CN116777307A - Hospital doctor comprehensive analysis and evaluation method based on big data - Google Patents
Hospital doctor comprehensive analysis and evaluation method based on big data Download PDFInfo
- Publication number
- CN116777307A CN116777307A CN202311048922.5A CN202311048922A CN116777307A CN 116777307 A CN116777307 A CN 116777307A CN 202311048922 A CN202311048922 A CN 202311048922A CN 116777307 A CN116777307 A CN 116777307A
- Authority
- CN
- China
- Prior art keywords
- department
- doctor
- treatment
- patient
- target hospital
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 158
- 238000004458 analytical method Methods 0.000 title claims abstract description 57
- 238000003745 diagnosis Methods 0.000 claims abstract description 69
- 238000011282 treatment Methods 0.000 claims description 197
- 201000010099 disease Diseases 0.000 claims description 92
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 92
- 238000000034 method Methods 0.000 claims description 60
- 230000008569 process Effects 0.000 claims description 22
- 239000003814 drug Substances 0.000 claims description 12
- 230000001225 therapeutic effect Effects 0.000 claims description 12
- 229940126585 therapeutic drug Drugs 0.000 claims description 9
- 229940079593 drug Drugs 0.000 claims description 8
- 238000011369 optimal treatment Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000004044 response Effects 0.000 claims description 4
- 238000011269 treatment regimen Methods 0.000 claims description 4
- 238000007435 diagnostic evaluation Methods 0.000 claims description 3
- 229940124597 therapeutic agent Drugs 0.000 claims description 3
- 230000007812 deficiency Effects 0.000 abstract description 4
- 230000036541 health Effects 0.000 abstract description 4
- 230000007547 defect Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000007792 addition Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2219—Large Object storage; Management thereof
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Databases & Information Systems (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Quality & Reliability (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention belongs to the technical field of analysis and evaluation of doctors in hospitals, and relates to a comprehensive analysis and evaluation method of doctors in hospitals based on big data. According to the invention, through the basic information, the diagnosis information and the satisfaction feedback information of each doctor in each department of the target hospital, the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital are analyzed, so that the comprehensive evaluation coefficient of each doctor in each department of the target hospital is obtained, the comprehensive evaluation grade of each doctor in each department of the target hospital is further obtained, the doctor's skill level of the target hospital is ensured to be applied to the actual capability, the doctor is encouraged to continuously learn and grow up, the professional literacy and the technical level are improved, the doctor is helped to further improve the deficiency and correct in time, the experience and satisfaction of a patient are helped to be improved, the trust relationship of the doctor and the patient is enhanced, the medical level and the quality of service are improved, and the health and the safety of the patient are ensured.
Description
Technical Field
The invention belongs to the technical field of analysis and evaluation of doctors in hospitals, and relates to a comprehensive analysis and evaluation method of doctors in hospitals based on big data.
Background
People need to seek medical attention for various reasons in daily life, doctors are to study and study medical science and technology, life is saved as professional people, and the rescue and the rest are the internal work of the doctors. The working capacity of a doctor will determine the health and even life safety of the patient to be treated, so that it is necessary to evaluate and analyze the working capacity of the doctor in the hospital, find the deficiency and prompt improvement.
The existing doctor working capacity analysis and evaluation method can basically meet the requirements, but still has certain defects: (1) The prior doctor working ability analysis and evaluation method is mainly used for evaluating basic information of a doctor according to the academic history, school study results, academic levels and the like of the doctor, but lacks consideration of the working basic information conditions of the doctor after participating in work, such as job title grade, total number of patients who take part in work, job title grade is approval of professional quality and medical knowledge of the doctor, total number of patients who take part in diagnosis is another reflecting mode of the working ability of the doctor, and the working ability and learning ability of the doctor cannot be comprehensively calculated, so that the prior doctor working ability analysis and evaluation method cannot obtain the medical skill level after the working of the doctor to be applied to the actual ability, and cannot avoid the situation that the doctor with strong learning ability is a paper talk in work.
(2) The traditional doctor working capacity analysis and evaluation method is lack of evaluating the rationality and accuracy of medication in the treatment process of a patient when evaluating the doctor's ability to visit, so that the evaluation of the doctor's ability to visit is not accurate enough, and the evaluation of the estimated success rate of treatment is also lack, and the patient is informed of the success rate of treatment and the risk of treatment as the professional criterion requirement of the doctor.
(3) The existing doctor working capacity analysis and evaluation method lacks consideration on the aspects of the times of consultation of a doctor, the waiting time of the patient during the consultation and the like when evaluating the satisfaction degree of the patient, cannot ensure whether the doctor fulfills working responsibilities or not, cannot evaluate whether the doctor has enough care on the patient or not, and further cannot truly know the experience feeling and the satisfaction degree of the patient.
Disclosure of Invention
In view of this, in order to solve the problems presented in the above-mentioned background art, a comprehensive analysis and evaluation method for a hospital doctor based on big data is now proposed.
The aim of the invention can be achieved by the following technical scheme: the invention provides a comprehensive analysis and evaluation method for a hospital doctor based on big data, which comprises the following steps: (1) basic information acquisition: acquiring basic information of doctors in each department of a target hospital in a current monitoring period, recording the basic information as basic information of the doctors in each department of the target hospital, and numbering each department of the target hospital as followsThe doctor numbers +.>。
(2) Basic information analysis: basic information evaluation coefficients of doctors in each department of the target hospital are analyzed.
(3) Diagnostic information acquisition: the diagnosis information of each patient to be diagnosed of each doctor in each department of the target hospital is obtained.
(4) Diagnostic information analysis: and analyzing the diagnosis evaluation coefficients of the doctors in each department of the target hospital.
(5) Satisfaction feedback information acquisition: and acquiring satisfaction feedback information of each patient to be treated of each doctor in each department of the target hospital.
(6) Satisfaction feedback information analysis: and analyzing satisfaction evaluation coefficients of various doctors in various departments of the target hospital.
(7) Comprehensive capability evaluation analysis: according to the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital, the comprehensive evaluation coefficient of each doctor in each department of the target hospital is analyzed, and further the comprehensive evaluation grade of each doctor in each department of the target hospital is obtained.
Preferably, the basic information of each doctor in each department of the target hospital includes a job title level, a service life and a total number of patients who are received.
Preferably, the specific analysis process of the basic information evaluation coefficient of each doctor in each department of the target hospital is as follows: (21) The job title level, the service life and the total number of patients who are received for each doctor in each department of the target hospital are obtained.
(22) Comparing the job rank of each doctor in each department of the target hospital with the set corresponding score of each job rank to obtain the corresponding score of each doctor in each department of the target hospital, and marking as。
(23) Basic information evaluation coefficient of each doctor in each department of analysis target hospitalWherein->The service life of the jth doctor in the ith department of the target hospital and the total number of patients who are received, respectively,/->Respectively, minimum score, minimum years of practice and total number of patients who received the minimum, which are set to allow the doctor to participate in the job assessment,>the weight coefficient is a basic information evaluation coefficient corresponding to the job title level, the service life and the total number of patients to be diagnosed, and e is a natural constant.
Preferably, the diagnosis information of each patient to be diagnosed of each doctor in each department of the target hospital includes a diagnosis result, a treatment scheme and a treatment period.
The diagnosis results include disease type, etiology and stage of disease.
Treatment regimens include methods of treatment and various therapeutic agents.
Preferably, the specific analysis process of the diagnosis evaluation coefficients of each doctor in each department of the target hospital is as follows: (41) And extracting the diagnosis results, the treatment scheme and the treatment period of each patient to be diagnosed of each doctor in each department of the target hospital.
(42) And matching the disease type and the disease cause of each patient to be diagnosed of each doctor in each department of the target hospital with each disease cause corresponding to each disease type in each disease level stored in the medical database, so as to obtain the disease level of each patient to be diagnosed of each doctor in each department of the target hospital.
(43) The disease grade and disease type of each patient to be treated of each doctor in each department of the target hospital are matched with the reference treatment scheme and the reference treatment period corresponding to each type of disease in each disease grade stored in the target hospital information base, and the reference treatment scheme and the reference treatment period of each patient to be treated of each doctor in each department of the target hospital are obtained.
(44) Analyzing and obtaining the overlapping degree of the treatment scheme of the patient in each department of the target hospital according to the treatment scheme and the reference treatment scheme of the patient in each department of the target hospital, and marking asWherein，/>The number of each patient to be diagnosed is c is the number of patients to be diagnosed, and the expected treatment success rate of each patient to be diagnosed of each doctor in each department of the target hospital is analyzed according to the diagnosis result of each patient to be diagnosed of each doctor in each department of the target hospital>。
(45) Analysis of diagnostic evaluation coefficients of doctors in departments of target hospitalsWherein->For the purpose ofTreatment cycle of f-th patient to be diagnosed of jth doctor in ith department of standard hospital,/->For the reference treatment cycle of the f-th patient to be treated, which the j-th doctor belongs to, in the i-th department of the target hospital extracted from the medical database, +.>Weight of diagnosis evaluation coefficient corresponding to the set treatment plan overlap ratio, treatment period and predicted treatment success rate, +.>。
Preferably, the specific process for obtaining the overlapping ratio of the treatment schemes of the patients to be treated, which are affiliated by the doctors, in the departments of the target hospital is as follows: obtaining the treatment methods and the treatment method quantity in the treatment scheme of the patient in each department of the target hospital from the case list of the patient in each department of the target hospital, comparing the treatment methods and the treatment method quantity with the treatment method quantity in the reference treatment scheme, and obtaining the quantity of the coincident treatment method in the treatment scheme of the patient in each department of the target hospital and the reference treatment scheme。
The number of therapeutic drugs in the therapeutic scheme of each patient to be treated, the number of reference therapeutic drugs in the reference therapeutic scheme and the number of coincident therapeutic drugs in the therapeutic scheme and the reference therapeutic scheme in each department of the target hospital can be obtained in the same way and are respectively recorded as、/>And->。
Preset forThe permissible deviation values of the treatment methods and the treatment medicines in the treatment schemes corresponding to the various diseases in each disease grade and the reference treatment scheme are respectively recorded as。
Analyzing the overlapping degree of treatment schemes of the patients of the doctors in the departments of the target hospitalWhereinFor the number of treatment methods in the treatment scheme of the f-th patient to which the j-th doctor belongs in the ith department of the target hospital,the number of treatment methods in the reference treatment scheme for the f-th patient to which the j-th doctor belongs in the ith department of the target hospital.
Preferably, the specific analysis process of the predicted treatment success rate of each patient to be treated of each doctor in each department of the target hospital is as follows: extracting the disease grade and the disease stage of each patient to be treated of each doctor in each department of the target hospital.
Comparing the disease grade of each patient in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease grade, obtaining the influence coefficient of the expected treatment success rate corresponding to the disease grade of each patient in each department of the target hospital, and recording as。
Comparing the disease stage of each doctor in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease stage to obtain the targetThe influence coefficient of the disease stage of each doctor in each department of the hospital corresponding to the expected treatment success rate is recorded as。
Extracting the treatment scheme determination time and the predicted treatment time of each patient in each department of the target hospital from the medical record list of each patient in each department of the target hospital, and further differentiating the treatment scheme determination time and the predicted treatment time to obtain the predicted treatment interval duration of each patient in each department of the target hospital, wherein each doctor belongs to each patient in each department of the target hospital, and recording the predicted treatment interval duration as。
Extracting the optimal treatment time period threshold corresponding to each disease in each disease stage stored in the medical database, screening to obtain the optimal treatment time period threshold of each patient to be diagnosed of each doctor in each department of the target hospital, and marking as。
Analyzing the expected treatment success rate of each patient to be treated of each doctor in each department of the target hospital。
Preferably, the satisfaction feedback information of the patient to be treated of each doctor in each department of the target hospital comprises the frequency of inquiry, waiting time at the time of treatment and corresponding answer satisfaction of the inquiry of the patient at the time of treatment.
Preferably, the specific analysis process of the satisfaction evaluation coefficient of each doctor in each department of the target hospital is as follows: (61) Extracting the frequency of inquiry of each patient to be diagnosed of each doctor in each department of the target hospital, waiting time in diagnosis and corresponding answer conformity of the inquiry of the patient in diagnosis, and respectively recording as、/>And->。
(62) Comparing preset reference frequency of inquiry corresponding to various diseases in each disease grade, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, combining the disease grade and disease type of each patient in each doctor in each department of target hospital to obtain reference frequency of inquiry corresponding to each patient in each doctor in each department of target hospital, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, and respectively recording as、/>And。
(63) Analyzing satisfaction evaluation coefficient of each doctor in each department of target hospitalWhereinThe weight coefficients of satisfaction evaluation coefficients corresponding to the response satisfaction of the patient query at the time of consultation, waiting time at the time of consultation and the query of the patient at the time of consultation are respectively, e is a natural constant, and->Obtaining the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals according to the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals, wherein the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals is obtained according to the deviation of the frequency of the permission consultation of the diseases of the various types in the preset disease grades>。
Preferably, the specific process of obtaining the comprehensive evaluation grade of each doctor in each department of the target hospital is as follows: (71) Basic information evaluation coefficients, diagnosis evaluation coefficients and satisfaction evaluation coefficients of doctors in each department of the target hospital are extracted from the target hospital information base.
(72) Analysis target hospital department doctors comprehensive evaluation coefficientWherein->And e is a natural constant, which is a deviation correction value of the set basic information evaluation coefficient, diagnosis evaluation coefficient and satisfaction evaluation coefficient respectively.
(73) And matching the comprehensive evaluation coefficients of the doctors in the departments of the target hospital with the set comprehensive evaluation coefficient ranges corresponding to the comprehensive evaluation grades to obtain the comprehensive evaluation grades of the doctors in the departments of the target hospital, and storing the comprehensive evaluation grades in a target hospital information base.
Compared with the prior art, the invention has the following advantages and positive effects: (1) According to the invention, the basic information evaluation coefficients of the doctors in the departments of the target hospital are analyzed according to the job title level, the service life and the total number of the patients to be diagnosed of the doctors in the departments of the target hospital, so that the basic information of the doctors in the departments of the target hospital is evaluated, the situation that the learning ability and the working ability of the doctors are not matched is avoided, and the medical skill level of the doctors in the departments of the target hospital is applied to the actual ability.
(2) According to the diagnosis results, the treatment schemes, the treatment periods and the expected treatment success rates of the patients with the diagnosis of each doctor in each department of the target hospital, the diagnosis evaluation coefficients of each doctor in each department of the target hospital are analyzed, so that the diagnosis capability of each doctor in each department of the target hospital is evaluated, the rationality and the accuracy of medication of the patients in the treatment process are ensured, the accuracy of the evaluation of the doctor's diagnosis capability is improved, the continuous study and growth of the doctor are encouraged, and the professional literacy and technical level are improved.
(3) According to the method and the system, the satisfaction evaluation coefficient of each doctor in each department of the target hospital is analyzed according to the frequency of inquiry of the patient to be diagnosed, the waiting time in the diagnosis and the corresponding answer compliance of the patient inquiry in the diagnosis, so that the satisfaction evaluation of the patient to the doctor is truly obtained, the doctor is facilitated to further improve the deficiency, the experience and satisfaction of the patient are improved, and the doctor-patient trust relationship is enhanced.
(4) According to the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital, the comprehensive evaluation coefficient of each doctor in each department of the target hospital is analyzed, so that the comprehensive evaluation grade of each doctor in each department of the target hospital is obtained, the moral and occupational operations of the doctor are supervised, the problems and the defects of each doctor in each department of the target doctor are found, the correction is timely carried out, the medical level and the service quality are improved, and the health and the safety of patients are guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to FIG. 1, the invention provides a hospital doctor comprehensive method based on big dataAn analytical evaluation method comprising the steps of: (1) basic information acquisition: acquiring basic information of doctors in each department of a target hospital in a current monitoring period, recording the basic information as basic information of the doctors in each department of the target hospital, and numbering each department of the target hospital as followsThe doctor numbers +.>。
(2) Basic information analysis: basic information evaluation coefficients of doctors in each department of the target hospital are analyzed.
As a preferred embodiment of the present invention, the basic information of each doctor in each department of the target hospital includes the job title level, the service life and the total number of patients who are received.
It should be further noted that the specific acquisition modes of the job title grade, the service life and the total number of patients to be diagnosed are as follows: and extracting the job title level, the service life and the total number of patients who are received from each doctor in each department of the target hospital from the target hospital information base.
As a preferred embodiment of the present invention, the specific analysis process of the basic information evaluation coefficients of each doctor in each department of the target hospital is as follows: (21) The job title level, the service life and the total number of patients who are received for each doctor in each department of the target hospital are obtained.
(22) Comparing the job rank of each doctor in each department of the target hospital with the set corresponding score of each job rank to obtain the corresponding score of each doctor in each department of the target hospital, and marking as。
(23) Basic information evaluation coefficient of each doctor in each department of analysis target hospitalWherein->The service life of the jth doctor in the ith department of the target hospital and the total number of patients who are received, respectively,/->Respectively, minimum score, minimum years of practice and total number of patients who received the minimum, which are set to allow the doctor to participate in the job assessment,>the weight coefficient is a basic information evaluation coefficient corresponding to the job title level, the service life and the total number of patients to be diagnosed, and e is a natural constant.
According to the invention, the basic information evaluation coefficients of the doctors in the departments of the target hospital are analyzed according to the job title level, the service life and the total number of the patients to be diagnosed of the doctors in the departments of the target hospital, so that the basic information of the doctors in the departments of the target hospital is evaluated, the situation that the learning ability and the working ability of the doctors are not matched is avoided, and the medical skill level of the doctors in the departments of the target doctor is applied to the actual ability.
(3) Diagnostic information acquisition: the diagnosis information of each patient to be diagnosed of each doctor in each department of the target hospital is obtained.
(4) Diagnostic information analysis: and analyzing the diagnosis evaluation coefficients of the doctors in each department of the target hospital.
As a preferred embodiment of the present invention, the diagnosis information of each patient to be diagnosed of each doctor in each department of the target hospital includes a diagnosis result, a treatment plan and a treatment cycle.
The diagnosis results include disease type, etiology and stage of disease.
Treatment regimens include methods of treatment and various therapeutic agents.
It should be further noted that the specific acquisition modes of the etiology, the disease type and the stage of the disease are as follows: each doctor in each department of the target hospital inquires about the related problems of the illness state of each patient to which each doctor belongs, presumes the illness type of the patient according to the problems, further carries out related examination, obtains the etiology, the illness type and the illness stage of each patient to which each doctor belongs in each department of the target hospital, and records the etiology, the illness type and the illness stage of each patient to which each doctor belongs.
The specific acquisition modes of the treatment scheme and the treatment period are as follows: according to the diagnosis results of the patients in the departments of the target hospital, the doctors adopt a targeted treatment method and each treatment medicine according to the professional knowledge of the doctors, and the treatment period judgment is carried out by combining the personal conditions of the patients, so that the treatment scheme and the treatment period of the patients in the departments of the target hospital are obtained and recorded.
As a preferred embodiment of the present invention, the specific analysis process of the diagnosis evaluation coefficients of each doctor in each department of the target hospital is as follows: (41) And extracting the diagnosis results, the treatment scheme and the treatment period of each patient to be diagnosed of each doctor in each department of the target hospital.
(42) And matching the disease type and the disease cause of each patient to be diagnosed of each doctor in each department of the target hospital with each disease cause corresponding to each disease type in each disease level stored in the medical database, so as to obtain the disease level of each patient to be diagnosed of each doctor in each department of the target hospital.
(43) The disease grade and disease type of each patient to be treated of each doctor in each department of the target hospital are matched with the reference treatment scheme and the reference treatment period corresponding to each type of disease in each disease grade stored in the target hospital information base, and the reference treatment scheme and the reference treatment period of each patient to be treated of each doctor in each department of the target hospital are obtained.
(44) Analyzing and obtaining the overlapping degree of the treatment scheme of the patient in each department of the target hospital according to the treatment scheme and the reference treatment scheme of the patient in each department of the target hospital, and marking asWherein，/>The number of each patient to be diagnosed is c is the number of patients to be diagnosed, and the expected treatment success rate of each patient to be diagnosed of each doctor in each department of the target hospital is analyzed according to the diagnosis result of each patient to be diagnosed of each doctor in each department of the target hospital>。
(45) Analysis of diagnostic evaluation coefficients of doctors in departments of target hospitalsWherein->For the treatment cycle of the f-th patient to which the jth doctor belongs in the ith department of the target hospital,/for the patient to be treated>For the reference treatment cycle of the f-th patient to be treated, which the j-th doctor belongs to, in the i-th department of the target hospital extracted from the medical database, +.>Weight of diagnosis evaluation coefficient corresponding to the set treatment plan overlap ratio, treatment period and predicted treatment success rate, +.>。
As a preferred embodiment of the invention, the specific acquisition process of the treatment scheme coincidence degree of each patient to be treated of each doctor in each department of the target hospital is as follows: obtaining the treatment methods and the treatment method quantity in the treatment scheme of the patient in each department of the target hospital from the case list of the patient in each department of the target hospital, comparing the treatment methods and the treatment method quantity with the treatment method quantity in the reference treatment scheme, and obtaining the quantity of the coincident treatment method in the treatment scheme of the patient in each department of the target hospital and the reference treatment scheme。
The number of therapeutic drugs in the therapeutic scheme of each patient to be treated, the number of reference therapeutic drugs in the reference therapeutic scheme and the number of coincident therapeutic drugs in the therapeutic scheme and the reference therapeutic scheme in each department of the target hospital can be obtained in the same way and are respectively recorded as、/>And->。
Presetting permissible deviation values of the number of treatment methods and the number of treatment medicines in the corresponding treatment schemes and the reference treatment schemes of various diseases in each disease grade, screening the permissible deviation values to obtain the treatment schemes of the patients with the diagnosis in each department of the target hospital, and the permissible deviation values of the number of treatment methods and the number of treatment medicines in the reference treatment schemes, wherein the treatment schemes and the permissible deviation values are respectively recorded as。
Analyzing the overlapping degree of treatment schemes of the patients of the doctors in the departments of the target hospitalWhereinFor the number of treatment methods in the treatment scheme of the f-th patient to which the j-th doctor belongs in the ith department of the target hospital,the number of treatment methods in the reference treatment scheme for the f-th patient to which the j-th doctor belongs in the ith department of the target hospital.
As a preferred embodiment of the present invention, the specific analysis process of the predicted treatment success rate of each patient to be treated of each doctor in each department of the target hospital is as follows: extracting the disease grade and the disease stage of each patient to be treated of each doctor in each department of the target hospital.
Comparing the disease grade of each patient in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease grade, obtaining the influence coefficient of the expected treatment success rate corresponding to the disease grade of each patient in each department of the target hospital, and recording as。
Comparing the disease stage of each doctor in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease stage to obtain the influence coefficient of the expected treatment success rate corresponding to each disease stage of each doctor in each department of the target hospital, and recording as。
Extracting the treatment scheme determination time and the predicted treatment time of each patient in each department of the target hospital from the medical record list of each patient in each department of the target hospital, and further differentiating the treatment scheme determination time and the predicted treatment time to obtain the predicted treatment interval duration of each patient in each department of the target hospital, wherein each doctor belongs to each patient in each department of the target hospital, and recording the predicted treatment interval duration as。
Extracting the optimal treatment time period threshold corresponding to each disease in each disease stage stored in the medical database, screening to obtain the optimal treatment time period threshold of each patient to be diagnosed of each doctor in each department of the target hospital, and marking as。
Analyzing the expected treatment success rate of each patient to be treated of each doctor in each department of the target hospital。
According to the diagnosis results, the treatment schemes, the treatment periods and the expected treatment success rates of the patients with the diagnosis of each doctor in each department of the target hospital, the diagnosis evaluation coefficients of each doctor in each department of the target hospital are analyzed, so that the diagnosis capability of each doctor in each department of the target hospital is evaluated, the rationality and the accuracy of medication of the patients in the treatment process are ensured, the accuracy of the evaluation of the doctor's diagnosis capability is improved, the continuous study and growth of the doctor are encouraged, and the professional literacy and technical level are improved.
(5) Satisfaction feedback information acquisition: and acquiring satisfaction feedback information of each patient to be treated of each doctor in each department of the target hospital.
(6) Satisfaction feedback information analysis: and analyzing satisfaction evaluation coefficients of various doctors in various departments of the target hospital.
As a preferred embodiment of the invention, the satisfaction feedback information of the patient to be diagnosed of each doctor in each department of the target hospital comprises the frequency of inquiry diagnosis, the waiting time of the doctor at the time of the diagnosis and the corresponding answer satisfaction of the patient inquiry at the time of the diagnosis.
It should be further described that the specific acquisition mode of the frequency of inquiry is as follows: acquiring the frequency of inquiry of the patients of the doctor in each department of the target hospital according to the inquiry record table of the doctor in each department of the target doctor aiming at the patients of the doctor in each department of the target hospital.
The specific acquisition mode of the waiting time during the treatment is as follows: the appointment time of the patient to be treated of each doctor in each department of the target doctor is taken from the target hospital information base, and the appointment time is differed from the actual time of the patient to be treated recorded by each doctor in each department of the target doctor, so as to obtain the waiting time of the patient to be treated of each doctor in each department of the target doctor.
The specific acquisition mode of the corresponding answer conformity of the patient inquiry during the treatment is as follows: the condition of a patient in the treatment is recorded by arranging a camera in the treatment room, videos of the patients in the treatment room, which are treated by the doctors in the target hospital, are called from the monitoring room, answers of the questions asked by the doctors in the treatment room are extracted from the videos, the answers are recorded as answers of the questions of the doctors, and the answers are matched with the reference answers of the questions stored in the information base of the target hospital.
The specific matching method comprises the following steps: extracting each keyword in each question answer of a doctor according to a preset keyword principle, comparing each keyword with each keyword in each question reference answer, if a certain keyword in a certain question answer of the doctor is consistent with a certain keyword in the question reference answer, indicating that the keyword in the question answer of the doctor is successfully matched, counting the successful keyword matching quantity in the question answer of the doctor, and calculating the correct index of the question answer of the doctorWherein->And (3) respectively matching the successful number of keywords and the total number of keywords in the question solutions of the doctor, comparing the correct index of the question solutions of the doctor with a set correct index threshold, if the correct index threshold is larger than or equal to the set correct index threshold, marking the question solutions of the doctor as correct, counting the number of the correct question solutions of the doctor, and taking the number as the number of the correct questions solutions of the query questions of a certain patient to which a doctor belongs in a department of a target doctor.
Extracting the number of correct answers of the inquiry questions of a patient to be treated, which a doctor belongs in a department of a target doctor, and recording the number asAnalyzing response compliance in patient inquiry of each patient to be treated of each doctor in each department of the target doctorWherein->The number of inquiry questions when the patient is in the doctor's department of the target doctor, the doctor of the j belongs to the f-th patient.
As a preferred embodiment of the present invention, the specific analysis process of the satisfaction evaluation coefficient of each doctor in each department of the target hospital is as follows: (61) Extracting the frequency of inquiry of each patient to be diagnosed of each doctor in each department of the target hospital, waiting time in diagnosis and corresponding answer conformity of the inquiry of the patient in diagnosis, and respectively recording as、/>And->。
(62) Comparing preset reference frequency of inquiry corresponding to various diseases in each disease grade, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, combining the disease grade and disease type of each patient in each doctor in each department of target hospital to obtain reference frequency of inquiry corresponding to each patient in each doctor in each department of target hospital, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, and respectively recording as、/>And。
(63) Analyzing satisfaction evaluation coefficient of each doctor in each department of target hospitalWhereinRespectively, the frequency of inquiry and waiting time during the treatmentWeight coefficient of satisfaction evaluation coefficient corresponding to response compliance of patient query at inter-and visit, e is natural constant, +.>Obtaining the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals according to the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals, wherein the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals is obtained according to the deviation of the frequency of the permission consultation of the diseases of the various types in the preset disease grades>
According to the method and the system, the satisfaction evaluation coefficient of each doctor in each department of the target hospital is analyzed according to the frequency of inquiry of the patient to be diagnosed, the waiting time in the diagnosis and the corresponding answer compliance of the patient inquiry in the diagnosis, so that the satisfaction evaluation of the patient to the doctor is truly obtained, the doctor is facilitated to further improve the deficiency, the experience and satisfaction of the patient are improved, and the doctor-patient trust relationship is enhanced.
(7) Comprehensive capability evaluation analysis: according to the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital, the comprehensive evaluation coefficient of each doctor in each department of the target hospital is analyzed, and further the comprehensive evaluation grade of each doctor in each department of the target hospital is obtained.
As a preferred embodiment of the present invention, the specific process of obtaining the comprehensive evaluation level of each doctor in each department of the target hospital is as follows: (71) Basic information evaluation coefficients, diagnosis evaluation coefficients and satisfaction evaluation coefficients of doctors in each department of the target hospital are extracted from the target hospital information base.
(72) Analysis target hospital department doctors comprehensive evaluation coefficientWherein->Respectively set basic information evaluation coefficients,And (3) diagnosing the deviation correction value of the evaluation coefficient and the satisfaction evaluation coefficient, wherein e is a natural constant.
(73) And matching the comprehensive evaluation coefficients of the doctors in the departments of the target hospital with the set comprehensive evaluation coefficient ranges corresponding to the comprehensive evaluation grades to obtain the comprehensive evaluation grades of the doctors in the departments of the target hospital, and storing the comprehensive evaluation grades in a target hospital information base.
It should be further described that the specific process of obtaining the comprehensive evaluation level of each doctor in each department of the target hospital is: presetting the comprehensive evaluation coefficient range corresponding to each comprehensive evaluation grade, and respectively marking as，/>，And->And->。
If it isAnd if so, the comprehensive evaluation grade of the jth doctor in the ith department of the target hospital is excellent.
If it isAnd if so, the comprehensive evaluation grade of the jth doctor in the ith department of the target hospital is good. />
If it isAnd if the comprehensive evaluation grade of the jth doctor in the ith department of the target hospital is medium.
If it isAnd if the comprehensive evaluation grade of the jth doctor in the ith department of the target hospital is unqualified.
The comprehensive evaluation grades of doctors in each department of the target hospital are monitored and analyzed in a preset fixed period, after the comprehensive evaluation grades of the doctors in each department of the target hospital in each monitoring period are obtained, the doctors with excellent comprehensive evaluation grades, good comprehensive evaluation grades, medium comprehensive evaluation grades and unqualified comprehensive evaluation grades of the target hospital are counted, the doctors with excellent comprehensive evaluation grades are expressed, encouragement and promotion of improvement are conducted for the doctors with good comprehensive evaluation grades, self problems and insufficient analysis are conducted for the doctors with medium comprehensive evaluation grades, the doctors are led to know about weak links, improvement is conducted, and comprehensive ability training study is conducted for the doctors with unqualified comprehensive evaluation grades.
According to the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital, the comprehensive evaluation coefficient of each doctor in each department of the target hospital is analyzed, so that the comprehensive evaluation grade of each doctor in each department of the target hospital is obtained, the moral and occupational operations of the doctor are supervised, the problems and the defects of each doctor in each department of the target doctor are found, the correction is timely carried out, the medical level and the service quality are improved, and the health and the safety of patients are guaranteed.
It should be noted that, the basis of each value preset in the present invention is industry standard.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (10)
1. A hospital doctor comprehensive analysis and evaluation method based on big data is characterized in that: comprising the following steps:
(1) Base groupThe information acquisition: acquiring basic information of doctors in each department of a target hospital in a current monitoring period, recording the basic information as basic information of the doctors in each department of the target hospital, and numbering each department of the target hospital as followsThe doctor numbers +.>；
(2) Basic information analysis: basic information evaluation coefficients of doctors in each department of a target hospital are analyzed;
(3) Diagnostic information acquisition: acquiring diagnosis information of each patient to be diagnosed of each doctor in each department of a target hospital;
(4) Diagnostic information analysis: analyzing diagnosis evaluation coefficients of doctors in each department of a target hospital;
(5) Satisfaction feedback information acquisition: acquiring satisfaction feedback information of each patient to be treated of each doctor in each department of a target hospital;
(6) Satisfaction feedback information analysis: analyzing satisfaction evaluation coefficients of doctors in each department of a target hospital;
(7) Comprehensive capability evaluation analysis: according to the basic information evaluation coefficient, the diagnosis evaluation coefficient and the satisfaction evaluation coefficient of each doctor in each department of the target hospital, the comprehensive evaluation coefficient of each doctor in each department of the target hospital is analyzed, and further the comprehensive evaluation grade of each doctor in each department of the target hospital is obtained.
2. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 1, which is characterized in that: the basic information of each doctor in each department of the target hospital comprises the job title level, the service life and the total number of patients who are received.
3. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 2, which is characterized in that: the specific analysis process of the basic information evaluation coefficient of each doctor in each department of the target hospital is as follows:
(21) Acquiring the job title level, the service life and the total number of patients who are received by each doctor in each department of the target hospital;
(22) Comparing the job rank of each doctor in each department of the target hospital with the set corresponding score of each job rank to obtain the corresponding score of each doctor in each department of the target hospital, and marking as；
(23) Basic information evaluation coefficient of each doctor in each department of analysis target hospitalWherein->The service life of the jth doctor in the ith department of the target hospital and the total number of patients who are received, respectively,/->Respectively, minimum score, minimum years of practice and total number of patients who received the minimum, which are set to allow the doctor to participate in the job assessment,>the weight coefficient is a basic information evaluation coefficient corresponding to the job title level, the service life and the total number of patients to be diagnosed, and e is a natural constant.
4. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 1, which is characterized in that: the diagnosis information of each patient to be diagnosed of each doctor in each department of the target hospital comprises a diagnosis result, a treatment scheme and a treatment period;
wherein the diagnosis result includes disease type, etiology and stage of disease;
treatment regimens include methods of treatment and various therapeutic agents.
5. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 4, which is characterized in that: the specific analysis process of the diagnosis evaluation coefficients of the doctors in each department of the target hospital is as follows:
(41) Extracting the diagnosis results, treatment schemes and treatment periods of each patient to be diagnosed of each doctor in each department of the target hospital;
(42) Matching the disease type and the disease cause of each patient to be diagnosed of each doctor in each department of the target hospital with each disease cause corresponding to each disease type in each disease level stored in the medical database to obtain the disease level of each patient to be diagnosed of each doctor in each department of the target hospital;
(43) Matching the disease grade and disease type of each patient to be diagnosed of each doctor in each department of the target hospital with the reference treatment scheme and the reference treatment period corresponding to each type of disease in each disease grade stored in the target hospital information base to obtain the reference treatment scheme and the reference treatment period of each patient to be diagnosed of each doctor in each department of the target hospital;
(44) Analyzing and obtaining the overlapping degree of the treatment scheme of the patient in each department of the target hospital according to the treatment scheme and the reference treatment scheme of the patient in each department of the target hospital, and marking asWherein，/>The number of each patient to be diagnosed is c is the number of patients to be diagnosed, and the expected treatment success rate of each patient to be diagnosed of each doctor in each department of the target hospital is analyzed according to the diagnosis result of each patient to be diagnosed of each doctor in each department of the target hospital>；
(45) Analysis of diagnostic evaluation coefficients of doctors in departments of target hospitalsWherein->For the treatment cycle of the f-th patient to which the jth doctor belongs in the ith department of the target hospital,/for the patient to be treated>For the reference treatment cycle of the f-th patient to be treated, which the j-th doctor belongs to, in the i-th department of the target hospital extracted from the medical database, +.>Weight of diagnosis evaluation coefficient corresponding to the set treatment plan overlap ratio, treatment period and predicted treatment success rate, +.>。
6. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 5, which is characterized in that: the specific acquisition process of the treatment scheme coincidence degree of each patient to be treated of each doctor in each department of the target hospital is as follows:
obtaining the treatment methods and the treatment method quantity in the treatment scheme of the patient in each department of the target hospital from the case list of the patient in each department of the target hospital, comparing the treatment methods and the treatment method quantity with the treatment method quantity in the reference treatment scheme, and obtaining the quantity of the coincident treatment method in the treatment scheme of the patient in each department of the target hospital and the reference treatment scheme；
The number of therapeutic drugs in the therapeutic scheme of each patient to be treated, the number of reference therapeutic drugs in the reference therapeutic scheme and the number of overlapped therapeutic drugs in the therapeutic scheme and the reference therapeutic scheme in each department of the target hospital are obtained in the same way and are respectively recorded as、/>And->；
Presetting permissible deviation values of the number of treatment methods and the number of treatment medicines in the corresponding treatment schemes and the reference treatment schemes of various diseases in each disease grade, screening the permissible deviation values to obtain the treatment schemes of the patients with the diagnosis in each department of the target hospital, and the permissible deviation values of the number of treatment methods and the number of treatment medicines in the reference treatment schemes, wherein the treatment schemes and the permissible deviation values are respectively recorded as；
Analyzing the overlapping degree of treatment schemes of the patients of the doctors in the departments of the target hospitalWhereinFor the number of treatment methods in the treatment regimen of the f-th patient to which the j-th doctor belongs in the i-th department of the target hospital,/for the patient in the patient>The number of treatment methods in the reference treatment scheme for the f-th patient to which the j-th doctor belongs in the ith department of the target hospital.
7. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 6, which is characterized in that: the specific analysis process of the predicted treatment success rate of each patient to be treated of each doctor in each department of the target hospital is as follows:
extracting the disease grade and the disease stage of each patient to be treated of each doctor in each department of the target hospital;
comparing the disease grade of each patient in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease grade, obtaining the influence coefficient of the expected treatment success rate corresponding to the disease grade of each patient in each department of the target hospital, and recording as；
Comparing the disease stage of each doctor in each department of the target hospital with the set influence coefficient of the expected treatment success rate corresponding to each disease stage to obtain the influence coefficient of the expected treatment success rate corresponding to each disease stage of each doctor in each department of the target hospital, and recording as；
Extracting the treatment scheme determination time and the predicted treatment time of each patient in each department of the target hospital from the medical record list of each patient in each department of the target hospital, and further differentiating the treatment scheme determination time and the predicted treatment time to obtain the predicted treatment interval duration of each patient in each department of the target hospital, wherein each doctor belongs to each patient in each department of the target hospital, and recording the predicted treatment interval duration as；
Extracting the optimal treatment time period threshold corresponding to each disease in each disease stage stored in the medical database, screening to obtain the optimal treatment time period threshold of each patient to be diagnosed of each doctor in each department of the target hospital, and marking as；
Analyzing the expected treatment success rate of each patient to be treated of each doctor in each department of the target hospital。
8. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 5, which is characterized in that: the satisfaction feedback information of the patient to be diagnosed of each doctor in each department of the target hospital comprises the frequency of inquiry diagnosis, waiting time in diagnosis and corresponding answer satisfaction of the inquiry of the patient in diagnosis.
9. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 8, which is characterized in that: the specific analysis process of the satisfaction evaluation coefficient of each doctor in each department of the target hospital is as follows:
(61) Extracting the frequency of inquiry of each patient to be diagnosed of each doctor in each department of the target hospital, waiting time in diagnosis and corresponding answer conformity of the inquiry of the patient in diagnosis, and respectively recording as、/>And->；
(62) Comparing preset reference frequency of inquiry corresponding to various diseases in each disease grade, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, combining the disease grade and disease type of each patient in each doctor in each department of target hospital to obtain reference frequency of inquiry corresponding to each patient in each doctor in each department of target hospital, allowable waiting time in treatment and corresponding reference answer degree of inquiry of patients in treatment, and respectively recording as、/>And；
(63) Analyzing satisfaction evaluation coefficient of each doctor in each department of target hospitalWhereinThe weight coefficients of satisfaction evaluation coefficients corresponding to the response satisfaction of the patient query at the time of consultation, waiting time at the time of consultation and the query of the patient at the time of consultation are respectively, e is a natural constant, and->Obtaining the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals according to the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals, wherein the deviation of the frequency of the permission consultation of the patients of the doctors in the departments of the target hospitals is obtained according to the deviation of the frequency of the permission consultation of the diseases of the various types in the preset disease grades>。
10. The comprehensive analysis and evaluation method for hospital doctors based on big data according to claim 1, which is characterized in that: the specific acquisition process of the comprehensive evaluation grade of each doctor in each department of the target hospital is as follows:
(71) Basic information evaluation coefficients, diagnosis evaluation coefficients and satisfaction evaluation coefficients of doctors in each department of the target hospital are extracted from the target hospital information base;
(72) Analysis target hospital department doctors comprehensive evaluation coefficientWherein->Deviation correction values of the set basic information evaluation coefficient, the set diagnosis evaluation coefficient and the set satisfaction evaluation coefficient are respectively, and e is a natural constant;
(73) And matching the comprehensive evaluation coefficients of the doctors in the departments of the target hospital with the set comprehensive evaluation coefficient ranges corresponding to the comprehensive evaluation grades to obtain the comprehensive evaluation grades of the doctors in the departments of the target hospital, and storing the comprehensive evaluation grades in a target hospital information base.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311048922.5A CN116777307B (en) | 2023-08-21 | 2023-08-21 | Hospital doctor comprehensive analysis and evaluation method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311048922.5A CN116777307B (en) | 2023-08-21 | 2023-08-21 | Hospital doctor comprehensive analysis and evaluation method based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116777307A true CN116777307A (en) | 2023-09-19 |
| CN116777307B CN116777307B (en) | 2023-11-07 |
Family
ID=87991632
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311048922.5A Active CN116777307B (en) | 2023-08-21 | 2023-08-21 | Hospital doctor comprehensive analysis and evaluation method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116777307B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117497203A (en) * | 2023-11-15 | 2024-02-02 | 江苏鼎驰电子科技有限公司 | Medical data interconnection and intercommunication method and system based on artificial intelligence and intelligent medical treatment |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101546418A (en) * | 2009-05-19 | 2009-09-30 | 北京朗福天健医药科技有限公司 | Clinic doctor check evaluating system and evaluating process |
| CN105512796A (en) * | 2015-11-25 | 2016-04-20 | 上海蓬海涞讯数据技术有限公司 | System and method for realizing performance evaluation of doctors of operating departments of hospital |
| CN105787262A (en) * | 2016-02-20 | 2016-07-20 | 成都中医药大学 | Traditional Chinese medicine clinical digital evaluation system and evaluation method thereof on basis of big data analysis |
| CN106777921A (en) * | 2016-11-29 | 2017-05-31 | 成都中医药大学 | The constructing system and method for a kind of three tree net evaluation models based on traditional Chinese medical science big data |
| CN107463770A (en) * | 2017-07-11 | 2017-12-12 | 武汉金豆医疗数据科技有限公司 | A kind of evaluation method and system based on medical diagnosis on disease associated packets |
| CN112053040A (en) * | 2020-08-19 | 2020-12-08 | 重庆市中迪医疗信息科技股份有限公司 | Evaluation method and system based on disease diagnosis related grouping |
| CN114005509A (en) * | 2021-10-30 | 2022-02-01 | 平安国际智慧城市科技股份有限公司 | Treatment scheme recommendation system, method, device and storage medium |
| CN114093496A (en) * | 2021-11-23 | 2022-02-25 | 纳里健康科技有限公司 | Method and system for processing medical information of Internet hospital |
| CN114093472A (en) * | 2021-10-13 | 2022-02-25 | 阿里健康科技(杭州)有限公司 | Triage information display method and client for Internet medical treatment |
| CN115409429A (en) * | 2022-10-31 | 2022-11-29 | 北京春天医药科技发展有限公司 | Hospital nursing satisfaction examination management system and method |
| CN116070096A (en) * | 2023-02-22 | 2023-05-05 | 医途(杭州)科技有限公司 | Method and system for helping hospital build patient portrait through big data analysis |
-
2023
- 2023-08-21 CN CN202311048922.5A patent/CN116777307B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101546418A (en) * | 2009-05-19 | 2009-09-30 | 北京朗福天健医药科技有限公司 | Clinic doctor check evaluating system and evaluating process |
| CN105512796A (en) * | 2015-11-25 | 2016-04-20 | 上海蓬海涞讯数据技术有限公司 | System and method for realizing performance evaluation of doctors of operating departments of hospital |
| CN105787262A (en) * | 2016-02-20 | 2016-07-20 | 成都中医药大学 | Traditional Chinese medicine clinical digital evaluation system and evaluation method thereof on basis of big data analysis |
| CN106777921A (en) * | 2016-11-29 | 2017-05-31 | 成都中医药大学 | The constructing system and method for a kind of three tree net evaluation models based on traditional Chinese medical science big data |
| CN107463770A (en) * | 2017-07-11 | 2017-12-12 | 武汉金豆医疗数据科技有限公司 | A kind of evaluation method and system based on medical diagnosis on disease associated packets |
| CN112053040A (en) * | 2020-08-19 | 2020-12-08 | 重庆市中迪医疗信息科技股份有限公司 | Evaluation method and system based on disease diagnosis related grouping |
| CN114093472A (en) * | 2021-10-13 | 2022-02-25 | 阿里健康科技(杭州)有限公司 | Triage information display method and client for Internet medical treatment |
| CN114005509A (en) * | 2021-10-30 | 2022-02-01 | 平安国际智慧城市科技股份有限公司 | Treatment scheme recommendation system, method, device and storage medium |
| CN114093496A (en) * | 2021-11-23 | 2022-02-25 | 纳里健康科技有限公司 | Method and system for processing medical information of Internet hospital |
| CN115409429A (en) * | 2022-10-31 | 2022-11-29 | 北京春天医药科技发展有限公司 | Hospital nursing satisfaction examination management system and method |
| CN116070096A (en) * | 2023-02-22 | 2023-05-05 | 医途(杭州)科技有限公司 | Method and system for helping hospital build patient portrait through big data analysis |
Non-Patent Citations (1)
| Title |
|---|
| 曹瑾: "甘肃省基层医疗服务质量评价研究——基于匿名标准化患者方法", 《中国优秀硕士期刊全文数据库 医药卫生科技辑》, pages 1 - 4 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117497203A (en) * | 2023-11-15 | 2024-02-02 | 江苏鼎驰电子科技有限公司 | Medical data interconnection and intercommunication method and system based on artificial intelligence and intelligent medical treatment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116777307B (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Babor et al. | Just the facts: Enhancing measurement of alcohol consumption using self-report methods | |
| Abelson | Examination of avoidable and unavoidable turnover. | |
| Gorman | Information needs of physicians | |
| Lambert et al. | Measuring Outcome in Professional Practice: Considerations in Selecting and Using Brief Outcome Instruments. | |
| Shaw et al. | Veterinarian-client-patient communication patterns used during clinical appointments in companion animal practice | |
| Bergner | Development, testing, and use of the Sickness Impact Profile | |
| Garb et al. | Judgment research and neuropsychological assessment: A narrative review and meta-analyses. | |
| Dawson | Physician judgment in clinical settings: methodological influences and cognitive performance | |
| Garb | Computer-administered interviews and rating scales. | |
| CN116777307B (en) | Hospital doctor comprehensive analysis and evaluation method based on big data | |
| Lapham et al. | Alcohol abuse screening instruments: normative test data collected from a first DWI offender screening program. | |
| US20110166880A1 (en) | Method and System for Analyzing Health Related Data of Patients | |
| Anderson et al. | Integrating quantitative pupillometry into regular care in a neurotrauma intensive care unit | |
| Litt et al. | Momentary mood and coping processes in TMD pain. | |
| Mears et al. | Drug treatment in the criminal justice system: The current state of knowledge | |
| Lanyon | Detecting deception: Current models and directions. | |
| Brayden et al. | Interpretations of colposcopic photographs: evidence for competence in assessing sexual abuse? | |
| Brown et al. | Primary care physicians' knowledge and behavior related to Alzheimer's disease | |
| Osterlind et al. | Incidence and practical issues of mental health for school-aged youth in juvenile justice detention | |
| Fals-Stewart | Detection of neuropsychological impairment among substance-abusing patients: Accuracy of the Neurobehavioral Cognitive Status Examination. | |
| Aspray et al. | Rapid assessment methods used for health-equity audit: diabetes mellitus among frail British care-home residents | |
| Higginson et al. | The clinical utility of quality of life measures | |
| Wyer et al. | Do workshops in evidence-based practice equip participants to identify and answer questions requiring consideration of clinical research? A diagnostic skill assessment | |
| CN112259186A (en) | Novel sleep disorder diagnosis and treatment system | |
| CN110675930A (en) | Decision-making method, system and device for non-small cell lung cancer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |