CN105787262B - Tcm clinical practice Digital evaluation system and its evaluation method based on big data analysis - Google Patents
Tcm clinical practice Digital evaluation system and its evaluation method based on big data analysis Download PDFInfo
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Abstract
Tcm clinical practice Digital evaluation system of the present invention, including five first class index：Clinical efficacy indexes, advantage disease index, traditional Chinese medical science diagnosis and treatment method index, working attitude index and work figureofmerit；And score calculation and analysis are carried out to doctor's ability by big data analytical technology on the basis of tcm clinical practice Digital evaluation system.The present invention is from metrics evaluation is divided to the deep layer by layer of overall merit, the combination of desk evaluation and external evaluation, operating efficiency, the working attitude of the clinical efficacy of analysis doctor, advantage disease, traditional Chinese medical science diagnosis and treatment method and doctor comprehensively, and use big data treatment technology, the weight calculation detailed to each index, evidence-based data conclusion is drawn, so as to the ability to work and level of doctor in scientific evaluation.
Description
Technical Field
The invention relates to the technical field of traditional Chinese medicine clinical treatment, in particular to a traditional Chinese medicine clinical digital evaluation system and an evaluation method based on big data analysis.
Background
The database of the ten-thousand-square in 1995-2015 was searched in full text with "clinical skill", "evaluation" or "assessment" as a keyword, and 500 hits were hit, of which only 40 hits were hit related to "chinese medicine". The study of the Chinese scholars on the evaluation of the clinical capability of doctors with the traditional Chinese medicine characteristics is less. The study of a complete traditional Chinese medicine evaluation system by means of a plurality of modern means and methods is not performed by a plurality of students, and the combination of the study and the computer information processing technology is very little.
The evaluation system represents the design of a doctor comprehensive quality and work performance evaluation index system of a large comprehensive hospital [12], the design of a doctor comprehensive evaluation system of an MB hospital [15], the survey of the performance evaluation satisfaction degree of doctors in two public hospitals in the city of Hefei and the analysis of influence factors [14], the experience of the construction of a research type traditional Chinese medicine hospital based on a doctor competence quality model [16] and the like, and mainly takes the comprehensive competence quality of Dede "," can "," duty "," performance "and" cheap "as the basis of an appraisal doctor, wherein the performance quality mainly reflects the clinical capability of the doctor, but most of documents do not deeply research the characteristics of syndrome differentiation and treatment of traditional Chinese medicine, and do not evaluate the capability of the doctor according to specific quantitative indexes, so that the innovation and development of the traditional Chinese medicine are hindered.
The requirements of the advanced evaluation of the Chinese medicine of Chuan (2015) document "the related requirements of the qualification of the technical professional task of the advanced professional technology in traditional Chinese medicine of the Sichuan province in 2015" document 12 in the Sichuan province in clear conditions of the Chinese medicine promotion of the whole province mainly comprise the requirements and the quantity of the thesis of doctors, scientific research, basic service of oral support and advanced study, and the indexes are used for judging the height of the clinical capability of the doctors and are required to be perfect. The southern journal of 12 and 16 months in 2015 indicates that the current assessment and assessment of the job titles of the domestic doctors are mainly based on scientific research papers and subjects, which means that even if the clinical capability of a doctor in traditional Chinese medicine is stronger, the doctor is difficult to promote without articles.
The government offices implement performance wages in public health and basic medical and health institutions in 10 months in 2009, 96% of hospitals in China are public hospitals, annual financial fund allocation only accounts for 7% -8% of the total income of the public hospitals, and the rest 90% of hospitals are charged by medical services and medicine income, and in this case, part of hospitals are bound to put economic benefits at very important positions [ 5 ]. Some doctor performance assessment and evaluation systems formulated by traditional Chinese medicine hospitals mainly depend on economic benefits and the like created for hospitals.
The medical college of Zhejiang university, province 1 month in 2015, affiliated with the Ming Jianan, the second hospital, ministry of government and government, proposed a system for evaluating doctors in China, and rarely considered the defect of professional practice capacity, and pointed out that the evaluation of doctors should be more powerful.
Disclosure of Invention
At present, the evaluation research of the clinical capability of Chinese doctors by domestic academic, media and policy documents is very little, and the existing evaluation system has the defects of attaching importance to scientific research, thesis, economic benefit and the like, but is continuously striving for improvement. Social media, hospital doctors and the like pay attention to the clinical capability of doctors, but still seriously lack verifiable data support and detailed evaluation indexes. Only the evaluation is refined and an operable implementation mechanism exists, the evaluation mechanism established on the clinical capability of the traditional Chinese medicine can go farther, and the original intention of the doctor for returning the professional value is realized.
The invention aims to provide a traditional Chinese medicine clinical digital evaluation system, and a scientific and reasonable evaluation index system is established.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the method comprises five primary indexes: clinical efficacy index, dominant disease index, traditional Chinese medicine diagnosis and treatment method index, working attitude index and workload index.
Further, the secondary indexes of the clinical efficacy indexes comprise: the rate of return visits, the rate of visits, the improvement of symptoms and the good rate of patients;
the secondary indexes of the dominant disease species indexes comprise: ranking the rate of re-diagnosis of single disease species, ranking the rate of seeing a doctor of single disease species, diagnosis and treatment time, improving symptoms and judging the good rate of patients;
the secondary indexes of the traditional Chinese medicine diagnosis and treatment method indexes comprise: the rate of coincidence of the traditional Chinese medicine and the western medicine for the symptom of the medicine of the physical and legal prescription and the rate of utilization of the combination of the traditional Chinese medicine and the western medicine are determined;
the second-level indexes of the working attitude indexes comprise: uploading medical record star grade and patient goodness;
the secondary indicators of the workload indicators include: the diagnosis rate and the number of uploaded medical records.
Furthermore, the indexes are weighted, the clinical curative effect is preferred, the traditional Chinese medicine diagnosis and treatment method is assisted, and the workload and the working attitude are given in turn.
Furthermore, the corresponding medication coincidence rate of the rational-law prescription and the medicine is determined by a method of property-flavor meridian tropism according to the prescription prescribed by a doctor, the property-flavor meridian tropism method refers to analyzing the conformity of syndrome type and medication according to the combined dosage data of the medicine, a comparison graph of property-flavor meridian tropism and disease-location meridian tropism is generated, and the corresponding rate of the corresponding medication of the doctor is measured through the comparison graph of property-flavor meridian tropism and disease-location meridian tropism.
Further, the weight of the index is set as follows:
the traditional Chinese medicine clinical digital evaluation method based on big data analysis comprises the following steps:
firstly, establishing a traditional Chinese medicine clinical digital evaluation index system framework: the evaluation system framework comprises five primary indexes: clinical efficacy indexes, dominant disease indexes, traditional Chinese medicine diagnosis and treatment method indexes, working attitude indexes and workload indexes;
the secondary indexes of the clinical efficacy indexes comprise: the rate of return visit, the rate of visit, the improvement of symptoms and the rate of good appraisal of the patient;
the secondary indexes of the dominant disease species indexes comprise: ranking the rate of double-diagnosis of single disease, ranking the rate of seeing a doctor of single disease, diagnosis and treatment time, improving symptoms and improving the patient's appreciation rate;
the secondary indexes of the traditional Chinese medicine diagnosis and treatment method indexes comprise: the rate of coincidence of the traditional Chinese medicine and the western medicine for the symptom of the medicine of the physical and legal prescription and the rate of utilization of the combination of the traditional Chinese medicine and the western medicine are determined;
the secondary indexes of the working attitude indexes comprise: uploading medical record star grade and patient goodness;
the secondary indicators of the workload indicators include: the treatment rate and the number of uploaded medical records;
secondly, setting the weight of each index: setting the weight of the first-level index and then setting the weight of the second-level index;
thirdly, evaluating and analyzing the ability of the Chinese medical doctor by using a big data analysis method to obtain a conclusion;
the big data analysis method comprises the following steps: the method comprises the steps of collecting clinical electronic medical record data, preprocessing the collected data, integrating the preprocessed data, establishing a data analysis model, calculating scores of indexes, reasonably explaining and evaluating the scores, and presenting evaluation results through visualization effects.
Furthermore, in the third step, the patient favorable rating indexes are graded, scores are distributed to each grade, the corresponding score of each grade is the score of the patient favorable rating index, and the score distributed to each grade is not more than the weight of the patient favorable rating index; the uploaded medical record quantity indexes are graded, scores are distributed to each grade, the scores corresponding to each grade are the scores of the uploaded medical record quantity, the uploaded medical record quantity indexes are the quantity of uploaded cases in unit time, and the scores distributed to each grade are not more than the weight scores of the uploaded medical record quantity indexes.
Further, the data preprocessing comprises cleaning, converting and loading the clinical electronic medical record data, and combining a traditional Chinese medicine theory, method, prescription and specification table to perform clinical four-diagnosis information: the data of symptoms, syndrome types, prescriptions and medicines are combed, and the method comprises the following steps: treating positive and different names of the traditional Chinese medicines, treating syndrome type structuring and treating symptoms;
treating the positive and the negative synonyms of the traditional Chinese medicines: processing wrongly written characters and omitted characters;
and (3) syndrome type structuring treatment: compositing or splitting content;
symptom treatment: split by word processing.
Further, in the third step, the SQL Server 2008R2 database and the mining tool ETL are adopted for processing data; the cleaning data adopts a reflection technology based on a programming language and a Python script technology.
Further, in the third step, the score of the re-diagnosis rate or the visit rate index is calculated according to the formula (1):
in the formula (1), the first and second groups,
further: score of the re-diagnosis rate or visit rate index, p: the average doctor review rate or the doctor visit rate of similar departments, q is the doctor review rate or the doctor visit rate, m: the number of the patients who go back or visit in the hospital, n: the number of doctor's re-diagnosis or doctor's visit Q is the weight of the re-diagnosis rate or doctor's visit rate;
calculating the total score of the single disease re-diagnosis rate ranking and the single disease treatment rate ranking according to the formula (2):
advantage＝(A+B)*Q*2 (2)
in the formula (2), the first and second groups,the advertisement: the sum of the ranking score of the rate of double-disease and the ranking score of the rate of seeing single-disease, A: the rate of the single disease re-diagnosis is ranked and scored, B: ranking the diagnosis rates of single disease species, sg: amount of single disease, tsg: the amount of a single disease to be treated in the same department of the hospital, k: total number of doctors in a single disease in the whole province, n: doctor province rank of single disease category, m: the number of the patients who have a repeated diagnosis or the number of the patients who have a visit, Q is the weight of the ranking of the rate of repeated diagnosis of a single disease or the ranking of the rate of visit of a single disease,the maximum diagnosis rate;
calculating the score of the symptomatic medication coincidence rate index of the rational and legal prescription according to the formula (3):
in the formula (3), 0<p i &1,1 is more than or equal to i is less than or equal to n, n: maximum number of medical records, p i : the medication of the medical record numbered i conforms to the symptoms; symptomatic, the coincidence rate score of the physical and legal prescription drugs for symptomatic medication, total: the doctor always uploads the number of courses of disease, Q is the weight of the symptomatic medication coincidence rate of the rational and legal prescriptions;
calculating the score of the index of the utilization rate of the combination of Chinese and Western medicine according to the formula (4):
unite＝ratio*Q*2 (4)
in formula (4), 0-ratio-1, unity: scoring the index of the combined utilization rate of the traditional Chinese medicine and the western medicine, wherein the ratio is the coincidence rate of the combined utilization of the traditional Chinese medicine and the western medicine, and Q is the weight score of the combined utilization rate of the traditional Chinese medicine and the western medicine;
calculating the star-grade score of the uploaded medical records according to a formula (5):
in the formula (5), the first and second groups of the chemical reaction materials are selected from the group consisting of,
UP, uploading medical record star score and total i The total number of medical records with the star level i uploaded by doctors, total: doctor uploads the number of medical calendars in total, i: i is more than or equal to 1 and less than or equal to n, and i is an integer.
Compared with the prior art, the invention has the following beneficial effects:
1. the doctor is normalized to fill in the electronic medical records, the support effect of the electronic medical records in medical work is better exerted, the information construction work of hospitals taking the electronic medical records as the core is promoted, and the medical record filling method is an improvement of advanced medical and health systems and stone paving;
2. through the doctor prescription, the scientificity of the traditional Chinese medicine prescription is judged by the positive or negative interaction among the medicine property attributes such as the composition and the quantity of the medicines, the analytic nature, the taste, the channel tropism and the like and the nature-taste channel tropism method, the characteristics of the traditional Chinese medicine dialectical treatment are highlighted, and the popularization value is realized;
3. scientific and accurate diagnosis and treatment data are collected according to clinical medical records, the dominant disease species of doctors are mined and analyzed by a data warehouse design and a decision analysis algorithm and the like, the advantages and the differences among hospitals are displayed, and the diagnosis and treatment of single disease species in traditional Chinese medicine are further standardized;
4. the data source for evaluation is real and reliable, and the evaluation conclusion is available for check, so that a basis is provided for reward distribution;
5. the real-time sharing of medical information is realized, the improvement and sublimation of the clinical experience of doctors are driven, and in the process of seeing a doctor, the doctor obtains the medical information provided by doctors in other hospitals, so that conditions are created for correct diagnosis, the doctor is an information provider and is also a direct user of the information, and the evaluation system not only improves the medical quality, but also can obviously reduce medical errors;
6. the evaluation system intuitively displays the clinical skills of doctors through indexes such as the number of doctor reexamination, dominant disease species, the patient goodness of comment and the like of a big data analysis technology, finds excellent traditional Chinese medicine clinicians, enables the whole medical process to be transparent, has stronger constraint on the doctors, has more definite responsibility, promotes medical institutions and doctors to reasonably apply medicines, reasonably check and reasonably diagnose, improves the medical service quality, effectively controls the too fast increase of medical expenses, and changes the original 'treatment with medicines' into 'treatment with skills';
7. the invention constructs a digitalized evaluation system of the traditional Chinese medicine clinical skill, adopts data analysis and algorithm on the basis of a system architecture, carries out layer-by-layer deepening from sub-index evaluation to comprehensive evaluation, organically combines internal evaluation and external evaluation, comprehensively analyzes the clinical curative effect, dominant disease species, traditional Chinese medicine diagnosis and treatment methods of doctors and the working efficiency and working attitude of the doctors, adopts a big data processing technology, calculates the detailed weight of each index, and obtains a data conclusion which can be followed, thereby scientifically evaluating the working capacity and level of the doctors.
Drawings
FIG. 1 is a framework of a digital evaluation index system for clinical use in TCM;
FIG. 2 is a data processing flow diagram of a big data analysis method;
FIG. 3 is a flow chart of a single patient visit and follow-up rate ranking score algorithm;
FIG. 4 is a dominant disease species map A;
FIG. 5 is a dominant disease species map B;
FIG. 6 is a comparison chart of sex-flavor meridian tropism and disease location meridian tropism.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings.
Example 1
As shown in FIG. 1, the digital evaluation system for Chinese medicine clinical disclosed by the invention comprises 5 primary indexes for measuring the clinical skill of a middle doctor: respectively the clinical curative effect, the dominant disease species, the traditional Chinese medicine diagnosis and treatment method and the working attitude and workload of doctors. Attitude determination action, action promotion method, method determination effect, effect determination effect, five indexes are respectively judged by a plurality of secondary indexes.
The secondary indexes of clinical efficacy indexes comprise: the rate of return visits, the rate of visits, the improvement of symptoms and the good rate of patients;
the secondary indexes of the dominant disease species indexes comprise: ranking the rate of double-diagnosis of single disease, ranking the rate of seeing a doctor of single disease, diagnosis and treatment time, improving symptoms and improving the patient's appreciation rate;
the secondary indexes of the traditional Chinese medicine diagnosis and treatment method indexes comprise: the rate of coincidence of the traditional Chinese medicine and the western medicine for the symptom of the medicine of the physical and legal prescription and the rate of utilization of the combination of the traditional Chinese medicine and the western medicine are determined;
the secondary indexes of the working attitude indexes comprise: uploading medical record star grade and patient rating;
the secondary indicators of the workload indicators include: the treatment rate and the number of uploaded medical records.
The dominant disease species are used for measuring the disease species which are skilled for treatment by doctors through comprehensive analysis of symptom improvement, single disease species visiting rate ranking, diagnosis and treatment time and patient goodness of treatment of the diseases treated by the doctors (see the attached figures 4 and 5). Through data practice, zhang Xiang doctors are good at treating children cough in conclusion drawn in figure 4, li Xiaolin doctors are good at treating dizziness in conclusion drawn in figure 5, and the index of dominant disease species can accurately explain the advantages of doctors in treating and researching certain disease species, and is a core index for evaluating clinical skills of doctors.
The diagnosis and treatment time refers to the time taken by a doctor to diagnose a patient and is obtained by the difference between the start time of an outpatient service and the end time of the outpatient service. The diagnosis and treatment time is long, which indicates whether a doctor is familiar with and skilled in the disease, can reflect the advantages of the doctor in the disease, and is also an index for judging the working efficiency of the doctor.
The doctor diagnosis rate can calculate the number of the doctor diagnosing and treating people, the doctor diagnosis rate is high, the number of the patients seeing a doctor is large, whether the doctor overtakes or not can be judged through the algorithm analysis of the doctor diagnosis rate and the patient diagnosis time, and the doctor diagnosis rate is an important index for judging the workload of the doctor. The ranking index of the number of patients who see a doctor for a certain disease type, which is called the single disease type rate ranking for short, is one of the indexes for calculating the dominant disease type.
The number of the patients who go back to the doctor in the same hospital and the same doctor for a period of time is more than two times, and the more the number of the patients who go back to the doctor indicates that the patients trust the doctor and the doctor goes to the doctor for many times to see a doctor. The rate of re-diagnosis is a microscopic index in the evaluation system, and is also an important index, which can explain the service attitude, the curative effect of medication and the like of doctors to a certain extent. The number of the doctor who makes a single disease review is also an index for evaluating the dominant disease of the doctor, and the clinical capability of the doctor can be explained from the review rate.
The symptom improvement means that after the medicine is taken by a doctor, the symptom before the re-diagnosis is judged to stop or reduce by filling the medical record. The system generates a numerical value representing the severity degree according to the combined parameters of the magnitude of the symptoms and the severity degree of the symptoms and a weighting table corresponding to the symptoms, and judges whether the symptoms are improved or not according to the numerical value. The index can obviously prove and evaluate the clinical skill of doctors and has strong persuasion to the clinical curative effect.
The uploading medical record star rating is a rating score for the quality of the electronic medical record uploaded by a doctor in a calculation time period. The medical records are not only the summary of clinical practice, but also the legal basis for exploring disease laws and handling medical disputes, and have important functions on medical treatment, prevention, teaching, scientific research, hospital management and the like. The evaluation system is divided into five star-level standards according to the completeness, accuracy and authenticity of filling of various parameters of actual medical records by referring to basic medical record writing specifications issued by the ministry of health. The complete and meticulous medical records are the most basic basis for clinical diagnosis and treatment, so the star grade uploaded to the medical records is taken as a working attitude index for evaluating the fact that doctors are serious, scientific, precise and meticulous in the system.
The utilization rate of the combination of the traditional Chinese medicine and the western medicine can reflect whether a doctor uses the western medicine technology or not, and the treatment process of the patient by using the methods of western medicine clinical examination, western medicine and the like. The Chinese and western medicine have different lengths, the combination of the Chinese and western medicine is beneficial to promoting the progress of medical science, and the index can be used for evaluating whether a doctor researches the traditional Chinese medicine according to a modern scientific method.
The coincidence rate weight score of the symptomatic medicine of the rational and legal prescriptions is larger, and is a core index for evaluating the clinical skill of a doctor in traditional Chinese medicine. The index utilizes the prescription prescribed by the doctor, and the conformity between syndrome type and medication is analyzed by the method of nature-flavor channel tropism according to the data of medicine combination dosage and the like, so as to generate a comparison chart of nature-flavor channel tropism and disease location channel tropism (see figure 6). The comparison of the nature-flavor meridian tropism and the disease location meridian tropism means that the 46-bit codes of the disease symptoms and the medication are compared on a histogram to accurately judge the disease location and the disease nature of the patient and the improvement and the development of the disease condition. Different medicines have different treatment sites and drug effects. The syndrome type of a single or multiple syndrome types can be known about the damaged part and the damaged potential. Thereby measuring the compliance rate of the doctors to the symptomatic medicine. The coincidence rate of the theory, method and prescription for symptomatic administration highlights the characteristic of 'treatment by syndrome differentiation' of the traditional Chinese medicine, and inspects the scientificity and rationality of the administration of the medicine by doctors.
The patient goodness of appraisal is used as the microscopic index of the appraisal system, and is also an external index, the patient appraisal is the comparison of the patient experience value and the expected value, the satisfaction appraisal of the clinical skill level such as the doctor working attitude, the medical service and the like is formed by the experience value, the appraisal system objectively and fairly collects the opinions and suggestions of the patient to the hospital and the doctor, and clears the collected data to form the real and effective external appraisal to the doctor. The evaluation of the patient's good appraisal rate provides a feasible basis for further improving the clinical medical skills of doctors, improving the service attitude and for the examination of departments and the management and development of hospitals.
The evaluation system forms five primary indexes of clinical efficacy, dominant disease species, traditional Chinese medicine diagnosis and treatment method and the workload and working attitude of doctors according to the secondary indexes, and evaluates the clinical skills of the traditional Chinese medicine doctors, and the indexes are independent and connected with each other to form an inseparable whole. The index system has hierarchy, and the depth from sub-index evaluation to comprehensive evaluation is layered, and the internal evaluation and the external evaluation are organically combined and comprehensively analyzed to jointly form an organic unified evaluation system. The evaluation system is preliminarily applied to evaluation of doctors in traditional Chinese medicine hospitals in a certain district and county in metropolis at present, the evaluation result is close to the actual effect, good effect is obtained, the consistent and favorable comment of hospitals is obtained, and the system has good application and popularization values.
In the evaluation process, weights need to be set for all indexes, and the setting of the weights is based on the principle that the clinical curative effect is prior, the traditional Chinese medicine diagnosis and treatment method is assisted, and the workload and the working attitude are secondary.
The embodiment of the digital evaluation method of the traditional Chinese medicine clinical based on big data analysis is used for carrying out scoring calculation and analysis on the capability of a doctor by a big data analysis technology on the basis of a digital evaluation system of the traditional Chinese medicine clinical. The method specifically comprises the following steps:
firstly, establishing a traditional Chinese medicine clinical digital evaluation system framework shown in figure 1;
secondly, setting the weight of each index: setting the weight of the first-level index, and then setting the weight of the second-level index;
thirdly, evaluating and analyzing the capability of the Chinese medical doctor by using a big data analysis method to obtain a conclusion;
as shown in fig. 2, the big data analysis method includes: the method comprises the steps of collecting clinical electronic medical record data, preprocessing the data, integrating the data, establishing a data analysis model, and reasonably explaining and visualizing the data.
Collecting clinical electronic medical record data: in this embodiment, the clinical electronic medical record data generally refers to a collection of all medical information systems of a hospital, including HIS, doctor report, medical advice, assay, and other systems, and the clinical electronic medical record is used as a data acquisition object and is the most important component of medical big data. The clinical case data of the hospital is processed in an xml file form in the information system, a uniform and convenient uploading interface is provided, real-time file processing condition query, uploading batch management and problem data rollback are supported, and other data format processing and interface parties are compatible.
Data preprocessing: and cleaning, converting and loading the acquired clinical electronic medical record data. Analyzing the vast amounts of medical data collected presents a number of challenges. First, medical information systems are not typically designed for scientific research and data analysis. From the perspective of data analysis, medical data is generally complex, the data has high isomerism, and a great amount of missing information and inconsistent information exist; second, understanding medical data often requires knowledge in different fields. In view of the above problems, the present embodiment establishes a distributed computing platform and provides a pre-processing ETL of clinical data, including data cleaning, conversion, and loading, and further combines with a prescription and drug specification table of traditional Chinese medicine, to sort out clinical information symptoms, syndrome types, prescriptions, and drug data of four diagnostic methods: 1. processing the positive and the negative names of the traditional Chinese medicines, and processing wrongly written characters, omitted characters and the like; 2. carrying out syndrome type structuring treatment, and compounding or splitting the content; 3. the symptom part adopts word splitting processing and the like, so that the whole preprocessing process meets the requirements of scheduling automation and maintainability, and useful information is extracted through batch analysis and visualization tools to make a correct decision for evaluation.
Data integration: and partitioning and storing the preprocessed data, and establishing an index and cache mechanism. Preferably, the storage is partitioned according to the key field, and the partitioned data block is generally set according to the time parameter. When the system processes data, the SQL Server 2008R2 database with higher performance and the excellent mining tool ETL are adopted to perform partition operation on mass data, reduce system load, establish an index and cache mechanism, increase virtual memory and the like to improve access speed, and on the basis, a reflection technology and a Python script technology based on a programming language are adopted to realize data cleaning so as to fundamentally solve possible errors of the mass data.
Establishing a data analysis model: and (4) performing a data mining algorithm on the basis of data integration to obtain the score of the evaluation index in the traditional Chinese medicine clinical digital evaluation system.
Reasonable interpretation and visualization: and reasonably explaining and evaluating the scores, and presenting evaluation results through a visualization effect.
The algorithm for calculating each index is as follows:
grading the patient goodness index, and distributing scores to each grade, wherein the score corresponding to each grade is the score of the patient goodness index, and the score distributed to each grade does not exceed the weight of the patient goodness index; the uploaded medical record quantity indexes are graded, scores are distributed to each grade, the scores corresponding to each grade are the scores of the uploaded medical record quantity, the uploaded medical record quantity indexes are the quantity of uploaded cases in unit time, and the scores distributed to each grade are not more than the weight scores of the uploaded medical record quantity indexes.
Calculating the score of the re-diagnosis rate or the visit rate index according to the formula (1):
in the formula (1), the first and second groups,
further: score of the re-diagnosis rate or the visit rate index, p: the average doctor review rate or the doctor visit rate of similar departments, q is the doctor review rate or the doctor visit rate, m: the number of the patients who go back or visit in the hospital, n: the number of doctor's doctor who will see a doctor or doctor's doctor, Q: the weight score of the rate of double or visit;
calculating the total score of the single disease re-diagnosis rate ranking and the single disease treatment rate ranking according to the formula (2):
advantage＝(A+B)*Q*2 (2)
in the formula (2), the first and second groups,advantage: the sum of the ranking score of the rate of double-disease and the ranking score of the rate of seeing single-disease, A: rank scores of the rate of return visits of single disease species, B: ranking the diagnosis rates of single disease species, sg: the amount of a single patient to see a doctor, tsg: the amount of a single disease to be treated in the same department of the hospital, k: the total number of doctors in a single disease in the whole province, n: doctor province rank of single disease category, m: the number of the patients who have a repeated diagnosis or the number of the patients who have a visit, Q is the weight of the ranking of the rate of repeated diagnosis of a single disease or the ranking of the rate of visit of a single disease,the maximum diagnosis rate;
calculating the score of the symptomatic medication compliance rate index of the rational and legal prescription according to the formula (3):
in the formula (3), 0<p i &1, 1-n, i-n: maximum number of medical records, p i : the medication of the medical record numbered i conforms to the symptoms; symptomatic, the coincidence rate score of the physical and legal prescription drugs for symptomatic medication, total: the doctor always uploads the number of courses of disease, Q is the weight of the symptomatic medication coincidence rate of the rational and legal prescriptions;
calculating the score of the index of the utilization rate of the combination of Chinese and Western medicine according to the formula (4):
unite＝ratio*Q*2 (4)
in the formula (4), 0-straw-type-1-unite: scoring the index of the combined utilization rate of the traditional Chinese medicine and the western medicine, wherein the ratio is the coincidence rate of the combined utilization of the traditional Chinese medicine and the western medicine, and Q is the weight score of the combined utilization rate of the traditional Chinese medicine and the western medicine;
calculating the star-grade score of the uploaded medical records according to a formula (5):
in the formula (5), the first and second groups of the chemical reaction materials are selected from the group consisting of,
UP, uploading medical record star score and total i The doctor uploads the total number of medical records with star level i, total: doctor uploads the number of medical calendars in total, i: i is more than or equal to 1 and less than or equal to n, and i is an integer.
Example 2
In the embodiment, a large amount of clinical electronic medical record data of more than thirty traditional Chinese medicine hospitals in a plurality of counties of Sichuan province are collected through a big data analysis and acquisition platform, and a traditional Chinese medicine clinical skill digital evaluation system based on big data analysis comprehensively considers the clinical curative effect, dominant disease species, traditional Chinese medicine diagnosis and treatment method, the working attitude and the working amount in all directions to construct a traditional Chinese medicine clinical digital evaluation system framework (shown in an attached figure 1) and an index ratio (shown in a table 1). On the basis of the evaluation framework, a data acquisition platform and a data mining analysis intelligent platform are utilized, the evaluation object is systematically measured and analyzed by cleaning the acquired data of the evaluation object, and finally, the evaluation object is notarized to obtain a scientific and reasonable evaluation conclusion.
TABLE 1 index ratio of Chinese medicine clinical digital evaluation system
The weight setting rules and proportions in table 1:
firstly, listing examination indexes of clinical capability of doctors, then sorting the indexes according to importance by a pairwise comparison method, wherein the indexes are ranked in front of each other, the weight is correspondingly larger, and the weight of each index is considered when setting the weight according to the principle that the clinical curative effect is prior, the traditional Chinese medicine treatment method is assisted, and the working efficiency and the working attitude are inferior:
1. the weight is generally between 5% and 30%, so that the risk of evaluation and evaluation is prevented from being too concentrated due to high weight, and the work quality indexes influencing evaluation are not concerned due to low weight;
2. the set weight generally takes a multiple of 5, so that the calculation is convenient;
3. the weight setting is higher for the indexes with strong importance and strong comprehensiveness for evaluating the capability of doctors and the indexes with direct and obvious influence, such as dominant disease species, symptom improvement, symptomatic medication coincidence rate of rational and legal prescriptions and the like.
The big data analysis algorithm in this embodiment is as follows:
1. calculating the clinical curative effect (full score of 30 points): the patient's good score is 10 points, the symptom is improved by 10 points, the re-diagnosis rate is 5 points, and the visit rate is 5 points.
1.1 patient goodness scoring algorithm, as shown in Table 2:
table 2: patient goodness scoring algorithm
| Serial number | Condition | Score of |
| 1 | The favorable rating is more than or equal to 90 percent | 10 minutes |
| 2 | The good appraisal rate is more than or equal to 70 percent and less than 90 percent | 8 is divided into |
| 3 | The good appraisal rate is more than or equal to 50 percent and less than 70 percent | 6 minutes |
| 4 | The good appraisal rate is more than or equal to 30 percent and less than 50 percent | 4 is divided into |
| 5 | The good appraisal rate is more than or equal to 10 percent and less than 30 percent | 2 is divided into |
| 6 | The good evaluation rate is more than 0 and less than 10 percent | 1 point is |
| 7 | Favorable rating =0 | 0 point of |
1.2 symptom improvement: in the system, no index for improving symptoms exists, so that the doctor is given full mark in a unified way during calculation.
1.3 visit rate or follow-up rate scoring algorithm, as shown in table 3:
table 3: score algorithm for diagnosis rate or re-diagnosis rate
2. Calculating dominant disease species (score of 25): the single disease treatment rate or the re-diagnosis rate is ranked 5 points, the patient good rating is 5 points, the symptom is improved 5 points, and the diagnosis and treatment time is 5 points.
2.1 Single disease visit rate or follow-up rate ranking score algorithm, as shown in Table 4:
table 4: ranking and scoring algorithm for single-disease-type diagnosis rate or double-diagnosis rate
As shown in FIG. 3, in the process of calculating the individual disease treatment rate, the total number of people in the individual disease treatment is calculatedThen, screening is carried out to screen out the maximum treatment rate, whereinReferring to the maximum visit rate, the system may be implemented by a bubble sort program.
The hospital visit rate x = sg/tsg is to solve the unfair problem caused by different regional advantages in the whole province ranking. On the basis of eliminating regional differences, the maximum treatment rate is sorted and screened out.
2.2 patient well-scored Algorithm, as shown in Table 7.
3. Calculating the traditional Chinese medicine diagnosis and treatment method (the full score is 20): the coincidence rate of the drugs of the physical and legal methods for the symptomatic medication is 15 minutes, and the combined utilization rate of the traditional Chinese medicine and the western medicine is 5 minutes.
The algorithm of the coincidence rate of the therapeutic and symptomatic medicines and the score of the Chinese and western medicine combined use rate is shown in table 5:
table 5: algorithm for scoring symptomatically-using coincidence rate of traditional Chinese medicine and western medicine
4. Calculating the working attitude (full score is 10): the medical record is classified by 5 stars and the patient rating is 5.
4.1 upload case history star score algorithm, as shown in table 6:
table 6: star grade scoring algorithm for uploading medical records
4.2 patient goodness score algorithm, as shown in Table 7:
table 7: patient goodness score algorithm
| Serial number | Condition | Score of |
| 1 | The favorable rating is more than or equal to 90 percent | 5 points of |
| 2 | The good evaluation rate is more than or equal to 60 percent and less than 90 percent | 4 is divided into |
| 3 | The good appraisal rate is more than or equal to 30 percent and less than 60 percent | 3 points of |
| 4 | The evaluation rate is more than or equal to 0 percent and less than 30 percent | 1 minute (1) |
| 5 | Favorable score =0 | 0 point (min) |
5. Calculation workload (full score 15): the number of the medical records uploaded is 10 points, and the treatment rate is 5 points
5.1 medical record uploading quantity scoring algorithm, as shown in Table 8:
table 8: medical record uploading quantity scoring algorithm
| Serial number | Condition | Score of |
| 1 | The number of uploaded medical records in unit time is more than or equal to 200 | 5 points of |
| 2 | 100&= uploading the number of medical records less than 200 in unit time | 4 is divided into |
| 3 | 50&= uploading the number of medical records less than 100 in unit time | 3 points of |
| 4 | 0&= uploading the number of medical records less than 50 in unit time | 1 point is |
5.2 visit rate scoring algorithm, as shown in Table 3.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it is therefore intended that all such changes and modifications as fall within the true spirit and scope of the invention be considered as within the following claims.
Claims (4)
1. The Chinese medicine skill clinical digital evaluation method based on big data analysis is characterized by comprising the following steps: the method comprises the following steps:
firstly, establishing a traditional Chinese medicine clinical digital evaluation system: the evaluation system framework comprises five primary indexes: clinical efficacy indexes, dominant disease indexes, traditional Chinese medicine diagnosis and treatment method indexes, working attitude indexes and workload indexes;
the secondary indexes of the clinical curative effect indexes comprise: the rate of return visit, the rate of visit, the improvement of symptoms and the rate of good appraisal of the patient;
the secondary indexes of the dominant disease species indexes comprise: ranking the rate of re-diagnosis of single disease species, ranking the rate of seeing a doctor of single disease species, diagnosis and treatment time, improving symptoms and judging the good rate of patients;
the secondary indexes of the traditional Chinese medicine diagnosis and treatment method indexes comprise: the rate of coincidence of the traditional Chinese medicine and the western medicine for the symptom of the medicine of the physical and legal prescription and the rate of utilization of the combination of the traditional Chinese medicine and the western medicine are determined;
the second-level indexes of the working attitude indexes comprise: uploading medical record star grade and patient rating;
the secondary indicators of the workload indicators include: the diagnosis rate and the number of uploaded medical records;
secondly, setting the weight of each index: setting the weight of the first-level index and then setting the weight of the second-level index;
thirdly, evaluating and analyzing the capability of the Chinese medical doctor by using a big data analysis method to obtain an evaluation result;
the big data analysis method comprises the following steps: acquiring clinical electronic medical record data, preprocessing the acquired data, integrating the preprocessed data, establishing a data analysis model, calculating the score of an index, reasonably explaining and evaluating the score, and presenting an evaluation result through a visualization effect;
in the third step, the score of the re-diagnosis rate or the visit rate index is calculated according to the formula (1):
in the formula (1), the first and second groups,
further: score of the re-diagnosis rate or visit rate index, p: the average doctor review rate or the doctor visit rate of similar departments, q is the doctor review rate or the doctor visit rate, m: the number of the patients who are in a hospital for a return visit or a visit, n: the number of doctor's revisits or visits, Q: the weight of the rate of the return visit or the rate of the visit;
calculating the total score of the single disease re-diagnosis rate ranking and the single disease treatment rate ranking according to the formula (2):
advantage＝(A+B)*Q*2 (2)
in the formula (2), the first and second groups of the compound,
advantage: the sum of the ranking score of the rate of double-disease and the ranking score of the rate of seeing single-disease, A: the rate of the single disease re-diagnosis is ranked and scored, B: the diagnosis rate of each disease is ranked and scored, sg: amount of single disease, tsg: the amount of a single disease to be treated in the same department of the hospital, k: the total number of doctors in a single disease in the whole province, n: doctor province rank of single disease category, m: the number of the patients who have a repeated diagnosis or a number of the patients who have a visit Q is the weight of the ranking of the rate of repeated diagnosis of the single disease or the ranking of the rate of visit of the single disease;the maximum diagnosis rate;
calculating the score of the symptomatic medication compliance rate index of the rational and legal prescription according to the formula (3):
in the formula (3), 0<p i &1, 1-n, i-n: maximum number of medical records, p i : the medication of the medical record with the number i conforms to the symptom; symptomatic, the coincidence rate score of the physical and legal prescription drugs for symptomatic medication, total: the doctor always uploads the number of courses of disease, Q is the weight of the symptomatic medication coincidence rate of the rational and legal prescriptions;
calculating the score of the index of the utilization rate of the combination of Chinese and Western medicine according to the formula (4):
unite＝ratio*Q*2 (4)
in the formula (4), 0-straw-type-1-unite: scoring the index of the combined utilization rate of the traditional Chinese medicine and the western medicine, wherein the ratio is the coincidence rate of the combined utilization of the traditional Chinese medicine and the western medicine, and Q is the weight score of the combined utilization rate of the traditional Chinese medicine and the western medicine;
calculating the score of the uploaded medical record star level according to the formula (5):
in the formula (5), the first and second groups,
UP, uploading medical record star score and total i The total number of medical records with the star level i uploaded by doctors, total: doctor's total number of uploaded medical records, i: representing the star level reached by uploading the medical records, i is more than or equal to 1 and less than or equal to n, and i is an integer.
2. The digital clinical evaluation method for skills in traditional Chinese medicine based on big data analysis according to claim 1, wherein: in the third step, the patient high rating index is graded, and each grade is assigned with a score, the score corresponding to each grade is the score of the patient high rating index, and the score assigned at each grade is not more than the weight of the patient high rating index; the uploaded medical record quantity indexes are graded, scores are distributed to each grade, the score corresponding to each grade is the score of the uploaded medical record quantity, the uploaded medical record quantity indexes are the quantity of uploaded cases in unit time, and the scores distributed to each grade do not exceed the weight score of the uploaded medical record quantity indexes.
3. The digital clinical evaluation method for skills in traditional Chinese medicine based on big data analysis according to claim 1, wherein: in the third step, the SQL Server 2008R2 database and the mining tool ETL are adopted for processing data; the cleaning data adopts a reflection technology based on a programming language and a Python script technology.
4. The digital clinical evaluation method for skills in traditional Chinese medicine based on big data analysis according to claim 1, wherein: the data preprocessing comprises the steps of cleaning, converting and loading clinical electronic medical record data, and combining a traditional Chinese medicine science and method prescription standard table to carry out clinical four-diagnosis information: the data of symptoms, syndrome types, prescriptions and medicines are combed, and the method comprises the following steps: treating positive and different names of the traditional Chinese medicines, treating syndrome type structuring and treating symptoms;
treating the positive and the different names of the traditional Chinese medicines: processing wrongly written characters and omitted characters;
and (3) syndrome type structuring treatment: compositing or splitting content;
symptom treatment: split by word processing.
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| CN106485403A (en) * | 2016-09-27 | 2017-03-08 | 成都金盘电子科大多媒体技术有限公司 | Hospital evaluation system and evaluation method based on medical big data |
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| WO2019084863A1 (en) * | 2017-11-02 | 2019-05-09 | 刘莉 | Electronic medical record evaluation system |
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| CN110322970A (en) * | 2019-06-13 | 2019-10-11 | 郑思思 | A kind of method for estimating curative effect and system increasing patient's inner experience |
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| CN111460260A (en) * | 2020-03-31 | 2020-07-28 | 上海智芝全智能科技有限公司 | Data processing system, method and medium for multi-type data |
| CN111785395B (en) * | 2020-05-08 | 2022-09-09 | 四川大学华西第二医院 | Monitoring method, system, equipment and storage medium for clinical application of antibacterial drugs |
| CN111667898B (en) * | 2020-06-19 | 2021-08-27 | 重庆中联信息产业有限责任公司 | Classification processing method of medical medication rule information |
| CN112053758B (en) * | 2020-08-27 | 2024-04-16 | 北京颢云信息科技股份有限公司 | Intelligent construction method of single disease seed database |
| CN116777307B (en) * | 2023-08-21 | 2023-11-07 | 河北省儿童医院(河北省第五人民医院、河北省儿科研究所) | Hospital doctor comprehensive analysis and evaluation method based on big data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625714A (en) * | 2009-08-13 | 2010-01-13 | 北京中医药大学 | Remote traditional Chinese medical diagnosis and treatment system |
| CN102342858A (en) * | 2010-08-06 | 2012-02-08 | 上海中医药大学 | Chinese medicine sound diagnosis acquisition and analysis system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI275396B (en) * | 2001-10-17 | 2007-03-11 | Braingenesis Biotechnology Co | Composition of traditional Chinese medicines for preventing and treating cerebrovascular disease |
-
2016
- 2016-02-20 CN CN201610094749.6A patent/CN105787262B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625714A (en) * | 2009-08-13 | 2010-01-13 | 北京中医药大学 | Remote traditional Chinese medical diagnosis and treatment system |
| CN102342858A (en) * | 2010-08-06 | 2012-02-08 | 上海中医药大学 | Chinese medicine sound diagnosis acquisition and analysis system |
Non-Patent Citations (2)
| Title |
|---|
| 中医优势病种信息化研究;包中龙等;《亚太传统医药》;20150731;第11卷(第14期);第1-3页 * |
| 某大型综合性医院医生综合素质和工作绩效评价指标体系的设计;滕怀金等;《中国医院》;20101231;第14卷(第3期);第50-51页 * |
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