Internet medical insurance health service platform and method based on big data

Abstract

The invention relates to the technical field of internet medical insurance health services, and particularly discloses an internet medical insurance health service platform and method based on big data, and the platform comprises an online doctor distribution module, a database, a distribution pharmacy distribution module, and a patient medical insurance settlement module. According to the method, the adaptive online doctors and prescription medicine distribution pharmacies are allocated to the patients, and the medical insurance settlement process is started online, so that the medical experience of the patients is comprehensively improved, the adaptive online doctors accurately diagnose the illness state by virtue of rich experience and good public praise, professional treatment suggestions are provided, and the medical insurance settlement efficiency is improved. A proper prescription medicine distribution pharmacy ensures timely and accurate delivery of medicines, the medicine taking time and energy of a patient are saved, the reimbursement link is greatly simplified by starting a medical insurance settlement process online, the patient does not need to manually arrange materials and queue up to a medical insurance department for handling, the system automatically checks the reimbursement amount, and settlement is rapidly completed. And the patient can deeply feel convenient, efficient and intimate medical services.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet medical insurance and health services, and relates to an Internet medical insurance and health service platform and method based on big data. Background Art

[0002] With the rapid development of Internet technology and the advent of the big data era, the traditional medical insurance service model faces many challenges, such as low service efficiency, poor information flow, and uneven distribution of medical resources. Patients often need to run between different medical institutions and medical insurance departments during the medical treatment process, go through cumbersome procedures, and spend a lot of time and energy. Therefore, there is an urgent need for an Internet medical insurance health service platform based on big data to improve the quality and efficiency of medical insurance services, optimize the allocation of medical resources, and enhance patients' medical experience.

[0003] For example, the Chinese patent publication number CN115274043A discloses a prescription circulation system based on an Internet platform, including: a prescription circulation platform, a medical insurance settlement information platform, an online diagnosis and treatment platform, a hospital and a pharmacy. The prescription circulation platform includes a prescription unified interface platform, a prescription sharing service platform and a drug alliance service platform. After adopting the above structure, the present invention has the following advantages: patients can view prescriptions issued in hospitals or online diagnosis and treatment platforms through the prescription unified interface platform, and according to their own prescriptions, they can choose the nearest pharmacy to buy medicines through the prescription sharing service platform and the drug alliance service platform. The medicines are delivered to patients through special medicines delivered by pharmacies, third-party delivery or self-pickup by patients, saving the purchase time of prescription drugs and bringing convenience to patients. The prescription circulation platform realizes information communication, and the patient's symptom information can be transmitted to the doctor in a timely and clear manner. The doctor prescribes the medicine according to the patient's symptom information, reducing the difficulty of the doctor's prescription.

[0004] For example, the Chinese patent publication number CN114420267A discloses a Chinese medicine clinic management system, including: a user management system, a remote appointment system, an internal management system, an online diagnosis system and an express delivery system. The remote appointment system is used for patients to make appointments remotely via the Internet. The internal management system includes list management, medical staff management and patient management. The online diagnosis system is used for patients to remotely connect to the medical terminal through smart devices during the appointment time period after making an appointment online to obtain the doctor's diagnosis information and prescription. The express delivery system is used to upload the doctor's diagnosis information and prescription to the medical clinic pharmacy. After the medical clinic pharmacy receives the order and sorts it out, it will express the prescription order. The present invention enables patients to see a doctor online and offline by building a diagnosis and treatment platform for Chinese medicine clinics, quickly synchronize patients' medical data, break through the geographical limitations of medical services, and improve the efficiency of doctors' consultations.

[0005] The above existing technologies still have the following problems: 1. When currently assigning online doctors to patients, the online doctors’ work experience, evaluation status and online service activity are not taken into consideration to assign online doctors suitable for the patients, which reduces the comprehensiveness of the online doctors’ comprehensive service efficiency evaluation, and at the same time reduces the accuracy of the doctors’ diagnostic information and prescriptions, resulting in a decline in the quality of medical services on the medical insurance and health service platform, and damages the rights and interests of patients.

[0006] 2. Currently, when selecting a suitable delivery pharmacy for a patient, only the distance between the pharmacy and the patient is considered, without considering the accuracy of the pharmacy's historical dispensing of medicines. This cannot ensure that the patient receives the correct prescription drugs, thereby delaying the patient's treatment and affecting the patient's recovery process. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an Internet medical insurance health service platform and method based on big data are proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The first aspect of the present invention provides an Internet medical insurance health service platform based on big data, including: an online doctor allocation module, which is used to record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness and symptoms uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient.

[0009] The database is used to store the sets of disease symptoms corresponding to various diseases in various medical disciplines.

[0010] The delivery pharmacy allocation module is used to extract the geographic location of the current consulting patient after the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, and retrieve the geographic location, inventory of various prescription drugs and historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, so as to allocate an appropriate online delivery pharmacy for the current consulting patient.

[0011] The patient medical insurance settlement module is used to start the medical insurance settlement process once the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered.

[0012] The second aspect of the present invention provides an Internet medical insurance health service method based on big data, including: S1, online doctor allocation: record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness symptoms uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient.

[0013] S2. Distribution of delivery pharmacies: When the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, the geographical location of the current consulting patient is extracted, and the geographical location, inventory of various prescription drugs and historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient are retrieved to allocate an appropriate online delivery pharmacy for the current consulting patient.

[0014] S3. Patient medical insurance settlement: When the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered, the medical insurance settlement process will be initiated immediately.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention improves the patient's medical experience in all aspects by allocating an appropriate online doctor and prescription drug delivery pharmacy to the current consulting patient and initiating the medical insurance settlement process online. The appropriate online doctor, with his rich experience and good reputation, can accurately diagnose the condition and provide professional treatment advice, allowing patients to enjoy high-quality medical services. The appropriate prescription drug delivery pharmacy ensures that the drugs are delivered in a timely and accurate manner, saving the patient's time and energy in getting the drugs. The online initiation of the medical insurance settlement process greatly simplifies the reimbursement process. The patient does not need to manually organize the materials or go to the medical insurance department to queue up for processing. The system automatically calculates the reimbursement amount and quickly completes the settlement, reducing the patient's financial pressure and allowing the patient to deeply feel the convenient, efficient and considerate medical service.

[0016] (2) The present invention allocates online doctors suitable for the current consulting patients by combining the online doctors' work experience, evaluation and online service activity, thereby improving the comprehensiveness of the online doctors' comprehensive service efficiency evaluation and the accuracy of the doctors' diagnosis information and prescriptions, avoiding the decline in the medical service quality of the medical insurance and health service platform, thereby protecting the basic rights and interests of patients.

[0017] (3) The present invention not only considers the distance between the pharmacy and the current consulting patient, but also considers the accuracy of the pharmacy's historical drug dispensing when selecting an appropriate delivery pharmacy for the current consulting patient, thereby ensuring that the current consulting patient receives accurate prescription drugs, thereby avoiding delays in the treatment of the current consulting patient and avoiding affecting the recovery process of the current consulting patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system structure connection of the present invention.

[0020] Figure 2 It is a schematic diagram of the method steps of the present invention.

[0021] Figure 3 This is a flow chart for judging the doctors to be assigned corresponding to the currently consulting patients in the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the first aspect of the present invention provides an Internet medical insurance health service platform based on big data, including: an online doctor allocation module, a database, a distribution pharmacy allocation module and a patient medical insurance settlement module.

[0024] The online doctor allocation module is connected to the database, the online doctor allocation module is connected to the delivery pharmacy allocation module, and the delivery pharmacy allocation module is connected to the patient medical insurance settlement module.

[0025] The online doctor allocation module is used to record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness symptoms uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient.

[0026] In a specific embodiment of the present invention, the professional information includes years of clinical work experience, total number of consultations, number of successful consultations, patient satisfaction score, peer evaluation score, average response time for consultations, and online time within the consultation cycle.

[0027] It should be noted that the years of clinical work experience, total number of consultations, number of successful consultations, patient satisfaction scores, peer evaluation scores, average response time for consultations and online time during the consultation cycle are all extracted from the online doctor management files of the target medical insurance platform.

[0028] In a specific embodiment of the present invention, the specific process of assigning an adapted online doctor to the current consulting patient is as follows: Figure 3As shown, A1, the illness uploaded by the current consulting patient on the target medical insurance platform is matched and compared with the illness symptom set corresponding to each type of disease in each medical discipline stored in the database. If the illness uploaded by the current consulting patient on the target medical insurance platform is in the illness symptom set corresponding to a certain type of disease in a certain medical discipline, then the disease in this medical discipline is regarded as the disease suffered by the current consulting patient, and the online doctors corresponding to this disease are regarded as the doctors to be assigned corresponding to the current consulting patient. Otherwise, it indicates that the disease in this medical discipline is not the disease suffered by the current consulting patient.

[0029] A2. Extract the clinical working experience, total number of consultations, number of successful consultations, patient satisfaction score, peer evaluation score, average consultation response time and online time within the consultation cycle from the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and analyze the comprehensive service efficiency indicators of each doctor to be assigned corresponding to the current consulting patient.

[0030] In a specific embodiment of the present invention, the specific process of analyzing the comprehensive service efficiency index of each to-be-assigned doctor corresponding to the current consulting patient is as follows: B1. Extract the clinical working years, total consultation times, successful consultation times, patient satisfaction scores, peer evaluation scores, average consultation response times, and online time in the consultation cycle of each online doctor corresponding to each disease in each medical discipline, and record them as N respectively. i , T i and Here, i represents the number of the doctor to be assigned, i=1,2,...,n.

[0031] B2. Calculate the work experience richness β of each doctor to be assigned to the current consulting patient i , Evaluation of excellence χ i and online service activity δ i .

[0032] In a specific embodiment of the present invention, the formula for calculating the work experience richness of each doctor to be assigned corresponding to the current consulting patient is: Among them, N′ and K1 represent the reference clinical working experience and the proportion of successful consultation times, respectively.

[0033] It should be noted that the calculation process of calculating the evaluation excellence of each doctor to be assigned corresponding to the current consulting patient is as follows: Where n represents the number of doctors to be assigned.

[0034] It should be noted that the specific process of calculating the online service activity of each to-be-assigned doctor corresponding to the current consulting patient is as follows: the average consultation response time of each to-be-assigned doctor corresponding to the current consulting patient is calculated to obtain the average consultation response time of the to-be-assigned doctor, which is recorded as

[0035] The average online time of each doctor to be assigned during the consultation cycle corresponding to the current consulting patient is calculated to obtain the average online time of the doctor to be assigned during the consultation cycle, which is recorded as

[0036] Calculate the online service activity δ of each doctor to be assigned to the current consulting patient i ,

[0037] B3. Calculate the comprehensive service efficiency index θ of each doctor to be assigned to the current consulting patient i , Among them, a1, a2 and a3 represent the weights of the set work experience richness, evaluation excellence and online service activity corresponding to the comprehensive service effectiveness index assessment, a1+a2+a3=1.

[0038] In a specific embodiment of the present invention, the setting value of a1 is 0.33, the setting value of a2 is 0.33, and the setting value of a3 is 0.34. When calculating the comprehensive service efficiency index of each doctor to be assigned corresponding to the current consulting patient, the importance of work experience richness, evaluation excellence and online service activity is almost the same. The richness of work experience means that the doctor has deep professional qualities accumulated through many cases and can play a key role in the diagnosis of complex diseases and the formulation of treatment plans. The evaluation excellence reflects the patient's intuitive feeling of the doctor's past services, and the high evaluation reflects the recognition of the doctor's comprehensive abilities such as communication, diagnosis and treatment. The online service activity ensures that the doctor can respond to patient consultations in a timely manner and ensure the timeliness and continuity of the service.

[0039] A3. From the comprehensive service efficiency indexes of the doctors to be assigned corresponding to the current consulting patient, extract the doctor to be assigned corresponding to the maximum comprehensive service efficiency index as the online doctor suitable for the current consulting patient.

[0040] The embodiment of the present invention allocates online doctors suitable for the current consulting patients by combining the online doctors' work experience, evaluation and online service activity, thereby improving the comprehensiveness of the online doctors' comprehensive service efficiency evaluation, and at the same time improving the accuracy of the doctors' diagnosis information and prescriptions, avoiding the decline in the medical service quality of the medical insurance and health service platform, thereby protecting the basic rights and interests of patients.

[0041] The database is used to store disease symptom sets corresponding to various diseases in various medical disciplines. The data sources in the database of this embodiment are shown in Table 1 below.

[0042] Table 1 Data sources in the database

[0043]

[0044] The delivery pharmacy allocation module is used to extract the geographical location of the current consulting patient after the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, and retrieve the geographical location, inventory of various prescription drugs and historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, so as to allocate an appropriate online delivery pharmacy for the current consulting patient.

[0045] It should be noted that the geographic location of the currently consulting patient is obtained through Bluetooth GPS technology, and the geographic locations of various medical insurance settlement pharmacies within the geographical location of the currently consulting patient and the inventory of various prescription drugs are extracted from the medical insurance settlement pharmacy management system connected to the target medical insurance platform.

[0046] In a specific embodiment of the present invention, the historical drug dispensing information includes the number of drug dispensing times, pictures of each drug uploaded at each drug dispensing time, and pictures of each drug fed back by the patient after receiving the drug at each drug dispensing time.

[0047] It should be noted that the number of drug dispensing times, the pictures of each drug uploaded at each dispensing time, and the pictures of each drug reported by the patients after receiving the drugs at each dispensing time are extracted from the pharmacy management files of the target medical insurance platform.

[0048] In a specific embodiment of the present invention, the specific process of assigning an adapted online delivery pharmacy to the current consulting patient is as follows: C1. Compare the inventory of various prescription drugs of each medical insurance settlement pharmacy within the geographical location of the current consulting patient with the required quantity of various prescription drugs prescribed by the online doctor. If the inventory of various prescription drugs of a medical insurance settlement pharmacy is greater than the required quantity of various prescription drugs prescribed by the online doctor, then the medical insurance settlement pharmacy is recorded as the target pharmacy, thereby obtaining the target pharmacies within the geographical location of the current consulting patient.

[0049] C2. Extract the geographical location of each target pharmacy from the geographical location of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, and combine it with the geographical location of the current consulting patient to obtain the delivery distance between each target pharmacy and the current consulting patient, and record it as L j , where j represents the number of the target pharmacy, j = 1, 2, ..., m.

[0050] It should be noted that the delivery distance between each target pharmacy and the current consulting patient is obtained as follows: after the geographical location of each target pharmacy and the geographical location of the current consulting patient are simultaneously input into the target map API, the target map API automatically uses the distance calculation function to calculate the delivery distance between the two.

[0051] C3. Extract the number of drug dispensing times, the pictures of each drug uploaded at each dispensing, and the pictures of each drug reported by the patient after receiving the drug from the historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, and calculate the dispensing accuracy of each target pharmacy within the geographical location of the current consulting patient.

[0052] In a specific embodiment of the present invention, the specific process of calculating the accuracy of drug dispensing of each target pharmacy within the geographical location of the currently consulting patient is as follows: D1. Feature extraction and matching are performed on the drug images uploaded by each target pharmacy for each drug dispensing and the drug images fed back by the patients after receiving the drugs for each drug dispensing, to obtain the consistent drugs corresponding to each drug dispensing of each target pharmacy.

[0053] It should be noted that the consistent drugs corresponding to each dispensing of each target pharmacy can be automatically obtained directly through professional computer vision libraries and integrated software, wherein the specific steps include: 1) Data preprocessing: normalizing the pictures of each drug uploaded by each target pharmacy for each dispensing and the pictures of each drug reported by the patients after receiving the drugs, adjusting all pictures to the same size, and uniformly converting the color space of the pictures so that different pictures have consistency in data format and feature representation; 2) Extraction of key drug features: extracting key drug features from the preprocessed pictures using a deep learning-based target detection algorithm. The algorithm is trained and learned on a large number of picture data sets labeled with key drug information, thereby being able to accurately identify and locate the key features in the pictures. The key features of the drug are obtained and represented in the form of text or vector. 3) Image feature matching: For the pharmacy-uploaded pictures and patient feedback pictures of the same drug, their feature vectors are extracted respectively, and the two feature vectors are matched and analyzed using a similarity calculation method (such as cosine similarity, Euclidean distance, etc.) to calculate the similarity score between them. The higher the similarity score, the more similar the drugs in the two pictures are in appearance features, that is, the drugs sent by the pharmacy and the drugs received by the patient have a high degree of consistency in appearance. In actual operation, a similarity threshold can be set. When the similarity score of the two pictures is higher than the threshold, the drugs corresponding to them are considered to match in appearance features, and the drugs corresponding to the two pictures are recorded as consistent drugs. Otherwise, they are considered to be mismatched.

[0054] D2. If all drugs corresponding to a certain drug delivery at a target pharmacy are consistent drugs, then the drug delivery at the target pharmacy is recorded as accurate drug delivery. The number of accurate drug delivery at each target pharmacy is counted and recorded as

[0055] D3. Record the number of times each target pharmacy dispenses medicine within the geographical location of the current consulting patient as

[0056] D4. Calculate the accuracy of drug delivery for each target pharmacy within the geographical location of the current consulting patient. Among them, e represents the natural constant, and K2 represents the percentage of accurate drug dispensing times set as a reference.

[0057] C4. Calculate the comprehensive service quality index ω of each target pharmacy within the geographical location of the current consulting patient j , Among them, L′ and They respectively represent the set reference delivery distance and delivery accuracy.

[0058] C5. Extract the target pharmacy with the maximum comprehensive service quality index from the comprehensive service quality indexes of the target pharmacies within the geographical location of the current consulting patient, and use it as the online delivery pharmacy suitable for the current consulting patient.

[0059] The embodiment of the present invention not only considers the distance between the pharmacy and the current consulting patient, but also considers the accuracy of the pharmacy's historical drug dispensing when selecting an appropriate delivery pharmacy for the current consulting patient, thereby ensuring that the current consulting patient receives accurate prescription drugs, thereby avoiding delays in the treatment of the current consulting patient, and thus avoiding affecting the recovery process of the current consulting patient.

[0060] The patient medical insurance settlement module is used to start the medical insurance settlement process immediately after the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered.

[0061] In a specific embodiment of the present invention, the specific process of prompting the current consulting patient to settle the medical insurance is as follows: first, the current consulting patient logs in to the target medical insurance platform and enters the medical insurance settlement page at the designated settlement entrance; secondly, the target medical insurance platform sends the medical insurance type, medical insurance policy provisions, medical insurance reimbursement amount and self-payment amount to the current consulting patient; then, after confirming that the information is correct, the current consulting patient clicks the "Confirm Settlement" button; the target medical insurance platform will send the settlement request to the settlement system of the medical insurance department for verification and processing; if the medical insurance settlement is successful, the patient will receive a prompt message of successful settlement; at the same time, the medical insurance reimbursement amount will be directly deducted from the medical insurance account of the current consulting patient, and the self-payment amount will be paid through the payment method supported by the platform.

[0062] It should be noted that the payment methods supported by the platform include but are not limited to WeChat payment, Alipay payment and bank card payment.

[0063] The embodiment of the present invention improves the patient's medical experience in all aspects by allocating an appropriate online doctor and prescription drug delivery pharmacy to the current consulting patient and initiating the medical insurance settlement process online. The appropriate online doctor, with his rich experience and good reputation, can accurately diagnose the condition and provide professional treatment suggestions, so that patients can enjoy high-quality medical services. The appropriate prescription drug delivery pharmacy ensures that the drugs are delivered in time and accurately, saving the patient's time and energy in picking up the drugs. The online initiation of the medical insurance settlement process greatly simplifies the reimbursement link. The patient does not need to manually organize the materials or go to the medical insurance department to queue up for processing. The system automatically calculates the reimbursement amount and quickly completes the settlement, reducing the patient's financial pressure and allowing the patient to deeply feel the convenient, efficient and considerate medical service.

[0064] Reference Figure 2 As shown, the second aspect of the present invention provides an Internet medical insurance health service method based on big data, including: S1, online doctor allocation: record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness and symptoms uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient.

[0065] S2. Distribution of delivery pharmacies: When the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, the geographical location of the current consulting patient is extracted, and the geographical location, inventory of various prescription drugs and historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient are retrieved to allocate an appropriate online delivery pharmacy for the current consulting patient.

[0066] S3. Patient medical insurance settlement: When the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered, the medical insurance settlement process will be initiated immediately.

[0067] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An Internet medical insurance and health service platform based on big data, characterized in that: include: The online doctor allocation module is used to record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient; A database for storing disease symptom sets corresponding to various diseases in various medical disciplines; The pharmacy distribution module is used to extract the geographic location of the current consulting patient after the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, and retrieve the geographic location, inventory of various prescription drugs and historical drug dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, and allocate an appropriate online distribution pharmacy for the current consulting patient; The patient medical insurance settlement module is used to start the medical insurance settlement process once the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered.

2. The Internet medical insurance and health service platform based on big data according to claim 1 is characterized by: The professional information includes years of clinical work experience, total number of consultations, number of successful consultations, patient satisfaction scores, peer evaluation scores, average response time for consultations, and online time during the consultation cycle.

3. The Internet medical insurance and health service platform based on big data according to claim 2 is characterized by: The specific process of assigning an appropriate online doctor to the current consulting patient is as follows: A1. Match and compare the illness uploaded by the current consulting patient on the target medical insurance platform with the illness set corresponding to each disease in each medical discipline stored in the database. If the illness uploaded by the current consulting patient on the target medical insurance platform is in the illness set corresponding to a certain disease in a certain medical discipline, then the disease in that medical discipline is regarded as the disease suffered by the current consulting patient, and the online doctors corresponding to that disease are regarded as the doctors to be assigned to the current consulting patient. Otherwise, it indicates that the disease in that medical discipline is not the disease suffered by the current consulting patient. A2. Extract clinical working years, total consultation times, successful consultation times, patient satisfaction scores, peer evaluation scores, average consultation response time, and online time during the consultation cycle from the professional information of online doctors corresponding to various diseases in various medical disciplines on the target medical insurance platform, and analyze the comprehensive service efficiency indicators of each doctor to be assigned corresponding to the current consulting patient; A3. From the comprehensive service efficiency indexes of the doctors to be assigned corresponding to the current consulting patient, extract the doctor to be assigned corresponding to the maximum comprehensive service efficiency index as the online doctor suitable for the current consulting patient.

4. The Internet medical insurance and health service platform based on big data according to claim 3 is characterized by: The specific process of analyzing the comprehensive service efficiency index of each to-be-assigned doctor corresponding to the current consulting patient is as follows: B1. Extract the clinical working years, total consultation times, successful consultation times, patient satisfaction scores, peer evaluation scores, average consultation response times, and online time in the consultation cycle of each online doctor corresponding to each disease in each medical discipline, and record them as N respectively. i , T i and Wherein, i represents the number of the doctor to be assigned, i=1,2,...,n; B2. Calculate the work experience richness β of each doctor to be assigned to the current consulting patient i , Evaluation of excellence χ i and online service activity δ i ; B3. Calculate the comprehensive service efficiency index θ of each doctor to be assigned to the current consulting patient i , Among them, a1, a2 and a3 represent the weights of the set work experience richness, evaluation excellence and online service activity corresponding to the comprehensive service effectiveness index assessment, a1+a2+a3=1.

5. The Internet medical insurance and health service platform based on big data according to claim 4 is characterized by: The formula for calculating the work experience richness of each doctor to be assigned corresponding to the current consulting patient is: Among them, N′ and K1 represent the reference clinical working experience and the proportion of successful consultation times, respectively.

6. The Internet medical insurance and health service platform based on big data according to claim 1 is characterized by: The historical drug dispensing information includes the number of drug dispensing times, pictures of each drug uploaded at each drug dispensing, and pictures of each drug fed back by patients after receiving the drugs at each drug dispensing.

7. The Internet medical insurance and health service platform based on big data according to claim 6 is characterized by: The specific process of allocating an appropriate online delivery pharmacy to the current consulting patient is as follows: C1. Compare the inventory of various prescription drugs of each medical insurance settlement pharmacy within the geographical location of the current consulting patient with the required quantity of various prescription drugs prescribed by the online doctor. If the inventory of various prescription drugs of a medical insurance settlement pharmacy is greater than the required quantity of various prescription drugs prescribed by the online doctor, then record the medical insurance settlement pharmacy as the target pharmacy, thereby obtaining the target pharmacies within the geographical location of the current consulting patient; C2. Extract the geographical location of each target pharmacy from the geographical location of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, and combine it with the geographical location of the current consulting patient to obtain the delivery distance between each target pharmacy and the current consulting patient, and record it as L j , where j represents the number of the target pharmacy, j = 1, 2, ..., m; C3. Extract the number of drug dispensing times, the pictures of each drug uploaded at each dispensing, and the pictures of each drug reported by the patient after receiving the drug from the historical dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient, and calculate the dispensing accuracy of each target pharmacy within the geographical location of the current consulting patient. C4. Calculate the comprehensive service quality index ω of each target pharmacy within the geographical location of the current consulting patient j , Among them, L′ and They represent the set reference delivery distance and delivery accuracy respectively; C5. Extract the target pharmacy with the maximum comprehensive service quality index from the comprehensive service quality indexes of the target pharmacies within the geographical location of the current consulting patient, and use it as the online delivery pharmacy suitable for the current consulting patient.

8. The Internet medical insurance and health service platform based on big data according to claim 7 is characterized by: The specific process of calculating the dispensing accuracy of each target pharmacy within the geographical location of the current consulting patient is as follows: D1. Extract and match the features of each drug image uploaded by each target pharmacy and each drug image fed back by the patient after receiving the drug, and obtain the consistent drugs corresponding to each drug delivery of each target pharmacy; D2. If all drugs corresponding to a certain drug delivery at a target pharmacy are consistent drugs, then the drug delivery at the target pharmacy is recorded as accurate drug delivery. The number of accurate drug delivery at each target pharmacy is counted and recorded as D3. Record the number of times each target pharmacy dispenses medicine within the geographical location of the current consulting patient as D4. Calculate the accuracy of drug delivery for each target pharmacy within the geographical location of the current consulting patient. Among them, e represents the natural constant, and K2 represents the percentage of accurate drug dispensing times set as a reference.

9. The Internet medical insurance and health service platform based on big data according to claim 1 is characterized by: The specific process of prompting the current consulting patient to settle the medical insurance is as follows: first, the current consulting patient logs in to the target medical insurance platform and enters the medical insurance settlement page at the designated settlement entrance; secondly, the target medical insurance platform sends the medical insurance type, medical insurance policy provisions, medical insurance reimbursement amount and self-payment amount to the current consulting patient; then, after confirming that the information is correct, the current consulting patient clicks the "Confirm Settlement" button, and the target medical insurance platform will send the settlement request to the settlement system of the medical insurance department for verification and processing; if the medical insurance settlement is successful, the patient will receive a prompt message of successful settlement, and the medical insurance reimbursement amount will be directly deducted from the medical insurance account of the current consulting patient, and the self-payment amount will be paid through the payment method supported by the platform.

10. A big data-based Internet medical insurance health service method, characterized in that: include: S1. Online doctor allocation: record the target Internet medical insurance health service platform as the target medical insurance platform, extract the professional information of each online doctor corresponding to each disease in each medical discipline on the target medical insurance platform, and extract the illness uploaded by the current consulting patient on the target medical insurance platform, and allocate an appropriate online doctor to the current consulting patient; S2. Distribution of delivery pharmacies: When the online doctor assigned to the current consulting patient prescribes the required quantity of various prescription drugs, the geographical location of the current consulting patient is extracted, and the geographical location, inventory of various prescription drugs and historical drug dispensing information of each medical insurance settlement pharmacy within the geographical location of the current consulting patient are retrieved to allocate an appropriate online delivery pharmacy for the current consulting patient; S3. Patient medical insurance settlement: When the current consulting patient confirms that the prescription drugs from the online delivery pharmacy have been delivered, the medical insurance settlement process will be initiated immediately.

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