Nursing manpower allocation system based on man-hour analysis and post evaluation

Abstract

The invention relates to the technical field of nursing manpower allocation, and discloses a man-hour analysis and post evaluation-based nursing manpower allocation system, which comprises a data acquisition module, a data analysis module and an allocation model module, and is characterized in that the data acquisition module, the data analysis module and the allocation model module can determine a development environment and a technical framework through Python; establishing a basic module; the data acquisition module is mainly used for collecting patient data, nursing personnel data and hospital data. According to the nursing manpower allocation system based on man-hour analysis and post evaluation, by scientifically and reasonably allocating nursing manpower resources and optimizing man-hour arrangement, the workload of nursing personnel can be effectively reduced, and the working efficiency is improved; a hospital can utilize existing human resources to the maximum extent on the premise of ensuring the medical service quality, resource waste is reduced, and the overall operation efficiency is improved; and meanwhile, the optimal configuration scheme is beneficial to reducing the occupational tireness rate and loss rate of nursing personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing staff allocation, and specifically to a nursing staff allocation system based on working hours analysis and post evaluation. Background Art

[0002] With the development of hospitals shifting from scale expansion to quality and efficiency connotative development, the quality of human resources has become a key factor in measuring the level of medical service capabilities and is also one of the core competitiveness of hospitals; as an important part of health human resources, optimizing the allocation of nursing human resources is an important way to improve medical service capabilities, enhance market competitiveness, and achieve the strategic development goals of hospitals.

[0003] As early as in the "General Hospital Organization and Staffing Principles (Trial Draft)" promulgated in 1978, it was stipulated that nursing staff should account for 50% of health professional and technical personnel. This standard was further clarified in the evaluation of the third-level hospitals and the construction of key clinical specialties in the third-level hospitals in Hubei Province. The ratio of the total number of nurses in the hospital to the number of open beds should not be less than 1:0.6, among which the ratio of beds to nurses in the ward should not be less than 0.5, and clinical nurses should account for more than 95% of the total number of nurses in the hospital; if the nursing human resources are configured according to the ratio of beds to nurses of 1:0.5, it may lead to unbalanced labor intensity, coexistence of personnel shortage and resource waste, seriously affecting the enthusiasm of nursing staff and the improvement of nursing quality; therefore, it is urgent to measure the allocation of nursing human resources through scientific methods and optimize the allocation efficiency, so a nursing staff allocation system based on working hours analysis and post evaluation is proposed to solve the above problems. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the prior art, the present invention provides a nursing staff allocation system based on working hours analysis and post evaluation, which has the advantages of improving the efficiency of hospital human resources, enhancing the quality of nursing services, reducing the nursing error rate, and increasing patient satisfaction.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: A nursing staff allocation system based on working hours analysis and post evaluation, including a data collection module, a data analysis module, and a deployment model module.

[0008] Preferably, the data collection module, the data analysis module, and the deployment model module can determine the development environment and technical framework through Python and establish a basic module.

[0009] Preferably, the data collection module mainly collects patient data, nursing staff data, and hospital data.

[0010] Preferably, the data collection steps of the data collection module are as follows:

[0011] 1.1. Determine the types of data to be collected: patient data, caregiver data, hospital data;

[0012] 1.2. Extract data into the data collection module: Extract the required data from the hospital information system, nursing information system, human resource management system, electronic health records, and physical documents;

[0013] 1.3. Clean the data: Remove duplicate records, standardize the units of different data, and handle missing values;

[0014] 1.4. Store and update the data in a timely manner: Use a database to store the data, design a data update process to ensure that the data is kept up-to-date, and refresh the data regularly to reflect new changes.

[0015] Preferably, the data analysis module mainly analyzes man-hour data, workload intensity data, and department risk level data, and the data analysis module can use SPSS for measurement and analysis.

[0016] Preferably, the data analysis steps of the data analysis module are as follows:

[0017] 2.1. Man-hour measurement: Calculate the average man-hour and compare it with the standard man-hour;

[0018] 2.2. Analyze the workload intensity: Include patient number analysis, nursing level analysis, shift arrangement analysis, calculate the average workload, and identify peak and trough periods;

[0019] 2.3. Department risk level analysis: Score the risk level of the department and combine the risk level with the workload;

[0020] 2.4. Generate a report on the analyzed data structure.

[0021] Preferably, the patient number analysis is to count the number of patients per day and per week and identify peak periods, the nursing level analysis is to analyze the number of patients in different nursing levels and their impact on the workload, the shift arrangement analysis is to analyze the impact of work arrangements in different shifts on the workload, the calculation of the average workload is to calculate the average workload of each department based on the patient number, nursing level, and shift arrangement, and the identification of peak and trough periods is to identify the peak and trough periods of the workload through time series analysis.

[0022] Preferably, the patient data includes age, gender, disease severity, length of hospital stay, and required nursing services. The caregiver data includes working hours records, skills, qualifications, work experience, and work arrangements. The hospital data includes the number of beds, department distribution, and historical patient flow records.

[0023] (III) Beneficial Effects

[0024] Compared with the prior art, the present invention provides a nursing staff allocation system based on working hours analysis and job evaluation, which has the following beneficial effects:

[0025] 1. The nursing staff allocation system based on working hours analysis and job evaluation can effectively reduce the workload of nursing staff and improve work efficiency by scientifically and reasonably allocating nursing human resources and optimizing working hours arrangements; the hospital can make the most of existing human resources to the greatest extent, reduce resource waste, and improve overall operational efficiency while ensuring the quality of medical services; at the same time, the optimized allocation plan can relieve work pressure, improve job satisfaction, help reduce the burnout rate and turnover rate of nursing staff, promote the stability and sustainable development of the nursing team, and provide high-quality nursing talent support for the hospital.

[0026] 2. The nursing staff allocation system based on working hours analysis and job evaluation can ensure that each department has sufficient nursing staff, improve the timeliness and accuracy of nursing services, reduce the nursing error rate, and improve patient satisfaction by scientifically and reasonably allocating nursing human resources; high-quality nursing services will further enhance the competitiveness and reputation of the hospital, attracting more patients to choose the hospital for treatment; through data-driven analysis and optimization models, it can provide empirical support for policy-making and promote the standardized and scientific development of nursing human resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a functional module block diagram of the nursing staff allocation system of the present invention;

[0028] Figure 2 It is a data collection step diagram of the data collection module of the present invention;

[0029] Figure 3 It is a data analysis step diagram of the data analysis module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0031] Please refer to Figures 1-3 , a nursing staff allocation system based on working hours analysis and job evaluation, including a data collection module, a data analysis module, and a deployment model module.

[0032] Specifically, the data collection module, the data analysis module, and the deployment model module can determine the development environment and technical framework through Python and establish a basic module.

[0033] Specifically, the data collection module mainly collects patient data, nursing staff data, and hospital data.

[0034] Specifically, the data collection steps of the data collection module are as follows:

[0035] 1.1. Determine the data types to be collected: patient data, nursing staff data, hospital data;

[0036] 1.2. Extract the data into the data collection module: extract the required data from the hospital information system, nursing information system, human resource management system, electronic health record, and physical documents;

[0037] 1.3. Clean the data: remove duplicate records, standardize the units of different data, and process missing values;

[0038] 1.4. Store the data and update it in a timely manner: use a database to store the data, design a data update process to ensure that the data is kept up-to-date, and refresh the data regularly to reflect new changes.

[0039] Specifically, the data analysis module mainly analyzes the man-hour data, work load intensity data, and department risk level data. The data analysis module can use SPSS for measurement and analysis.

[0040] Specifically, the data analysis steps of the data analysis module are as follows:

[0041] 2.1. Man-hour measurement: calculate the average man-hour and compare it with the standard man-hour;

[0042] 2.2. Analyze the work load intensity: including patient number analysis, nursing level analysis, shift arrangement analysis, calculate the average work load, and identify peak hours and off-peak hours;

[0043] 2.3 Department risk level analysis: Score the risk levels of departments, combine the risk levels with the workload, use color coding or icons to indicate departments with different risk levels, and automatically push notifications through the system;

[0044] 2.4 Generate a report on the analyzed data structure.

[0045] Specifically, the patient number analysis is to count the number of patients per day and week, identify peak hours; the nursing level analysis is to analyze the number of patients with different nursing levels and their impact on the workload; the shift arrangement analysis is to analyze the impact of different shift arrangements on the workload; calculate the average workload according to the patient number, nursing level and shift arrangement to calculate the average workload of each department; identify peak and trough hours by time series analysis to identify the peak and trough hours of the workload.

[0046] Specifically, patient data includes age, gender, disease severity, length of hospital stay, and required nursing services; nursing staff data includes working hours records, skills, qualifications, work experience, work arrangements; hospital data includes the number of beds, department distribution, and historical patient flow records.

[0047] Specifically, the specific steps for the deployment model module to deploy nursing manpower are as follows:

[0048] 1) Construct a mathematical model: The objective function is

[0049] where x ij represents the number of nursing staff i assigned to department j, and c ij represents the cost of assigning nursing staff i to department j; set constraint conditions according to the actual situation, for example: the minimum and maximum number of nursing staff required for each department, resource availability limitations (such as working hours, rest hours), and nursing level requirements set according to the patient situation, and the constraint conditions are expressed as

[0050] where d j is the minimum nursing staff requirement for department j;

[0051] 2) Apply the model: Input the prepared data into the model, obtain the optimal solution, check the personnel allocation situation, and during the implementation stage after deployment, monitor the human resource status and patient needs of each department in real time;

[0052] 3) Continuously optimize: Track the data on the effectiveness of the new deployment plan, collect key performance indicators (KPIs) such as patient satisfaction, nursing quality, and employee satisfaction, and regularly update and optimize the model as the hospital situation and patient needs change.

[0053] In summary, the nursing staff allocation system based on working hours analysis and post evaluation can effectively reduce the workload of nursing staff and improve work efficiency by scientifically and reasonably allocating nursing human resources and optimizing work hours. Under the premise of ensuring the quality of medical services, the hospital can make the most of existing human resources, reduce resource waste, and improve overall operational efficiency. At the same time, the optimized allocation plan can relieve work pressure and improve job satisfaction by reducing the overwork hours of nursing staff, which helps to reduce the burnout rate and turnover rate of nursing staff, promote the stability and sustainable development of the nursing team, and provide high-quality nursing talent support for the hospital.

[0054] Moreover, by scientifically and reasonably allocating nursing human resources, it can ensure that each department has enough nursing staff, improve the timeliness and accuracy of nursing services, reduce the nursing error rate, and enhance patient satisfaction. High-quality nursing services will further enhance the competitiveness and reputation of the hospital, attracting more patients to choose the hospital for treatment. Through data-driven analysis and optimization models, it can provide empirical support for policy-making, promote the standardized and scientific development of nursing human resource management, advance the process of building a large digital human resource management information system, and improve the accuracy of nursing human resource management.

[0055] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0056] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made therein without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A nursing manpower deployment system based on work time analysis and job evaluation, characterized by: It includes data collection module, data analysis module and deployment model module.

2. According to claim 1, a nursing manpower deployment system based on work time analysis and job evaluation is characterized in that: The data acquisition module, data analysis module and deployment model module can determine the development environment and technical framework through Python to establish basic modules.

3. The nursing manpower deployment system based on work time analysis and job evaluation according to claim 1 is characterized in that: The data collection module mainly collects patient data, nursing staff data and hospital data.

4. The nursing manpower deployment system based on work time analysis and job evaluation according to claim 1 is characterized in that: The data acquisition steps of the data acquisition module are as follows: 1.

1. Determine the type of data that needs to be collected: patient data, nursing staff data, hospital data; 1.

2. Extract data into the data collection module: Extract required data from hospital information systems, nursing information systems, human resource management systems, electronic health records, and physical documents; 1.

3. Clean the data: remove duplicate records, standardize the units of different data, and handle missing values; 1.

4. Store and update data in a timely manner: Use a database to store data, design a data update process to ensure that the data remains up to date, and refresh the data regularly to reflect new changes.

5. The nursing manpower deployment system based on work time analysis and job evaluation according to claim 1 is characterized in that: The data analysis module mainly analyzes manpower working hours data, workload intensity data, and department risk level data, and the data analysis module can use SPSS for measurement and analysis.

6. The nursing manpower deployment system based on work time analysis and job evaluation according to claim 1 is characterized in that: The data analysis steps of the data analysis module are as follows: 2.

1. Working hours calculation: calculate the average working hours and compare it with the standard working hours; 2.

2. Analyze workload intensity: including patient quantity analysis, nursing level analysis, shift schedule analysis, calculate average workload and identify peak and trough hours; 2.

3. Department risk level analysis: Score the risk level of the department and combine the risk level with the workload; 2.

4. Generate a report on the analyzed data structure.

7. A nursing manpower deployment system based on work time analysis and job evaluation according to claim 6, characterized in that: The patient quantity analysis is to count the number of patients every day and every week and identify the peak time periods. The nursing level analysis is to analyze the number of patients with different nursing levels and their impact on workload. The shift arrangement analysis is to analyze the impact of different shift work arrangements on workload. The calculation of average workload is to calculate the average workload of each department based on the number of patients, nursing level and shift arrangement. The identification of peak time periods and trough time periods is to identify the peak time periods and trough time periods of workload through time series analysis.

8. The nursing manpower deployment system based on work time analysis and job evaluation according to claim 3 is characterized by: The patient data includes age, gender, severity of illness, length of hospital stay and required nursing services; the nursing staff data includes working time records, skills, qualifications, work experience, and work arrangements; and the hospital data includes the number of beds, department distribution, and patient flow history records.

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