Performance management method and system based on Internet of Things platform
By collecting real-time data from patients and medical staff through an IoT platform, and dynamically adjusting performance evaluations based on task difficulty and health changes, the system solves the problems of real-time and comprehensiveness in traditional medical performance management, enabling accurate assessment and personalized incentives for medical staff's work performance.
Patent Information
- Application Number
- CN202511089988.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-05
- Publication Date
- 2025-11-18
AI Technical Summary
Existing medical performance management technologies rely on manual recording and periodic data statistics, which are not real-time and the data is not comprehensive enough. They cannot accurately reflect the working status of doctors and the real-time health data of patients, and lack dynamic tracking and optimization of medical quality.
By collecting patients' physiological data and medical work data in real time through IoT devices, and combining the task difficulty, quality and changes in patients' health, the performance evaluation weights are dynamically adjusted to provide intelligent treatment plan suggestions. The performance evaluation results are generated through instant feedback and comprehensive evaluation, and training suggestions and incentive mechanisms are automatically generated.
It enables timely reflection and objective evaluation of medical staff's work performance, reduces subjective bias, provides personalized incentives and training measures, and enhances the ability to dynamically track and optimize the medical process.
Smart Images
Figure CN120975747A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data analysis technology, and more specifically, to a performance management method and system based on an Internet of Things (IoT) platform. Background Technology
[0002] In the medical field, with the rapid development of IoT technology, hospitals and medical institutions have begun to apply IoT platforms to achieve real-time monitoring and management of equipment, sensors, and patient health data. By collecting various types of information, including patient physiological data, medical equipment data, and staff operational data, IoT platforms perform data fusion and comprehensive analysis, helping medical managers to understand the hospital's operational status, the work status of medical staff, and the health status of patients in real time.
[0003] Existing healthcare performance management technologies typically rely on manual recording and periodic data statistical analysis, which suffers from poor real-time performance and incomplete data. During the medical process, the performance of medical staff cannot be monitored and evaluated in a timely manner. Traditional performance management is often based on static indicators, failing to accurately reflect doctors' work status and patients' real-time health data. Therefore, existing technologies cannot provide a comprehensive, real-time performance analysis tool, lacking dynamic tracking and optimization of healthcare quality. Summary of the Invention
[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a performance management method and system based on an Internet of Things (IoT) platform to address the problems mentioned in the background section.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a performance management method based on an Internet of Things (IoT) platform, specifically comprising: The system collects patients' physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. Based on the responsibilities of different medical staff, the task points for each work task are defined, including: task difficulty, task completion quality, and changes in patient health. According to the defined task points, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. By combining patients' physiological data with medical work data, intelligent treatment plan suggestions are provided. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance and health changes, and the work performance is informed through real-time feedback. The task score is automatically adjusted according to the situation, and a comprehensive performance evaluation result is generated by combining patient health data, medical staff work data and patient feedback. Training suggestions are automatically generated based on the comprehensive performance evaluation results of medical staff, and incentive mechanisms are designed to motivate medical staff to continuously improve their work performance.
[0006] Preferably, as a preferred embodiment of the performance management method based on an Internet of Things (IoT) platform described in this invention, it includes collecting patient physiological data and medical work data through IoT devices, preprocessing the collected data, and uploading it to a cloud platform, specifically including: Intelligent medical devices are used to acquire patients' physiological data in real time, including heart rate, blood pressure, body temperature, blood sugar, and respiratory rate, and these data are represented as... The hospital's information system collects work data from medical staff, including medication usage, treatment steps, and treatment time. It is represented as a time series that changes over time, where t represents a time point, HR represents heart rate, BP represents blood pressure, Temp represents body temperature, Blood Sugar represents blood glucose, RespRate represents respiratory rate, WorkTime represents treatment time, MedUsage represents drug dosage, and Procedures represent treatment steps. The data is filtered to remove noise, fill in missing values, and undergoes standardization. All preprocessed data is then uploaded to the cloud platform's database via an IoT platform and stored in a table structure. Each row contains the patient ID, timestamp, patient physiological data, and medical staff work data.
[0007] Preferably, as a preferred embodiment of the performance management method based on an Internet of Things platform described in this invention, it includes defining task point criteria for each work task according to the responsibilities of different medical personnel. These criteria include: task difficulty, task completion quality, and changes in patient health. Based on the defined task point criteria, task points are set for each work task to dynamically adjust the weight of different work tasks in performance evaluation. Specifically, this includes: Based on the responsibilities of each medical staff member, the work tasks are divided into multiple dimensions: ,in, This represents the integral of task i. This is the difficulty level of task i. It refers to the quality of task i's completion. It is the impact of changes in the patient's health status on task i; The score for each task is determined based on its difficulty, quality, and changes in the patient's health, and a weighted average formula is used to calculate the score for each task. ,in, This represents the score for the task. The weights are respectively based on three dimensions: difficulty, quality, and health changes, to meet the requirements. ; Design a dynamic adjustment formula that adjusts dynamically according to changes in the patient's health, so that the impact of health changes on the score changes over time. The specific formula is as follows: ,in, This represents the dynamic weight of the health status at time t. It is the initial weight of the health status. The change in the patient's health status relative to baseline, where k is an adjustment factor, and is adjusted by normalization. and The weighting ratio and These are the initial weights for the task difficulty dimension and the initial weights for the quality control dimension, respectively. Boundary constraints are added to the formula, and settings are made accordingly. The minimum value is 0.1 and the maximum value is 0.3.
[0008] Preferably, as a preferred embodiment of the performance management method based on an Internet of Things platform described in this invention, it includes providing intelligent treatment plan suggestions by combining patient physiological data and medical work data, and, based on this, conducting performance evaluation according to the doctor's professional judgment and adjustments, the patient's treatment compliance, and health changes, and informing the employee of their work performance through real-time feedback, specifically including: By analyzing patients' physiological data Assess the patient's current health status Based on the patient's health status and medical staff work data Generate personalized treatment plans The treatment plan includes recommended drug dosage, treatment steps, and treatment duration, wherein, It is a comprehensive function, evaluated using a neural network algorithm, and represented as a nonlinear transformation across multiple layers: ,in, It is the activation function of the Jth layer. It is the first layer bias vector. It is the first-level weight matrix, which maps the input to the space of treatment plans; Based on the recommendations in the treatment plan and their own professional judgment, the medical staff adjusted the treatment plan, resulting in the adjusted treatment plan. And record the actual implementation status. The deviation is used to obtain an execution score: ,in, and These represent the recommended dosage and the actual dosage of medication, respectively. and These represent the suggested treatment steps and the actual treatment steps, respectively. and These represent the suggested treatment time and the actual treatment time, respectively. This indicates the deviation of the doctor from following the recommendations during the treatment process; the smaller the value, the more closely the doctor followed the recommendations. Assess changes in the patient's health status after treatment. ,in, This indicates changes in health status; a positive value indicates improved health, while a negative value indicates deteriorating health. Overall Execution Score and health changes Calculate the doctor's overall performance score And set a scoring threshold. Provide immediate feedback based on the doctor's performance score; if the performance score falls below the scoring threshold: It reminds doctors to improve treatment plans and generates detailed feedback reports, listing implementation deviations and health changes to help doctors understand the problems and make adjustments.
[0009] Preferably, as a preferred embodiment of the performance management method based on an Internet of Things platform described in this invention, it includes automatically adjusting task scores according to circumstances and generating a comprehensive performance evaluation result by combining patient health data, medical staff work data, and patient feedback, specifically including: Equipment management software continuously tracks the operational status of equipment to obtain operational data, including equipment status, maintenance records, and usage records. It also sets standard parameter ranges for equipment operation, monitors the equipment's operational status in real time, and identifies any deviations from the normal range as abnormalities using algorithms. Further details include: Set the standard parameter range of the equipment Each of them Here, m represents the standard parameters for equipment operation, and m is the total number of standard parameters for equipment operation. Current parameters of monitoring equipment Compared to the standard parameter range, when the equipment status... Exceeding the standard range: When an anomaly is detected, the degree of anomaly for each parameter of the device is calculated. The total anomaly of the equipment is obtained by weighted summation of the anomalies of the m parameters. ,in, The weight of the j-th parameter, Let j be the anomaly degree of the j-th parameter; Real-time monitoring of the patient's physiological data, combined with patient feedback, and using sentiment analysis technology to identify any abnormal health changes, further includes: Monitoring patient physiological data Obtain patient feedback information and obtain the patient's emotional state through sentiment analysis. And assess their emotional changes through sentiment analysis technology. Set an emotional threshold When emotional state changes exceed a threshold, it is identified as an abnormal change in health. Based on the results of anomaly detection, combined with equipment data, patient data, and medical staff work data, the task score is automatically adjusted. A comprehensive scoring model is established, which calculates a task score by weighting and summarizing data from multiple aspects, including equipment, patients, and medical staff. ,in, These are the weights for equipment, patients, and medical staff, respectively. It is the total abnormality of the equipment. It is the patient's emotional state. It is data from medical staff. It involves task scoring, and based on the automatically adjusted scores, generates a final comprehensive performance evaluation result, which is then divided into different performance levels: A, B, and C. The task score is near perfect, the equipment is in normal condition, the patient is emotionally stable, and the medical staff performed the task without deviation, indicating that the medical process went very smoothly and is classified as Grade A. The task score was moderate, the equipment was in basically normal condition, the patient's emotions fluctuated slightly, and the medical staff performed the task relatively accurately, with minor deviations. It was classified as Grade B. Low task score, serious equipment malfunction, abnormally intense patient emotions, and large differences in the performance of medical staff indicate significant problems in the medical process. Immediate improvement measures should be taken, and the grade should be classified as C. If the performance grade is C for two consecutive times, medical access will be forcibly locked until the designated training is completed. The lower the level, the higher the level of the anomaly, triggering an alarm mechanism to remind relevant personnel to take further action.
[0010] Preferably, as a preferred embodiment of the performance management method based on an Internet of Things platform described in this invention, it includes automatically generating training suggestions based on the comprehensive performance evaluation results of medical staff and designing an incentive mechanism to motivate medical staff to continuously improve their work performance, specifically including: Based on performance level, different training suggestions are automatically generated. For A-level medical staff, advanced skills training and career development suggestions are provided; for B-level medical staff, intermediate skills training and basic process improvement are recommended; and for C-level medical staff, specialized improvement training is recommended. The material reward is set as a bonus, and the reward amount is adjusted according to the task score. Where R is the reward amount. Rate the task. This is the reward coefficient, which is adjusted according to the performance level.
[0011] This embodiment also provides a performance management system based on an Internet of Things platform, specifically including a data acquisition module, an points management module, an intelligent treatment suggestion and assessment module, a dynamic scoring and adjustment module, and a performance feedback module; The data acquisition module collects patient physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. The points management module defines the task points for each work task based on the responsibilities of different medical staff. The criteria include: task difficulty, task completion quality, and changes in patient health. Based on the defined task points criteria, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. The intelligent treatment suggestion and evaluation module combines the patient's physiological data and medical work data to provide intelligent treatment plan suggestions. Based on this, it conducts performance evaluation according to the doctor's professional judgment and adjustment, the patient's treatment compliance and health changes, and informs the doctor of their work performance through real-time feedback. The dynamic scoring and adjustment module automatically adjusts task scores based on the situation and generates a comprehensive performance evaluation result by combining patient health data, medical staff work data, and patient feedback. The performance feedback module automatically generates training suggestions based on the comprehensive performance evaluation results of medical staff and designs incentive mechanisms to encourage medical staff to continuously improve their work performance.
[0012] On the other hand, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements the steps of a performance management method based on an Internet of Things platform as described above.
[0013] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of a performance management method based on an Internet of Things platform as described above.
[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: By collecting patient physiological data and medical work data in real time through IoT devices and uploading the data to the cloud for processing, this invention overcomes the limitations of traditional medical performance management, which relies on manual recording and periodic statistical analysis. It uses dynamic scoring based on patient health data and medical work data, combined with factors such as task difficulty, quality, and changes in patient health, to conduct precise performance evaluations. The dynamic adjustment of task scores and weights makes performance evaluations more objective and fair, and can promptly reflect the work performance of medical staff, avoiding subjective biases in traditional methods. Through personalized incentives and training measures, this invention helps medical staff to promptly identify their strengths and weaknesses in their work. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0016] Figure 1 This is a flowchart of the method of the present invention.
[0017] Figure 2 This is a system structure diagram of the present invention.
[0018] Figure 3 This is a flowchart of the dynamic weight calculation process.
[0019] Table 1 is a data recording table of the simulation experiment of this invention.
[0020] Table 2 is the training verification table for this invention.
[0021] Table 3 is a comparison table of the dynamic evaluation effectiveness of the present invention. Detailed Implementation
[0022] 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.
[0023] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0024] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application. Example
[0025] This embodiment provides, for example Figure 1 The performance management method based on an IoT platform, as shown, specifically includes: The system collects patients' physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. Based on the responsibilities of different medical staff, the task points for each work task are defined, including: task difficulty, task completion quality, and changes in patient health. According to the defined task points, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. By combining patients' physiological data with medical work data, intelligent treatment plan suggestions are provided. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance and health changes, and the work performance is informed through real-time feedback. The task score is automatically adjusted according to the situation, and a comprehensive performance evaluation result is generated by combining patient health data, medical staff work data and patient feedback. Training suggestions are automatically generated based on the comprehensive performance evaluation results of medical staff, and incentive mechanisms are designed to motivate medical staff to continuously improve their work performance.
[0026] Preferably, the process of collecting patient physiological data and medical work data through IoT devices, preprocessing the collected data, and uploading it to a cloud platform automates the process, reducing the workload of manual recording and data processing and improving the work efficiency of medical staff. Specifically, this includes: Intelligent medical devices are used to acquire patients' physiological data in real time, including heart rate, blood pressure, body temperature, blood sugar, and respiratory rate, and these data are represented as... The hospital's information system collects work data from medical staff, including medication usage, treatment steps, and treatment time. It is represented as a time series that changes over time, where t represents a time point, HR represents heart rate, BP represents blood pressure, Temp represents body temperature, Blood Sugar represents blood glucose, RespRate represents respiratory rate, WorkTime represents treatment time, MedUsage represents drug dosage, and Procedures represent treatment steps. The data is filtered to remove noise, fill in missing values, and undergoes standardization. All preprocessed data is then uploaded to the cloud platform's database via an IoT platform and stored in a table structure. Each row contains the patient ID, timestamp, patient physiological data, and medical staff work data.
[0027] Preferably, the step of defining task point criteria for each work task based on the responsibilities of different medical personnel includes: task difficulty, task completion quality, and changes in patient health. Based on these defined criteria, task points are set for each work task to dynamically adjust the weight of different work tasks in performance evaluation. Specifically, this includes: Based on the responsibilities of each medical staff member, the work tasks are divided into multiple dimensions: ,in, This represents the integral of task i. This is the difficulty level of task i. It refers to the quality of task i's completion. It is the impact of changes in the patient's health status on task i; The score for each task is determined based on its difficulty, quality, and changes in the patient's health, and a weighted average formula is used to calculate the score for each task. ,in, This represents the score for the task. The weights are respectively based on three dimensions: difficulty, quality, and health changes, to meet the requirements. ; Design a dynamic adjustment formula that adjusts dynamically according to changes in the patient's health, so that the impact of health changes on the score changes over time. The specific formula is as follows: ,in, This represents the dynamic weight of the health status at time t. It is the initial weight of the health status. The change in the patient's health status relative to baseline, where k is an adjustment factor, and is adjusted by normalization. and The weighting ratio and These are the initial weights for the task difficulty dimension and the initial weights for the quality control dimension, respectively. Boundary constraints are added to the formula, and settings are made accordingly. The minimum value is 0.1, and the maximum value is 0.3. Figure 3 As shown, when the patient's blood pressure changes When the threshold is exceeded, the formula is used. The weights are automatically adjusted; see Example 3 for specific parameter settings.
[0028] Preferably, the method combines patient physiological data with medical work data to provide intelligent treatment plan suggestions. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance, and health changes. Performance is communicated through real-time feedback, specifically including: By analyzing patients' physiological data Assess the patient's current health status Based on the patient's health status and medical staff work data Generate personalized treatment plans The treatment plan includes recommended drug dosage, treatment steps, and treatment duration, wherein, It is a comprehensive function, evaluated using a neural network algorithm, and represented as a nonlinear transformation across multiple layers: ,in, It is the activation function of the Jth layer. It is the first layer bias vector. It is the first-level weight matrix, which maps the input to the space of treatment plans; Based on the recommendations in the treatment plan and their own professional judgment, the medical staff adjusted the treatment plan, resulting in the adjusted treatment plan. And record the actual implementation status. Treatment plans adjusted through comparison With actual implementation The deviation is used to obtain an execution score: ,in, and These represent the recommended dosage and the actual dosage of medication, respectively. and These represent the suggested treatment steps and the actual treatment steps, respectively. and These represent the suggested treatment time and the actual treatment time, respectively. This indicates the deviation of the doctor from following the recommendations during the treatment process; the smaller the value, the more closely the doctor followed the recommendations. Assess changes in the patient's health status after treatment. ,in, This indicates changes in health status; a positive value indicates improved health, while a negative value indicates deteriorating health. Overall Execution Score and health changes Calculate the doctor's overall performance score And set a scoring threshold. Provide immediate feedback based on the doctor's performance score; if the performance score falls below the scoring threshold: It reminds doctors to improve treatment plans and generates detailed feedback reports, listing implementation deviations and health changes to help doctors understand the problems and make adjustments.
[0029] Preferably, the step of automatically adjusting the task score based on the situation and combining patient health data, medical staff work data, and patient feedback to generate a comprehensive performance evaluation result specifically includes: Equipment management software continuously tracks the operational status of equipment to obtain operational data, including equipment status, maintenance records, and usage records. It also sets standard parameter ranges for equipment operation, monitors the equipment's operational status in real time, and identifies any deviations from the normal range as abnormalities using algorithms. Further details include: Set the standard parameter range of the equipment Each of them Here, m represents the standard parameters for equipment operation, and m is the total number of standard parameters for equipment operation. Current parameters of monitoring equipment Compared to the standard parameter range, when the equipment status... Exceeding the standard range: When an anomaly is detected, the degree of anomaly for each parameter of the device is calculated. The total anomaly of the equipment is obtained by weighted summation of the anomalies of the m parameters. ,in, The weight of the j-th parameter, Let j be the anomaly degree of the j-th parameter; Real-time monitoring of the patient's physiological data, combined with patient feedback, and using sentiment analysis technology to identify any abnormal health changes, further includes: Monitoring patient physiological data Obtain patient feedback information and obtain the patient's emotional state through sentiment analysis. And assess their emotional changes through sentiment analysis technology. Set an emotional threshold When emotional state changes exceed a threshold, it is identified as an abnormal change in health. Based on the results of anomaly detection, combined with equipment data, patient data, and medical staff work data, the task score is automatically adjusted. A comprehensive scoring model is established, which calculates a task score by weighting and summarizing data from multiple aspects, including equipment, patients, and medical staff. ,in, These are the weights for equipment, patients, and medical staff, respectively. It is the total abnormality of the equipment. It is the patient's emotional state. It is data from medical staff. It involves task scoring, and based on the automatically adjusted scores, generates a final comprehensive performance evaluation result, which is then divided into different performance levels: A, B, and C. The task score is near perfect, the equipment is in normal condition, the patient is emotionally stable, and the medical staff performed the task without deviation, indicating that the medical process went very smoothly and is classified as Grade A. The task score was moderate, the equipment was in basically normal condition, the patient's emotions fluctuated slightly, and the medical staff performed the task relatively accurately, with minor deviations. It was classified as Grade B. Low task score, serious equipment malfunction, abnormally intense patient emotions, and large differences in the performance of medical staff indicate significant problems in the medical process. Immediate improvement measures should be taken, and the grade should be classified as C. If the performance grade is C for two consecutive times, medical access will be forcibly locked until the designated training is completed. The lower the level, the higher the level of the anomaly, triggering an alarm mechanism to remind relevant personnel to take further action.
[0030] Preferably, the step of automatically generating training suggestions based on the comprehensive performance evaluation results of medical staff and designing an incentive mechanism to motivate medical staff to continuously improve their work performance specifically includes: Based on performance level, different training suggestions are automatically generated. For A-level medical staff, advanced skills training and career development suggestions are provided; for B-level medical staff, intermediate skills training and basic process improvement are recommended; and for C-level medical staff, specialized improvement training is recommended. The material reward is set as a bonus, and the reward amount is adjusted according to the task score. Where R is the reward amount. Rate the task. This is the reward coefficient, which is adjusted according to the performance level. Example
[0031] This embodiment provides a performance management system based on an Internet of Things (IoT) platform, specifically including a data acquisition module, an points management module, an intelligent treatment suggestion and assessment module, a dynamic scoring and adjustment module, and a performance feedback module; The data acquisition module collects patient physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. The points management module defines the task points for each work task based on the responsibilities of different medical staff. The criteria include: task difficulty, task completion quality, and changes in patient health. Based on the defined task points criteria, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. The intelligent treatment suggestion and evaluation module combines the patient's physiological data and medical work data to provide intelligent treatment plan suggestions. Based on this, it conducts performance evaluation according to the doctor's professional judgment and adjustment, the patient's treatment compliance and health changes, and informs the doctor of their work performance through real-time feedback. The dynamic scoring and adjustment module automatically adjusts task scores based on the situation and generates a comprehensive performance evaluation result by combining patient health data, medical staff work data, and patient feedback. The performance feedback module automatically generates training suggestions based on the comprehensive performance evaluation results of medical staff and designs incentive mechanisms to encourage medical staff to continuously improve their work performance.
[0032] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0033] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of implementing a performance management method based on an Internet of Things platform as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Example
[0034] The following is another embodiment of the present invention, which provides a performance management method based on an Internet of Things platform. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.
[0035] This experiment aims to verify the effectiveness of the performance management method based on the Internet of Things platform. Through data collection, preprocessing, task scoring, intelligent decision support, performance evaluation and feedback technologies, it enhances the ability of personalized performance evaluation and real-time feedback in medical work. The experiment uses simulated and actual collected patient physiological data, medical work data, task execution data and patient health change data. By analyzing the consistency between task scores and actual work results, the accuracy and effectiveness of the system in evaluating and improving the performance of medical staff are verified. The simulation experiment steps are implemented according to the content of the performance management method based on the Internet of Things platform provided in Example 1, and the specific steps include: Collect patient physiological data and medical work data: Collect patient physiological data and medical staff work data in real time through IoT devices, preprocess the collected data, and upload it to the cloud platform; Define personalized work tasks and scoring mechanisms: Based on the responsibilities of different medical staff, define personalized work tasks. The difficulty of the task, the quality of task completion, and changes in the patient's health serve as the basis for task scoring. The weight of different work tasks in performance evaluation will be dynamically adjusted according to changes in the patient's condition. The specific steps include: a. Task difficulty assessment: Set an initial difficulty score based on the complexity and duration of the task; b. Task quality assessment: Set a task quality score based on the accuracy and effectiveness of task completion; c. Patient health change assessment: Assess the patient's health changes through physiological data and treatment effects; Intelligent Treatment Plans and Performance Evaluation: Combining patient physiological data and medical work data, intelligent treatment plan suggestions are provided. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance, and changes in health. The performance evaluation informs medical staff of their work performance through an instant feedback mechanism, specifically including: a. Provide treatment recommendations based on data analysis; b. Conduct performance evaluations by combining patient compliance with treatment outcomes and changes in health. c. Provide real-time feedback to guide medical staff in improving their work performance; Automatically identify and adjust scores for abnormal data: Task scores are automatically adjusted based on the situation, combining patient health data, medical staff work data, and patient feedback to generate a comprehensive performance evaluation result, specifically including: a. Abnormal data detection: Through real-time data analysis, identify existing data anomalies, including equipment malfunctions and abnormal changes in patient health; b. Automatic task rating adjustment: Adjusts task ratings based on abnormal situations to ensure the accuracy of performance evaluation; c. Comprehensive Performance Generation: Combining all data factors to generate the final performance evaluation result; Automatically generated training suggestions and incentive mechanisms: Based on the performance evaluation results of medical staff, personalized training suggestions are automatically generated, and incentive mechanisms are designed to motivate medical staff to continuously improve their work performance, specifically including: a. Performance evaluation feedback: Provide specific feedback on work performance, identifying strengths and areas for improvement; b. Generate training recommendations: Based on performance evaluation results, provide training and improvement recommendations for medical staff; c. Incentive mechanism design: Based on performance evaluation, set corresponding rewards or incentive measures to enhance the work enthusiasm of medical staff.
[0036] The specific data from the above simulation experiment are as follows:
[0037] Table 1 Quantitative verification of technical effects: 1. Dynamic weighted responsiveness: When ΔH ≥ 0.8, It reached its maximum value of 0.3 within 1 minute; Results: By simulating 24-hour monitoring of ICU patients and pre-setting 100 critical events, with a real-time data sampling frequency of 1 time / minute, the sensitivity of the critical event scoring was improved by 83%. The sensitivity formula is as follows: .
[0038] 2. Training Mechanism:
[0039] Table 2 Compared with traditional methods:
[0040] Table 3 Experimental Analysis: By comparing the consistency between the model's predictions and the actual performance of medical staff, the accuracy and reliability of the system in assessing and improving the performance of medical staff were verified. The experiment showed that the performance management method based on the Internet of Things (IoT) platform can provide accurate and personalized performance evaluations based on changes in patients' health, the work performance of medical staff, and real-time data feedback. The performance management system based on the IoT platform has high accuracy and robustness in dynamically adjusting task scores, identifying abnormal data, and providing real-time feedback, which can effectively improve the work performance of medical staff and provide higher quality medical services to patients.
[0041] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.
Claims
1. A performance management method based on an Internet of Things (IoT) platform, characterized in that: Specifically, it includes: The system collects patients' physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. Based on the responsibilities of different medical staff, the task points for each work task are defined, including: task difficulty, task completion quality, and changes in patient health. According to the defined task points, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. By combining patients' physiological data with medical work data, intelligent treatment plan suggestions are provided. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance and health changes, and the work performance is informed through real-time feedback. The task score is automatically adjusted according to the situation, and a comprehensive performance evaluation result is generated by combining patient health data, medical staff work data and patient feedback. Training suggestions are automatically generated based on the comprehensive performance evaluation results of medical staff, and incentive mechanisms are designed to motivate medical staff to continuously improve their work performance.
2. The performance management method based on an Internet of Things platform according to claim 1, characterized in that: The process of collecting patient physiological data and medical work data through IoT devices, preprocessing the collected data, and uploading it to the cloud platform specifically includes: Intelligent medical devices are used to acquire patients' physiological data in real time, including heart rate, blood pressure, body temperature, blood sugar, and respiratory rate, and these data are represented as... The hospital's information system collects work data from medical staff, including medication usage, treatment steps, and treatment time. It is represented as a time series that changes over time, where t represents a time point, HR represents heart rate, BP represents blood pressure, Temp represents body temperature, Blood Sugar represents blood glucose, RespRate represents respiratory rate, WorkTime represents treatment time, MedUsage represents drug dosage, and Procedures represent treatment steps. The data is filtered to remove noise, fill in missing values, and undergoes standardization. All preprocessed data is then uploaded to the cloud platform's database via an IoT platform and stored in a table structure. Each row contains the patient ID, timestamp, patient physiological data, and medical staff work data.
3. The performance management method based on an Internet of Things platform according to claim 1, characterized in that: The process involves defining task point criteria for each work task based on the responsibilities of different medical personnel. These criteria include: task difficulty, task completion quality, and changes in patient health. Based on these defined criteria, task points are assigned to each work task to dynamically adjust the weight of different tasks in performance evaluation. Specifically, this includes: Based on the responsibilities of each medical staff member, the work tasks are divided into multiple dimensions: ,in, This represents the integral of task i. This is the difficulty level of task i. It refers to the quality of task i's completion. It is the impact of changes in the patient's health status on task i; The score for each task is determined based on its difficulty, quality, and changes in the patient's health, and a weighted average formula is used to calculate the score for each task. ,in, This represents the score for the task. The weights are respectively based on three dimensions: difficulty, quality, and health changes, to meet the requirements. ; Design a dynamic adjustment formula that adjusts dynamically according to changes in the patient's health, so that the impact of health changes on the score changes over time. The specific formula is as follows: ,in, This represents the dynamic weight of the health status at time t. It is the initial weight of the health status. The change in the patient's health status relative to baseline, where k is an adjustment factor, and is adjusted by normalization. and The weighting ratio and These are the initial weights for the task difficulty dimension and the initial weights for the quality control dimension, respectively. Boundary constraints are added to the formula, and settings are made accordingly. The minimum value is 0.1 and the maximum value is 0.
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4. The performance management method based on an Internet of Things platform according to claim 1, characterized in that: The system combines patient physiological data with medical work data to provide intelligent treatment plan suggestions. Based on this, performance evaluation is conducted according to the doctor's professional judgment and adjustments, the patient's treatment compliance, and health changes. Performance is communicated through real-time feedback, specifically including: By analyzing patients' physiological data Assess the patient's current health status Based on the patient's health status and medical staff work data Generate personalized treatment plans The treatment plan includes recommended drug dosage, treatment steps, and treatment duration, wherein, It is a composite function; Based on the recommendations in the treatment plan and their own professional judgment, the medical staff adjusted the treatment plan, resulting in the adjusted treatment plan. And record the actual implementation status. The deviation is used to obtain an execution score: ,in, and These represent the recommended dosage and the actual dosage of medication, respectively. and These represent the suggested treatment steps and the actual treatment steps, respectively. and These represent the suggested treatment time and the actual treatment time, respectively. This indicates a deviation by the doctor in following recommendations during the treatment process; Assess changes in the patient's health status after treatment. ,in, This indicates changes in health status; a positive value indicates improved health, while a negative value indicates deteriorating health. Overall Execution Score and health changes Calculate the doctor's overall performance score And set a scoring threshold. Provide immediate feedback based on the doctor's performance score; if the performance score falls below the scoring threshold: It reminds doctors to improve treatment plans and generates detailed feedback reports, listing implementation deviations and health changes to help doctors understand the problems and make adjustments.
5. The performance management method based on an Internet of Things platform according to claim 1, characterized in that: The process of automatically adjusting task scores based on circumstances, and combining patient health data, medical staff work data, and patient feedback to generate a comprehensive performance evaluation result, specifically includes: The equipment management software continuously tracks the working status of the equipment to obtain the equipment's working data, including: equipment status, maintenance records and equipment usage records. It also sets the standard parameter range for equipment operation, monitors the equipment's operating status in real time, and identifies abnormalities when the equipment status deviates from the normal range through algorithms. Real-time monitoring of the patient's physiological data, combined with patient feedback, and using sentiment analysis technology to identify any abnormal health changes, further includes: Monitoring patient physiological data Obtain patient feedback information and obtain the patient's emotional state through sentiment analysis. And assess their emotional changes through sentiment analysis technology. Set an emotional threshold When emotional state changes exceed a threshold, it is identified as an abnormal change in health. Based on the results of anomaly detection, combined with equipment data, patient data, and medical staff work data, the task score is automatically adjusted. A comprehensive scoring model is established, which calculates a task score by weighting and summarizing data from multiple aspects, including equipment, patients, and medical staff. ,in, These are the weights for equipment, patients, and medical staff, respectively. It is the total abnormality of the equipment. It is the patient's emotional state. It is data from medical staff. It is a task scoring system. Based on the automatically adjusted score, a final comprehensive performance evaluation result is generated and divided into different performance levels: A, B, and C. The lower the level, the higher the abnormality, triggering an alarm mechanism to remind relevant personnel to take action.
6. The performance management method based on an Internet of Things platform according to claim 5, characterized in that: The standard parameter range for setting the device's operation is used to monitor the device's operating status in real time. When the device status deviates from the normal range, it is identified as abnormal by an algorithm. This further includes: Set the standard parameter range of the equipment Each of them Here, m represents the standard parameters for equipment operation, and m is the total number of standard parameters for equipment operation. Current parameters of monitoring equipment Compared to the standard parameter range, when the equipment status... Exceeding the standard range: When an anomaly is detected, the degree of anomaly for each parameter of the device is calculated. The total anomaly of the equipment is obtained by weighted summation of the anomalies of the m parameters. ,in, The weight of the j-th parameter, Let be the anomaly degree of the j-th parameter.
7. The performance management method based on an Internet of Things platform according to claim 1, characterized in that: The system automatically generates training suggestions based on the comprehensive performance evaluation results of medical staff and designs incentive mechanisms to motivate medical staff to continuously improve their work performance, specifically including: Based on performance level, different training suggestions are automatically generated. For A-level medical staff, advanced skills training and career development suggestions are provided; for B-level medical staff, intermediate skills training and basic process improvement are recommended; and for C-level medical staff, specialized improvement training is recommended. The material reward is set as a bonus, and the reward amount is adjusted according to the task score. Where R is the reward amount. Rate the task. This is the reward coefficient, which is adjusted according to the performance level.
8. A performance management system based on an Internet of Things (IoT) platform is applied to the performance management method based on an IoT platform as described in any one of claims 1-7, characterized in that: Specifically, it includes a data acquisition module, an points management module, an intelligent treatment suggestion and assessment module, a dynamic scoring and adjustment module, and a performance feedback module; The data acquisition module collects patient physiological data and medical work data through IoT devices, preprocesses the collected data, and uploads it to the cloud platform. The points management module defines the task points criteria for each work task based on the responsibilities of different medical staff. The criteria include: task difficulty, task completion quality, and changes in patient health. Based on the defined task points criteria, the task points for each work task are set to dynamically adjust the weight of different work tasks in performance evaluation. The intelligent treatment suggestion and evaluation module combines the patient's physiological data and medical work data to provide intelligent treatment plan suggestions. Based on this, it conducts performance evaluation according to the doctor's professional judgment and adjustment, the patient's treatment compliance and health changes, and informs the doctor of their work performance through real-time feedback. The dynamic scoring and adjustment module automatically adjusts task scores based on the situation and generates a comprehensive performance evaluation result by combining patient health data, medical staff work data, and patient feedback. The performance feedback module automatically generates training suggestions based on the comprehensive performance evaluation results of medical staff and designs incentive mechanisms to encourage medical staff to continuously improve their work performance.