Inpatient medical service enhancement method and medium
By using the address information of hospitalized patients in the hospital to find fellow villagers and medical staff for on-site condolence services, and improving measures were proposed based on the feedback results analysis, the problem of high cost of traditional medical services was solved, and the goal of improving patient satisfaction and reducing medical service costs was achieved.
Patent Information
- Application Number
- CN202510278301.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-10
- Publication Date
- 2025-06-03
AI Technical Summary
Traditional medical service methods are costly, and it is difficult to reduce medical service costs while improving patient satisfaction, and it is difficult to effectively improve hospital service quality.
By obtaining the ID card address and residence address information of the hospitalized patients, searching the hospital medical staff database, finding fellow villagers' medical staff closest to the hospitalized patient's address, providing on-site condolence services, and analyzing the feedback results, targeted service improvement measures are proposed.
On the basis of reducing personnel costs, improve hospital service quality, improve patient satisfaction, and improve medical services through feedback to achieve the dual effects of cost reduction and service quality improvement.
Smart Images
Figure CN120089416A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of information services, and particularly relates to a method and medium for enhancing medical services for inpatients. 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 their ability to serve patients, including improving the convenience of medical treatment, the sense of identity of medical treatment, and the satisfaction of medical treatment. However, traditional means of serving patients, such as increasing service personnel traditionally, will increase more costs, and the cost investment of using the method of artificial medical guidance 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
[0003] The technical problem to be solved by this application is to provide a method and medium for enhancing medical services for inpatients, which have the characteristics of reducing medical service costs and improving the service quality of hospitals on the basis of improving patient satisfaction.
[0004] In a first aspect, in one embodiment, a method for enhancing medical services for inpatients is provided, including: Obtaining the first identity information of the inpatient, 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 inpatient, searching the hospital medical staff database, finding the list of medical staff with the preset number threshold closest to the ID address of the inpatient and / or closest to the residential address, and determining the local medical staff who will conduct on-site condolences services for the inpatient based on this list of medical staff; Sending the first service feedback information to the first client where the local medical staff conducting on-site condolences services are located to obtain the first service feedback result, and sending the second service feedback information to the second client where the inpatient receiving on-site condolences services is located to obtain the second service feedback result; Analyzing the inpatient based on the first service feedback result returned by the first client, obtaining the overall characteristics of the inpatient, and proposing targeted service improvement measures; analyzing the medical service situation based on the second service feedback result returned by the second client, and obtaining targeted medical service improvement measures.
[0005] In one embodiment, the first service feedback information includes the individual information of the inpatient receiving on-site condolences services, where the individual information includes the condition diagnosis, family economic situation, personal emotional situation, familiarity with the hospital where the patient is located, expectations for inpatient treatment, and accompanying personnel situation.
[0006] In one embodiment, the first service feedback result includes: feedback results on the condition diagnosis being very serious, serious or general, the family economic situation being poor, average or good, the personal emotional situation being poor, average or good, the familiarity with the hospital being unfamiliar, average or familiar, the expectation for hospitalization being very high, relatively high or average, and the accompaniment situation being no accompanying person, occasional accompanying person or constant accompanying person.
[0007] In one embodiment, analyzing the in-patient based on the first service feedback result returned by the first client to obtain the overall characteristics of the in-patient and propose targeted service improvement measures, including: Performing clustering analysis based on the first service feedback result of the in-patient to identify the overall characteristics of the in-patient, and giving targeted service improvement measures based on the overall characteristics of the patient, including: inputting the first service feedback result of the in-patient into the first clustering analysis model to identify the overall characteristics of the in-patient; the method for obtaining the first clustering analysis model includes: Collecting the first service feedback results of hospital medical staff on each historical in-patient and preprocessing them to obtain training data as the input of the autoencoder; The autoencoder includes an encoder and a decoder. Training the autoencoder with the training data as the input includes: the encoder compresses the input training data into a low-dimensional representation, extracts key features, and realizes dimensionality reduction; the decoder reconstructs the low-dimensional representation back to the original data dimension, enabling the model to learn the data feature distribution and making the reconstructed data as close as possible to the original input training data; Performing density clustering on the low-dimensional representation obtained from the trained autoencoder based on the density-based clustering algorithm to obtain a clustering result; Analyzing each cluster in the clustering result, counting the distribution of in-patients in each category in terms of condition diagnosis, family economic situation, personal emotional situation, familiarity with the hospital, expectation for hospitalization, and accompaniment situation, and analyzing and annotating the overall characteristics of patients in each category.
[0008] In one embodiment, analyzing the in-patient based on the first service feedback result returned by the first client includes: obtaining the evaluation total score of the first service feedback result and determining the degree of attention required according to the score range where the total score is located.
[0009] In one embodiment, the second service feedback result includes hospital service evaluation information, and the medical service evaluation information includes medical technology situation, medical service situation, medical convenience situation, medical order situation, medical environment situation, and the service attitude of local medical staff.
[0010] In one embodiment, the second service feedback result includes: feedback results on the medical technology situation being very high, high, average, poor, or very poor; feedback results on the medical service situation being very high, high, average, poor, or very poor; feedback results on the medical convenience situation being very convenient, relatively convenient, average, relatively inconvenient, or very inconvenient; feedback results on the medical order situation being very good, good, average, poor, or very poor; feedback results on the medical environment situation being very good, good, average, poor, or very poor; and feedback results on the service attitude of local medical staff being very good, good, average, poor, or very poor.
[0011] In one embodiment, analyzing the medical service situation based on the second service feedback result returned by the second client to obtain targeted medical service improvement measures includes: Performing clustering analysis based on the second service feedback result to obtain targeted medical service improvement measures, including: inputting the second service feedback result into a second clustering analysis model to obtain targeted medical service improvement measures; the method for obtaining the second clustering analysis model includes: Collecting the second service feedback results of each historical in-patient and performing preprocessing to obtain training data, which is used as the input of the autoencoder; The autoencoder includes an encoder and a decoder. Training the autoencoder with the training data as the input includes: the encoder compresses the input training data into a low-dimensional representation, extracts key features, and realizes dimensionality reduction; the decoder then reconstructs the low-dimensional representation back to the original data dimension, enabling the model to learn the data feature distribution and making the reconstructed data as close as possible to the original input training data; Performing density clustering on the low-dimensional representation obtained from the trained autoencoder based on the density-based clustering algorithm to obtain a clustering result; Analyzing each cluster in the clustering result, counting the distribution of hospital services in terms of medical technology situation, medical service situation, medical convenience situation, medical order situation, medical environment situation, and the service attitude of local medical staff for each category, and analyzing and annotating the targeted medical service improvement measures for each category.
[0012] In one embodiment, analyzing the medical service situation based on the second service feedback result returned by the second client includes: obtaining the total evaluation score of the second service feedback result and determining the degree of attention required according to the score range where the total score is located.
[0013] 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 inpatient medical service enhancement method described in any one of the above embodiments.
[0014] The beneficial effects of the present invention are as follows: Based on the ID card address and residential address information of inpatients, the hospital medical staff database is searched to find the list of medical staff within the preset number threshold that is closest to the ID card address of the inpatient and / or closest to the residential address, and the fellow-townsman medical staff for on-site condolence services for the inpatients is determined based on this list of medical staff. This enables finding fellow-townsman medical staff whose addresses are the same as or close to those of the inpatients based on the address information of the inpatients, so that the fellow-townsman medical staff can go to console the inpatients, which can improve the hospital service quality on the basis of reducing personnel costs, and thus can reduce the medical service cost on the basis of improving patient satisfaction. Since it is based on on-site condolence services, the first service feedback result returned by the first client where the fellow-townsman medical staff is located is obtained to analyze the inpatients, and the overall characteristics of the inpatients are obtained to propose targeted service improvement measures; moreover, the second service feedback result returned by the second client where the inpatients receiving on-site condolence services are located is obtained to analyze the medical service situation and obtain targeted medical service improvement measures, which enables further improving the hospital service quality based on the feedback from inpatients and "fellow-townsman" medical staff. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a schematic flowchart of the inpatient medical service enhancement method of "fellow-townsman serving fellow-townsman" according to an embodiment of the present application; Figure 2 It is a schematic flowchart of the method for obtaining the first clustering analysis model according to an embodiment of the present application; Figure 3 It is a schematic flowchart of the method for obtaining the second clustering analysis model according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0016] The present invention will be further described in detail below in conjunction with the accompanying drawings by specific embodiments. Similar elements in different embodiments are denoted by related similar element numbers. In the following embodiments, many detailed descriptions are provided 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, or methods. In some cases, some operations related to the present application are not shown or described in the specification to avoid overwhelming the core part of the present application with excessive descriptions. 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 based on the descriptions in the specification and the general technical knowledge in the art.
[0017] In addition, the features, operations, or characteristics described in the specification can be combined in any appropriate 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 obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for clearly describing a certain embodiment and do not mean that they are the necessary sequences, unless it is stated that a certain sequence must be followed.
[0018] 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.
[0019] For the convenience of explaining the inventive concept of the present application, the medical service technology will be briefly described below.
[0020] 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 (such as mobile phone) software. However, with the aggravation of the aging degree, this kind of convenience can no longer meet the service needs of patients. Patients need more manual services. However, if the number of service personnel is increased or manual medical guidance is adopted, the investment cost will be relatively high. The applicant found in the research that if the medical staff providing medical services for inpatients is from the same hometown as the inpatients, due to the natural closeness of fellow villagers, the inpatients' sense of identity and belonging to the hospital will be stronger. Therefore, "fellow villager" medical staff can be arranged for inpatients as much as possible, so that on the basis of improving patient satisfaction, the medical service cost can be reduced. However, the applicant also found in the research that in providing medical services for inpatients, how to further improve the hospital service quality based on the feedback from inpatients and "fellow villager" medical staff has become an urgent problem to be solved.
[0021] In view of this, an inpatient medical service enhancement method and medium are provided in an embodiment of the present application. First, based on the ID card address and residential address information of inpatients, search the hospital medical staff database to find the list of medical staff within the preset quantity threshold that is closest to the ID card address of the inpatient and / or closest to the residential address, and determine the fellow-townsman medical staff who will provide on-site consolation services to the inpatient based on this list of medical staff. In this way, based on the address information of the inpatient, fellow-townsman medical staff with the same or close addresses as the inpatient can be found, so that the fellow-townsman medical staff can visit the inpatient, which can improve the hospital service quality on the basis of reducing personnel costs, and thus can reduce the medical service cost on the basis of improving patient satisfaction. Second, based on the on-site consolation service, obtain the first service feedback result returned by the first client where the fellow-townsman medical staff is located, analyze the inpatient, obtain the overall characteristics of the inpatient, and propose targeted service improvement measures; and obtain the second service feedback result returned by the second client where the inpatient receiving the on-site consolation service is located, analyze the medical service situation, and obtain targeted medical service improvement measures. In this way, the hospital service quality can be further improved based on the feedback from inpatients and "fellow-townsman" medical staff.
[0022] To implement the method of the embodiment of the present application, in one embodiment, it can be implemented based on a medical service system, which may include a service terminal, at least one first client, and at least one second client. Among them, the service terminal can be at least configured to obtain the first identity information of inpatients, and based on the ID card address and residential address information in the first identity information, search the hospital medical staff database to find the list of medical staff within the preset quantity threshold that is closest to the ID card address of the inpatient and / or closest to the residential address, and determine the fellow-townsman medical staff who will provide on-site consolation services to the inpatient based on this list of medical staff. The first client is at least configured to receive the first service feedback information sent by the service terminal and send the first service feedback result to the service terminal, and the second client is at least configured to receive the second service feedback information sent by the service terminal and send the second service feedback result to the service terminal. The service terminal is also configured to analyze the inpatient based on the first service feedback result, obtain the overall characteristics of the inpatient, propose targeted service improvement measures, and analyze the medical service situation based on the second service feedback information to obtain targeted medical service improvement measures.
[0023] In one embodiment of the present application, the inpatient medical service enhancement method of "fellow-townsman serving fellow-townsman" can be implemented based on the above medical service system. Please refer to Figure 1 , and this method may include: Step S10, obtain the first identity information of the inpatient.
[0024] Among them, the first identity information includes the name, ID number, ID address and residential address information of the in-patient. Based on this, fellow-townsman medical staff of this hospital whose address information is the same as or close to that of the in-patient can be found according to the ID address and residential address information of the in-patient.
[0025] Step S20: Based on the ID address and residential address information of the in-patient, search the hospital medical staff database, find the list of medical staff within the preset quantity threshold that is the closest to the ID address of the in-patient and / or the closest to the residential address, and determine the fellow-townsman medical staff who will conduct on-site condolence services for the in-patient based on this list of medical staff.
[0026] In the case where the ID address and residential address of the in-patient are the same, search is conducted based on the identical address. In the case where the ID address and residential address of the in-patient are different, search is conducted separately. Whether it is the ID address or the residential address of the in-patient, the object for distance calculation can be either the ID address of the medical staff or the residential address of the medical staff. Based on this, more medical staff with the closest distance can be found, increasing the possibility of finding true fellow-townsman medical staff.
[0027] The method adopted in step S20 can adopt some methods of the prior art, such as similarity calculation, etc., or a map can be called to directly conduct distance calculation. In one embodiment, it can be a combination of the similarity algorithm and the distance calculation by calling the map. For example, first conduct similarity calculation, and then call the map to conduct precise distance calculation. Based on this, first obtain those with similarity exceeding the preset similarity threshold, and then conduct precise distance calculation on those exceeding the similarity threshold to reduce the occupation of computer resources.
[0028] Step S30: Send the first service feedback information to the first client where the fellow-townsman medical staff conducting on-site condolence services are located to obtain the first service feedback result, and send the second service feedback information to the second client where the in-patient receiving on-site condolence services is located to obtain the second service feedback result.
[0029] In one embodiment, the first service feedback information includes the individual information of the in-patient receiving on-site condolence services, and this individual information includes the condition diagnosis situation, family economic situation, personal emotion situation, familiarity with the hospital where the patient is located, expectations for in-patient treatment, and the situation of accompanying personnel.
[0030] In one embodiment, after receiving the first service feedback information sent by the service terminal, the first client can open the corresponding page, which includes a feedback unit for the condition diagnosis, a feedback unit for the family economic situation, a feedback unit for the personal emotional situation, a feedback unit for the familiarity with the hospital where the client is located, a feedback unit for the expectations for inpatient treatment, and a feedback unit for the accompanying personnel situation, so as to provide feedback on the corresponding situations.
[0031] In one embodiment, the first service feedback results include: feedback results indicating that the condition diagnosis is very serious, serious, or general; feedback results indicating that the family economic situation is poor, average, or good; feedback results indicating that the personal emotional situation is poor, average, or good; feedback results indicating that the familiarity with the hospital where the client is located is unfamiliar, average, or familiar; feedback results indicating that the expectations for inpatient treatment are very high, relatively high, or average; and feedback results indicating that the accompanying personnel situation is no accompanying personnel, occasional accompanying personnel, or constant accompanying personnel.
[0032] In one embodiment, the corresponding scoring situation can be obtained through the feedback results of various personal information. For example, in the condition diagnosis, a very serious condition is scored 1 point, a serious condition is scored 2 points, and a general condition is scored 3 points; in the family economic situation, a poor economic situation is scored 1 point, an average economic situation is scored 2 points, and a good economic situation is scored 3 points; in the personal emotional situation, a poor emotional state is scored 1 point, an average emotional state is scored 2 points, and a good emotional state is scored 3 points; in the familiarity with the hospital where the client is located, unfamiliarity is scored 1 point, average familiarity is scored 2 points, and familiarity is scored 3 points; in the expectations for inpatient treatment, very high expectations are scored 1 point, relatively high expectations are scored 2 points, and average expectations are scored 3 points; in the accompanying personnel situation, no accompanying personnel is scored 1 point, occasional accompanying personnel is scored 2 points, and constant accompanying personnel is scored 3 points. Thus, the total score of the first service feedback results can be obtained, and the degree of attention required can be determined according to the score range in which the total score falls.
[0033] In one embodiment, if the total score is between 6 and 9 points, key attention is required; if the total score is between 10 and 12 points, close attention is required; if the total score is between 13 and 18 points, attention is required.
[0034] In one embodiment, the second service feedback results include hospital service evaluation information, which includes medical technology situation, medical service situation, medical convenience situation, medical order situation, medical environment situation, and the service attitude of local medical staff.
[0035] In one embodiment, after receiving the second service feedback information sent by the service terminal, the second client can open the corresponding page, which includes a feedback unit for the medical technology situation, a feedback unit for the medical service situation, a feedback unit for the medical convenience situation, a feedback unit for the medical order situation, a feedback unit for the medical environment situation, and a feedback unit for the service attitude of local medical staff, so as to provide feedback on the corresponding situations.
[0036] In one embodiment, the second service feedback result includes: feedback results on the medical technology level being very high, high, average, poor, or very poor; feedback results on the medical service level being very high, high, average, poor, or very poor; feedback results on the medical convenience level being very convenient, relatively convenient, average, relatively inconvenient, or very inconvenient; feedback results on the medical order level being very good, good, average, poor, or very poor; feedback results on the medical environment level being very good, good, average, poor, or very poor; and feedback results on the service attitude of local medical staff being very good, good, average, poor, or very poor.
[0037] In one embodiment, the corresponding scoring situation can be obtained through the feedback results of various medical service evaluation information. For example, in terms of medical technology level, very high is 5 points, high is 4 points, average is 3 points, poor is 2 points, and very poor is 1 point; in terms of medical service level, very high is 5 points, high is 4 points, average is 3 points, poor is 2 points, and very poor is 1 point; in terms of medical convenience level, very convenient is 5 points, relatively convenient is 4 points, average is 3 points, relatively inconvenient is 2 points, and very inconvenient is 1 point; in terms of medical order level, very good is 5 points, good is 4 points, average is 3 points, poor is 2 points, and very poor is 1 point; in terms of medical environment level, very good is 5 points, good is 4 points, average is 3 points, poor is 2 points, and very poor is 1 point; and in terms of the service attitude of local medical staff, very good is 5 points, good is 4 points, average is 3 points, poor is 2 points, and very poor is 1 point. Thus, the total evaluation score of the second service feedback result can be obtained, and the degree of attention required can be determined according to the score range where the total score is located.
[0038] In one embodiment, if the total score is between 6 - 12 points, it indicates that the customer evaluation is poor and key attention is required to find the reasons for targeted improvement; if the total score is between 13 - 18 points, it indicates that the customer evaluation is average and close attention is required to find the reasons for targeted improvement; if the total score is above 19 points, it indicates that the customer evaluation is good and continuous improvement is required.
[0039] Thus, the first-round screening can be carried out based on the scores to find the objects that need to be closely monitored and focused on, thereby reducing the occupancy of the computer's computing resources, and further finding the targeted service improvement measures and targeted medical service improvement measures.
[0040] Step S40: Analyze the in-patient based on the first service feedback result returned by the first client to obtain the overall characteristics of the in-patient and propose targeted service improvement measures; analyze the medical service situation based on the second service feedback result returned by the second client to obtain targeted medical service improvement measures.
[0041] In one embodiment, the in - hospital patients are analyzed based on the first service feedback results returned by the first client to obtain the overall characteristics of the in - hospital patients, and targeted service improvement measures are proposed, including: performing clustering analysis based on the first service feedback results of the in - hospital patients to identify the overall characteristics of the in - hospital patients, and giving targeted service improvement measures based on the overall characteristics of the patients, including: inputting the first service feedback results of the in - hospital patients into the first clustering analysis model to identify the overall characteristics of the in - hospital patients.
[0042] In one embodiment, please refer to Figure 2 , the method for obtaining the first clustering analysis model includes: Step S100, collect the first service feedback results of hospital medical staff for each historical in - hospital patient, and perform pre - processing to obtain training data, which is used as the input of the auto - encoder.
[0043] Data pre - processing may include cleaning the original data, removing noise data and outliers, etc., and then performing normalization or standardization operations to map the data to a specific range. For example, subtract the mean from the data points to make them concentrated around 0, or divide by the variance to make them concentrated around 1, or divide the data points by the maximum value to make them concentrated between 0 and 1, ensuring that the input data meets the input requirements of the auto - encoder.
[0044] Step S200, the auto - encoder includes an encoder and a decoder, and use the training data as the input of the auto - encoder to train the auto - encoding.
[0045] Step S200 may include: the encoder compresses the input training data into a low - dimensional representation, extracts key features, and realizes dimensionality reduction; the decoder then reconstructs the low - dimensional representation back to the original data dimension, enabling the model to learn the data feature distribution and making the reconstructed data as close as possible to the original input training data.
[0046] In one embodiment, the training process of the auto - encoder may include: Step S2001, train the encoder. Randomly initialize the weights of the encoder, calculate the loss between the encoder output and the target encoding vector using the training data. Commonly used loss functions include mean square error (MSE) and cross - entropy loss, etc. Use the gradient descent algorithm to update the encoder weights to minimize the loss function, and repeat this process until the encoder weights converge, compressing the input high - dimensional data into a low - dimensional representation of the encoding vector.
[0047] Step S2002, train the decoder. The training process of the decoder is similar to that of the encoder. Using the encoding vector as the input, the goal is to restore it to the original high - dimensional data. Calculate the loss between the decoder output and the original high - dimensional data, and update the decoder weights through the gradient descent algorithm so that it can accurately reconstruct the original data from the encoding vector.
[0048] Step S2003: Train the encoder and decoder as an integrated model. Using the preprocessed data, calculate the loss between the output of the autoencoder and the target high-dimensional data, and update the weights of the integrated model using the gradient descent algorithm. Continuously iterate until the weights of the autoencoder converge, enabling the autoencoder to learn the underlying structure and features of the data, effectively compress the high-dimensional data into a low-dimensional encoded vector, and be able to reconstruct it well.
[0049] Step S300: Based on the density-based clustering algorithm, perform density clustering on the low-dimensional representations obtained from the trained autoencoder to obtain the clustering results.
[0050] The features of the low-dimensional representations obtained from the autoencoder have undergone dimensionality reduction processing, retaining the key information of the data.
[0051] Adopt a density-based clustering algorithm, such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise). This algorithm can identify different clustering clusters according to the density distribution of data points and can detect noise points in the data. The parameters of the DBSCAN algorithm, such as the neighborhood radius (eps) and the minimum number of points (minPts), can be set according to the data characteristics and actual requirements. Determine the optimal parameter values through multiple experiments and evaluations.
[0052] Step S400: Analyze each clustering cluster in the clustering results, count the distribution of inpatients in each category in terms of disease diagnosis, family economic situation, personal emotional situation, familiarity with the hospital, expectations for inpatient treatment, and accompanying personnel situation, and analyze and label the overall characteristics of patients in each category.
[0053] In some embodiments, for patient groups with overall characteristics of severe illness, poor mood, and no accompanying personnel, the targeted service improvement measures that can be given include: strengthening medical care, increasing the frequency of medical staff rounds, and closely monitoring the changes in the condition; arranging psychiatrists for psychological counseling to relieve the negative emotions of patients; coordinating hospital volunteers or nursing workers to provide necessary life assistance and companionship for patients. For patient groups with overall characteristics of poor economy and very high expectations for inpatient treatment, the targeted service improvement measures that can be given include: communicating with the hospital's medical insurance department to assist patients in understanding medical insurance policies and reimbursement procedures and striving for more fee waivers; reasonably selecting cost-effective treatment plans and medications on the premise of ensuring treatment effects; explaining the treatment process and expected effects to patients in detail to avoid psychological gaps due to overly high expectations. For patient groups with overall characteristics of unfamiliarity with the hospital and average family economy, the targeted service improvement measures that can be given include: arranging a special person to introduce the hospital environment and medical treatment process and distributing a detailed medical treatment guide when patients are admitted to the hospital; providing a clear expense list and explanation to let patients understand the reasons for each expense; regularly carrying out health lectures to improve patients' awareness of their own diseases and treatments.
[0054] In one embodiment, the medical service situation is analyzed based on the second service feedback result returned by the second client to obtain targeted medical service improvement measures, including: performing clustering analysis based on the second service feedback result to obtain targeted medical service improvement measures, including: inputting the second service feedback result into a second clustering analysis model to obtain targeted medical service improvement measures.
[0055] In one embodiment, please refer to Figure 3 , and the acquisition method of the second clustering analysis model includes: Step S1000, collect the second service feedback results of each historical in-patient and perform preprocessing to obtain training data, which is used as the input of the autoencoder.
[0056] Step S2000, the autoencoder includes an encoder and a decoder, and the training data is used as the input of the autoencoder to train the autoencoding.
[0057] The specific method steps of step S2000 can refer to the specific method steps of step S200.
[0058] Step S3000, based on the density-based clustering algorithm, perform density clustering on the low-dimensional representation obtained by the trained autoencoder to obtain a clustering result.
[0059] The specific method steps of step S3000 can refer to the specific method steps of step S300.
[0060] Step S4000: Analyze each cluster in the clustering result, count the distribution of hospital services in each category in terms of medical technology, medical services, medical convenience, medical order, medical environment, and the service attitude of local medical staff, and analyze and label the targeted medical service improvement measures for each category.
[0061] In one embodiment, for each cluster, the distribution of inpatients in each scoring dimension can be analyzed in depth. For example, count the proportion of patients with different levels of medical technology scores in each cluster, as well as the similar distributions of scores in other dimensions, so as to summarize the overall evaluation characteristics of patients in each category for hospital services.
[0062] For clusters with generally low scores, the targeted medical service improvement measures that can be given include: in terms of medical technology, organize internal expert consultations and case discussions, improve the professional skills of medical staff, and invite external experts to conduct training lectures; in terms of medical services, strengthen the training of medical staff's service awareness, set up patient feedback and complaint channels and handle them in a timely manner; in terms of medical convenience, optimize the appointment registration and examination processes, and increase self-service equipment; in terms of medical order, assign more security personnel to maintain order and improve the guiding service; in terms of medical environment, strengthen the management of ward cleaning and sanitation and improve facilities and equipment; in terms of the service attitude of local medical staff, carry out special training on service attitude and establish a supervision and assessment mechanism.
[0063] For clusters with high medical technology scores but low scores in other aspects, the targeted medical service improvement measures that can be given include: in terms of medical services, establish a patient satisfaction survey mechanism, and conduct criticism and education and training on medical staff with poor service attitudes; in terms of medical convenience, open an online service platform and provide services such as pushing inspection and test results; in terms of medical order, optimize the medical treatment process and set up special order guides; in terms of medical environment, improve the hospital's signage system and increase rest areas and green plants; in terms of the service attitude of local medical staff, strengthen team building and create a good working atmosphere.
[0064] For clusters with high scores in medical environment and order but low scores in medical technology and services, the targeted medical service improvement measures that can be given include: in terms of medical technology, formulate a plan to improve the professional skills of medical staff and encourage them to participate in academic exchange activities; in terms of medical services, carry out service etiquette training, establish a patient service center, and provide one-stop services.
[0065] For the method for enhancing the medical services for inpatients of "fellow villagers serving fellow villagers" based on any of the above embodiments, first, based on the ID card address and residential address information of inpatients, search the hospital medical staff database to find the list of medical staff within the preset number threshold that is closest to the ID card address and / or residential address of the inpatients, and determine the fellow villager medical staff who will conduct on-site consolation services for the inpatients based on this list of medical staff. In this way, based on the address information of the inpatients, fellow villager medical staff whose addresses are the same as or close to those of the inpatients can be found, so that the fellow villager medical staff can visit the inpatients, which can improve the hospital service quality on the basis of reducing personnel costs, thereby enabling the reduction of medical service costs on the basis of improving patient satisfaction. Second, based on the on-site consolation services, obtain the first service feedback result returned by the first client where the fellow villager medical staff is located, analyze the inpatients to obtain the overall characteristics of the inpatients, and propose targeted service improvement measures; and obtain the second service feedback result returned by the second client where the inpatients receiving the on-site consolation services are located, analyze the medical service situation, and obtain targeted medical service improvement measures. In this way, the hospital service quality can be further improved based on the feedback from the inpatients and the "fellow villager" medical staff.
[0066] In an embodiment of the present application, a computer-readable storage medium is provided. A program is stored on the storage medium, and the stored program includes the method that can be loaded and processed by a processor in any of the above embodiments.
[0067] Those skilled in the art can understand that all or part of the functions of the above methods 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. The storage medium may include: read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc. The above functions are realized by a computer executing the program. For example, when the program is stored in the memory of the device and the program in the memory is executed by the processor, all or part of the above 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, downloaded or copied and saved to the memory of the local device, or the system of the local device is updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be realized.
[0068] 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 to which the present invention pertains, based on the idea of the present invention, several simple deductions, deformations or substitutions can also be made.
Claims
1. A method for enhancing medical services for inpatients, characterized in that: include: Acquire the first identity information of the inpatient, wherein the first identity information includes the name, ID card number, ID card address and residence address information of the inpatient; Based on the ID card address and residence address information of the inpatient, search the hospital medical staff database to find a list of medical staff with a preset number threshold closest to the ID card address and / or closest to the residence address of the inpatient, and determine the fellow medical staff who will provide on-site condolence services for the inpatient based on the list of medical staff; Sending first service feedback information to a first client where the fellow medical staff providing on-site condolence service is located to obtain a first service feedback result, and sending second service feedback information to a second client where the inpatient receiving on-site condolence service is located to obtain a second service feedback result; Analyze the inpatient based on the first service feedback result returned by the first client, obtain the overall characteristics of the inpatient, and propose targeted service improvement measures; The medical service situation is analyzed based on the second service feedback result returned by the second client to obtain targeted medical service improvement measures.
2. The method for enhancing medical services for inpatients according to claim 1, characterized in that: The first service feedback information includes individual information of hospitalized patients receiving on-site condolence services, and the individual information includes disease diagnosis, family financial situation, personal emotional state, familiarity with the hospital, expectations for hospitalization, and accompanying personnel.
3. The method for enhancing medical services for inpatients according to claim 2, characterized in that: The first service feedback results include: the diagnosis of the disease is very serious, serious or general, the family economic situation is poor, general or good, the personal emotional situation is poor, general or good, the familiarity with the hospital is unfamiliar, general or familiar, the expectation for hospitalization is very high, high or general, and the companionship is no, occasional or all the time.
4. The method for enhancing medical services for inpatients according to claim 3, characterized in that: The inpatient is analyzed based on the first service feedback result returned by the first client, the overall characteristics of the inpatient are obtained, and targeted service improvement measures are proposed, including: Performing cluster analysis based on the first service feedback result of the inpatient to identify the overall characteristics of the inpatient, and providing targeted service improvement measures based on the overall characteristics of the patient, including: inputting the first service feedback result of the inpatient into a first cluster analysis model to identify the overall characteristics of the inpatient; the method for obtaining the first cluster analysis model includes: Collect the first service feedback results of hospital medical staff on each historical inpatient and pre-process them to obtain training data as the input of the autoencoder; The autoencoder includes an encoder and a decoder. The training data is used as the input of the autoencoder to train the autoencoder, including: the encoder compresses the input training data into a low-dimensional representation, extracts key features, and realizes dimensionality reduction; the decoder reconstructs the low-dimensional representation back to the original data dimension, allowing the model to learn the data feature distribution, so that the reconstructed data is as close as possible to the original input training data; Based on the density clustering algorithm, the low-dimensional representation obtained by the trained autoencoder is subjected to density clustering to obtain the clustering result; Each cluster in the clustering results was analyzed, and the distribution of hospitalized patients in each category in terms of disease diagnosis, family economic situation, personal emotional state, familiarity with the hospital, expectations for hospitalization and accompanying personnel was counted. The overall characteristics of patients in each category were analyzed and labeled.
5. The method for enhancing medical services for inpatients according to claim 3, characterized in that: The analyzing of the inpatient based on the first service feedback result returned by the first client includes: obtaining the total evaluation score of the first service feedback result, and determining the degree of attention required according to the score range of the total score.
6. The method for enhancing medical services for inpatients according to claim 1, characterized in that: The second service feedback result includes hospital service evaluation information, and the medical service evaluation information includes medical technology conditions, medical service conditions, medical convenience conditions, medical order conditions, medical environment conditions and service attitude conditions of fellow medical staff.
7. The method for enhancing medical services for inpatients according to claim 6, characterized in that: The second service feedback results include: feedback results of medical technology being very high, relatively high, average, poor or very poor, feedback results of medical service being very high, relatively high, average, poor or very poor, feedback results of medical convenience being very convenient, relatively convenient, average, relatively inconvenient or very inconvenient, feedback results of medical order being very good, good, average, poor or very poor, feedback results of medical environment being very good, good, average, poor or very poor, and feedback results of service attitude of fellow villagers and medical staff being very good, good, average, poor or very poor.
8. The method for enhancing medical services for inpatients according to claim 7, characterized in that: The analyzing of the medical service situation based on the second service feedback result returned by the second client to obtain targeted medical service improvement measures includes: Performing cluster analysis based on the second service feedback result to obtain targeted medical service improvement measures includes: inputting the second service feedback result into a second cluster analysis model to obtain targeted medical service improvement measures; the method for obtaining the second cluster analysis model includes: The second service feedback results of each historical inpatient are collected and preprocessed to obtain training data as the input of the autoencoder; The autoencoder includes an encoder and a decoder. The training data is used as the input of the autoencoder to train the autoencoder, including: the encoder compresses the input training data into a low-dimensional representation, extracts key features, and realizes dimensionality reduction; the decoder reconstructs the low-dimensional representation back to the original data dimension, allowing the model to learn the data feature distribution, so that the reconstructed data is as close as possible to the original input training data; Based on the density clustering algorithm, the low-dimensional representation obtained by the trained autoencoder is subjected to density clustering to obtain the clustering result; Analyze each cluster in the clustering results, and count the distribution of hospital services in each category in terms of medical technology, medical services, medical convenience, medical order, medical environment, and service attitude of fellow medical staff, and analyze and mark the targeted medical service improvement measures for each category.
9. The method for enhancing medical services for inpatients according to claim 7, characterized in that: The analyzing of the medical service situation based on the second service feedback result returned by the second client includes: obtaining the total evaluation score of the second service feedback result, and determining the degree of attention required according to the score range of the total score.
10. A computer-readable storage medium, characterized in that: The medium stores a program, which can be loaded by a processor and execute the inpatient medical service enhancement method as described in any one of claims 1 to 9.