Medical specialist ability index evaluation method

Abstract

The invention relates to the technical field of medical management, and discloses a medical specialist ability index evaluation method, which is characterized by comprising the following steps: data acquisition: collecting multi-dimensional information; constructing an index system, and establishing a multi-dimensional evaluation system; weight distribution: determining a weight coefficient by adopting a scientific method; and calculating a specialist ability index through comprehensive scoring model calculation. The system is characterized in that data acquisition is completed through a hospital system and a database; the basic capability indexes comprise equipment configuration and the like; clinical ability indexes comprise diagnosis and treatment quantity and the like; the scientific research capability indexes comprise scientific research projects and the like; the service capability indexes comprise patient satisfaction and the like; the specialized ability index has a specific calculation formula. The method has the advantages that comprehensive, scientific and dynamic assessment of the clinical specialized ability of the medical institution is realized through the specialized ability assessment method based on multi-dimensional data acquisition, objective weight distribution and a quantitative scoring model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical management, in particular to a medical specialty capability index evaluation method. BACKGROUND

[0002] The clinical specialty capability evaluation of medical institutions is the core link of medical quality improvement, but the traditional evaluation method has significant limitations. First, the single evaluation dimension problem is prominent, most of which only rely on basic indicators such as the number of diagnosis and treatment, the size of the bed, or through expert subjective scoring, which is difficult to fully reflect the multi-dimensional capabilities of the specialty in clinical technology, scientific research innovation, service radiation, etc. For example, some evaluations measure the level of surgery only by the number of operations, ignoring the proportion of difficult and complex cases, postoperative rehabilitation effects and other key dimensions, resulting in a disconnection between the evaluation results and the actual capabilities.

[0003] In addition, the subjectivity of weight allocation is strong, and the traditional method often sets the index weight based on experience, lacking scientific quantitative basis. For example, the weight ratio of scientific research capability and clinical capability may be unbalanced due to personal preference, resulting in evaluation results that cannot truly reflect the needs of discipline development. At the same time, the static evaluation model is difficult to adapt to the rapid iteration of medical technology, such as the application of new diagnosis and treatment technology and the adjustment of policy guidance, resulting in evaluation results lagging behind the actual development. In summary, the existing evaluation methods are deficient in comprehensiveness, objectivity and dynamics, and there is an urgent need for a specialty capability evaluation method that integrates multi-dimensional data, scientific weight allocation and dynamic modeling to improve the precision and timeliness of medical management. SUMMARY

[0004] To solve the above problems, the present application proposes a specialty capability evaluation method based on multi-dimensional data collection, objective weight allocation and quantitative scoring model, which realizes comprehensive, scientific and dynamic evaluation of the clinical specialty capability of medical institutions, and provides a medical specialty capability index evaluation method for data support for medical decision-making.

[0005] To solve the above technical problems, the technical solution proposed by the present application is as follows: a medical specialty capability index evaluation method, comprising the following steps: Step 1: Data collection, collect the specialty capability related data of medical institutions, covering multi-dimensional information such as the number of diagnosis and treatment, case complexity, medical quality, scientific research level, equipment configuration, etc. Step 2: Index system construction, establish a multi-dimensional evaluation index system including basic capability, clinical capability, scientific research capability, service capability, etc., comprehensively reflecting different aspects of specialty capability; Step 3: Weight allocation, according to the importance or influence of each index, use the analytic hierarchy process or other mathematical methods to scientifically determine the weight coefficient of each index, ensuring the objectivity of the evaluation; Step 4: Comprehensive scoring model calculation. Using the weighted average method or other mathematical models, the various indicators are quantified and comprehensively scored to calculate the professional competence index.

[0006] Preferably, the data collection in step one is completed through the hospital information system and related databases to achieve efficient and accurate data acquisition.

[0007] Preferably, the basic capability indicators in step two include, but are not limited to, equipment configuration, personnel structure, and other aspects that measure the basic conditions for a specialized degree.

[0008] Preferably, the clinical competence indicators in step two include elements that directly reflect the level of specialty clinical services, such as the number of cases treated, case complexity, and medical quality.

[0009] Preferably, the research capability indicators in step two cover the number of research projects, the quality of published papers, patent achievements, and other content that reflects the research and innovation capabilities of the major.

[0010] Preferably, the service capability indicators in step two include factors that measure the effectiveness of specialized social services, such as patient satisfaction and the service coverage capacity.

[0011] Preferably, the formula for calculating the specialty competence index in step four is SCI=∑(from i=1 to n)(xi×wi), where xi is the score value of the i-th indicator and wi is the weight coefficient of the i-th indicator. This formula enables the quantitative assessment of specialty competence.

[0012] The advantages of this invention compared to the prior art are: Multi-dimensional and full-cycle coverage: Integrating data from four dimensions—clinical services, scientific research and innovation, basic conditions, and social services—covering dozens of specific indicators such as the number of diagnoses and treatments, case complexity, research projects, and patient satisfaction, avoiding the one-sidedness of traditional assessments and achieving a full-cycle profile of specialty capabilities.

[0013] Scientific and flexible weight allocation: It supports expert experience-based weighting using the analytic hierarchy process (AHP) and data-driven weighting using the entropy weighting method. It can be flexibly switched according to the evaluation objectives, preserving industry consensus while revealing key influencing factors through data variability, thereby improving the objectivity and relevance of weight allocation.

[0014] Dynamic assessment and real-time response: Machine learning-based models can access real-time data, automatically identify trends in changes in specialty capabilities, dynamically adjust indicator weights, solve the lag problem of traditional static models, and adapt to the needs of rapid iteration in medical technology. Detailed Implementation

[0015] Example 1

[0016] Comprehensive assessment of orthopedics department at a top-tier hospital Step 1: Data Collection Data from the orthopedics department of a tertiary hospital in 2023 was collected through the hospital information system, research management platform, and satisfaction survey. Basic capabilities: 80 open beds, 5 chief physicians, accounting for 20% of the total number of physicians, and equipped with advanced equipment such as the da Vinci surgical robot; Clinical capabilities: 3,000 surgeries per year, of which 45% are complex surgeries; postoperative infection rate is 0.5%; and the rate of Class A medical records is 98%. Research capabilities: Undertook 2 national-level research projects, published 15 high-level papers, and obtained 3 invention patents; Service capabilities: Patient satisfaction rate reached 92%, patients from other provinces accounted for 18%, and 20 orthopedic health popular science lectures were conducted throughout the year.

[0017] Step Two: Construction of the Indicator System The assessment is broken down into 12 specific indicators across four dimensions: basic capabilities, clinical capabilities, research capabilities, and service capabilities. For example, clinical capabilities include three sub-indicators: the number of cases treated, the complexity of cases, and the quality of medical care, which correspond to data such as the number of surgeries, the proportion of complex surgeries, and the qualification rate of medical records, respectively.

[0018] Step 3: Weight Allocation Ten healthcare management experts were invited to score the importance of the indicators, and the weights were calculated using the analytic hierarchy process (AHP). The experts determined that clinical competence had the greatest impact on specialty development, with a weight of 40%; research competence was second, with a weight of 25%; basic competence and service competence were weighted at 20% and 15%, respectively. The weights of each sub-indicator were further refined based on the expert scores; for example, within clinical competence, case complexity accounted for 40%, the number of consultations for 30%, and medical quality for 30%.

[0019] Step 4: Calculation of Overall Score Each indicator was standardized and scored, ranging from 0 to 100 points, and a comprehensive score was calculated based on weighted averages. For example, the clinical competence score was calculated as follows: surgical volume score multiplied by 30%, plus the percentage of complex surgeries multiplied by 40%, plus the medical record qualification rate score multiplied by 30%, and then multiplied by the clinical competence weight of 40%. The calculated scores were: basic orthopedic competence 87, clinical competence 94, research competence 91, and service competence 88. The final specialty competence index was calculated as: 87 multiplied by 20%, 94 multiplied by 40%, 91 multiplied by 25%, and 88 multiplied by 15%, resulting in a score of 91.

[0020] Step 5: Application of Results The assessment results showed that the orthopedic department had outstanding clinical capabilities, but its basic capabilities, particularly its personnel structure, needed optimization, with a low proportion of young physicians. Based on this, the hospital formulated a training program for young physicians and prioritized their application for national-level key clinical specialties. Example 2

[0021] Objective assessment of internal medicine department of county-level hospital Data collection and standardization Five county-level hospitals' internal medicine departments were selected, and data from 2023 was collected and standardized. For example, Hospital A scored 70 points in basic capabilities due to low equipment configuration; 85 points in clinical capabilities, with sufficient treatment volume but few complex cases; 60 points in research capabilities due to a lack of national-level research projects; and 80 points in service capabilities due to high patient satisfaction. Hospital D scored 80 points in basic capabilities due to relatively complete equipment; 82 points in clinical capabilities; 65 points in research capabilities; and 90 points in service capabilities, serving the surrounding counties.

[0022] Weighting Weights were automatically calculated based on data variability. Analysis revealed the greatest difference among the five hospitals in service capacity, with a standard deviation of 8.5, indicating that service outreach capacity is a key differentiating indicator for internal medicine departments in county-level hospitals; therefore, its weight was automatically adjusted to 25%. Clinical capacity was the second most significant difference, with a weight of 35%. Basic medical capabilities and research capabilities had weights of 22% and 18%, respectively.

[0023] Overall Rating and Comparison Taking Hospital D as an example, the comprehensive score is calculated as follows: basic competence multiplied by 22%, clinical competence multiplied by 35%, research competence multiplied by 18%, and service competence multiplied by 25%, i.e., 80 multiplied by 22% + 82 multiplied by 35% + 65 multiplied by 18% + 90 multiplied by 25%, resulting in 81 points. Compared with the traditional experience-based weighting, i.e., clinical competence 40% and basic competence 20%, the entropy weighting method highlights the importance of service competence, which is consistent with the policy orientation of county-level medical consortium construction, suggesting that hospitals should strengthen cooperation in primary care referrals and health service coverage. Example 3

[0024] Dynamic assessment of the cardiovascular department of a tertiary hospital Data preprocessing and model training Quarterly data from the cardiology department of a hospital over the past five years were collected, including 12 indicators such as patient volume, implementation of new technologies, and research output. A dynamic evaluation model was constructed using the random forest algorithm. The model learned from historical data and found that the application of new technologies, such as the popularization of minimally invasive interventional surgery and guideline correlation studies, significantly contribute to the improvement of specialty capabilities.

[0025] Dynamic weight adjustment In the first quarter of 2024, the department introduced an AI-assisted diagnostic system, increasing the rate of Class A medical cases from 95% to 98%; at the same time, it began performing transcatheter aortic valve replacement surgery, raising the proportion of complex cases from 30% to 40%. The model automatically recognized the improvement in clinical technology and medical quality, increasing the weight of clinical capabilities from 35% to 40%, and decreasing the weight of equipment application in basic capabilities from 20% to 15%.

[0026] Evaluation Results and Application Calculations showed that the specialty capability index for this quarter was 93 points, an increase of 5 points compared to the previous quarter. Visual charts showed that the efficiency of scientific research results transformation decreased by 10% year-on-year, and the number of patent applications decreased, prompting the system to issue an automatic warning. Based on this, the hospital adjusted its scientific research incentive policies, established a special fund for new technology transformation, and promoted the simultaneous development of clinical research and patent applications.

[0027] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating medical specialty competence index, characterized in that, Includes the following steps: Step 1: Data collection. Collect data related to the medical institution's specialty capabilities, covering multiple dimensions such as the number of diagnoses and treatments, case complexity, medical quality, research level, and equipment configuration. Step 2: Constructing an indicator system. Establish a multi-dimensional evaluation indicator system that includes basic capabilities, clinical capabilities, research capabilities, and service capabilities, comprehensively reflecting different aspects of specialty capabilities. Step 3: Weight allocation. Based on the importance or influence of each indicator, use the analytic hierarchy process or other mathematical methods to scientifically determine the weight coefficient of each indicator to ensure the objectivity of the evaluation. Step 4: Comprehensive scoring model calculation. Using the weighted average method or other mathematical models, the various indicators are quantified and comprehensively scored to calculate the professional competence index.

2. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The data collection in step one is completed through the hospital information system and related databases, achieving efficient and accurate data acquisition.

3. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The basic capability indicators in step two include, but are not limited to, equipment configuration, personnel structure, and other aspects that measure the basic conditions for a specialized degree.

4. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The clinical competence indicators in step two include elements that directly reflect the level of specialty clinical services, such as the number of cases treated, case complexity, and medical quality.

5. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The research capability indicators in step two cover aspects that reflect the research and innovation capabilities of a specialized college, such as the number of research projects, the quality of published papers, and patent achievements.

6. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The service capacity indicators in step two include factors that measure the effectiveness of specialized social services, such as patient satisfaction and the service coverage capacity.

7. The method for evaluating a medical specialty competence index according to claim 1, characterized in that: The formula for calculating the Specialty Competency Index in step four is SCI=∑(from i=1 to n)(xi×wi), where xi is the score of the i-th indicator and wi is the weight coefficient of the i-th indicator. This formula enables the quantitative assessment of Specialty Competency.