timeliness, workload difference, nursing resource utilization rate, fatigue level and delayed task ratio, etc. This application embodiment does not limit this.

[0045] The scene control module M10 is used to construct typical or custom scenarios such as holidays, night shifts, and peak task periods to enable multi-scenario comparative simulation.

[0046] In this embodiment, by integrating elements such as task generation, path time modeling, nursing staff competence and fatigue threshold setting, and coupling task-level simulation path modeling and fatigue evolution mechanism, the actual effects of scheduling schemes on task completion rate, response time, and resource load can be dynamically evaluated in simulated real nursing scenarios. It supports the simulation operation and performance evaluation of different scheduling strategies in various clinical scenarios, realizes dynamic simulation and quantitative evaluation of scheduling strategies at the task level, and improves the scientificity, visualization and practicality of scheduling strategies.

[0047] Reference Figure 2 This document illustrates a flowchart of a clinical scheduling simulation method for nursing human resource allocation, provided in an embodiment of this application. It is applied to applications such as... Figure 1 The clinical scheduling simulation system shown may specifically include the following steps:

[0048] Step S201: Obtain the structured nursing task model and clinical scheduling parameters.

[0049] In some embodiments of this application, a structured nursing task model needs to be constructed first to address the fundamental issues of simulation modeling. Clinical scheduling parameters, as input conditions, can be used to form the basic simulation data structure required for this model. Therefore, the clinical scheduling parameters need to be acquired and set before the simulation starts.

[0050] Clinical scheduling parameters can be obtained based on initial settings, specifically including nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules. Nursing human resources include the number of nurses, nurse ID numbers, nurse levels, and scheduling records / positions, which can be extracted from the Hospital Human Resources Management System (HRIS). Nursing task data mainly indicates nursing task configuration information, such as task type, execution frequency, task priority, average duration, number of patients, and patient care level. The nursing ward layout structure mainly indicates the spatial geographic setting information of the nursing ward, such as area layout, spatial distance, and travel speed. Scheduling rules include maximum continuous working time, rest intervals, rotation frequency, and an average of no more than 8 patients per nurse, which are not limited in this embodiment.

[0051] In practical applications, the clinical scheduling parameters can be input through the input configuration module M1, thereby importing the basic data required for simulation. The data can be input in text or file format to establish a complete task, manpower, and space input structure, which serves as the initial setting part of the entire clinical scheduling simulation system.

[0052] Optionally, the simulation time window and preset clinical scenarios can be set via the input configuration module M1. Different clinical scenarios can be, for example, 24-hour day-night shifts, holidays, peak periods, etc. Peak periods can include daytime peaks (such as peak workload during the day shift) and situations where the night shift is tight (few staff). The holiday mode refers to scenarios where shifts are concentrated and there is a shortage of manpower. Ordinary day shifts are scenarios that meet basic tasks. Specific details can be shown in Table 1 below. It should be noted that Table 1 is only an exemplary setting, and the specific parameters can be adjusted according to the actual situation. This application embodiment does not impose any limitations on this.

[0053] Table 1 Clinical Scenario Settings

[0054]

[0055] The nursing human resources, nursing task data, nursing ward layout and scheduling rules vary in different clinical scenarios, and this application embodiment does not impose any restrictions on these.

[0056] Optionally, it also supports customizing parameters to construct scenarios based on actual conditions. If there are problems such as severe manpower shortage or scheduling conflicts in the input scenario, the simulation system can issue early warning prompts, such as prompts for uncovered tasks or consecutive night shifts under this configuration; for example, the custom parameters of the scenario may include the number and job level composition of nurses, characteristics of nursing objects (such as the total number of patients and the proportion of nursing levels), ward spatial structure (such as node layout), shift arrangement mode (i.e., A / P / N shift arrangement and rotation rules), and the incidence and type composition of emergencies (supporting linkage with M7), etc., which are not limited in this embodiment.

[0057] Step S202: After setting the simulation time window and preset clinical scenario, a structured nursing task is generated based on the clinical scheduling parameters through the structured nursing task model.

[0058] In some embodiments of this application, the nursing task generation module M2 can automatically generate structured nursing task flows based on the number of patients, patient care levels, and preset clinical scenarios. Specifically, this can be achieved by setting the single execution time, daily execution frequency, total daily time, and task priority for each nursing task category based on the number of patients, patient care levels, and preset clinical scenarios, thereby generating structured nursing tasks.

[0059] For example, nursing tasks can be categorized into areas such as condition assessment and observation, medication and infusion, nutrition and excretion care, airway care, comfort and safety care, specimen collection, specialized nursing techniques, psychological and emotional support, health education and outreach, and nursing documentation. Each nursing task category can be defined separately within the program. Patient care levels can include special care, level one care, level two care, and level three care. In a defined clinical scenario, task flows at each level can be systematically generated, and corresponding basic task packages can be assigned based on the patient's care level, covering routine nursing tasks such as condition assessment, medication and infusion, turning and repositioning, and nutrition and excretion, as shown in Table 2 below. It should be noted that the routine nursing tasks listed in Table 2 are just examples; specific adjustments can be made based on actual departmental characteristics, patient needs, or user customization. This application embodiment does not impose any limitations on these adjustments.

[0060] Table 2 Nursing Task Categories, Task Priorities, and Duration

[0061]

[0062] Task priority is used to indicate the urgency of nursing tasks and can be divided into 1-5 levels, with higher numbers indicating greater urgency. For example, a task priority of 5 indicates that the nursing task requires immediate response. All nursing tasks are uniformly defined within the program. The total daily nursing time for each patient care level is shown in Table 3 below. It should be noted that the total daily nursing time and recommended nursing staff levels in Table 3 are examples and can be adjusted according to the actual department characteristics and nursing stratification standards. This application embodiment does not impose any limitations on this.

[0063] Table 3. Total daily nursing time for each patient's nursing level (min / day)

[0064]

[0065] As shown in Table 3, different nursing staff levels can be divided into N0, N1, N2, N3, and N4. N0 level includes assistant nurses and nurses with less than one year of experience after obtaining their licenses, representing the entry-level stage of their profession. N1 level covers nurses with 1-5 years of experience and registered nurses with 1-3 years of experience, possessing basic clinical nursing skills. N2 level refers to registered nurses with more than 3 years of experience or nurses with more than 5 years of experience, capable of independently handling the care of critically ill patients. N3 level requires appointment as a head nurse with 5 years of experience, or holding a specialist nurse position, capable of balancing clinical, teaching, and research tasks. N4 level refers to those appointed as associate chief nurses or above, typically senior experts in the nursing field, responsible for guiding complex cases and leading discipline development. Nursing staff at different levels have varying levels of proficiency, and therefore, the corresponding task time weighting differs when performing the same tasks. For example, the time weight for tasks N3–N4 can be set to 0.8 (indicating higher efficiency), the time weight for tasks N1–N2 can be set to 1.0 (as a baseline), and the time weight for tasks N0 can be set to 1.2 (indicating lower efficiency). Therefore, nurses at higher levels take less time to perform the same tasks.

[0066] It should be noted that it also supports adaptive adjustment of various task parameters based on actual clinical pathways or user definitions to ensure a realistic reflection of task load simulation.

[0067] Step S203: By simulating the task allocation mechanism and combining it with the fatigue evolution mechanism, structured nursing tasks are allocated to each nursing staff.

[0068] In some embodiments of this application, structured nursing tasks can be allocated based on simulation modeling of responsibility-based nursing. Specifically, the task allocation module M3 can simulate the task allocation mechanism and combine it with the fatigue evolution mechanism to allocate structured nursing tasks to nursing staff in each responsibility group based on the patient's nursing level, task priority, and the workload of nursing staff in each responsibility group.

[0069] Optionally, the task allocation mechanism includes a task assignment mechanism for responsibility groups, a team leader collaboration mechanism, and a floating task pool scheduling mechanism; the fatigue evolution mechanism can be used to indicate the workload of nursing staff.

[0070] Among them, the simulation of the task assignment mechanism of the responsibility group can realize the simulation of clinical nursing work, in which nurses complete various nursing tasks based on the established schedule and division of responsibility beds, so as to restore the responsibility group shift structure and division of responsibilities in the real nursing ward; the simulation of the team leader collaboration mechanism can support the real-time collaboration mechanism within the group. For example, when a responsible team member encounters a complex or urgent task (such as a patient's sudden discomfort) and cannot complete it independently, in order to be closer to the clinical collaboration scenario, the task response logic and intra-group collaboration mechanism under the responsibility-based nursing model can be simulated to realize task scheduling and real-time optimization based on the patient's nursing level, the urgency of the task and the workload; the floating task pool scheduling mechanism refers to the basic logic of "priority of responsible nursing staff + emergency floating replacement". At this time, the target nursing staff can be selected from the currently idle nursing staff to assign the interrupted structured nursing tasks.

[0071] In responsibility-based nursing, a nursing ward is considered a nursing unit, and each nursing unit is divided into several responsibility groups. Each responsibility group consists of a group leader and one or more group members. A responsibility group is jointly responsible for a designated area of ​​beds, for example, covering 20-22 beds. Each nurse in the group is assigned to at least one responsible bed. The group leader can be a nurse at a preset level, while the group members can be nurses at a lower level. If the preset level is N3, then the group leader's nurse level is N3 or N4, and the group members' nurse levels are N1-N2. The group leader not only undertakes some patient care work but also handles high-difficulty, high-risk tasks and provides professional guidance to group members, especially intervening promptly in emergencies, when skills are lacking, or under heavy workloads. This embodiment of the application does not impose any limitations on this.

[0072] As an example, a task assignment mechanism simulating responsibility groups can be used to allocate responsibility beds to group leaders and members based on patient care levels. Structured nursing tasks for each responsibility bed with lower than preset priorities can be assigned to the corresponding group members. If the patient care level of the responsibility bed is special care or level one treatment, structured nursing tasks with higher priority than the preset priority are prioritized for the group leader. Structured nursing tasks with lower than preset priority are routine nursing tasks, which are assumed by the group members; structured nursing tasks with higher than preset priority are core and high-risk tasks for special or level one patients, and are prioritized for the group leader. Each nurse is assigned to a specific bed. To recreate the real scenario where nurses are fully responsible for all nursing affairs of the patients under their care, the task allocation module M3 defaults to assigning routine nursing tasks for the fixed bed, such as patient observation, medication administration, nutritional excretion, and documentation, to the responsible team member in charge of the corresponding bed. However, if a high-risk patient with a nursing level of Special Grade or Grade 1 is encountered (as shown in Table 1), the core tasks, such as resuscitation tasks, will be prioritized and assigned to the team leader of the responsible group. The task limit for the team leader is usually 6-8 beds, prioritizing coverage of high-level patients and reserving a certain amount of work redundancy to deal with emergencies or support team members.

[0073] As another example, a simulated team leader collaboration mechanism can be used. When a member of the responsible team is unable to complete the task independently, such as due to task overlap, excessive fatigue, or a break interval, the task allocation module M3 can first determine the status of the responsible team leader. If the responsible team leader is determined to be in an idle or low-load state based on the workload, an assistance task for the corresponding structured nursing task can be assigned to the responsible team leader. That is, the assistance task is automatically dispatched, and the team leader takes over part or all of the task, forming a temporary two-person nursing team to perform the relevant structured nursing task. If the responsible team leader is determined to be in a high-load state based on the workload, the floating task pool scheduling mechanism is entered.

[0074] It should be noted that when the responsible caregiver is unable to take on a new task due to task overlap, excessive fatigue (e.g., fatigue index FI>1.0), or rest interval triggering, the task will be automatically added to the preset scheduling pool and enter the floating task pool scheduling mechanism.

[0075] As another example, interrupted structured nursing tasks can be added to a preset scheduling pool by simulating a floating task pool scheduling mechanism, and the interrupted structured nursing tasks can be assigned to target nurses selected from those currently in an idle state.

[0076] Specifically, currently idle nurses can be weighted and scored based on access time, current workload, and task urgency to calculate a scoring index. Then, based on this index, target nurses are selected from the currently idle nurses, and interrupted structured nursing tasks are assigned to them. For example, the scoring index can be weighted based on the following three core factors: access time refers to the estimated path time from the nurse's current location to the assigned bed, with data sourced from the path planning module M4; current workload refers to the total time spent on currently assigned tasks divided by the maximum shift work hours (e.g., 480 minutes), with lighter workloads receiving higher priority; and task urgency is reflected by task priority, with higher priority numbers indicating more urgent tasks. For example, a priority of 4-5 grants the interrupted structured nursing task a higher scheduling priority. The weight of access time can be 30%, current workload 50%, and task urgency 20%. In this case, the aforementioned three core factors are used to calculate a comprehensive score and complete task assignment. It should be noted that the above weight allocation is only an exemplary setting. In actual applications, it can be customized or dynamically adjusted according to different nursing scenarios, task types or historical performance data. This application embodiment does not impose any restrictions on this.

[0077] It should be noted that although the urgency of a task is an attribute of the task itself, it can be used as a multiplier in the overall scoring function to exert a consistent influence on the scores of all candidate nurses. By using the urgency of a task as a global regulator of the scheduling score, it affects the overall score level of the task in the scheduling system, ensuring that urgent tasks are prioritized during peak periods while delaying the allocation of non-urgent tasks, thereby achieving the optimal match between task response and resource scheduling.

[0078] Optionally, if there are insufficient floating nursing staff, the system can record the task as starting with a delay and trigger a scheduling warning. Furthermore, the system can also have task interruption and reassignment functions. During simulation, if other structured nursing tasks with higher priority than the currently executing target structured nursing task are dynamically detected (e.g., respiratory arrest resuscitation), the currently executing target structured nursing task is interrupted, the original target structured nursing task is marked as interrupted, and the interrupted structured nursing task is added to a preset scheduling pool to trigger reassignment, entering the floating task pool scheduling mechanism to await task rescheduling. Interruption records will be used for subsequent metric statistics, such as delay rate, interruption rate, and resuscitation response efficiency.

[0079] Step S204: Construct a spatial topology map and generate simulation path information for nursing staff within the nursing ward based on the spatial topology map.

[0080] In order to provide accurate access time as a key input for the task allocation module M3, task execution module M5 and fatigue assessment module M6, the path planning module M4 can dynamically simulate the target movement path and access time of the nurse from the current location to the target point of the target structured nursing task based on the algorithm. The access time is used to indicate the total time spent on the target movement path.

[0081] In some embodiments of this application, spatial modeling is used to construct a spatial topology map of the nursing space. Specifically, the entire nursing ward can be abstracted as a network graph structure. Each node in the network graph structure can represent a key spatial node in the nursing ward, such as beds, nursing staff stations, treatment rooms, procedure rooms, and rest areas. Each edge in the network graph structure can represent passageways, corridors, doorways, corners, etc., between key spatial nodes, and each edge can be assigned a distance (meters) or standard travel time (seconds). The graph structure can be constructed by importing hospital building blueprints, 3D scanning, or manual setting; this application does not impose any limitations on this.

[0082] For calculating the movement path, commonly used pre-set graph search algorithms embedded in the system, such as Dijkstra's algorithm and heuristic algorithms, can be used to calculate at least one graph path between the nurse's current location in the nursing ward and the target point of the structured nursing task. Each graph path consists of several consecutive nodes, and each path segment carries distance and travel time. At this point, the target movement path can be selected from at least one graph path, for example, by optimizing the target selection based on shortest time, shortest distance, and fewest turns. Optionally, once the nurse is assigned to perform a task, the path planning module M4 will call the path planning algorithm to output the target movement path.

[0083] To calculate the travel time, considering that the movement speed of nursing staff is affected not only by individual differences, but also by factors such as fatigue level and task urgency, we can model the movement speed of nursing staff to obtain a dynamic speed factor model. Then, we can use the dynamic speed factor model to obtain the total time taken for nursing staff to travel to the target.

[0084] For example, the dynamic velocity factor model introduced by the system can be expressed as follows:

[0085]

[0086]

[0087] The standard base speed can be set to 1.2 m / s by default. The fatigue adjustment coefficient can be determined based on the fatigue index FI output by the fatigue assessment module M6. If the fatigue index FI is higher than 1.0, the fatigue adjustment coefficient can be automatically reduced to 0.7–0.9 times. The urgency weighting coefficient is positively correlated with the movement speed of the nursing staff. For example, if the task priority is a level 5 task such as rescue / blood transfusion, the movement speed of the nursing staff can be temporarily increased to 1.3–1.5 times. This application embodiment does not limit this.

[0088] In this embodiment, the path planning result of the path planning module M4 may include an optimal path node sequence, such as nursing staff stations. Corridor A Ward 1 Bed 1; Total path distance (meters) and estimated time (minutes); Current access status record of nursing staff, such as on the move, arriving at the task point, deviating from the shortest path, etc.; and path type label, such as access time <2min for short distance, access time between 2 and 5min for medium distance, and access time >5min for long distance, etc.

[0089] Step S205: Run the simulation according to the time step set within the simulation time window.

[0090] In some embodiments of this application, the entire simulation system can be driven by the time advancement module M8 to advance the simulation process according to a set time step, such as every 1 minute, linking task scheduling, execution status and indicator recording to simulate a complete shift cycle.

[0091] Specifically, the time-driven module M8 can drive the entire simulation system to simulate the task response, path movement, execution process and status changes of nursing staff in each shift, so as to dynamically update nursing tasks and staff status, calculate the scheduling simulation evaluation index in real time, and realize the systematic restoration and quantitative evaluation of the effectiveness of the scheduling plan.

[0092] Step S206: During the simulation, when the current simulation time reaches the start time of the target structured nursing task, the simulated nursing staff executes the target structured nursing task according to the simulation path information, and outputs the scheduling simulation evaluation index.

[0093] The scheduling simulation evaluation index is used to measure the simulation operation and performance evaluation of different scheduling strategies in various typical clinical scenarios, so as to realize the dynamic simulation and quantitative evaluation of scheduling strategies at the task level.

[0094] The scheduling simulation evaluation index is obtained based on the simulation operation of the simulated task. In some embodiments of this application, the actual execution process of each nursing task can be dynamically simulated through the task execution module M5, which can fully record the entire process of task generation, allocation, response, execution and completion, and track the status changes of nursing staff and task interference. Then, by analyzing the task completion status and timeliness, the rationality of scheduling, the balance of resource allocation and the efficiency of clinical response can be evaluated.

[0095] Specifically, the task execution module M5 uses a simulation mechanism based on time progression and status updates to perform simulation operations. When the simulation system advances to the startable time of a certain nursing task, it can respond to the current simulation time to reach the start time of the target structured nursing task. It simulates nursing staff traveling to the task location of the target structured nursing task according to the target movement path and arrival time. During the process of nursing staff traveling to the task location of the target structured nursing task according to the target movement path and arrival time, it can update the current location and estimated arrival time of the nursing staff, execute the target structured nursing task, and output the scheduling simulation evaluation index.

[0096] The system can record the start time of a task and continuously accumulate the time consumed. If the required standard time for the task is reached (see Table 2), the task status can be marked as completed. After the target structured nursing task is completed, the simulation system can automatically generate the structured data output content.

[0097] For example, after each structured nursing task is completed, the simulation system can automatically generate the following data records:

[0098] Table 4 Data Output Content

[0099]

[0100] The evaluation indicators for scheduling simulation are manifested as operational performance indicators, which can serve as the core basis for evaluating the operational efficiency and safety of the scheduling plan. For example, operational performance indicators may include task completion-related indicators, nurse workload-related indicators, nurse fatigue assessment-related indicators, and nursing safety risk scores. Task completion-related indicators include, but are not limited to, task completion rate, task delay rate, and emergency response time. Nursing workload-related indicators include, but are not limited to, average workload, maximum workload difference, and average access time. Nursing fatigue assessment-related indicators include, but are not limited to, the number of interrupted tasks and the number of reassignments, the proportion of highly fatigued nurses, and the number of risk warnings.

[0101] For example, the task completion rate (%) can be used to measure whether the schedule covers all care needs; specifically, Task latency rate (%) can be used to reflect execution efficiency and scheduling response quality. Specifically, Emergency response timeliness refers to the average time from task generation to execution start, i.e., the time it takes for nurses to reach the task target point. It can serve as a key indicator of the response capability of high-priority tasks. Average number of tasks (per person) refers to the average number of tasks undertaken by each nurse, which can reflect the balance of task allocation. Maximum load difference (per person) refers to the difference between the nurses with the most and fewest tasks per unit time, which can measure the balance of resource utilization. Average access time (min) refers to the average travel time for nurses from receiving a task to reaching the target point, which can measure the impact of spatial layout on efficiency. The percentage of highly fatigued nurses (%) can be used to assess whether the scheduling causes excessive fatigue. Specifically, the percentage of highly fatigued nurses = the number of nurses whose fatigue index exceeds the threshold (e.g., 1.0) / the total number of nurses. The nursing safety risk score can be calculated by weighting indicators such as task delay rate, emergency failure rate, and percentage of highly fatigued nurses. The weights can be adjusted according to actual needs. The emergency failure rate (%) refers to the percentage of emergency tasks that are not completed due to response time exceeding the time limit (e.g., 5 minutes) or due to task conflicts.

[0102] In some embodiments of this application, if it is dynamically detected that there are other structured nursing tasks with higher priority than the currently executed target structured nursing task during the execution of the target structured nursing task, the target structured nursing task currently being executed by the nurse can be interrupted, and the currently executed target structured nursing task, i.e. the interrupted task, can be added to a preset scheduling pool to trigger reallocation and enter the floating task pool scheduling mechanism.

[0103] In some embodiments of this application, during the simulation operation, the fatigue assessment module M6 can also be used to assess the work fatigue level of nursing staff in real time, support the analysis of the rationality of the scheduling and dynamic load monitoring, and provide data basis for subsequent adjustment of the scheduling plan.

[0104] Specifically, a fatigue index for nursing staff can be derived based on the total time spent on the target structured nursing tasks performed daily and the staff's shift fatigue coefficient. This fatigue index is applied in real-time to task allocation and path speed adjustment during simulation to achieve dynamic feedback of the fatigue evolution mechanism. The total time spent on the target structured nursing tasks can be preset based on the patient care level and task volume using the task generation module M2, and the total time comprehensively reflects the nursing intensity. The shift fatigue coefficient can be used to reflect the aggravating effect of different shift categories on nursing staff fatigue. The calculated fatigue index can be used to indicate the nursing staff's fatigue level and provide fatigue exceeding warnings, and can be applied in real-time to task allocation and path speed adjustment during simulation to achieve dynamic feedback of the fatigue evolution mechanism.

[0105] For example, assuming the fatigue index is FI, the total time spent by nurses on the daily structured nursing tasks is W (minutes), the nurse's shift category is B, and the shift fatigue coefficient is C(B), the aggravating effect of different shift categories on nurse fatigue can be shown in Table 5 below. It should be noted that the relationship between shift categories and shift fatigue coefficients listed in Table 5 is only an illustrative setting used to illustrate the aggravating effect of different shifts on nurse fatigue. In practical applications, shift divisions and fatigue coefficients can be customized and adjusted according to the hospital's scheduling system, individual differences among nurses, and historical workload data; this embodiment does not impose such limitations.

[0106] Table 5 Relationship between shift type and shift fatigue coefficient

[0107]

[0108] The formula for calculating the fatigue index FI can be:

[0109]

[0110] The shift fatigue coefficient is used as a weighting factor in the calculation to reflect the aggravating effect of different shift categories on nursing staff fatigue; 480 minutes is the standard daily working time benchmark, which can be adjusted according to the hospital's regulations.

[0111] Fatigue levels are determined based on the fatigue index range. The criteria can be as follows: when FI < 0.75, the workload is reasonable, the fatigue risk is low, and the nurse's fatigue level is low fatigue; when 0.75 ≤ FI < 1.0, the fatigue level is moderate, and rest and adjustment are recommended, and the nurse's fatigue level is moderate fatigue; when 1.0 ≤ FI < 1.25, the fatigue is severe, which may affect work performance and requires timely intervention, and the nurse's fatigue level is high fatigue; when FI ≥ 1.25, the fatigue is excessive, posing a safety hazard, and the shift schedule needs to be adjusted or additional rest is required, and the nurse's fatigue level is extreme fatigue. It should be noted that the above fatigue level ranges are only exemplary settings. In actual applications, they can be customized according to different clinical scenarios, nursing intensity, and individual differences. This application embodiment does not impose any limitations on this.

[0112] Optionally, based on the fatigue level and fatigue over-limit warning of nursing staff, the simulation system can define and limit the effective rest time. The effective rest time refers to the period of time when there are no continuous work tasks and sufficient recovery is possible. For example, by monitoring the shift interval of nursing staff through the scheduling information, it can ensure that there is at least 24 consecutive hours of rest after the night shift (N shifts); and avoid consecutive night shifts to prevent fatigue accumulation.

[0113] In real clinical settings, nursing staff not only need to complete routine tasks but also frequently respond to various unpredictable emergencies (such as patient falls, resuscitation, emergency examinations, catheter dislodgement, etc.). These tasks disrupt existing plans and work rhythms, making them a key factor in evaluating the flexibility and safety margin of scheduling schemes. In some embodiments of this application, during simulation operation, the emergency event simulation module M7 can simulate high-frequency or high-risk emergencies in nursing scenarios. This allows the simulation system to dynamically generate emergency tasks based on the event occurrence probabilities set for different nursing levels and inject them into the task scheduling system, testing the real-time responsiveness and resource allocation efficiency of the scheduling system.

[0114] Specifically, the core logic of event probability setting, periodic judgment, emergency task generation, and dynamic allocation can be used to run the relevant processes. First, the types of emergencies and their corresponding probabilities can be set through an event type setting table. Then, the probability judgment engine, within the simulation time window (each simulation cycle), assesses whether a target emergency event has been triggered based on the patient's care level and the event probability. If the target emergency event is triggered, a corresponding emergency task can be created through the task generator and added to a preset scheduling pool. Emergency tasks in the preset scheduling pool can be prioritized and allocated according to their urgency level; that is, all emergency tasks enter the scheduling process and are allocated based on their urgency level.

[0115] The event type setting table can predefine basic attributes such as the category, priority, and average duration of emergencies, as shown in Table 6 below. It should be noted that the event types and parameters listed in Table 6 are merely illustrative examples. In practical applications, they can be expanded and adjusted according to the characteristics of different departments, patient groups, or historical data. This application embodiment does not impose any limitations on this.

[0116] Table 6. Types and Parameters of Emergencies

[0117]

[0118] Task priority indicates the urgency of a nursing task and is categorized into 1-5 levels, with higher numbers signifying greater urgency. For example, a priority of 5 indicates an immediate response to the task. All nursing tasks are uniformly defined within the program. Different nursing staff levels are categorized as N0, N1, N2, N3, and N4. The skill levels of nurses at different levels vary, resulting in different task time weights when performing the same task. This leads to different execution efficiency coefficients for different nurse levels. For instance, the task time weight for N3–N4 can be set to 0.8 (indicating higher efficiency), for N1–N2 it can be set to 1.0 (as a baseline), and for N0 it can be set to 1.2 (indicating lower efficiency). Higher-level nurses have shorter task execution times.

[0119] The trigger probability setting mechanism can be manifested as the simulation system setting a corresponding daily average trigger probability for each type of emergency event based on the patient's current nursing level, as shown in Table 7 below. It should be noted that the trigger probabilities of various events listed in Table 7 are only examples, and can be adjusted according to departmental characteristics, historical data, or user-defined parameters. This application embodiment does not impose any limitations on this.

[0120] Table 7 Trigger Probability Settings

[0121]

[0122] In each simulation cycle, the simulation system calculates whether various emergencies are triggered for inpatients according to their nursing level; the same patient can trigger a maximum of one emergency task in the same cycle to avoid task stacking distortion, and this application embodiment does not impose such restrictions.

[0123] In this embodiment, the simulation system addresses the fundamental issues of simulation modeling by constructing a structured nursing task model. Based on this, it comprehensively considers factors such as ward space structure, shift scheduling, and task flow paths, supporting simulation settings for various typical or custom clinical scenarios (e.g., regular day shifts, night shifts, holidays, peak periods). This allows users to flexibly input parameters such as nursing task type and intensity, human resource allocation, and spatial layout. The system automatically runs the simulation, gradually advancing the task allocation and execution process. Furthermore, by collecting key performance indicators such as task completion rate, response time, nursing staff workload, and fatigue index, the system systematically evaluates the safety and adaptability of scheduling plans under different scenarios, providing data support for manpower allocation and scheduling optimization. Finally, through comprehensive analysis of simulation output indicators, the simulation system assists in evaluating the performance of different scheduling plans, providing managers with intuitive and quantitative reference data, supporting the optimization and adjustment of scheduling strategies, and improving the scientific nature and efficiency of nursing manpower allocation.

[0124] In some embodiments of this application, in order to enable those skilled in the art to further understand the clinical scheduling simulation method for nursing human resource allocation provided in the embodiments of this application, combined with Figure 3 The following explanation is provided:

[0125] Reference Figure 3 The diagram illustrates the process of the clinical scheduling simulation method provided in the embodiments of this application.

[0126] The initial setup module M1 is used to import the basic data required for simulation and establish a complete input structure for tasks, manpower, and space, serving as the initial setup for the entire system. For example, clinical scheduling parameters can be input in the form of a text file, which may include nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules, etc. This embodiment of the application does not impose any limitations on this.

[0127] The nursing task generation module M2 can generate structured nursing tasks based on clinical scheduling parameters through a structured nursing task model after setting the simulation time window and preset clinical scenarios. Specifically, it can automatically generate structured nursing task flows according to the number of patients, nursing level and scenario settings.

[0128] The task allocation module M3 is based on the simulation model of responsibility-based nursing. It can simulate the entire process of nursing staff completing various nursing tasks in clinical nursing work based on a predetermined shift schedule and division of responsibility beds. It particularly emphasizes the task response logic and intra-group collaboration mechanism under the responsibility-based nursing model, so that the simulation system can fully restore the responsibility team structure and division of responsibilities in the real nursing ward, and perform task scheduling and real-time optimization according to the patient's nursing level, the urgency of the task and the workload of nursing staff.

[0129] The path planning module M4 dynamically calculates the optimal path and estimated time for nurses to travel from their current location to the target point of their nursing task within the nursing ward. This provides accurate arrival times as key inputs for the task allocation module M3, task execution module M5, and fatigue assessment module M6. Specifically, the path planning module M4 outputs path planning results, including the optimal path node sequence, such as nurse station locations. Corridor A Ward 1 Bed 1; Total path distance (meters) and estimated time (minutes); Current access status record of nursing staff, such as in progress, arriving at the task point, deviating from the shortest path, etc.; and path type label, such as access time <2min for short distance, access time between 2 and 5min for medium distance, and access time >5min for long distance, etc.

[0130] It should be noted that the path planning module M4 performs different tasks in the task allocation module M3 and the task execution module M5, respectively. For example, in the task allocation phase of the task allocation module M3, which is used for scheduling prediction, the goal of the task allocation module M3 is to find suitable nurses to perform a task. The required information includes the nurse's speed (path length) to the task target point, workload, and task urgency. The purpose of calling the path planning module M4 is to calculate the estimated travel time from the nurse's current location to the task location, which is then used in the scheduling scoring function. That is, in this stage, the path planning module M4 only roughly estimates the expected travel time from the nurse to the task point as a scheduling scoring reference and does not output the path trajectory or animation. In the subsequent task execution phase of the task execution module M5, which is used for actual simulation, the path planning module M4 performs the actual use. The goal of the task execution module M5 is to simulate the process of a nurse actually walking to the task location, performing the task, and then returning or receiving a new task. The required information includes the nurse's departure time, route, and estimated arrival time. The purpose of calling the path planning module M4 is to obtain the nurse's actual path planning trajectory to support the state progression in the simulation and calculate the response time. That is, at this stage, the path planning module M4 will be called again to generate the nurse's actual path trajectory and precise arrival time to support the state progression and response time recording in the simulation. At this time, the arrival time calculated by the path planning module M4 will consider actual simulation factors such as whether the nurse is performing other tasks and whether the current point is obstructed. This embodiment of the application does not impose any limitations on this.

[0131] The task execution module M5 is based on a simulation mechanism of time progression and status update. When the simulation system advances to the startable time of a certain nursing task, the task execution can be started according to the process of task triggering and nursing staff response, nursing staff movement and arrival, task execution and monitoring, interruption and reassignment processing, and task completion and status update.

[0132] The task triggering and nursing staff response process is as follows: if the nursing task has been successfully assigned to a nursing staff member by the task allocation module M3, the simulation system can call the path planning module M4 to calculate the estimated travel time based on the optimal path between the nursing staff member's current location and the task target point. At the same time, the simulation system can start recording the task response time, that is, considering whether the nursing staff member is performing other tasks, recording the time interval from task generation to the nursing staff member starting to act, and recording the nursing staff member entering the state of heading to the task location. It should be noted that some tasks do not have a fixed start time point, but are generated based on the distribution of daily nursing needs. The simulation system can perform rolling triggering and invocation based on the nursing staff's workload and the urgency of the task.

[0133] The nursing staff movement and arrival process is characterized by the simulation system continuously updating the current location and estimated arrival time as the nursing staff moves to the task location along the path. After the nursing staff arrives at the task location, the simulation system can switch the task status from pending to executing and record the actual start time of the task. The task execution and monitoring process is characterized by the simulation system synchronously updating the current work status (e.g., busy), task progress time, and estimated completion time during task execution. Furthermore, during task execution, the simulation system can dynamically detect if a higher-priority task (e.g., emergency care) is inserted. If a higher-priority task is inserted, an interruption and reassignment process is initiated. The interruption and reassignment process is characterized by the current task being forcibly interrupted, the status being marked as interrupted, and... The system can record the reasons for interruptions, such as being interrupted by a Level 5 emergency task. The interrupted task will return to the task pool, waiting for idle nurses to reassign it or for the original nurses to return later. The simulation system records the interruption time and execution duration as the basis for analyzing subsequent task delays, overlaps, and fatigue loads. The task completion and status update process is represented by the task being successfully completed within the set time. The simulation system records the actual completion time and marks the task as completed on time. If the nurse fails to complete the task as expected due to path delays, task backlog, fatigue, or reduced efficiency, it is marked as delayed completion. If the current task is interrupted by a rescue task and has not been resumed, it can be recorded as incomplete and included in the interrupted task analysis. After each task is completed, the simulation system can automatically generate structured data output content.

[0134] During simulation, the fatigue assessment module M6 can also be used to assess the work fatigue level of nursing staff in real time, supporting the analysis of scheduling rationality and dynamic load monitoring, providing data basis for subsequent adjustments to the scheduling plan. The fatigue assessment module M6 can output the daily fatigue index value and corresponding fatigue level classification for each nursing staff member, as well as fatigue exceeding the standard warning, supporting dynamic adjustment of the scheduling.

[0135] The Emergency Event Simulation Module M7 can simulate high-frequency or high-risk emergencies in nursing scenarios. This allows the simulation system to dynamically generate emergency tasks based on the probability of occurrence set for different nursing levels, and inject these tasks into the task scheduling system to test the real-time responsiveness and resource allocation efficiency of the scheduling system. The results of the Emergency Event Simulation Module M7 are fed back to the scheduling system in the form of tasks and recorded in real time: task ID and type; triggering patient and responsible nurse; actual response time; whether the ongoing task was interrupted; changes in the fatigue of the executing nurse (linked to the fatigue assessment module M6); and impact indicators on the scheduling plan (such as task stacking, response delay, etc.).

[0136] The M8 time-tracking module drives the entire simulation system to simulate nursing staff's task responses, path movements, execution processes, and status changes across shifts. This dynamically updates nursing tasks and staff status, calculates real-time scheduling simulation evaluation indicators, and achieves a systematic reproduction and quantitative evaluation of the scheduling plan's effectiveness. Simulation can begin at a set time, such as 8:00 AM, and the system can advance the timeline at fixed time intervals, such as Δt = 1 minute. At each time step, the simulation system automatically executes tasks including task scheduling decisions, path calculation and accessibility checks, task execution progression, updates to nursing staff status and fatigue, recording of scheduling simulation evaluation indicators, shift control and handover, and data output.

[0137] The task scheduling and determination process involves the simulation system scanning for any unassigned regular tasks (generated by the task generation module M2) or emergency tasks (generated by the emergency event simulation module M7) at the current simulation time point as it progresses along the timeline. It then dynamically assigns and reschedules tasks by calling the task allocation module M3. The path calculation and accessibility determination process involves the simulation system calling the path planning module M4 to obtain the estimated access time from the nurse's current location to the task location for an assigned structured nursing task. If the current time is greater than or equal to the assigned time plus the path travel time, the nurse's status is updated to "in progress." If the current time is less than or equal to the assigned time plus the path travel time, the nurse's status is updated to "in progress." If the allocation time plus path consumption time is used, the nurse's status remains "in progress" and the task status is "pending execution." Task execution progress is represented by simulating the advancement of the task in progress, recording the task start time, and continuously accumulating the consumption time. If the required standard consumption time for the task is reached (refer to Table 2), the task status can be marked as completed, and the nurse's status switches to "idle." If a higher-priority nursing task, such as emergency care, is encountered during execution, the current task status can be changed to "interrupted," and the task is returned to the task pool, i.e., added to the preset scheduling pool for reassignment. The nurse's status and fatigue update process is represented by the simulation system updating the nurse's status based on each time step. The system displays the current status of nurses, such as idle, on their way, performing, or interrupted, as well as their cumulative working hours. It can also call the fatigue assessment module M6 to update the nurses' fatigue index and fatigue level. The scheduling simulation evaluation index recording process involves the simulation system automatically collecting and recording operational performance indicators at each time step, serving as the core basis for evaluating the efficiency and safety of the scheduling plan. The shift control and handover process involves the simulation system automatically switching to the next shift (e.g., night shift P) when the simulation time reaches the end time of the current shift (e.g., shift A is 08:00–16:00), and updating the nurses' attendance sheet and status (e.g., the initial position for the new shift is the nurses' station). The system can determine whether all unfinished tasks should be taken over by the next shift or enter the redistribution pool based on the set logic. The data output process is represented by a complete simulation operation trajectory at a time resolution, showing the task history, fatigue level changes, and task status changes of each nurse throughout the shift, the task lifecycle tracking log (such as allocation time, response time, execution start and end, completion status, etc.), and a visualization output interface for scheduling performance indicators (such as load balance, task completion efficiency, and emergency response capability). The output format is not limited to reports, but can also be charts, log files, or real-time monitoring interfaces. This application embodiment does not impose any restrictions on this.

[0138] The M9 indicator output module can systematically output core performance indicators related to scheduling quality after the simulation is completed. Specifically, it can evaluate the advantages and disadvantages of different scheduling schemes by integrating and analyzing multi-dimensional data such as task execution process, nurse workload, and emergency response, providing objective decision-making basis for nursing managers. By constructing a dynamic simulation model with "task-nurse-space-time" as the core, it can realistically reproduce the task flow and personnel allocation in clinical nursing, quantitatively reflect key indicators such as task completion rate, response timeliness, and fatigue load, realize dynamic verification and optimization suggestions for scheduling schemes, effectively fill the shortcomings of traditional scheduling methods, achieve dynamic simulation evaluation, and accurately reflect the actual effect of scheduling.

[0139] The M10 simulation scenario control module can be used to construct various typical or user-defined nursing work scenarios as input backgrounds for the simulation system. Specifically, by flexibly setting key parameters such as ward layout, shift scheduling, staffing, and frequency of emergencies, it enables pre-running and pre-evaluation of scheduling plans under different nursing environments. Through the built-in implementation of various typical clinical scenarios (such as peak-hour emergency care, holiday staff shortages, and night shifts), and by supporting user-defined parameters, it can simulate the performance of different scheduling strategies in various complex environments, ensuring the scientific validity and broad applicability of simulation results. This provides hospital management with more targeted and forward-looking decision-making support, enabling multi-scenario simulation and improving clinical adaptability and decision support capabilities.

[0140] In this embodiment, by integrating elements such as task generation, path time modeling, nursing staff hierarchy and fatigue threshold setting, and coupling task-level simulation path modeling and fatigue evolution mechanism, the actual effect of scheduling schemes on task completion rate, response time and resource load can be dynamically evaluated in simulated real nursing scenarios. It supports the simulation operation and performance evaluation of different scheduling strategies in various clinical scenarios, realizes dynamic simulation and quantitative evaluation of scheduling strategies at the task level, and improves the scientificity, visualization and practicality of scheduling strategies.

[0141] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0142] This application also provides an electronic device, see embodiments thereof. Figure 4The provided electronic device 400 includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and capable of running on the processor 420. When the computer program 411 is executed by the processor, it implements the various processes of the above-described clinical scheduling simulation method embodiment for nursing human resource allocation and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0143] This application also provides a computer-readable storage medium, see embodiments thereof. Figure 5 The computer-readable storage medium 500 provides a computer program 411 that is stored on it. When the computer program 411 is executed by the processor, it implements the various processes of the above-described clinical scheduling simulation method embodiment for nursing human resource allocation and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0144] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0145] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division; in actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. Additionally, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0146] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0149] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0151] In the above embodiments, the implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product.

[0152] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0153] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes; these computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0155] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0156] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0157] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A clinical scheduling simulation method for nursing human resource allocation, characterized in that, The method includes: Obtain a structured nursing task model and clinical scheduling parameters; the clinical scheduling parameters include nursing human resources, nursing task data, nursing ward layout structure, and scheduling rules; After setting the simulation time window and preset clinical scenarios, structured nursing tasks are generated based on the clinical scheduling parameters through the structured nursing task model; wherein, the nursing human resources, nursing task data, nursing ward layout structure and scheduling rules are different in different clinical scenarios; The structured nursing tasks are assigned to each nursing staff by simulating a task allocation mechanism and combining it with a fatigue evolution mechanism. Construct a spatial topology map, and generate simulation path information for the nursing staff in the nursing ward based on the spatial topology map; The simulation will run according to the time step set within the simulation time window. During the simulation, when the current simulation time reaches the start time of the target structured nursing task, the nursing staff will simulate the execution of the target structured nursing task according to the simulation path information, and the scheduling simulation evaluation index will be output.

2. The method according to claim 1, characterized in that, The nursing task data is used to indicate nursing task configuration information, and the nursing task data includes the number of patients and the patient care level; The generation of structured nursing tasks based on the clinical scheduling parameters using the structured nursing task model includes: The structured nursing task model generates structured nursing tasks by setting the single execution time, daily execution frequency, total daily time, and task priority for each nursing task category based on the number of patients, patient care level, and preset clinical scenario.

3. The method according to claim 1, characterized in that, The task allocation mechanism includes a responsibility group task assignment mechanism, a group leader collaboration mechanism, and a floating task pool scheduling mechanism; the fatigue evolution mechanism is used to indicate the workload of the nursing staff; wherein, a nursing ward is considered as a nursing unit, and each nursing unit is divided into several responsibility groups. The nursing staff in each responsibility group consists of a responsibility group leader and one or more responsibility group members, and each responsibility group is responsible for a responsible bed area. The method of allocating structured nursing tasks to various nursing staff through a simulated task allocation mechanism combined with a fatigue evolution mechanism includes: By simulating the task assignment mechanism of the responsibility group, responsibility beds are assigned to the responsibility group leaders and members in each responsibility group based on the patient's nursing level, and structured nursing tasks with lower than preset priority for each responsibility bed are assigned to the corresponding responsibility group members; if the patient's nursing level of the responsibility bed is special care or level one care, structured nursing tasks with higher priority than preset priority are assigned to the responsibility group leader first. By simulating the team leader collaboration mechanism, if the responsible team leader is determined to be in an idle or low-load state based on the workload, an assistance task for the corresponding structured nursing task is assigned to the responsible team leader; if the responsible team leader is determined to be in a high-load state based on the workload, the floating task pool scheduling mechanism is entered. By simulating the floating task pool scheduling mechanism, interrupted structured nursing tasks are added to a preset scheduling pool, and target nurses are selected from the currently idle nurses to assign the interrupted structured nursing tasks.

4. The method according to claim 3, characterized in that, The step of selecting target nurses from currently idle nurses to assign the interrupted structured nursing task includes: The scoring index is calculated by weighting the access time, current workload, and urgency of the task for nurses who are currently idle. Based on the scoring indicators, target nurses are selected from the nurses who are currently idle, and the interrupted structured nursing tasks are assigned to the target nurses.

5. The method according to claim 1, characterized in that, The simulation path information includes the target movement path and the arrival time, wherein the arrival time is used to indicate the total time spent on the target movement path; The construction of the spatial topology map, and the generation of simulation path information for the nursing staff within the nursing ward based on the spatial topology map, includes: The nursing ward is abstracted as a network graph structure; wherein each point in the network graph structure represents a key spatial node of the nursing ward, each edge in the network graph structure represents a channel between key spatial nodes, and each edge is assigned a distance or standard travel time. A preset graph search algorithm is used to calculate at least one graph path between the nursing staff's current location in the nursing ward and the target point of the structured nursing task. The target movement path is then selected from the at least one graph path. Each graph path consists of several consecutive nodes, and each path segment carries distance and travel time. The movement speed of the nursing staff was modeled to obtain a dynamic speed factor model; The total time taken for the nursing staff to reach the target movement path is obtained through the dynamic speed factor model.

6. The method according to claim 1, characterized in that, The simulation path information includes the target movement path and access time; the response time reaches the start time of the target structured nursing task when the current simulation time arrives, simulating the nursing staff executing the target structured nursing task according to the simulation path information, and outputting scheduling simulation evaluation indicators, including: In response to the current simulation time reaching the start time of the target structured nursing task, the nursing staff will simulate traveling to the task location of the target structured nursing task according to the target movement path and the access time. During the process of the nursing staff traveling to the task location of the target structured nursing task according to the target movement path and travel time, the current location and estimated arrival time of the nursing staff are updated, the target structured nursing task is executed, and the scheduling simulation evaluation index is output. During the execution of the target structured care task, the following is also included: If it is dynamically detected that there are other structured nursing tasks with higher priority than the currently executed target structured nursing task, the target structured nursing task currently being executed by the nurse will be interrupted, and the interrupted task will be added to a preset scheduling pool to trigger reallocation.

7. The method according to any one of claims 1 to 6, characterized in that, During the simulation, the method further includes: Based on the total time spent on the structured nursing tasks performed by the nursing staff each day and the shift fatigue coefficient of the nursing staff, the fatigue index of the nursing staff is obtained; wherein, the shift fatigue coefficient is used to reflect the aggravating effect of different shift categories on the fatigue of the nursing staff, and the fatigue index is used to indicate the fatigue level of the nursing staff and the warning of excessive fatigue; the fatigue index is applied in real time to task allocation and path speed adjustment during the simulation operation to realize the dynamic feedback of the fatigue evolution mechanism.

8. The method according to any one of claims 1 to 6, characterized in that, During the simulation, the method further includes: Define the types of emergencies and the probability of occurrence for each type of emergency; Within the simulation time window, the determination of whether to trigger the target emergency event is made based on the patient's care level and the probability of the event occurring. If a target emergency event is triggered, a corresponding emergency task is created and added to a preset scheduling pool; emergency tasks in the preset scheduling pool are prioritized and allocated according to their urgency.

9. A clinical scheduling simulation system for nursing human resource allocation, characterized in that, The system includes an input configuration module, a task generation module, a task allocation module, a path planning module, a time progression module, a task execution module, and an indicator output module. The input configuration module is used to obtain the structured nursing task model and clinical scheduling parameters; the clinical scheduling parameters include nursing human resources, nursing task data, nursing ward layout structure and scheduling rules; The task generation module is used to generate structured nursing tasks based on the clinical scheduling parameters through the structured nursing task model after setting the simulation time window and preset clinical scenarios; wherein, the nursing human resources, nursing task data, nursing ward layout structure and scheduling rules are different in different clinical scenarios. The task allocation module is used to allocate the structured nursing tasks to each nursing staff by simulating a task allocation mechanism and combining it with a fatigue evolution mechanism. The path planning module is used to construct a spatial topology map and generate simulated path information of the nursing staff in the nursing ward based on the spatial topology map; The time advancement module is used to perform simulation operation according to the time step set within the simulation time window; The task execution module is used to respond to the start time point of the target structured nursing task when the current simulation time reaches the simulation time during the simulation operation, and simulate the nursing staff to execute the target structured nursing task according to the simulation path information. The indicator output module is used to output the simulation evaluation indicators for the scheduling.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the clinical scheduling simulation method for nursing human resource allocation as described in any one of claims 1 to 8.

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