A modular intelligent doctor recommendation system for oral medical service platform

Abstract

The present invention discloses a modular intelligent doctor recommendation system for an oral medical service platform, and relates to the technical field of medical information systems. The present invention allows patients to input the type of oral disease they need to see and then screens out candidate doctors, then evaluates them from three dimensions of efficiency, evaluation, and experience, and performs weighted calculation based on the evaluation results to obtain a bias comprehensive index. Patients can adjust the weights of an efficiency performance index, a patient feedback index, and an experience performance index when calculating the bias comprehensive index according to their own needs and preferences, and rank the candidate doctors according to the bias comprehensive index, push the ranking results to the patients, and after receiving the pushed results, the patients select the corresponding candidate doctors to make an appointment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information systems, and in particular to a modularized intelligent doctor recommendation system for an oral medical service platform. Background Art

[0002] The existing oral medical service platform intelligent doctor recommendation system still has the following deficiencies in the actual application process of patients:

[0003] Patients often lack comprehensive and accurate reference information when registering and choosing a doctor. They can only choose randomly or rely on limited recommendations from others. They cannot ensure that the selected doctor can meet their expectations to the greatest extent, and cannot personalize the ranking of doctors according to their own preferences, such as consultation efficiency, doctor's service attitude, treatment experience, etc., to accurately screen and recommend suitable doctors for patients.

[0004] To this end, a modular intelligent doctor recommendation system for oral medical service platform is introduced. Summary of the invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a modular oral medical service platform intelligent doctor recommendation system.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A modular oral medical service platform intelligent doctor recommendation system, comprising:

[0007] Information management module: including patient information unit, medical information unit and resource information unit;

[0008] The patient information unit is used to build a patient personal information database to store the patient's basic information, medical records, imaging data, and medical records;

[0009] The medical information unit is used to build a medical staff information database to store the basic information, positions and consultation records of each medical staff in the dental hospital;

[0010] The resource information unit is used to construct a dental equipment information database to store the number, location, service life and maintenance records of each device in the dental hospital;

[0011] Appointment registration module: Patients view the doctor schedule information of the dental hospital on the expected date of consultation, and generate the preference comprehensive index RWA of each doctor on the expected date of consultation based on their own needs and preferences; the doctors on the expected date of consultation are sorted based on the size of the preference comprehensive index RWA and pushed to the patient. The patient receives the pushed sorting results and selects the corresponding doctor to make an online appointment; their own needs and preferences include efficiency preference, evaluation preference and experience preference;

[0012] Resource scheduling module: obtains the operating status of each examination equipment in the dental hospital, and executes corresponding steps to intelligently schedule patients when the corresponding examination equipment fails;

[0013] Data middle platform module: establish a unified login portal for staff in the dental hospital and set corresponding permissions for each staff member.

[0014] As a preferred implementation of the present invention, the preference comprehensive index rwa of each doctor on the expected consultation date is generated according to one's own demand preference, specifically:

[0015] M1: The patient inputs the type of oral disease that he / she needs to see. After the input is completed, the doctors on the expected date of consultation are screened, and the doctors who meet the oral disease type entered by the patient are selected as the candidates;

[0016] M2: Obtain the patient's ranking in the registration of each candidate doctor, that is, the number of patients ahead of the patient after the patient selects the corresponding candidate doctor to make an online appointment, and use the number of patients who have seen each candidate doctor as the number of patients waiting for the patient to select each candidate doctor;

[0017] M3: Taking the current time point as the evaluation starting point, obtain the number of patients seen by each candidate doctor within the set time interval before the evaluation starting point and the corresponding treatment time for each group of patients, and make a comprehensive evaluation based on the number of patients waiting for each candidate doctor to obtain the efficiency performance index reta of each candidate doctor within the current set time interval;

[0018] M4: Obtain the evaluation of each group of patients by each candidate doctor within the set time interval before the evaluation starting point, and perform a comprehensive evaluation to obtain the patient feedback index retb of each candidate doctor within the current set time interval;

[0019] M5: Extract the length of practice of each candidate doctor, the number of types of oral diseases he / she has treated, and the number of complex cases he / she has successfully treated, and make a comprehensive evaluation to obtain the experience performance index retc of each candidate doctor within the current set time interval;

[0020] M6: Obtain the efficiency performance index reta, patient feedback index retb and experience performance index retc of each candidate doctor within the set time interval before the current evaluation starting point, and substitute them into the formula A weighted calculation is performed to obtain the comprehensive preference index RWA of each candidate doctor under different demand preferences; among which z1, z2 and z3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under efficiency bias; p1, p2 and p3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under evaluation bias; r1, r2 and r3 are the weight factors of the efficiency performance index reta, patient feedback index retb and experience performance index retc under experience bias;

[0021] The candidate doctors are sorted from large to small according to the comprehensive preference index RWA based on the patient's different needs and preferences. After the sorting is completed, they are pushed to the patient for selection.

[0022] As a preferred implementation of the present invention, the efficiency performance index reta of each candidate doctor in the current set time interval is obtained, specifically:

[0023] M3-301: For each group of consultation processing time of each candidate doctor within the set time interval before the evaluation starting point, the highest consultation processing time and the lowest consultation processing time in each group are screened out, and after screening, the average of the remaining consultation processing time of each group of each candidate doctor is calculated to obtain the average consultation processing time of each candidate doctor within the current set time interval;

[0024] M3-302: Obtain the number of patients seen by each candidate doctor within a set time interval before the evaluation starting point, preset the intervals of the number of patients seen in each group, and set a mean time added value corresponding to each interval of the number of groups; match the number of patients seen by each candidate doctor with the interval of the number of groups, so as to obtain the mean time added value of each candidate doctor;

[0025] M3-303: extract the average consultation time and average additional value corresponding to each candidate doctor and multiply them, and use the result of the multiplication as the estimated consultation time of each candidate doctor;

[0026] M3-304: Multiply the number of patients waiting for each candidate doctor by the corresponding estimated consultation time to obtain the estimated waiting time for each candidate doctor;

[0027] Preset the waiting time value intervals for each group corresponding to the estimated waiting time, and set each waiting time value interval to correspond to a consultation priority score; match the estimated waiting time for each candidate doctor with the waiting time value intervals for each group, so as to obtain the consultation priority score for each candidate doctor;

[0028] The treatment priority score matched by each candidate doctor is used as the efficiency performance index reta of each candidate doctor.

[0029] As a preferred implementation of the present invention, the patient feedback index retb of each candidate doctor within the current set time interval is obtained, specifically:

[0030] M4-401: Classify the evaluation types of each group of patients in the set time interval before the evaluation starting point for each candidate doctor. The evaluation types include the patient's evaluation of the candidate doctor's service attitude, professional recognition and communication ability. After the patient's consultation, the service attitude, professional recognition and communication ability are evaluated;

[0031] M4-402: After the classification is completed, extract the service attitude evaluation, professional recognition evaluation and communication ability evaluation scores of each candidate doctor corresponding to each group of patient evaluations, calculate the mean of the scores of each group of service attitude evaluation of each candidate doctor, and obtain the service attitude performance index of each candidate doctor; calculate the mean of the scores of each group of professional recognition evaluation of each candidate doctor, and obtain the professional recognition performance index of each candidate doctor; calculate the mean of the scores of each group of communication ability evaluation of each candidate doctor, and obtain the communication ability performance index of each candidate doctor;

[0032] Preset the weight coefficients corresponding to the service attitude performance index, professional recognition performance index and communication ability performance index respectively, multiply the service attitude performance index, professional recognition performance index and communication ability performance index of each candidate doctor by the corresponding preset weight coefficient respectively, and then sum them up to obtain the comprehensive evaluation index of each candidate doctor;

[0033] M4-403: extract the number of patient evaluations of each candidate doctor within the currently set time interval, preset the intervals of each group of evaluation numbers corresponding to the number of patient evaluations, and set a comprehensive added value corresponding to each interval of each group of evaluation numbers; match the number of patient evaluations of each candidate doctor with the interval of each group of evaluation numbers, so as to obtain the comprehensive added value of each candidate doctor;

[0034] M4-404: Multiply the comprehensive evaluation index of each candidate doctor by the corresponding matching comprehensive added value, and divide the multiplied value by the integer three to obtain the patient feedback index retb of each candidate doctor.

[0035] As a preferred implementation of the present invention, the experience performance index retc of each candidate doctor within the current set time interval is obtained, specifically:

[0036] M5-501: A linear normalization formula is used to calculate the length of practice, the number of oral disease types handled, and the number of complex cases successfully treated by each candidate doctor;

[0037] M5-502: Set the maximum and minimum values ​​of the length of medical practice, and calculate the medical practice evaluation value of each candidate doctor through the linear standardized formula (T1-T2) / (T3-T2); T1, T2 and T3 represent the length of medical practice of the candidate doctor, the minimum value of the corresponding length of medical practice and the maximum value of the corresponding length of medical practice respectively;

[0038] M5-503: Set the maximum and minimum values ​​of the number of oral disease types, and calculate the treatment evaluation value of each candidate doctor through the linear normalization formula (D1-D2) / (D3-D2); D1, D2 and D3 represent the number of oral disease types handled by the candidate doctor, the minimum number of oral disease types and the maximum number of oral disease types, respectively;

[0039] M5-504: Set the maximum and minimum values ​​of the number of complex cases, and calculate the treatment evaluation value of each candidate doctor through the linear normalization formula (Y1-Y2) / (Y3-Y2); where Y1, Y2 and Y3 represent the number of complex cases successfully treated by the candidate doctor, the minimum number of complex cases and the maximum number of complex cases respectively;

[0040] M5-505: Set the weight coefficients corresponding to the medical evaluation value, the processing evaluation value and the treatment evaluation value, respectively, multiply the medical evaluation value, the processing evaluation value and the treatment evaluation value of each candidate doctor by the corresponding weight coefficient, and then sum them up to obtain the ability evaluation value of each candidate doctor;

[0041] M5-506: preset the evaluation value intervals corresponding to the competence evaluation values, set each evaluation value interval to correspond to a competence score; match the competence evaluation value of each candidate doctor with each evaluation value interval, and use the matched competence score as the experience performance index retc of each candidate doctor.

[0042] As a preferred embodiment of the present invention, when a corresponding examination device fails, corresponding steps are executed to intelligently schedule the patient, specifically:

[0043] Q1: Mark the corresponding examination equipment that has failed as a faulty equipment, filter out the patient list originally assigned to the faulty equipment, arrange the patients in the patient list according to the order of appointment time, and obtain other similar available examination equipment corresponding to the faulty equipment in the dental hospital as the equipment to be dispatched;

[0044] Q2: Obtain the number of patients who have been scheduled for each device to be scheduled at the current time point, identify the type of each device to be scheduled and the specific number within the type, and set the reference single examination time corresponding to the examination devices of different types and numbers; based on the identification result, obtain the reference single examination time corresponding to each device to be scheduled, multiply the number of patients who have been scheduled for each device to be scheduled at the current time point by the corresponding reference single examination time, and obtain the fastest estimated usage time of each device to be scheduled at the current time point;

[0045] Q3: Taking the faulty equipment as the starting point, obtain the location of each device to be dispatched and use it as the target point, calculate the distance between the starting point and each group of target points, preset the distance value ranges for each group corresponding to the distance, set each group of distance value ranges to correspond to a group of walking time, match the distance between the starting point and each group of target points with the preset distance value ranges one by one, and obtain the walking time between the starting point and each group of target points.

[0046] As a preferred embodiment of the present invention, when a corresponding examination device fails, corresponding steps are executed to intelligently schedule the patient, further comprising:

[0047] Q4: The patient ranked first in the patient list is selected as the priority patient, and the fastest estimated usage time of each device to be scheduled at the current time point is sorted from short to long according to the duration, and the walking time corresponding to each device to be scheduled is compared with the fastest estimated usage time. If the walking time of a device to be scheduled is less than the fastest estimated usage time, the fastest estimated usage time of the device to be scheduled is used as the final usage reference time of the priority patient; if the walking time of a device to be scheduled is greater than the fastest estimated usage time, the walking time of the device to be scheduled is used as the final usage reference time of the priority patient;

[0048] Q5: Obtain the final reference time of use of each device to be scheduled, and select the device to be scheduled with the shortest final reference time as the scheduling device for the patient to be scheduled first, and adjust the patient's appointment information from the faulty device to the scheduling device, and increase the number of scheduled patients on the scheduling device by one;

[0049] Q6: Allocate the equipment to be scheduled according to the order of the patients in the patient list by following steps Q2-Q5.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention allows patients to input the type of oral disease they need to see and then screens out doctors to be selected, and then evaluates them from three dimensions: efficiency, evaluation, and experience. When calculating the efficiency performance index, factors such as the number of patients seen and the length of time for treatment are comprehensively considered; when calculating the patient feedback index, the patient's evaluation of the doctor's service attitude, professional recognition, and communication ability is classified and counted; when calculating the experience performance index, the length of medical practice, the number of disease types treated, and the number of complex cases successfully treated are analyzed, and finally a weighted calculation is performed to obtain a biased comprehensive index. Patients can adjust the weights of the efficiency performance index, the patient feedback index, and the experience performance index according to their own needs and preferences when calculating the biased comprehensive index, and rank the doctors to be selected according to the biased comprehensive index, and push the ranking results to the patients. After receiving the push results, the patients select the corresponding doctor to make an appointment;

[0052] The present invention adjusts the weight in the calculation process of the bias comprehensive index by comprehensively considering the three dimensions of efficiency bias, evaluation bias and experience bias, fully respecting the different emphases of patients in the process of medical treatment, thereby realizing personalized medical treatment selection;

[0053] When checking equipment failure, the present invention first marks the faulty equipment and filters out the original assigned patient list, then obtains relevant information of other similar available equipment to be scheduled, including the number of scheduled patients, equipment type and examination time corresponding to the number, calculates the fastest estimated usage time, and considers the distance from the faulty equipment to the equipment to be scheduled and the walking time, so as to reasonably allocate examination equipment to patients, reduce waiting time, improve the efficiency of the examination process, and take into account the geographical layout of the hospital. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0055] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] See also Figure 1 As shown, a modular intelligent doctor recommendation system for an oral medical service platform includes an information management module, an appointment registration module, a resource scheduling module, and a data middle platform module;

[0058] The information management module includes a patient information unit, a medical information unit, and a resource information unit;

[0059] The patient information unit is used to build a patient personal information database to store the patient's basic information, medical records, imaging data, and medical records;

[0060] Basic information includes name, gender, age, contact information, allergy history, etc.; medical records include previous oral disease diagnosis records, treatment plans, etc.; imaging data include X-rays, CT scan images, etc.; medical records include consultation time, medical institution, attending physician, diagnosis results, treatment costs, etc.;

[0061] The medical information unit is used to build a medical staff information database to store the basic information, positions and consultation records of each medical staff in the dental hospital;

[0062] The resource information unit is used to construct a dental equipment information database to store the number, location, service life and maintenance records of each device in the dental hospital;

[0063] The appointment registration module is used for patients to view the doctor schedule information of the dental hospital on the expected date of consultation through the mobile application, and generate the preference comprehensive index RWA of each doctor on the expected date of consultation according to their own needs and preferences; the doctors on the expected date of consultation are sorted based on the size of the preference comprehensive index RWA and then pushed to the patient, who receives the sorting result and selects the corresponding doctor to make an online appointment registration; their own needs and preferences include efficiency preference, evaluation preference and experience preference;

[0064] Specifically:

[0065] M1: The patient inputs the type of oral disease that he / she needs to see, such as tooth decay, periodontitis, etc. After inputting, the doctors on the expected date of consultation are screened, and the doctors who meet the oral disease type entered by the patient are selected as the candidates;

[0066] M2: Obtain the patient's ranking in the registration of each candidate doctor, that is, the number of patients ahead of the patient after the patient selects the corresponding candidate doctor to make an online appointment, and use the number of patients who have seen each candidate doctor as the number of patients waiting for the patient to select each candidate doctor;

[0067] M3: Taking the current time point as the evaluation starting point, obtain the number of patients seen by each candidate doctor within the set time interval before the evaluation starting point and the corresponding treatment time for each group of patients, and make a comprehensive evaluation based on the number of patients waiting for each candidate doctor to obtain the efficiency performance index reta of each candidate doctor within the current set time interval;

[0068] M3-301: For each group of consultation processing time of each candidate doctor within the set time interval before the evaluation starting point, the highest consultation processing time and the lowest consultation processing time in each group are screened out, and after screening, the average of the remaining consultation processing time of each group of each candidate doctor is calculated to obtain the average consultation processing time of each candidate doctor within the current set time interval;

[0069] It should be noted that the lower the average consultation processing time of the candidate doctor, the higher the processing efficiency of the candidate doctor when seeing patients.

[0070] M3-302: Obtain the number of patients seen by each candidate doctor within the set time interval before the evaluation starting point, preset the intervals of the number of patients seen in each group, and set a mean time added value corresponding to each interval of the number of groups; the mean time added value range is set at 1.179-1.895. The more patients the candidate doctor sees, the lower the mean time added value obtained by the corresponding matching; match the number of patients seen by each candidate doctor with the interval of the number of groups, so as to obtain the mean time added value of each candidate doctor;

[0071] It should be noted that the higher the number of patients seen by the candidate doctor, the higher the reference value of the candidate doctor's average time for treatment, and the lower the corresponding matching average time added value.

[0072] M3-303: extract the average consultation time and average additional value corresponding to each candidate doctor and multiply them, and use the result of the multiplication as the estimated consultation time of each candidate doctor;

[0073] M3-304: Multiply the number of patients waiting for each candidate doctor by the corresponding estimated consultation time to obtain the estimated waiting time for each candidate doctor; if the number of patients waiting is 0, take the integer 1;

[0074] Preset the estimated waiting time and the corresponding waiting time value intervals for each group, and set each waiting time value interval to correspond to a consultation priority score; the lower the estimated waiting time, the higher the corresponding matching consultation priority score, and the consultation priority score is set to 1-10, which is a positive integer; match the estimated waiting time of each candidate doctor with the waiting time value interval of each group, so as to obtain the consultation priority score of each candidate doctor;

[0075] The priority score of the matched doctors is used as the efficiency performance index reta of each doctor.

[0076] M4: Obtain the evaluation of each group of patients by each candidate doctor within the set time interval before the evaluation starting point, and perform a comprehensive evaluation to obtain the patient feedback index retb of each candidate doctor within the current set time interval;

[0077] M4-401: For each group of patient evaluations of each candidate doctor within a set time interval before the evaluation starting point, the evaluation types are classified. The evaluation types include patient evaluation of the candidate doctor's service attitude, professional recognition evaluation, and communication ability evaluation. After the patient's consultation, the service attitude, professional recognition, and communication ability evaluation are conducted. The evaluation adopts a scoring system, and the scoring range is set to 1-10, and it is a positive integer. The higher the score, the more satisfied the patient is.

[0078] M4-402: After the classification is completed, extract the service attitude evaluation, professional recognition evaluation and communication ability evaluation scores of each candidate doctor corresponding to each group of patient evaluations, calculate the mean of the scores of each group of service attitude evaluation of each candidate doctor, and obtain the service attitude performance index of each candidate doctor; calculate the mean of the scores of each group of professional recognition evaluation of each candidate doctor, and obtain the professional recognition performance index of each candidate doctor; calculate the mean of the scores of each group of communication ability evaluation of each candidate doctor, and obtain the communication ability performance index of each candidate doctor;

[0079] Preset the weight coefficients corresponding to the service attitude performance index, professional recognition performance index and communication ability performance index respectively, multiply the service attitude performance index, professional recognition performance index and communication ability performance index of each candidate doctor by the corresponding preset weight coefficient respectively, and then sum them up to obtain the comprehensive evaluation index of each candidate doctor;

[0080] M4-403: Extract the number of patient evaluations of each candidate doctor within the currently set time interval, preset the interval of each group of evaluation numbers corresponding to the number of patient evaluations, and set a comprehensive added value corresponding to each interval of each group of evaluation numbers; the comprehensive added value range is set at 1.139-1.295. The more patient evaluations the candidate doctor has, the higher the corresponding matched comprehensive added value; match the number of patient evaluations of each candidate doctor with the interval of each group of evaluation numbers, so as to obtain the comprehensive added value of each candidate doctor;

[0081] It should be noted that the higher the number of patient evaluations of the candidate doctor, the higher the reference value of the comprehensive evaluation index of the candidate doctor, and the higher the corresponding matching comprehensive added value.

[0082] M4-404: multiply the comprehensive evaluation index of each candidate doctor by the corresponding matching comprehensive added value, and divide the multiplied value by the integer three to obtain the patient feedback index retb of each candidate doctor;

[0083] It should be noted that the set time interval can be set to one month;

[0084] M5: Extract the length of practice of each candidate doctor, the number of types of oral diseases he / she has treated, and the number of complex cases he / she has successfully treated, and make a comprehensive evaluation to obtain the experience performance index retc of each candidate doctor within the current set time interval;

[0085] M5-501: A linear normalization formula is used to calculate the length of practice, the number of oral disease types handled, and the number of complex cases successfully treated by each candidate doctor;

[0086] M5-502: Set the maximum and minimum values ​​of the length of medical practice, and calculate the medical practice evaluation value of each candidate doctor through the linear standardized formula (T1-T2) / (T3-T2); T1, T2 and T3 represent the length of medical practice of the candidate doctor, the minimum value of the corresponding length of medical practice and the maximum value of the corresponding length of medical practice respectively;

[0087] M5-503: Set the maximum and minimum values ​​of the number of oral disease types, and calculate the treatment evaluation value of each candidate doctor through the linear normalization formula (D1-D2) / (D3-D2); D1, D2 and D3 represent the number of oral disease types handled by the candidate doctor, the minimum number of oral disease types and the maximum number of oral disease types, respectively;

[0088] M5-504: Set the maximum and minimum values ​​of the number of complex cases, and calculate the treatment evaluation value of each candidate doctor through the linear normalization formula (Y1-Y2) / (Y3-Y2); where Y1, Y2 and Y3 represent the number of complex cases successfully treated by the candidate doctor, the minimum number of complex cases and the maximum number of complex cases respectively;

[0089] M5-505: Set the weight coefficients corresponding to the medical evaluation value, the processing evaluation value and the treatment evaluation value, respectively, multiply the medical evaluation value, the processing evaluation value and the treatment evaluation value of each candidate doctor by the corresponding weight coefficient, and then sum them up to obtain the ability evaluation value of each candidate doctor;

[0090] M5-506: preset the evaluation value intervals corresponding to the competence evaluation values, and set each evaluation value interval to correspond to a competence score; match the competence evaluation value of each candidate doctor with each evaluation value interval, and use the matched competence score as the experience performance index retc of each candidate doctor; the larger the competence evaluation value, the higher the corresponding matched competence score;

[0091] M6: Obtain the efficiency performance index reta, patient feedback index retb and experience performance index retc of each candidate doctor within the set time interval before the current evaluation starting point, and substitute them into the formula A weighted calculation is performed to obtain the comprehensive preference index RWA of each candidate doctor under different demand preferences; among which z1, z2 and z3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under efficiency bias; p1, p2 and p3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under evaluation bias; r1, r2 and r3 are the weight factors of the efficiency performance index reta, patient feedback index retb and experience performance index retc under experience bias;

[0092] Sort the candidates from largest to smallest according to the comprehensive index RWA of the patients' different needs and preferences, and push them to the patients for selection after the sorting is completed;

[0093] It should be noted that after receiving the push results, patients can view the comprehensive preference index RWA of each candidate doctor under different their own needs and preferences, so that patients can quickly select the candidate doctor with the best different needs and preferences.

[0094] The resource scheduling module obtains the operating status of each examination equipment in the dental hospital, and executes corresponding steps to intelligently schedule patients when the corresponding examination equipment fails;

[0095] Specifically:

[0096] Q1: Mark the corresponding examination equipment that has failed as a faulty equipment, filter out the patient list originally assigned to the faulty equipment, arrange the patients in the patient list according to the order of appointment time, and obtain other similar available examination equipment corresponding to the faulty equipment in the dental hospital as the equipment to be dispatched;

[0097] Q2: Obtain the number of patients who have been scheduled for each device to be scheduled at the current time point, identify the type of each device to be scheduled and the specific number within the type, and set the reference single examination time corresponding to the examination equipment of different types and numbers; for example, the reference single examination time corresponding to the oral CT machine numbered 1 is 15 minutes, and the reference single examination time corresponding to the oral CT machine numbered 2 is 20 minutes, and the specific setting is based on the performance parameters of the examination equipment; based on the identification result, obtain the reference single examination time corresponding to each device to be scheduled, multiply the number of patients who have been scheduled for each device to be scheduled at the current time point by the corresponding reference single examination time, and obtain the fastest estimated usage time of each device to be scheduled at the current time point;

[0098] Q3: Taking the faulty device as the starting point, obtain the location of each device to be dispatched and use it as the target point, calculate the distance between the starting point and each group of target points, preset the distance value ranges corresponding to each group of distances, set each group of distance value ranges to correspond to a group of required walking time, match the distance between the starting point and each group of target points with the preset distance value ranges one by one, and obtain the walking time between the starting point and each group of target points;

[0099] Q4: The patient ranked first in the patient list is selected as the priority patient, and the fastest estimated usage time of each device to be scheduled at the current time point is sorted from short to long according to the duration, and the walking time corresponding to each device to be scheduled is compared with the fastest estimated usage time. If the walking time of a device to be scheduled is less than the fastest estimated usage time, the fastest estimated usage time of the device to be scheduled is used as the final usage reference time of the priority patient; if the walking time of a device to be scheduled is greater than the fastest estimated usage time, the walking time of the device to be scheduled is used as the final usage reference time of the priority patient;

[0100] Q5: Obtain the final reference time of use of each device to be scheduled, and select the device to be scheduled with the shortest final reference time as the scheduling device for the patient to be scheduled first, and adjust the patient's appointment information from the faulty device to the scheduling device, and increase the number of scheduled patients on the scheduling device by one;

[0101] Q6: Allocate the equipment to be scheduled according to the order of the patients in the patient list by following steps Q2-Q5;

[0102] It should be noted that by considering the number of patients who have made appointments for the equipment to be scheduled and the corresponding reference time required for a single examination, the fastest estimated usage time of each equipment to be scheduled can be calculated more accurately. This allows patients to be quickly and reasonably allocated when equipment fails, reducing the overall time patients wait for examinations and improving the efficiency of the examination process. For example, it avoids patients blindly waiting for faulty equipment to be repaired or disorderly transfers between equipment, so that patients can receive examination services as soon as possible, improving patient experience. While calculating the fastest estimated usage time of the equipment, the time required to walk from the faulty equipment to the equipment to be scheduled is considered, fully taking into account the geographical layout factors of the hospital.

[0103] It should be noted that patients are automatically scheduled and assigned according to the set rules and algorithms, and more reasonable decisions can be made without manual intervention. This not only reduces the errors and subjectivity that may occur in manual scheduling, but also enables rapid response to emergencies such as equipment failures, thereby improving the hospital's ability to deal with emergencies and management efficiency.

[0104] The data middle platform module is used to establish a unified login entrance for the staff in the dental hospital and set corresponding permissions for each staff member to limit the functions of viewing and use; the staff includes medical staff and customer service staff, and the permissions are pre-set based on the role of the specific staff member and can be adjusted later;

[0105] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A modularized intelligent doctor recommendation system for oral medical service platform, characterized in that: include: Information management module: including patient information unit, medical information unit and resource information unit; The patient information unit is used to build a patient personal information database to store the patient's basic information, medical records, imaging data, and medical records; The medical information unit is used to build a medical staff information database to store the basic information, positions and consultation records of each medical staff in the dental hospital; The resource information unit is used to construct a dental equipment information database to store the number, location, service life and maintenance records of each device in the dental hospital; Appointment registration module: Patients view the doctor schedule information of the dental hospital on the expected date of consultation, and generate the preference comprehensive index RWA of each doctor on the expected date of consultation based on their own needs and preferences; the doctors on the expected date of consultation are sorted based on the size of the preference comprehensive index RWA and pushed to the patient. The patient receives the pushed sorting results and selects the corresponding doctor to make an online appointment; their own needs and preferences include efficiency preference, evaluation preference and experience preference; Generate the comprehensive preference index rwa of each doctor on the expected date of consultation based on your own needs and preferences, specifically: M1: The patient inputs the type of oral disease that he / she needs to see. After the input is completed, the doctors on the expected date of consultation are screened, and the doctors who meet the oral disease type entered by the patient are selected as the candidates; M2: Obtain the patient's ranking in the registration of each candidate doctor, that is, the number of patients ahead of the patient after the patient selects the corresponding candidate doctor to make an online appointment, and use the number of patients who have seen each candidate doctor as the number of patients waiting for the patient to select each candidate doctor; M3: Taking the current time point as the evaluation starting point, obtain the number of patients seen by each candidate doctor within the set time interval before the evaluation starting point and the corresponding treatment time for each group of patients, and make a comprehensive evaluation based on the number of patients waiting for each candidate doctor to obtain the efficiency performance index reta of each candidate doctor within the current set time interval; M4: Obtain the evaluation of each group of patients by each candidate doctor within the set time interval before the evaluation starting point, and perform a comprehensive evaluation to obtain the patient feedback index retb of each candidate doctor within the current set time interval; M5: Extract the length of practice of each candidate doctor, the number of types of oral diseases he / she has treated, and the number of complex cases he / she has successfully treated, and make a comprehensive evaluation to obtain the experience performance index retc of each candidate doctor within the current set time interval; M6: Obtain the efficiency performance index reta, patient feedback index retb and experience performance index retc of each candidate doctor within the set time interval before the current evaluation starting point, and substitute them into the formula A weighted calculation is performed to obtain the comprehensive preference index RWA of each candidate doctor under different demand preferences; among which z1, z2 and z3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under efficiency bias; p1, p2 and p3 are the weight factors of the patient's efficiency performance index reta, patient feedback index retb and experience performance index retc under evaluation bias; r1, r2 and r3 are the weight factors of the efficiency performance index reta, patient feedback index retb and experience performance index retc under experience bias; Sort the candidates from largest to smallest according to the comprehensive index RWA of the patients' different needs and preferences, and push them to the patients for selection after the sorting is completed; Get the efficiency performance index reta of each candidate doctor in the current set time interval, specifically: M3-301: For each group of consultation processing time of each candidate doctor within the set time interval before the evaluation starting point, the highest consultation processing time and the lowest consultation processing time in each group are screened out, and after screening, the average of the remaining consultation processing time of each group of each candidate doctor is calculated to obtain the average consultation processing time of each candidate doctor within the current set time interval; M3-302: Obtain the number of patients seen by each candidate doctor within a set time interval before the evaluation starting point, preset the intervals of the number of patients seen in each group, and set a mean time added value corresponding to each interval of the number of groups; match the number of patients seen by each candidate doctor with the interval of the number of groups, so as to obtain the mean time added value of each candidate doctor; M3-303: extract the average consultation time and average additional value corresponding to each candidate doctor and multiply them, and use the result of the multiplication as the estimated consultation time of each candidate doctor; M3-304: Multiply the number of patients waiting for each candidate doctor by the corresponding estimated consultation time to obtain the estimated waiting time for each candidate doctor; Preset the waiting time value intervals for each group corresponding to the estimated waiting time, and set each waiting time value interval to correspond to a consultation priority score; match the estimated waiting time for each candidate doctor with the waiting time value intervals for each group, so as to obtain the consultation priority score for each candidate doctor; The priority score of the matched doctors is used as the efficiency performance index reta of each doctor. Resource scheduling module: obtains the operating status of each examination equipment in the dental hospital, and executes corresponding steps to intelligently schedule patients when the corresponding examination equipment fails; Data middle platform module: establish a unified login portal for staff in the dental hospital and set corresponding permissions for each staff member.

2. According to claim 1, a modular oral medical service platform intelligent doctor recommendation system is characterized in that: Get the patient feedback index retb of each candidate doctor within the current set time interval, specifically: M4-401: Classify the evaluation types of each group of patients in the set time interval before the evaluation starting point for each candidate doctor. The evaluation types include the patient's evaluation of the candidate doctor's service attitude, professional recognition and communication ability. After the patient's consultation, the service attitude, professional recognition and communication ability are evaluated; M4-402: After the classification is completed, extract the service attitude evaluation, professional recognition evaluation and communication ability evaluation scores of each candidate doctor corresponding to each group of patient evaluations, calculate the mean of the scores of each group of service attitude evaluation of each candidate doctor, and obtain the service attitude performance index of each candidate doctor; calculate the mean of the scores of each group of professional recognition evaluation of each candidate doctor, and obtain the professional recognition performance index of each candidate doctor; calculate the mean of the scores of each group of communication ability evaluation of each candidate doctor, and obtain the communication ability performance index of each candidate doctor; Preset the weight coefficients corresponding to the service attitude performance index, professional recognition performance index and communication ability performance index respectively, multiply the service attitude performance index, professional recognition performance index and communication ability performance index of each candidate doctor by the corresponding preset weight coefficient respectively, and then sum them up to obtain the comprehensive evaluation index of each candidate doctor; M4-403: extract the number of patient evaluations of each candidate doctor within the currently set time interval, preset the intervals of each group of evaluation numbers corresponding to the number of patient evaluations, and set a comprehensive added value corresponding to each interval of each group of evaluation numbers; match the number of patient evaluations of each candidate doctor with the interval of each group of evaluation numbers, so as to obtain the comprehensive added value of each candidate doctor; M4-404: Multiply the comprehensive evaluation index of each candidate doctor by the corresponding matching comprehensive added value, and divide the multiplied value by the integer three to obtain the patient feedback index retb of each candidate doctor.

3. According to claim 2, a modular oral medical service platform intelligent doctor recommendation system is characterized in that: Get the experience performance index retc of each candidate doctor in the current set time interval, specifically: M5-501: A linear normalization formula is used to calculate the length of practice, the number of oral disease types handled, and the number of complex cases successfully treated by each candidate doctor; M5-502: Set the maximum and minimum values ​​of the length of medical practice, and calculate the medical practice evaluation value of each candidate doctor through the linear standardized formula (T1-T2) / (T3-T2); T1, T2 and T3 respectively represent the length of medical practice of the candidate doctor, the minimum value set for the length of medical practice and the maximum value set for the length of medical practice; M5-503: Set the maximum and minimum values ​​of the number of oral disease types, and calculate the treatment evaluation value of each candidate doctor through the linear standardization formula (D1-D2) / (D3-D2); Where D1, D2 and D3 represent the number of oral disease types handled by the candidate doctor, the minimum number of oral disease types and the maximum number of oral disease types respectively; M5-504: Set the maximum and minimum values ​​of the number of complex cases, and calculate the treatment evaluation value of each candidate doctor through the linear normalization formula (Y1-Y2) / (Y3-Y2); where Y1, Y2 and Y3 represent the number of complex cases successfully treated by the candidate doctor, the minimum number of complex cases and the maximum number of complex cases respectively; M5-505: Set the weight coefficients corresponding to the medical evaluation value, the processing evaluation value and the treatment evaluation value, respectively, multiply the medical evaluation value, the processing evaluation value and the treatment evaluation value of each candidate doctor by the corresponding weight coefficient, and then sum them up to obtain the ability evaluation value of each candidate doctor; M5-506: preset the evaluation value intervals corresponding to the competence evaluation values, set each evaluation value interval to correspond to a competence score; match the competence evaluation value of each candidate doctor with each evaluation value interval, and use the matched competence score as the experience performance index retc of each candidate doctor.

4. According to claim 3, a modular oral medical service platform intelligent doctor recommendation system is characterized in that: When the corresponding examination equipment fails, corresponding steps are executed to intelligently dispatch the patient, specifically: Q1: Mark the corresponding examination equipment that has failed as a faulty equipment, filter out the patient list originally assigned to the faulty equipment, arrange the patients in the patient list according to the order of appointment time, and obtain other similar available examination equipment corresponding to the faulty equipment in the dental hospital as the equipment to be dispatched; Q2: Obtain the number of patients who have been scheduled for each device to be scheduled at the current time point, identify the type of each device to be scheduled and the specific number within the type, and set the reference single examination time corresponding to the examination devices of different types and numbers; based on the identification result, obtain the reference single examination time corresponding to each device to be scheduled, multiply the number of patients who have been scheduled for each device to be scheduled at the current time point by the corresponding reference single examination time, and obtain the fastest estimated usage time of each device to be scheduled at the current time point; Q3: Taking the faulty equipment as the starting point, obtain the location of each device to be dispatched and use it as the target point, calculate the distance between the starting point and each group of target points, preset the distance value ranges for each group corresponding to the distance, set each group of distance value ranges to correspond to a group of walking time, match the distance between the starting point and each group of target points with the preset distance value ranges one by one, and obtain the walking time between the starting point and each group of target points.

5. According to claim 4, a modular oral medical service platform intelligent doctor recommendation system is characterized in that: When the corresponding examination equipment fails, corresponding steps are executed to intelligently dispatch the patient, and also includes: Q4: The patient ranked first in the patient list is selected as the priority patient, and the fastest estimated usage time of each device to be scheduled at the current time point is sorted from short to long according to the duration, and the walking time corresponding to each device to be scheduled is compared with the fastest estimated usage time. If the walking time of a device to be scheduled is less than the fastest estimated usage time, the fastest estimated usage time of the device to be scheduled is used as the final usage reference time of the priority patient; if the walking time of a device to be scheduled is greater than the fastest estimated usage time, the walking time of the device to be scheduled is used as the final usage reference time of the priority patient; Q5: Obtain the final reference time of use of each device to be scheduled, and select the device to be scheduled with the shortest final reference time as the scheduling device for the patient to be scheduled first, and adjust the patient's appointment information from the faulty device to the scheduling device, and increase the number of scheduled patients on the scheduling device by one; Q6: Allocate the equipment to be scheduled according to the order of the patients in the patient list by following steps Q2-Q5.

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