Medical equipment performance intelligent risk evaluation method

Abstract

The invention discloses a medical equipment performance intelligent risk evaluation method, and belongs to the technical field of medical equipment performance risk evaluation, and the method comprises the following steps: 1) collecting necessary data: collecting the necessary data related to the performance of medical equipment; 2) key data preprocessing: preprocessing the collected data to enable the data to adapt to subsequent data analysis; (3) setting judgment indexes: setting various indexes used for data analysis; 4) intelligent analysis: analyzing the preprocessed data, and comparing the analyzed data with a set judgment index; according to the invention, through necessary data collection, key data preprocessing, determination index setting, intelligent analysis and result application, the medical equipment performance intelligent risk evaluation process is carried out, the performance and risk condition of the medical equipment are evaluated, powerful support is provided for equipment management of a medical institution, and the medical equipment performance risk evaluation method is suitable for popularization and application. And the comprehensive management efficiency of the equipment, the social benefit of the equipment and the medical experience of people are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical equipment performance risk assessment, and in particular relates to a medical equipment performance intelligent risk assessment method. Background Art

[0002] Medical equipment performance refers to the comprehensive benefits and results demonstrated by medical equipment in the medical service process. Risk assessment is a vital part of the medical equipment field. By evaluating the potential risks of the equipment, the safety of patients and medical staff can be guaranteed and the normal operation of the equipment can be ensured.

[0003] At present, the risk assessment process includes determining assessment objectives, identifying potential risks, assessing risk levels, formulating risk management plans, and monitoring risk implementation. Such an assessment process makes the risk assessment not detailed enough and cannot improve the comprehensive management efficiency of equipment, the social benefits of equipment, and the medical experience of the public. Summary of the invention

[0004] The purpose of the present invention is to propose a medical equipment performance intelligent risk assessment method in order to solve the above-mentioned problems.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a medical equipment performance intelligent risk assessment method, which comprises the following steps:

[0006] 1) Collection of necessary data: Collect necessary data related to the performance of medical equipment;

[0007] 2) Key data preprocessing: preprocess the collected data to make it suitable for subsequent data analysis;

[0008] 3) Judgment indicator setting: setting various indicators used for data analysis;

[0009] 4) Intelligent analysis: Analyze the preprocessed data, compare the analyzed data with the set judgment indicators, and obtain the evaluation results;

[0010] 5) Application of results: Formulate corresponding strategies based on the evaluation results.

[0011] As a further description of the above technical solution:

[0012] In step 1), data collection includes basic equipment information collection, operation data collection, maintenance data recording and clinical use feedback collection.

[0013] As a further description of the above technical solution:

[0014] In the step 2), data preprocessing includes data cleaning and data standardization.

[0015] As a further description of the above technical solution:

[0016] In the step 3), the setting of the determination index includes the setting of the performance index and the setting of the risk index.

[0017] As a further description of the above technical solution:

[0018] In step 4), intelligent analysis includes selecting an intelligent analysis model, model training and verification, and risk and performance assessment.

[0019] As a further description of the above technical solution:

[0020] In step 5), the formulated strategies include equipment management decision support, resource allocation optimization, and continuous monitoring and improvement.

[0021] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0022] In the present invention, through the collection of necessary data, preprocessing of key data, setting of judgment indicators, intelligent analysis and application of results, an intelligent risk evaluation process of medical equipment performance is carried out to evaluate the performance and risk status of medical equipment, provide strong support for the equipment management of medical institutions, and improve the comprehensive management efficiency of equipment, the social benefits of equipment and the medical experience of the people. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The figure is a flow chart of a medical equipment performance intelligent risk assessment method. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 The present invention provides a technical solution: a medical equipment performance intelligent risk assessment method, comprising the following steps:

[0026] 1) Collection of necessary data: Collect necessary data related to the performance of medical equipment;

[0027] 2) Key data preprocessing: preprocess the collected data to make it suitable for subsequent data analysis;

[0028] 3) Judgment indicator setting: setting various indicators used for data analysis;

[0029] 4) Intelligent analysis: Analyze the preprocessed data, compare the analyzed data with the set judgment indicators, and obtain the evaluation results;

[0030] 5) Application of results: Formulate corresponding strategies based on the evaluation results;

[0031] In step 1), data collection includes basic equipment information collection, operation data collection, maintenance data recording and clinical use feedback collection;

[0032] In the step 2), data preprocessing includes data cleaning and data standardization;

[0033] In the step 3), the setting of the determination index includes the setting of the performance index and the setting of the risk index;

[0034] In step 4), intelligent analysis includes selecting intelligent analysis models, model training and verification, and risk and performance assessment;

[0035] In step 5), the formulated strategies include equipment management decision support, resource allocation optimization, and continuous monitoring and improvement.

[0036] Example:

[0037] S01: Collection of necessary data: Collection of necessary data related to the performance of medical equipment; Collection of basic equipment information: Covering the equipment name, model, manufacturer, purchase time, service life, and equipment category. This basic information helps to initially understand the characteristics of the equipment and the stage of its life cycle, and provides a basic background for subsequent evaluation. Knowing the purchase time and service life of the equipment can roughly infer its degree of aging and possible risks; Operation data collection: including real-time monitoring data of the equipment's power-on time, number of operations, and operating status parameters. Continuous collection of such data can analyze the equipment's operating stability. For example, by monitoring the energy output stability parameters of a certain treatment device, it can be determined whether it can continuously and effectively complete the treatment task. If the parameter fluctuates greatly, it may indicate that the equipment is at risk and affect performance; Maintenance data record: record in detail the time, content, maintenance personnel and other information of the equipment's previous maintenance. Frequent maintenance or recent replacement of important parts may indicate problems with the equipment's reliability, which in turn affects its performance. These data can help accurately grasp the equipment's maintenance history and current status; Clinical use feedback collection: collect medical staff's evaluation and feedback on the equipment's ease of use, accuracy, imaging quality, treatment effect and other aspects in actual clinical operations. For example, if a doctor reports that a certain ultrasound device has unclear imaging, this not only affects diagnostic efficiency, but also suggests that the equipment may be at risk of performance degradation, which is crucial to its performance evaluation;

[0038] S02: Key data preprocessing: preprocess the collected data so that the data can adapt to subsequent data analysis; data cleaning: remove outliers, erroneous data and duplicate data from the collected data. For example, due to sensor failure, the collected operating temperature data of a certain device may appear to be obviously unreasonable and extremely high. Such abnormal data needs to be identified and eliminated to ensure the accuracy of subsequent analysis. Similarly, if there is repeated maintenance information, it should also be cleaned up to retain only accurate and valid data records; data standardization: standardize data of different types and sources to make them comparable. For example, the startup time data of different devices are uniformly converted according to a certain standard time unit, so that the operating conditions of different devices can be accurately compared in subsequent analysis. For some operating status parameters with different value ranges, such as pressure values, which may have different ranges in different devices, they also need to be standardized so that they can be analyzed on the same scale;

[0039] S03: Judgment indicator setting: setting various indicators for data analysis; performance indicator setting: setting specific indicators from the dimensions of medical effect, work efficiency, cost-effectiveness, patient satisfaction, etc. In terms of medical effect, diagnostic accuracy and treatment effectiveness indicators can be set; in terms of work efficiency, equipment operation speed and equipment availability indicators can be set; in terms of cost-effectiveness, indicators such as the purchase cost and use benefit ratio, operating cost and output benefit ratio can be set; in terms of patient satisfaction, indicators such as the patient's evaluation score of the equipment experience and the evaluation score of the perception of medical service quality can be set; risk indicator setting: including technical performance risk indicators, reliability risk indicators, availability risk indicators, cost-effectiveness risk indicators, and personnel-related risk indicators. These indicators are used to accurately quantify various risk conditions that the equipment may face and provide a specific basis for subsequent risk evaluation;

[0040] S04: Intelligent analysis: Analyze the preprocessed data, compare the analyzed data with the set judgment indicators, and obtain the evaluation results; Select the intelligent analysis model: According to the data characteristics and evaluation requirements, artificial intelligence models such as artificial neural networks, support vector machines, and decision trees can be selected; Model training and verification: Divide the preprocessed data into training sets and verification sets according to a certain ratio; Risk and performance evaluation: Use the trained and verified intelligent analysis model to input the relevant data of the equipment to be evaluated into the model, and the model will output the risk level of the equipment and the performance evaluation results;

[0041] S05: Result application: formulate corresponding strategies based on the evaluation results;

[0042] Equipment management decision support: According to the results of risk assessment and performance evaluation, formulate corresponding equipment management strategies. For high-risk equipment, it may be necessary to arrange more frequent maintenance, upgrade equipment in advance, or conduct in-depth overhaul. For equipment with poor performance, the specific reasons can be analyzed and targeted improvement measures can be taken, such as optimizing equipment parameter settings and strengthening operator training.

[0043] Optimize resource allocation: Rationally allocate medical resources, including maintenance personnel and spare parts inventory, based on the risk and performance of the equipment. For example, for equipment with higher risks, ensure that there is sufficient spare parts inventory so that it can be repaired in time when the equipment fails to reduce downtime. At the same time, reasonably arrange the work priorities of maintenance personnel to give priority to the normal operation of high-risk, high-performance equipment.

[0044] Continuous monitoring and improvement: Continuously monitor the operation, maintenance, and performance of the equipment, constantly update data, and improve the evaluation model; with the increase in the age of equipment and the upgrading of technology, the risk and performance of the equipment will also change. Through continuous monitoring and improvement, the actual situation of the equipment can be more accurately grasped, and the effectiveness and practicality of the evaluation method can be improved.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A medical equipment performance intelligent risk assessment method, characterized by: The steps include: 1) Collection of necessary data: Collect necessary data related to the performance of medical equipment; 2) Key data preprocessing: preprocess the collected data to make it suitable for subsequent data analysis; 3) Judgment indicator setting: setting various indicators used for data analysis; 4) Intelligent analysis: Analyze the preprocessed data, compare the analyzed data with the set judgment indicators, and obtain the evaluation results; 5) Application of results: Formulate corresponding strategies based on the evaluation results.

2. According to claim 1, a medical equipment performance intelligent risk assessment method is characterized in that: In step 1), data collection includes basic equipment information collection, operation data collection, maintenance data recording and clinical use feedback collection.

3. A medical equipment performance intelligent risk assessment method according to claim 1, characterized in that: In the step 2), data preprocessing includes data cleaning and data standardization.

4. A medical equipment performance intelligent risk assessment method according to claim 1, characterized in that: In the step 3), the setting of the determination index includes the setting of the performance index and the setting of the risk index.

5. The medical equipment performance intelligent risk assessment method according to claim 1 is characterized in that: In step 4), intelligent analysis includes selecting an intelligent analysis model, model training and verification, and risk and performance assessment.

6. A medical equipment performance intelligent risk assessment method according to claim 1, characterized in that: In step 5), the formulated strategies include equipment management decision support, resource allocation optimization, and continuous monitoring and improvement.

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