Medical service method for outpatient and medium

Abstract

The invention relates to an outpatient medical service method and a medium, and the method comprises the steps: carrying out the clustering analysis based on the basic medical information of an outpatient, recognizing whether the outpatient needs help service or not, and judging the time for obtaining an inspection report; if the time is before the first preset time of the day and the diagnosis result reaches a preset first illness state severity threshold value, searching the old and rural medical personnel to perform first targeted consultation and help service; if the time is after the first preset time of the day and before the second preset time of the day and the disease condition severity prediction reaches a preset second disease condition severity threshold value, searching for the old and rural medical staff to perform second targeted consultation and help service, and if the time is after the second preset time of the day, performing second targeted consultation and help service; and if yes, searching the old and rural medical personnel for third targeted consultation and help service. The outpatient needing help service can obtain help service in time, and the medical service cost is reduced on the basis that the satisfaction degree of the patient is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer services, and particularly to a medical service method and medium for outpatient patients. Background Art

[0002] With the development of the times and the aging process, in line with the principle of "minor illnesses can be treated in county-level hospitals, and major illnesses can be treated in city-level hospitals", in addition to accelerating the improvement of medical diagnosis and treatment capabilities, district and county-level hospitals also need to improve the ability to serve patients, including improving the convenience of medical treatment, the sense of identity and satisfaction of medical treatment, so as to quickly meet the needs of patients for seeing a doctor and receiving treatment nearby.

[0003] Patients who go to the hospital for medical treatment generally have the following three types of troubles: First, the troubles of patients who are diagnosed by doctors as relatively serious during outpatient visits in the hospital; second, the troubles of patients who cannot complete relevant tests and examinations on the same day or cannot obtain test and examination reports on the same day during outpatient visits in the hospital; third, the troubles of patients who have completed tests and examinations on the same day during outpatient visits and have obtained test and examination results on the same day, but cannot complete the doctor's diagnosis and the test and examination results show relatively serious conditions.

[0004] In response to the above troubles of outpatient patients, for traditional means of serving patients, such as increasing service personnel in the traditional way will increase more costs, and the cost investment of using the method of manual medical guidance and consultation is also relatively high. Therefore, there is an urgent need for a medical service method with low cost and improved patient's sense of identity and satisfaction in medical treatment. Summary of the Invention

[0005] The technical problem to be solved by the present application is to provide a medical service method and medium for outpatient patients, which have the characteristics of reducing the cost of medical services on the basis of improving patient satisfaction.

[0006] In a first aspect, in one embodiment, a medical service method for outpatient patients is provided, which can be applied to a medical service system. The method includes: Obtain the basic medical information of outpatient patients, where the basic medical information includes the patient's chief complaint information, current medical history, past medical history, family history, personal history, physical examination, psychosocial factors, systems review, and medication history; Perform cluster analysis based on the basic medical information of outpatient patients to identify whether the outpatient patient needs targeted consultation and assistance services. If so, proceed to the next step; Based on the test and examination information of outpatient patients, judge the time to obtain test and examination reports; If the time for obtaining the inspection and examination report is before the first preset time of the day, wait for the doctor's diagnosis result. If the time for obtaining the inspection and examination report is after the first preset time of the day and before the second preset time of the day, wait for the inspection and examination report and predict the severity of the condition based on the obtained inspection and examination report; If the doctor's diagnosis result obtained reaches the preset first severity threshold of the condition, search for fellow hometown medical staff to provide the first targeted consultation and assistance service for the outpatient; If the prediction of the severity of the condition reaches the preset second severity threshold of the condition, search for fellow hometown medical staff to provide the second targeted consultation and assistance service for the outpatient; If the time for obtaining the inspection and examination report is after the second preset time of the day, search for fellow hometown medical staff to provide the third targeted consultation and assistance service for the outpatient.

[0007] In one embodiment, the clustering analysis based on the basic medical information of the outpatient to identify whether the outpatient needs targeted consultation and assistance services includes: inputting the basic medical information of the outpatient into an unsupervised clustering classification model to obtain the clustering category and the result of whether targeted consultation and assistance services are needed; the method for obtaining the clustering classification model includes: Form the basic medical information of each historical outpatient into a first data set; Apply an unsupervised clustering algorithm to the first data set and train and learn to obtain a clustering classification model; Analyze and label the category characteristics and recommended measures of each clustering category in the clustering classification model; the recommended measures include whether targeted consultation and assistance services are needed.

[0008] In one embodiment, the method for judging the time for obtaining the inspection and examination report based on the inspection and examination information of the outpatient includes: Obtain the time for the outpatient to receive the inspection and examination report and the queuing order of the inspection and examination. Based on the average inspection and examination time, calculate the possible time for the outpatient to obtain the report, and use this possible time as the time for the outpatient to obtain the inspection and examination report; when the outpatient has multiple inspections and examinations, calculate the possible times for these multiple inspections and examinations respectively, and use the latest possible time as the time for the outpatient to obtain the inspection and examination report.

[0009] In one embodiment, the first preset time is the time corresponding to half an hour before the doctor of the outpatient finishes work on the same day, and the second preset time is the end time of the outpatient medical staff on the same day.

[0010] In one embodiment, the prediction of the severity of the condition based on the obtained inspection and examination report includes: Input the obtained inspection and examination reports into the corresponding neural network model for predicting the severity of the illness. The corresponding neural network model is a neural network model trained based on multiple historical inspection and examination reports with illness severity labels in combination with a loss function.

[0011] In one embodiment, the fellow villager medical staff providing the first targeted consultation and assistance service for the outpatient is a doctor, the fellow villager medical staff providing the second targeted consultation and assistance service for the outpatient is a doctor, and the fellow villager medical staff providing the third targeted consultation and assistance service for the outpatient is a nurse.

[0012] In one embodiment, the method for finding the fellow villager medical staff includes: Obtain the first identity information of the outpatient, where the first identity information includes the name, ID number, ID address, and residential address information of the inpatient; Based on the ID address and residential address information of the outpatient, search the hospital medical staff database to find a list of fellow villager medical staff within a preset number threshold that is the closest to the outpatient's ID address and / or the closest to the residential address; Based on the list of fellow villager medical staff, determine the service information for providing targeted consultation and assistance services to the outpatient within a future time threshold range. The service information includes the medical staff information for providing targeted consultation and assistance services and the specific time when they can provide services; Based on the determined service information, send the service information to the client where the outpatient is located, and send the first identity information of the outpatient to the client where the medical staff providing targeted consultation and assistance services is located; the first identity information also includes the age, gender, contact phone number, department, and medical record information of the outpatient.

[0013] In one embodiment, the searching of the hospital medical staff database based on the ID address and residential address information of the inpatient to find a list of medical staff within a preset number threshold that is the closest to the inpatient's ID address and / or the closest to the residential address includes: giving priority to finding medical staff whose ID address or residential address is in the same district, county, or township as that of the outpatient.

[0014] In one embodiment, the method further includes: if no medical staff from the same district, county, or township as the outpatient can be found, then use the associate professor doctor or nurse in the department where the outpatient is located as the medical staff for providing targeted consultation and assistance services.

[0015] In a second aspect, in one embodiment, a computer-readable storage medium is provided, in which a program is stored, and the program can be loaded and executed by a processor to perform the outpatient medical service method according to any one of the above embodiments.

[0016] The beneficial effects of the present invention are as follows: Since the basic medical information of outpatient patients is first obtained for clustering analysis to identify whether outpatient patients need targeted consultation and assistance services, a first round of screening is performed to screen out potential outpatient patients who need targeted consultation and assistance services, so as to reduce the occupancy of computer resources for subsequent screening. Secondly, since it is based on the acquisition time of inspection and examination reports, outpatient patients who need the first targeted consultation and assistance service, the second targeted consultation and assistance service, and the third targeted consultation and assistance service are obtained, and matching of fellow-townsman medical staff is performed, so that outpatient patients who need targeted consultation and assistance services can timely obtain the targeted consultation and assistance services of fellow-townsman medical staff, thereby reducing the medical service cost on the basis of improving patient satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic structural diagram of a medical service system according to an embodiment of the present application; Figure 2 is a schematic flow diagram of an outpatient medical service method of "fellow-townsman serving fellow-townsman" according to an embodiment of the present application; Figure 3 is a schematic flow diagram of a method for obtaining a clustering and classification model according to an embodiment of the present application.

[0018] In the illustration, package 01 is the hospital HIS terminal, 02 is the first client, 03 is the second client, and 04 is the server. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The present invention will be further described in detail below in conjunction with the accompanying drawings through specific embodiments. Similar elements in different embodiments are labeled with related similar element numbers. In the following embodiments, many details are described to enable a better understanding of the present application. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification to avoid the core part of the present application being overwhelmed by excessive description. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0020] In addition, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can also be reordered or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and drawings are only for clearly describing a certain embodiment and do not mean a necessary sequence, unless it is stated that a certain sequence must be followed.

[0021] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meaning.

[0022] To facilitate the description of the inventive concept of the present application, the following briefly describes the medical service technology.

[0023] In the current medical service technology, to increase convenience, the convenience of medical services is usually increased based on the medical guidance service of client software (such as mobile phones). However, with the increasing aging population, this kind of convenience alone can no longer meet the service needs of patients. Patients need more human services. However, if more service personnel are added or manual medical guides are used, the investment cost will be relatively high.

[0024] In view of this, an outpatient medical service method and medium are provided in an embodiment of the present application. The outpatient medical service method can be applied to a medical service system. First, the basic medical information of outpatient patients is obtained for clustering analysis to identify whether outpatient patients need targeted consultation and assistance services, so as to perform the first round of screening to screen out potential outpatient patients who need targeted consultation and assistance services, thereby reducing the occupancy of computer resources for subsequent screening. Secondly, based on the acquisition time of the inspection and examination reports, outpatient patients who need the first targeted consultation and assistance service, the second targeted consultation and assistance service, and the third targeted consultation and assistance service are obtained, and the matching of hometown medical staff is performed, so that outpatient patients who need targeted consultation and assistance services can obtain the targeted consultation and assistance services of hometown medical staff in a timely manner, thereby reducing the medical service cost on the basis of improving patient satisfaction.

[0025] The application environment of the embodiment of the present application will be described below. The embodiment of the present application can be applied to a medical service system. Please refer to Figure 1, the medical service system may include the hospital HIS (Hospital Information System) end 01, at least one first client 02 where medical staff are located, at least one second client 03 where outpatient patients are located, and the server 04. Among them, the server 04 can collect information from the medical information system and analyze and judge whether outpatient patients need targeted consultation and assistance services. If so, it matches the corresponding fellow-townsman medical staff and sends the required matching service information to the second client 02 where the outpatient patient is located and the first client 01 where the corresponding medical staff is located respectively.

[0026] Applied to the above medical service system, for the outpatient patient medical service method of "fellow-townsman serving fellow-townsman" in the embodiments of the present application, please refer to Figure 2 , may include: Step S10, obtaining the basic medical information of the outpatient patient.

[0027] Among them, the basic medical information may include the patient's chief complaint information, current medical history, past medical history, family history, personal history, physical examination, psychosocial factors, system review, medication history, etc.

[0028] The chief complaint information is the most prominent pain or the most obvious symptoms and signs felt by the patient, that is, the main reason that prompts the patient to seek medical treatment and the duration. The present illness history is the main part of the medical history. Around the chief complaint, it details the occurrence, development, evolution, and diagnosis and treatment process of the disease from the onset to the time of seeking medical treatment. It includes the onset situation and the duration of illness, the characteristics of the main symptoms, the causes and predisposing factors, the development and evolution of the condition, accompanying symptoms, the diagnosis and treatment process, and the general condition during the course of the disease, etc. The past history, also known as the previous history, includes the patient's past health status and the diseases (including various infectious diseases) that the patient has suffered from in the past, trauma surgeries, preventive vaccinations, allergy history, etc. Understanding the past history helps the doctor judge the relevance between the current disease and past diseases, as well as evaluate the patient's tolerance and adaptability to certain treatment methods. The family history mainly includes whether there are similar diseases and other genetically related diseases in the patient's family. It includes the health status of direct relatives such as parents, siblings, and children, whether they suffer from hereditary or family-predisposed diseases such as hypertension, diabetes, coronary heart disease, tumors, etc., and rare diseases with a familial genetic tendency. The family history is of great significance for judging the genetic risk and diagnosis of certain diseases. The personal history includes the patient's social experiences, such as place of birth, residence area and duration of residence, educational level, economic life, and hobbies; occupation and working conditions, such as type of work, working environment, exposure to industrial poisons and the duration; habits and hobbies, such as daily living and hygiene habits, regularity and quality of diet. Physical examination is that the doctor uses his own senses or with the help of traditional examination instruments (such as thermometer, sphygmomanometer, stethoscope, percussion hammer, etc.) to conduct a comprehensive physical examination of the patient to understand the patient's physical condition. It includes general examination (such as body temperature, pulse, respiration, blood pressure, height, weight, etc.), head and neck examination, chest examination, abdominal examination, spine and limb examination, nervous system examination, etc. Psychosocial factors mainly focus on the impact of the patient's psychological state and social environment on their health. Psychologically, it includes the patient's emotional state (such as anxiety, depression, fear, etc.), personality traits, cognitive ability, coping styles, etc.; social factors involve the patient's family relationships, social support system, work pressure, economic status, cultural background, etc. These factors may affect the occurrence, development, and treatment effect of the disease. System review. The system review is a comprehensive inquiry by the doctor according to each system of the human body to discover other possible diseases or symptoms in the patient and avoid missing important information. It covers various systems such as the respiratory system, circulatory system, digestive system, urinary system, hematological system, endocrine system, nervous system, and musculoskeletal system, and asks whether there are related symptoms and diseases in each system. The medication history refers to the patient's past and current medication use. It includes the names, dosage forms, doses, medication times, reasons for medication, drug adverse reactions, etc. of the medications that the patient has taken in the past, as well as the current medication use situation.

[0029] Step S20, perform cluster analysis based on the basic medical information of outpatient patients.

[0030] In step S20, the basic medical information of outpatient patients is input into an unsupervised clustering classification model to obtain the clustering categories and the results of whether targeted consultation and assistance services are required. In one embodiment, please refer to Figure 3 , and the acquisition method of this clustering classification model may include: Step S100, forming the basic medical information of each historical outpatient patient into a first data set.

[0031] In one embodiment, the first data set can be expressed as: Where X represents the first data set, j represents the index of the data item in the basic medical information, J represents the total number of data items in the basic medical information, 1 ≤ j ≤ J; i represents the index of each historical outpatient patient, and I represents the total number of historical outpatient patients, 1 ≤ i ≤ I.

[0032] In one embodiment, if I = 1000 and J = 9, there are a total of 9000 data points in the first data set.

[0033] Step S200, applying an unsupervised clustering algorithm to the first data set to train and learn to obtain a clustering classification model.

[0034] In one implementation, step S200 may include: Step S2001, taking each data point in the first data set as an initial clustering center.

[0035] In one embodiment, for any data point , its initial clustering center can be expressed as . Where the superscript 0 represents that the number of iterations is 0.

[0036] Step S2002, based on each initial clustering center, performing iterative clustering until the convergence condition is met to obtain a clustering classification model.

[0037] In one embodiment, taking any initial clustering center as an example, for the next iterative clustering, the mean vector of all data points within the range of a high-dimensional sphere with as the center and radius h can be calculated, and this mean vector is used as the new iterative clustering center .

[0038] From this, it can be obtained that step S2002 may include: First, based on any initial clustering center, calculate a new clustering center based on the preset range after each iterative clustering.

[0039] For the cluster center after any iteration of clustering , based on the mean vector of all data points within a range of radius h, and use this mean vector as the new cluster center ; where t represents the index of the iteration number.

[0040] In one embodiment, for the cluster center after any iteration of clustering , the specific method to obtain the new cluster center may include: defining a kernel function , and this kernel function can adopt a Gaussian kernel function, then there is: where d represents the dimension of the data, represents the Euclidean norm, and e represents the natural number exponent.

[0041] Measure the contribution degree of each data point within a range of radius h to the cluster center , and obtain the weight vector of each data point based on this contribution degree, then the obtained weight vector can be used to measure the contribution degree of the data point to the cluster center. In one embodiment, the weight vector can be expressed as: .

[0042] Then the new cluster center can be expressed as: .

[0043] Secondly, determine whether the new cluster center satisfies the convergence condition. If so, classify the data points that meet the preset conditions into the corresponding clusters based on the final cluster center.

[0044] In one embodiment, determining whether the new cluster center satisfies the convergence condition includes: for any updated cluster center , calculate the distance from the updated cluster center to the previous cluster center as the first distance; determine whether the first distance is less than a preset first distance threshold. If so, it is considered to satisfy the convergence condition.

[0045] In one embodiment, calculate the Euclidean distance between the updated cluster center and the previous cluster center as the first distance. If the first distances obtained for all cluster centers are less than the preset first distance threshold, it is considered to satisfy the convergence condition and stop the iteration. Otherwise, continue the next iteration.

[0046] In one embodiment, classifying data points that meet preset conditions into corresponding clusters based on the final cluster centers may include: Step S1000: For any data point, calculate the distances from this arbitrary data point to each of the converged cluster centers.

[0047] Calculate the distances from each data point to each of the converged cluster centers as the second distances through step S1000. For example, if there are 6 cluster centers, each data point corresponds to 6 second distances. In one embodiment, the second distance uses the Euclidean distance.

[0048] Step S2000: Assign each data point to the cluster to which the cluster center with the minimum distance belongs.

[0049] In one embodiment, for any data point, since the minimum distance can be obtained from the 6 second distances obtained, the cluster center corresponding to this minimum distance can be used as the cluster center of the cluster to which it belongs. Thus, the cluster to which each data point belongs can be obtained, and a cluster classification model can be obtained.

[0050] The model of the embodiment of the present application is trained based on the basic medical information of emergency and outpatient patients already saved in the hospital, so it has high scientificity and rationality. Compared with the existing practice that relies on the experience and judgment of medical staff, various factors can be considered more systematically, the influence of human factors can be reduced, and thus the error rate of parameter setting can be lowered.

[0051] Step S300: Analyze the category features and recommended measures of each cluster category in the labeled cluster classification model. Among them, the recommended measures include whether targeted consultation and assistance services are required.

[0052] For example, based on the patient's physical condition, distance from home, psychological demands, etc., it can be determined whether outpatient patients have the need for targeted consultation and assistance services.

[0053] In this way, the basic medical information of the current outpatient patient can be input into the already trained and labeled cluster analysis model to identify whether the current outpatient patient requires targeted consultation and assistance services.

[0054] Step S30: Determine whether the outpatient patient requires targeted consultation and assistance services. If so, proceed to step S40; if not, end this judgment process.

[0055] Through the cluster analysis model, if the conclusion is that the current outpatient patient requires targeted consultation and assistance services, it means that the current outpatient patient has potential needs for targeted consultation and assistance services.

[0056] Through the above process, a preliminary judgment is made on whether each outpatient needs targeted consultation and assistance services, so as to reduce the occupation of computer resources during further accurate judgment.

[0057] Step S40: Based on the test and examination information of the outpatient, judge the time to obtain the test and examination report. If the judged time to obtain the test and examination report is before the first preset time of the day, go to step S501; if the judged time to obtain the test and examination report is after the first preset time of the day and before the second preset time of the day, go to step S502; if the judged time to obtain the test and examination report is after the second preset time of the day, go to step S603.

[0058] In one embodiment, step S40 may include: obtaining the registration time of the outpatient's test and examination and the queuing order of the test and examination, calculating the possible time for the outpatient to obtain the report based on the average test and examination time, and using this possible time as the time for the outpatient to obtain the test and examination report; when there are multiple tests and examinations for the outpatient, calculate the possible times for these multiple tests and examinations respectively, and use the latest possible time as the time for the outpatient to obtain the test and examination report.

[0059] After obtaining the registration time of the outpatient's test and examination, the possible time for the outpatient to participate in the test and examination and obtain the report can be calculated based on the number of people queuing in front, combined with the service capacity and service efficiency of the hospital's test and examination equipment and personnel. If the possible time is on the same day, the possible time of "at XX:XX on the same day, the test and examination report can be obtained" can be obtained; if the possible time is on the next day, the possible time of "at XX:XX on the next day, the test and examination report can be obtained" can be obtained.

[0060] Step S501: Judge whether the obtained doctor's diagnosis result reaches the preset first disease severity threshold T1. If it is, go to step S601; if not, end the judgment process.

[0061] In one embodiment, the first preset time is the time corresponding to half an hour before the doctor of the outpatient finishes work on the same day. For example, if the doctor on duty in the morning on the same day does not sit in the afternoon and the off-duty time of the doctor on duty in the morning on the same day is 12:00 noon, the first preset time can be set to 11:30 am on the same day. If the doctor on duty sits all day and the off-duty time in the afternoon is 6:00 pm, the first preset time can be 5:30 pm on the same day.

[0062] If the test and examination report is obtained before the first preset time, it is considered that the outpatient can obtain the doctor's diagnosis result on the same day; if the test and examination report is obtained after the first preset time, it is considered that the outpatient cannot obtain the doctor's diagnosis result on the same day.

[0063] In one embodiment, if inspection and examination are not required, it is also defaulted that the time for obtaining the inspection and examination report is before the first preset time of the day, and then it is directly determined whether the doctor's diagnosis result reaches the preset first disease severity threshold T1 based on the obtained doctor's diagnosis result.

[0064] In one embodiment, the doctor can directly give the disease severity based on the diagnosis result to determine whether the diagnosis result reaches the first disease severity threshold T1. In one embodiment, a neural network model for judging the disease severity based on the disease diagnosis result can also be trained first, so as to automatically determine whether the diagnosis result reaches the first disease severity threshold T1 after the doctor's diagnosis result is obtained. The neural network model for judging the disease severity based on the disease diagnosis result can be trained based on the disease diagnosis result training samples with disease severity labels.

[0065] Step S502, perform disease severity prediction based on the obtained inspection and examination report, and judge whether the disease severity reaches the preset second disease severity threshold T2. If so, enter step S602. If not, end the judgment process.

[0066] In one embodiment, the second preset time can be the off-duty time of the doctor who made the inspection and examination report on the same day. For example, if the off-duty time of the doctor who made the inspection and examination report on the same day is 6 pm, then the second preset time can be 6 pm on the same day.

[0067] In one embodiment, a neural network model for judging the disease severity based on the inspection and examination report can be trained first, so as to automatically judge whether the disease reaches the second disease severity threshold T2 after the inspection and examination report is obtained. Therefore, the obtained inspection and examination report can be input into the corresponding neural network model for disease severity prediction. The corresponding neural network model is a neural network model trained based on multiple historical inspection and examination reports with disease severity labels and combined with a loss function.

[0068] In one embodiment, both the first disease severity threshold T1 and the second disease severity threshold T2 are "severe".

[0069] Step S601, find fellow-townsman medical staff to provide the first targeted consultation and assistance services for outpatient patients.

[0070] In one embodiment, the fellow-townsman medical staff who provide the first targeted consultation and assistance services for outpatient patients are doctors, who provide skill-based services for outpatient patients with a diagnosis result of "severe", such as giving disease consultation, further inspection and examination or hospitalization treatment suggestions, and assisting in handling hospitalization if the patient needs hospitalization treatment.

[0071] Step S602, finding fellow medical staff to provide second targeted consultation and assistance services to outpatients.

[0072] In one embodiment, the fellow medical staff who provides the second targeted consultation and assistance service to outpatients is a doctor, who provides skill-based services to outpatients with more "serious" diagnosis results, such as providing medical consultation, further testing or hospitalization recommendations, and assisting in arranging for hospitalization if the patient needs hospitalization.

[0073] Step S603, finding fellow medical staff to provide third-party targeted consultation and assistance services to outpatients.

[0074] In one embodiment, the fellow medical staff who provide the third targeted consultation and assistance service to outpatients is a nurse, who provides transactional services to outpatients, such as giving notifications to assist in reviewing test results, and assisting in making appointments with follow-up doctors until the patients obtain diagnosis results.

[0075] In one embodiment, the method for searching for fellow medical staff includes: Step S01, obtaining the first identity information of the outpatient, wherein the first identity information includes the name, ID number, ID address and residence address information of the inpatient.

[0076] Step S02, based on the ID card address and residence address information of the outpatient patient, search the hospital medical staff database to find a list of fellow medical staff who are closest to the outpatient patient's ID card address and / or closest to the residence address within a preset number threshold.

[0077] In one embodiment, in step S02, priority is given to searching for medical staff whose ID card address or residence address belongs to the same district, county or township as the outpatient patient's to enter the list of fellow medical staff.

[0078] In one embodiment, if no medical staff from the same district, county or township as the outpatient is found, the associate professor or nurse of the department where the outpatient is located will be used as the medical staff to provide targeted consultation and assistance services.

[0079] In one embodiment, the preset quantity threshold may be 3.

[0080] Step S03, based on the list of fellow medical staff, determine the service information of providing targeted consultation and assistance services to outpatients within the future time threshold range. The service information includes the information of medical staff providing targeted consultation and assistance services and the specific time when the services are available.

[0081] In one embodiment, the specific time for providing targeted consultation and assistance services to outpatient patients within a future time threshold range (e.g., within 1 hour) can be confirmed with the medical staff on the list of fellow-villager medical staff, so as to determine the service information based on a preset priority rule (such as priority based on time, earlier or later).

[0082] Step S04: Based on the determined service information, send the service information to the client where the outpatient patient is located, and send the first identity information of the outpatient patient to the client where the medical staff providing targeted consultation and assistance services is located. The first identity information further includes the age, gender, contact phone number, department where the outpatient patient is located, and medical record information of the outpatient patient.

[0083] Based on the outpatient patient medical service method of "fellow villagers serving fellow villagers" in any of the above embodiments, since the basic medical information of outpatient patients is first obtained for cluster analysis to identify whether outpatient patients need targeted consultation and assistance services, a first-round screening is performed to screen out potential outpatient patients who need targeted consultation and assistance services, so as to reduce the occupation of computer resources for subsequent screening. Secondly, since it is based on the acquisition time of the inspection and examination reports, outpatient patients who need the first targeted consultation and assistance service, the second targeted consultation and assistance service, and the third targeted consultation and assistance service are obtained, and the matching of fellow-villager medical staff is performed, so that outpatient patients who need targeted consultation and assistance services can timely obtain the targeted consultation and assistance services of fellow-villager medical staff, thereby reducing the medical service cost on the basis of improving patient satisfaction.

[0084] In one embodiment of the present application, a computer-readable storage medium is provided, and a program is stored on the storage medium. The stored program includes the method in any of the above embodiments that can be loaded and processed by a processor.

[0085] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be implemented in a hardware manner or in a computer program manner. When all or part of the functions in the above embodiments are implemented in a computer program manner, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc. The above functions can be realized by a computer executing the program. For example, the program is stored in the memory of the device, and when the processor executes the program in the memory, the above all or part of the functions can be realized. In addition, when all or part of the functions in the above embodiments are implemented in a computer program manner, the program can also be stored in a storage medium such as a server, another computer, magnetic disk, optical disk, flash drive or mobile hard disk, and saved to the memory of the local device by downloading or copying, or the system of the local device is updated in version. When the processor executes the program in the memory, all or part of the functions in the above embodiments can be realized.

[0086] The above uses specific examples to elaborate on the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art of the present invention, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made.

Claims

1. A method for providing medical services to outpatients, characterized in that: It can be applied to the medical service system, including: Obtaining basic medical information of outpatients, including the patient's chief complaint, current medical history, past medical history, family history, personal history, physical examination, psychosocial factors, system review, and medication history; Perform cluster analysis based on the basic medical information of outpatients to identify whether the outpatients need targeted consultation and help services. If so, proceed to the next step; Based on the outpatient test information, determine the time to obtain the test report; If the time to obtain the test report is determined to be before the first preset time of the day, wait for the doctor's diagnosis result; if the time to obtain the test report is determined to be after the first preset time of the day and before the second preset time of the day, wait for the test report and make a prediction of the severity of the disease based on the obtained test report; If the doctor's diagnosis result obtained reaches the preset first disease severity threshold, then find fellow medical staff to provide first targeted consultation and assistance services for outpatients; If the predicted severity of the illness reaches a preset second severity threshold of the illness, then finding fellow medical staff to provide second targeted consultation and assistance services to the outpatient; If the time for obtaining the test report is judged to be after the second preset time of the day, fellow medical staff will be found to provide third-party targeted consultation and assistance services to outpatients.

2. The outpatient medical service method according to claim 1, characterized in that: The cluster analysis based on the basic medical information of the outpatient to identify whether the outpatient needs targeted consultation and help services includes: inputting the basic medical information of the outpatient into an unsupervised cluster classification model to obtain cluster categories and results of whether targeted consultation and help services are needed; the method for obtaining the cluster classification model includes: The basic medical information of each historical outpatient is formed into a first data set; Applying an unsupervised clustering algorithm to the first data set, training and learning to obtain a clustering classification model; Analyze and annotate the category characteristics and recommended measures of each cluster category in the cluster classification model; the recommended measures include whether targeted consultation and assistance services are needed.

3. The outpatient medical service method according to claim 1, characterized in that: The method of determining the time to obtain the test report based on the test information of the outpatient patient includes: Obtain the outpatient patients' report registration time and the queuing order of the tests and examinations, calculate the possible time for the outpatient patients to obtain the reports based on the average test and examination time, and use the possible time as the time for the outpatient patients to obtain the test and examination reports; when the outpatient patients have multiple tests and examinations, calculate the possible times for the multiple tests and examinations respectively, and use the latest possible time as the time for the outpatient patients to obtain the test and examination reports.

4. The outpatient medical service method according to claim 1, characterized in that: The first preset time is the time corresponding to half an hour before the outpatient doctor gets off work that day, and the second preset time is the outpatient medical staff's off work time that day.

5. The outpatient medical service method according to claim 1, characterized in that: The prediction of the severity of the disease based on the obtained test report includes: The obtained test report is input into the corresponding neural network model to predict the severity of the disease. The corresponding neural network model is a neural network model trained based on multiple historical test reports with disease severity labels combined with a loss function.

6. The outpatient medical service method according to claim 1, characterized in that: The fellow medical staff who provide the first targeted consultation and help service to outpatients are doctors, the fellow medical staff who provide the second targeted consultation and help service to outpatients are doctors, and the fellow medical staff who provide the third targeted consultation and help service to outpatients are nurses.

7. The outpatient medical service method according to claim 1, characterized in that: The method for searching for the fellow medical staff includes: Obtaining first identity information of an outpatient, wherein the first identity information includes the name, ID number, ID address, and residence address information of an inpatient; Based on the ID card address and residence address information of the outpatient, search the hospital medical staff database to find a list of medical staff from the same hometown who are closest to the outpatient ID card address and / or closest to the residence address by a preset number threshold; Based on the list of fellow medical staff, determine service information for providing targeted consultation and assistance services to outpatients within a future time threshold, wherein the service information includes information on medical staff providing targeted consultation and assistance services and specific service times; Based on the determined service information, the service information is sent to the client where the outpatient is located, and the first identity information of the outpatient is sent to the client where the medical staff providing targeted consultation and assistance services is located; the first identity information also includes the outpatient's age, gender, contact number, department and medical record information.

8. The outpatient medical service method according to claim 7, characterized in that: The method of searching the hospital medical staff database based on the inpatient's ID card address and residential address information to find a list of medical staff who are closest to the inpatient's ID card address and / or closest to the residential address within a preset number threshold includes: giving priority to searching for medical staff whose ID card address or residential address is in the same district, county or township as the outpatient's ID card address or residential address.

9. The outpatient medical service method according to claim 8, characterized in that: The method also includes: if no medical staff from the same district, county or township as the outpatient is found, then the associate professor doctor or nurse of the department where the outpatient is located will be used as the medical staff to provide targeted consultation and assistance services.

10. A computer-readable storage medium, characterized in that: The medium stores a program, which can be loaded by a processor and execute the outpatient medical service method according to any one of claims 1 to 9.

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