CN117438073A

CN117438073A - Medical data processing method and device, storage medium and equipment

Info

Publication number: CN117438073A
Application number: CN202311434866.9A
Authority: CN
Inventors: 蒋伊石; 陈炜杰; 吕佳欣; 陈尔佳; 徐赫
Original assignee: Taikang Insurance Group Co Ltd; Taikang Pension Insurance Co Ltd
Current assignee: Taikang Insurance Group Co Ltd; Taikang Pension Insurance Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2024-01-23

Abstract

The disclosure relates to a medical data processing method and device, a storage medium and equipment, and relates to the technical field of medical big data processing, wherein the method comprises the following steps: screening candidate disease seeds from the historical medical records data, and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease seeds; determining basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result; determining a disease group fingerprint image of the target DRG disease group according to the feature analysis result, and recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint image; and configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group. The method improves the accuracy of the resulting target clinical path.

Description

Medical data processing method and device, storage medium and equipment

Technical Field

The embodiment of the disclosure relates to the technical field of medical big data processing, in particular to a medical data processing method, a medical data processing device, a computer readable storage medium and electronic equipment.

Background

In the existing medical data processing scheme, flexibility and intelligence are lacking, and the latest change of clinical practice cannot be reflected timely to cause the disconnection of a clinical path and actual demands; that is, the accuracy of the resulting clinical path is low.

It should be noted that the information of the present invention in the above background section is only for enhancing understanding of the background of the present disclosure, and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.

Disclosure of Invention

It is an object of the present disclosure to provide a medical data processing method, a medical data processing apparatus, a clinical path determining method, a clinical path determining apparatus, a computer-readable storage medium, and an electronic device, which further overcome, at least to some extent, the problem of low accuracy of the resulting clinical path due to limitations and drawbacks of the related art.

According to one aspect of the present disclosure, there is provided a medical data processing method including:

screening candidate disease seeds from the historical medical records data, and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease seeds;

determining basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result;

Determining a disease group fingerprint image of the target DRG disease group according to the feature analysis result, and recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint image;

and configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group.

In one exemplary embodiment of the present disclosure, screening candidate disease species from historical medical records data includes:

acquiring historical medical record data from a hospital management system, and analyzing the historical medical record data to obtain disease type data and disease group data included in the historical medical record data;

screening a first current disease seed included in the disease seed data to obtain a first screening result, and screening a second current disease seed included in the disease group data to obtain a second screening result;

and obtaining the candidate disease seeds based on the first screening result and/or the second screening result.

In an exemplary embodiment of the present disclosure, screening the first current disease seed included in the disease seed data to obtain a first screening result includes:

analyzing the disease data to obtain a first current disease and first disease attribute data of the first current disease, wherein the first current disease and the first disease attribute data are included in the disease data; the first disease attribute data comprises a first total cost of the first current disease, a first profit or first loss of the first current disease, and a first case number corresponding to the first current disease;

According to the first total cost, the first profit margin or the first loss margin and the first case number, sequencing the first current disease type to obtain a first sequencing result;

and screening the first current disease seeds arranged at the preset position from the first sorting result to serve as the first sorting result.

analyzing the historical medical records to obtain a first current disease type and first total cost of the first current disease type, a first current disease group and second total cost of the first current disease group, a first current department and third total cost of the first current department, which are included in the historical medical records;

determining a first target disease type from the first current disease type based on the first total cost, and determining a second target disease group from the first current disease group based on the second total cost;

determining a first target department from the first current department based on the third total cost, and determining a third target disease group and a second target disease species from the first target department;

determining a second target department and a third target disease from the second target disease group, and determining a third target department and a fourth target disease from the first target disease;

And cross screening is carried out on the second target department, the third target disease group, the fourth target disease group, the second target disease species and the third target disease species to obtain the candidate disease species.

In an exemplary embodiment of the present disclosure, determining a target DRG disease group based on a current diagnosis and treatment mode of the candidate disease species includes:

acquiring a current diagnosis and treatment mode of the candidate disease, and judging whether the current diagnosis and treatment mode is a preset diagnosis and treatment mode or not;

when the current diagnosis and treatment mode is determined to be a preset diagnosis and treatment mode, acquiring a current DRG disease group to which the candidate disease belongs in the current diagnosis and treatment mode, and taking the current DRG disease group as the target DRG disease group.

In one exemplary embodiment of the present disclosure, determining the group essential features of the target DRG group of diseases includes:

acquiring second disease group attribute data of the target DRG disease group; wherein the second group attribute data comprises a plurality of historical hospital days, historical hospital costs, historical number of inpatients, patient age, patient gender, patient diagnostic information, patient diagnosis information, patient preference information for inpatients associated with the target DRG group;

And determining the basic disease group characteristics of the target DRG disease group according to the second disease group attribute data.

In an exemplary embodiment of the present disclosure, analyzing the basic characteristics of the target DRG disease group to obtain the characteristic analysis result includes:

determining average hospitalization days and distribution of hospitalization days according to the historical hospitalization days, and determining average hospitalization cost, hospitalization cost occupation ratio and total cost distribution of the disease group according to the historical hospitalization cost;

determining the inpatient times ratio according to the historical inpatient times, and determining inpatient age distribution and inpatient sex distribution according to the patient age and the patient sex;

determining a disease group diagnosis distribution and a disease group operation distribution according to the patient diagnosis information and the patient diagnosis information, and determining whether a disease group deviation or a disease group preference exists according to the patient preference information;

determining the feature analysis result based on the average number of hospitalizations, average hospitalization cost, number of times of hospitalization duty, total hospitalization cost duty, hospitalization age distribution, hospitalization gender distribution, number of days of hospitalization distribution, total group cost distribution, group diagnosis distribution, group operation distribution, group offset, and group preference.

In an exemplary embodiment of the present disclosure, determining a group fingerprint map of the target DRG group according to the feature analysis result includes:

determining a first-level fingerprint image according to the distribution information of the total cost of the disease group in the characteristic analysis result under the preset medical cost category, and determining a second-level fingerprint image according to the distribution information of the total cost of the disease group under the preset second-level medical cost;

and determining tertiary catalog information based on the subsection information of the total cost distribution of the disease group under the preset tertiary medical catalog name, and determining the disease group fingerprint map of the target DRG disease group according to the primary fingerprint map, the secondary fingerprint map and the tertiary catalog information.

In one exemplary embodiment of the present disclosure, recommending candidate clinical paths for the target DRG group based on the group fingerprint map includes;

acquiring a profit case and a loss case in the target DRG disease group, and analyzing the profit case and the loss case to obtain profit reasons and loss reasons;

determining a profit case distribution of the profit cases and a loss case distribution of the loss cases, and determining a first average cost of the profit cases and a second average cost of the loss cases based on the group fingerprint map;

And determining the average cost difference of the deficiency and the surplus times based on the average cost of the first time and the average cost of the second time, and recommending candidate clinical paths for the target DRG disease group based on the average cost difference of the deficiency and the surplus times, the surplus reasons, the deficiency reasons, the surplus case distribution and the deficiency case distribution.

In an exemplary embodiment of the present disclosure, configuring the candidate clinical paths based on a preset standard medical rule to obtain target clinical paths corresponding to the target DRG disease group includes:

acquiring a preset standard medical rule; wherein the standard medical rules include national clinical pathways, medical evidence-based guidelines, and/or clinical pathway high-quality system reviews;

and configuring the patient diagnosis and treatment information included in the candidate clinical paths and project fees associated with the patient diagnosis and treatment information based on the standard medical rules to obtain target clinical paths corresponding to the target DRG disease groups.

In an exemplary embodiment of the present disclosure, the medical data processing method further includes:

docking the target clinical path to a doctor workstation so that the doctor workstation pushes a diagnosis and treatment project currently required to be performed by a inpatient associated with each attending doctor to terminal equipment corresponding to the attending doctor according to the current diagnosis and treatment progress of the inpatient;

The diagnosis and treatment project comprises a plurality of diagnosis and treatment project names, diagnosis and treatment expense, medicine names, medicine expense, consumable item names and consumable expense.

responding to a query request of a clinical path sent by terminal equipment, and analyzing the query request to obtain a DRG disease group to be queried;

inquiring a target clinical path corresponding to the DRG disease group to be inquired in a preset clinical path storage database, and feeding back the target clinical path to the terminal equipment so that the terminal equipment displays the target clinical path.

According to one aspect of the present disclosure, there is provided a method of determining a clinical path, comprising:

responding to patient medical record information of a current patient input by an attending doctor, and matching whether a target clinical path matched with the current patient exists in a clinical path storage database according to the patient medical record information; wherein the target clinical path is obtained by the medical data processing method according to any one of the above;

generating and displaying a sub-display interface when detecting that a target clinical path matched with the current patient exists;

Responding to the selection operation of the main treating doctor aiming at the sub-display interface, judging whether the current patient needs to be managed based on the target clinical path;

upon determining that the current patient needs to be managed based on the target clinical path, a patient clinical path required by the current patient is determined, and a current date the current patient entered the patient clinical path is determined.

In an exemplary embodiment of the present disclosure, the method for determining a clinical path further includes:

when the fact that the current patient does not need to be managed based on the target clinical path is determined, determining a non-diameter-entering reason of the current patient and storing the non-diameter-entering reason;

analyzing case data of a current patient without the entrance based on the reasons of the non-entrance to obtain characteristics of the patient without the entrance, and constructing a fingerprint map without the entrance based on the characteristics of the patient without the entrance;

and analyzing the abnormal hospitalization cost of the current patient without the entrance based on the fingerprint map without the entrance to obtain an abnormal cost analysis result, and configuring the clinical path of the current patient without the entrance based on the abnormal cost analysis result.

Judging whether the current treatment expense of the current patient which is included in the target clinical path exceeds the standard treatment expense in the target clinical path, and generating and displaying first early warning prompt information based on first personnel information exceeding the standard treatment expense when the current treatment expense is determined to exceed the standard treatment expense;

and responding to the touch operation acted on the first early warning prompt information, and displaying the name of the sickbed and the expense information of the current patient associated with the first early warning prompt information.

judging whether the current treatment expense of the current patient which is included in the target clinical path is lower than the standard treatment expense in the target clinical path, and generating and displaying second early warning prompt information based on second people information lower than the standard treatment expense when the current treatment expense is determined to be lower than the standard treatment expense;

and responding to the touch operation acted on the second early warning prompt information, and displaying the name of the sickbed and the expense information of the current patient associated with the second early warning prompt information.

According to one aspect of the present disclosure, there is provided a medical data processing apparatus including:

the target DRG disease group determining module is used for screening candidate disease seeds from the historical medical record data and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease seeds;

the disease group basic feature analysis module is used for determining the disease group basic features of the target DRG disease group and analyzing the disease group basic features of the target DRG disease group to obtain a feature analysis result;

the candidate clinical path recommending module is used for determining a disease group fingerprint image of the target DRG disease group according to the characteristic analysis result and recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint image;

and the target clinical path configuration module is used for configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group.

According to one aspect of the present disclosure, there is provided a determination apparatus of a clinical path, including:

the target clinical path matching module is used for responding to the patient medical record information of the current patient input by the main treating doctor, and matching whether a target clinical path matched with the current patient exists in a clinical path storage database according to the patient medical record information; wherein the target clinical path is obtained by the medical data processing method according to any one of the above;

The first interface display module is used for generating and displaying a sub-display interface when detecting that a target clinical path matched with the current patient exists;

the current patient management module is used for responding to the selection operation of the main treating doctor aiming at the sub-display interface and judging whether the current patient needs to be managed based on the target clinical path;

and the patient clinical path determining module is used for determining a patient clinical path required by the current patient and determining the current date of the current patient entering the patient clinical path when determining that the current patient needs to be managed based on the target clinical path.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of processing medical data of any one of the above, or the method of determining a clinical path of any one of the above.

According to one aspect of the present disclosure, there is provided an electronic device including:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of processing medical data of any one of the above, or the method of determining a clinical path of any one of the above, via execution of the executable instructions.

According to the medical data processing method provided by the embodiment of the disclosure, on one hand, candidate disease seeds are screened from historical disease case data, and a target DRG disease group is determined based on the current diagnosis and treatment mode of the candidate disease seeds; then determining the basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result; further, a disease group fingerprint diagram of the target DRG disease group is determined according to the feature analysis result, and candidate clinical paths are recommended for the target DRG disease group based on the disease group fingerprint diagram; finally, configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to target DRG disease groups, wherein the candidate clinical paths can be determined based on a disease group fingerprint image and updated based on the standard medical rules, so that the problems that the accuracy of the obtained clinical paths is low due to the fact that the clinical paths are not constructed flexibly and intelligently and the latest change of clinical practice cannot be timely reflected to cause the disconnection of the clinical paths from actual demands in the prior art are solved, and the accuracy of the target clinical paths is improved; on the other hand, the candidate clinical paths can be determined based on the disease group fingerprint image and updated based on the standard medical rules, so that timeliness of updating the candidate clinical paths is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

Fig. 1 schematically shows a flow chart of a method of processing medical data according to an example embodiment of the present disclosure.

Fig. 2 schematically illustrates an example diagram of a scenario for cross-screening according to an example embodiment of the present disclosure.

Fig. 3 schematically illustrates an exemplary diagram of a specific determination scenario of a medical procedure according to an exemplary embodiment of the present disclosure.

Fig. 4 schematically illustrates an exemplary view of a scenario of the results of a feature analysis of the resulting essential features of a group of diseases according to an exemplary embodiment of the present disclosure.

Fig. 5 schematically illustrates an example view of a scenario of a disease group fingerprint map according to an example embodiment of the present disclosure.

Fig. 6 schematically illustrates an example diagram of a scenario of a difference between a resulting profit case and a loss case according to an example embodiment of the present disclosure.

Fig. 7 schematically illustrates an example diagram of a scenario with a cost difference of both earnings and earnings according to an example embodiment of the present disclosure.

Fig. 8 schematically illustrates an example graph of target clinical path configuration recommendations according to an example embodiment of the present disclosure.

Fig. 9 schematically illustrates an example diagram of a scenario of a queried target clinical path according to an example embodiment of the present disclosure.

Fig. 10 schematically illustrates a flowchart of a method of determining a clinical path according to an example embodiment of the present disclosure.

Fig. 11 schematically illustrates a scene example diagram of specific content of a popped-up clinical pathway and corresponding pathway days, according to an example embodiment of the present disclosure.

Fig. 12 schematically illustrates an exemplary diagram of a specific scenario of a reason for no diameter entry according to an exemplary embodiment of the present disclosure.

Fig. 13 schematically illustrates an exemplary diagram of a scenario of specific statistics of non-diameter-entering causes according to an exemplary embodiment of the present disclosure.

Fig. 14 schematically illustrates an exemplary view of a scenario featuring details of a resulting non-incident medical record according to an exemplary embodiment of the present disclosure.

Fig. 15 schematically illustrates an example view of a scenario featuring details of another resulting non-incident medical record according to an example embodiment of the present disclosure.

Fig. 16 schematically illustrates an example diagram of a scenario of major cost anomalies for a resulting non-diameter case, according to an example embodiment of the present disclosure.

Fig. 17 schematically illustrates an example diagram of a display interface of displayed hospital bed names and cost information according to an example embodiment of the present disclosure.

Fig. 18 schematically illustrates an example diagram of a scenario of a resulting indicator warning indication according to an example embodiment of the present disclosure.

Fig. 19 schematically illustrates an example diagram of a resulting department overall pre-warning indication, according to an example embodiment of the present disclosure.

Fig. 20 schematically illustrates a specific example diagram of a patient discharge interface, according to an example embodiment of the present disclosure.

Fig. 21 schematically illustrates an example diagram of a scenario of resulting clinical path execution statistics according to an example embodiment of the present disclosure.

Fig. 22 schematically illustrates a block diagram of a medical data processing apparatus according to an example embodiment of the present disclosure.

Fig. 23 schematically shows a block diagram of a determination device of a clinical path according to an example embodiment of the present disclosure.

Fig. 24 schematically illustrates an electronic device for implementing the above-described processing method of medical data and determination method of clinical path according to an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

With the continuous development of medical science and technology, medical expenses have been rapidly increased. Based on this, a policy of medical insurance control fee is implemented in many countries internationally; that is, based on the policy of medical insurance control fee, a DRG (Diagnosis Related Groups, disease diagnosis related group) system can be commonly adopted for medical fee payment for hospitalization medical services; by the mode, the medical service branch is more transparent, and the purposes of encouraging hospitals and doctors to provide proper and reasonable medical service according to the actual illness state of patients and improving the quality and efficiency of the medical service are achieved. Meanwhile, along with the deep innovation of medical health system, china also promotes the innovation of medical insurance DRG (Diagnosis Related Groups, disease Diagnosis related group) or DIP (Diagnosis-Intervention Packet, disease score) payment mode, and the innovation requires the improvement of the operation efficiency of hospitals, the reasonable allocation of medical resources and the control of unreasonable increase of medical cost; meanwhile, related departments also require hospitals to strengthen medical quality management, optimize medical service flows, improve medical service efficiency and promote high-quality development of hospitals; on the premise, clinical path management as an important measure for ensuring medical quality and operation efficiency has also been developed, and at the same time, the application of clinical paths brings great convenience for standardizing medical behaviors and controlling medical costs.

From the practical experience of each country, under the premise of implementing a DRG clinical path, the competitive pressure among different hospitals is increased; therefore, each hospital must increase the efficiency and quality of medical services in order to obtain more patients in competition for the same disease species and thus more medical insurance payments; moreover, the clinical path implemented by the hospital can effectively support medical resource allocation, optimize medical service flow and control medical service cost. At present, although the clinical path management system based on the DRG on the market has certain advantages, the clinical path management system also has some defects or shortcomings, and mainly has the following aspects: on one hand, the establishment and updating of the clinical path are not timely; specifically, because the clinical path needs to be comprehensively formulated according to factors such as evidence-based medicine, expert consensus, regional characteristics and the like, and the factors change along with the change of time and environment, the clinical path needs to be continuously revised and updated; however, the clinical path management system in the current market often lacks flexibility and intelligence, and cannot reflect the latest progress and change of clinical practice in time, so that the clinical path is disjointed from the actual demand; on the other hand, the clinical path is not performed or monitored in place; specifically, because the clinical path involves a plurality of departments and links, medical staff is required to follow the specifications and the procedures in daily work, and an information system is required to record and analyze various indexes; however, the clinical path management systems in the market at present often lack effective incentive and constraint mechanisms, cannot effectively promote participation and cooperation of medical staff, cannot timely find and correct deviation or variation, and therefore the execution effect of the clinical path is compromised.

In summary, there is a great room for improvement in the clinical path management system based on DRG in the market at present, which needs to start from each link of formulation, execution, monitoring, etc., and improve the level of flexibility, intelligence, scientificity, objectivity, etc. to adapt to the needs of medical reform, and bring more value to hospitals and patients. Based on this, in the present exemplary embodiment, there is provided a method for processing medical data, which may be executed on a server, a server cluster, a cloud server, or the like; of course, those skilled in the art may also operate the methods of the present disclosure on other platforms as desired, which is not particularly limited in the present exemplary embodiment. Specifically, referring to fig. 1, the method for processing medical data may include the following steps:

s110, screening candidate disease seeds from historical medical records data, and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease seeds;

s120, determining basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result;

s130, determining a disease group fingerprint image of the target DRG disease group according to the feature analysis result, and recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint image;

And S140, configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group.

In the medical data processing method, on one hand, candidate disease seeds are screened from the historical disease case data, and a target DRG disease group is determined based on the current diagnosis and treatment mode of the candidate disease seeds; then determining the basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result; further, a disease group fingerprint diagram of the target DRG disease group is determined according to the feature analysis result, and candidate clinical paths are recommended for the target DRG disease group based on the disease group fingerprint diagram; finally, configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to target DRG disease groups, wherein the candidate clinical paths can be determined based on a disease group fingerprint image and updated based on the standard medical rules, so that the problems that the accuracy of the obtained clinical paths is low due to the fact that the clinical paths are not constructed flexibly and intelligently and the latest change of clinical practice cannot be timely reflected to cause the disconnection of the clinical paths from actual demands in the prior art are solved, and the accuracy of the target clinical paths is improved; on the other hand, the candidate clinical paths can be determined based on the disease group fingerprint image and updated based on the standard medical rules, so that timeliness of updating the candidate clinical paths is improved.

The method for processing medical data and the method for determining a clinical path according to the exemplary embodiments of the present disclosure will be explained and described in detail below with reference to the accompanying drawings.

Firstly, a business scenario applicable to the medical data processing method and the clinical path determining method described in the exemplary embodiments of the present disclosure may sell a DRG-based clinical path management tool directly to a hospital; or may directly provide clinical pathway management advisory services based on DRGs to hospitals; or may also provide a comprehensive service combining system products and counseling services to hospitals. Meanwhile, applicable personnel of the method described in the exemplary embodiments of the present disclosure may include, but are not limited to, hospital leaders, hospital management personnel such as medical insurance, medical personnel such as clinical department owners, and the like.

Next, technical problems solved by the exemplary embodiments of the present disclosure will be explained and illustrated. Specifically, the processing method of medical data and the determining method of clinical paths described in the exemplary embodiments of the present disclosure mainly solve the following technical problems: on one hand, how to establish standardized clinical paths suitable for different disease types and disease groups according to the DRG grouping principle, and realize the homogenization and optimization of the diagnosis and treatment process; on the other hand, how to combine the DRG payment standard and the weight coefficient to carry out accounting and analysis on the cost structure in the clinical path so as to realize the balance of medical quality and economic benefit; on the other hand, how to embed the clinical path into the hospital information system realizes the real-time monitoring and project management and control of key links such as medical advice, examination and inspection, operation anesthesia and the like, and avoids unnecessary resource consumption and medical risks.

Further, the technical implementation principles of the exemplary embodiments of the present disclosure are explained and illustrated. Specifically, in an exemplary embodiment, the processing procedure of the medical data described in the exemplary embodiment of the present disclosure may be implemented as follows: according to the relevant requirements of the DRG, combining with high-quality system reviews, clinical guidelines, national clinical path specifications and other evidence-based medical evidence, namely DRG data, medical practice of the hospital, consulting multidisciplinary clinical and administrative expert opinion, reformulating and optimizing the existing clinical path or establishing the clinical path of the hospital, especially referring to the standard post hospitalization day and standard post cost set by DRG disease group analysis, controlling key links so as to achieve standardization and homogenization of medical practice. In another example embodiment, the determination process of the clinical path described in the example embodiment of the present disclosure may be applied in the clinical path implementation and the clinical path quality control process; the clinical path implementation can embed the agreed clinical path scheme into a hospital informatization system, and carry out project prompt on links such as hospitalization orders, operations/anesthesia, inspection and the like through a special clinical path information system so as to promote the refinement, standardization and programming of treatment projects accepted by patients and improve the management and utilization of hospital resources; furthermore, the quality control of the clinical path can realize the feedback and the quality control of the clinical diagnosis and treatment process through the standardization of the clinical path and the recording and analysis of variation information. For example, the route establishment condition and the execution condition are counted according to the condition of constructing a clinical route in a hospital and the actual entrance and exit conditions of a case; taking the diameter-entering case as a standard pole, comparing and analyzing the diameter-non-entering case monthly, and analyzing the detailed characteristics of the diameter-non-entering case so as to form a feedback adjustment clinical path and realize quality improvement.

The processing method of the medical data shown in fig. 1 will be further explained and explained below. Specific:

in step S110, candidate disease seeds are screened from the historical medical records data, and a target DRG disease group is determined based on the current diagnosis and treatment mode of the candidate disease seeds.

In the present exemplary embodiment, first, candidate disease seeds are screened from the historical medical records data; specifically, the specific screening mode of the candidate disease seeds can be realized in the following two modes:

the first implementation way is: acquiring historical medical record data from a hospital management system, and analyzing the historical medical record data to obtain disease type data and disease group data included in the historical medical record data; screening a first current disease seed included in the disease seed data to obtain a first screening result, and screening a second current disease seed included in the disease group data to obtain a second screening result; and obtaining the candidate disease seeds based on the first screening result and/or the second screening result. The hospital management system described herein may include, but is not limited to, an electronic medical record system, a medical insurance settlement system, a medical insurance DRG settlement platform, a hospital information management system, and the like; meanwhile, the acquired historical medical records data can include, but is not limited to, a medical records front page, a medical insurance settlement list, medical insurance settlement details, medical insurance DRG grouping weights, payment standards, medical insurance return settlement data and the like; meanwhile, the disease type data described herein refers to medical data obtained by classifying based on disease types, and the disease group data refers to medical data obtained by classifying based on disease groups; meanwhile, the specific classifying process may be implemented according to the disease type or group to which each medical data belongs, which is not particularly limited in this example.

In an exemplary embodiment, the screening of the first current disease seed included in the disease seed data to obtain the first screening result may be implemented in the following manner: firstly, analyzing the disease data to obtain a first current disease and first disease attribute data of the first current disease, wherein the first current disease is included in the disease data; the first disease attribute data comprises a first total cost of the first current disease, a first profit or first loss of the first current disease, and a first case number corresponding to the first current disease; secondly, sorting the first current disease types according to the first total cost, the first profit margin or the first loss margin and the first case number to obtain a first sorting result; and then, screening a first current disease seed arranged at a preset position from the first sorting result to serve as the first sorting result.

The specific implementation of the determination of candidate disease based on the first implementation will be further explained and described below. Specifically, in the practical application process, when candidate disease types are determined, the DRG disease group which has more loss or higher income occupation, more case number and can establish a standardized diagnosis and treatment path and corresponding disease types can be considered as main targets for constructing a standardized clinical path; meanwhile, based on evidence-based guidelines and clinical paths, the latest progress of the disease research is consulted and known by combining with the historical DRG data of a hospital, and the paths are reconstructed after being discussed and authenticated by experts in the department of interdisciplinary group organization, so that the prospective, scientificity and systemicity of the clinical paths are ensured. In a specific candidate disease seed process, first, screening a first current disease seed according to a first total cost, a first profit margin or a first loss margin and a first number of people (first case number) of the first current disease seed to obtain a first screening result; in the screening process, selecting a first current disease with more loss or higher income and more cases as a first screening result; furthermore, in the process of screening the first current disease, it may be further determined according to the ratio of high-low magnification ratio and clinical experience, for example, if the disease group variation degree of the first current disease group is large, it is difficult to normalize the disease group, or if the disease group variation degree is too large, it is necessary to normalize the disease group, the first current disease group may be used as the first target disease group, and further, the second current disease included in the first target disease group may be screened, so as to obtain the second screening result.

Further, after the first screening result and the second screening result are obtained, the first screening result and the second screening result may be directly used as candidate disease seeds, or the candidate disease seeds may be obtained based on an intersection calculation result of the first screening result and the second screening result, or the candidate disease seeds may be obtained based on a union calculation result of the first screening result and the second screening result. In the actual application process, the selection may be performed according to actual needs, which is not particularly limited in this example.

The second implementation mode is as follows: analyzing the historical medical records to obtain a first current disease type and first total cost of the first current disease type, a first current disease group and second total cost of the first current disease group, a first current department and third total cost of the first current department, which are included in the historical medical records; determining a first target disease type from the first current disease type based on the first total cost, and determining a second target disease group from the first current disease group based on the second total cost; determining a first target department from the first current department based on the third total cost, and determining a third target disease group and a second target disease species from the first target department; determining a second target department and a third target disease from the second target disease group, and determining a third target department and a fourth target disease from the first target disease; and cross screening is carried out on the second target department, the third target disease group, the fourth target disease group, the second target disease species and the third target disease species to obtain the candidate disease species. That is, in the selection process of candidate disease, different dimensions can be selected to perform cross screening; for example, different departments, disease groups and disease types can be selected in the dimension, the TOP10 departments, disease groups and disease types of the whole hospital can be checked, the TOP10 disease groups and disease types of a certain department, the TOP10 departments and disease types of a certain disease group, the TOP10 departments and disease types of a certain disease type (3 rd position before ICD coding) and the like can be checked, and cross screening is realized so as to obtain candidate disease types. An exemplary diagram of a specific cross-screening scenario may be illustrated with reference to fig. 2.

Further, after the candidate disease is obtained, a target DRG disease group can be determined based on the current diagnosis and treatment mode of the candidate disease; specifically, the method can be realized by the following steps: firstly, acquiring a current diagnosis and treatment mode of the candidate disease, and judging whether the current diagnosis and treatment mode is a preset diagnosis and treatment mode; secondly, when the current diagnosis and treatment mode is determined to be a preset diagnosis and treatment mode, acquiring a current DRG disease group to which the candidate disease belongs in the current diagnosis and treatment mode, and taking the current DRG disease group as the target DRG disease group. That is, in the practical application process, the diagnosis and treatment mode needing special attention can be determined first, and then the corresponding target DRG disease group is determined from the diagnosis and treatment mode; an exemplary diagram of a specific determination scenario of the diagnosis and treatment manner may be shown in fig. 3. It should be noted that, the same disease can have multiple different diagnosis and treatment modes, and in the practical application process, a diagnosis and treatment mode (such as surgery treatment or hospitalization treatment) needing special attention can be selected, so that the target DRG disease group is determined based on the diagnosis and treatment mode and the candidate disease.

In step S120, the basic characteristics of the target DRG group are determined, and the basic characteristics of the target DRG group are analyzed to obtain a feature analysis result.

In the present exemplary embodiment, first, the group essential characteristics of the target DRG group are determined; specifically, the method can be realized by the following steps: firstly, acquiring second disease group attribute data of the target DRG disease group; wherein the second group attribute data comprises historical hospital days, historical hospital costs, historical number of inpatients, patient age, patient gender, patient diagnostic information, patient diagnosis information, patient treatment information, and patient preference information for inpatients associated with the target DRG group; and secondly, determining the disease group basic characteristics of the target DRG disease group according to the second disease group attribute data. Specifically, the basic characteristics of the group include average hospital days, average hospital costs, number of hospital accounts, total cost accounts, age distribution, sex distribution, hospital days distribution, total cost distribution, main diagnosis distribution, main operation distribution, whether there is an offset or preference, etc., and the selected dimension may be based on DRG group code, settlement year (optional), department name (optional), and settlement month (optional) in the process of acquiring the attribute data of the second group, which is not limited in this example. Further, the patient diagnosis and treatment information described herein may include operation information and other operation information required for the patient during the treatment.

Secondly, after obtaining the basic characteristics of the disease group of the target DRG disease group, analyzing the basic characteristics of the disease group of the target DRG disease group, thereby obtaining a characteristic analysis result; specifically, the method can be realized by the following steps: firstly, determining average hospitalization days and distribution of hospitalization days according to the historical hospitalization days, and determining average hospitalization cost, hospitalization cost ratio and total cost distribution of a disease group according to the historical hospitalization cost; secondly, determining the inpatient times ratio according to the historical inpatient times, and determining inpatient age distribution and inpatient sex distribution according to the patient age and the patient sex; then, according to the patient diagnosis information and the patient diagnosis and treatment information, determining a disease group diagnosis distribution and a disease group operation distribution, and determining whether a disease group deviation or a disease group preference exists according to the patient preference information; finally, determining the feature analysis result based on the average number of hospitalizations, average hospitalization cost, number of times of hospitalization duty ratio, total hospitalization cost duty ratio, hospitalization age distribution, hospitalization gender distribution, number of days of hospitalization distribution, total group cost distribution, group diagnosis distribution, group operation distribution, group offset, and group preference. An exemplary view of the scene of the feature analysis result of the obtained basic feature of the disease group may be shown in fig. 4.

In step S130, a group fingerprint map of the target DRG group is determined according to the feature analysis result, and candidate clinical paths are recommended for the target DRG group based on the group fingerprint map.

In the present exemplary embodiment, first, a group fingerprint map of a target DRG group is determined from the feature analysis result; specifically, the method can be realized by the following steps: firstly, determining a first-level fingerprint image according to distribution information of total cost of a disease group in a characteristic analysis result under a preset medical cost category, and determining a second-level fingerprint image according to distribution information of total cost of the disease group under a preset second-level medical cost; and secondly, determining tertiary catalog information based on the subsection information of the total cost distribution of the disease group under the preset tertiary medical catalog name, and determining the disease group fingerprint map of the target DRG disease group according to the primary fingerprint map, the secondary fingerprint map and the tertiary catalog information. Specifically, in the practical application process, the fingerprint image of the patient group is constructed mainly to understand the cost details of each target DRG patient group, that is, to understand that the medical cost of the patient is mainly spent in those categories; in this context, the specific distribution of costs can be understood from a number of different particle size levels, primary, secondary, detail, etc.

For example, in the practical application process, the total cost of the disease group can be obtained based on the DRG group coding, the settlement year (optional), the department name (optional) and the settlement month (optional), and then the total cost of the disease group is sequentially arranged according to the occupation ratio based on the cost distribution condition of fourteen cost classes (preset medical cost classes) according to the occupation ratio (for example, western medicines/Chinese medicinal decoction pieces/Chinese medicines can be sequentially connected together, and inspection/test is connected together) so as to obtain a first-level fingerprint image; further, based on the cost distribution condition of the total cost of the disease group under a secondary catalog (preset secondary medical cost), the total cost is sequentially arranged according to the ratio of the total cost to the secondary catalog to obtain a secondary fingerprint image; and finally, constructing tertiary catalog information based on the total cost of the disease group corresponding to the total cost and the total number of the used items under the three-catalog names (preset tertiary medical catalog names) with the standardized item names. Further, in the practical application process, if further drill-down is needed between the first level and the second level, or further drill-down is needed between the second level and the third level, the first level catalogue (such as western medicine fee) which is focused on can be directly clicked, and only the fee structure and the item name under the western medicine fee and the corresponding service condition can be correspondingly displayed in the second level fingerprint chart and the third level catalogue information. An exemplary view of a scene of the obtained fingerprint map of the disease group may be shown with reference to fig. 5.

And secondly, recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint map after the disease group fingerprint map is obtained. Specifically, the method can be realized by the following steps: firstly, acquiring a profit case and a loss case in the target DRG disease group, and analyzing the profit case and the loss case to obtain a profit reason and a loss reason; secondly, determining the profit case distribution of the profit cases and the loss case distribution of the loss cases, and determining the first average cost of the profit cases and the second average cost of the loss cases based on the disease group fingerprint map; and then determining the deficiency and surplus average cost difference based on the first average cost and the second average cost, and recommending candidate clinical paths for the target DRG disease group based on the deficiency and surplus average cost difference, the surplus reason, the deficiency reason, the surplus case distribution and the deficiency case distribution.

Hereinafter, a specific recommendation process of the candidate clinical path will be further explained and explained. Specifically, in the practical application process, firstly, basic characteristic differences of a profit case and a loss case can be checked to see whether the profit case and the loss case have individual disease reasons of patients, medical behavior preferences of doctors, diagnosis and operation differences and the like; meanwhile, the selection of the profit case and the loss case can be realized through dimensions such as room name, DRG group code, settlement year (optional), settlement month (optional) and the like; meanwhile, the profit case and the loss case recorded here are non-high-rate cases and non-low-rate cases; further, the high-rate cases refer to cases with total cost higher than the prescribed multiple (generally 2-3 times) of the DRG payment standard, the low-rate cases refer to cases with total cost lower than the prescribed multiple (generally 30%) of the DRG payment standard, and specific screening can be implemented according to the local medical insurance DRG payment policy, which is not particularly limited in this example; meanwhile, the profit and loss causes may be determined in terms of average number of stay days, average age, and average cost, such as determining the difference between profit and loss cases in terms of average number of stay days, average age, and average cost; wherein, the scene example graph of the difference between the obtained profit case and the loss case can be shown with reference to fig. 6;

Furthermore, in the process of determining the profit case distribution and the loss case distribution, the profit case distribution and the loss case distribution of the target DRG disease group in each department can be based on the target DRG disease group after screening, or the profit case distribution and the loss case distribution of the disease group in each home doctor can be based on the target DRG disease group after screening; meanwhile, if the department and the DRG group are screened at the same time, the situation that the dimension is the department after screening of the DRG group can be directly displayed; still further, in the process of determining the average cost difference of deficiency and profit times, the cost detail difference of the profit case and the deficiency case, especially the difference of primary, secondary and detail cost catalogues, can be compared and analyzed, and detail items which need to be focused and controlled are searched; meanwhile, the specific screening dimension can select DRG group codes, settlement years (optional), settlement months (optional) and the like; the average cost of the profit and loss cases described here is poor, i.e., the average cost of the profit cases minus the average cost of the loss cases. An exemplary diagram of the resulting cost difference for both the profit and the loss is shown in fig. 7.

Finally, after obtaining the expense difference, the profit reason, the loss reason, the profit case distribution and the loss case distribution of the deficiency times, candidate clinical paths can be recommended for the target DRG disease group.

In step S140, the candidate clinical paths are configured based on a preset standard medical rule, so as to obtain a target clinical path corresponding to the target DRG disease group.

Specifically, the candidate clinical paths are configured based on preset standard medical rules, and the target clinical paths corresponding to the target DRG disease group are obtained by the following modes: firstly, acquiring a preset standard medical rule; wherein the standard medical rules include medical evidence-based guidelines and/or clinical path high-quality system reviews; and secondly, configuring the patient diagnosis and treatment information included in the candidate clinical paths and project fees associated with the patient diagnosis and treatment information based on the standard medical rules to obtain target clinical paths corresponding to the target DRG disease groups. That is, in the practical application process, specific items of each category in the clinical path of the hospital can be configured by combining the medical evidence-based guidance, the high-quality system overview of the clinical path and the expert discussion result, so as to form a localized clinical path which accords with the practice of the hospital and has operability; wherein, the scene example diagram corresponding to the displayed clinical path can be referred to as fig. 8.

Further, after the target clinical path is obtained, the medical data processing method further includes: docking the target clinical path to a doctor workstation so that the doctor workstation pushes a diagnosis and treatment project currently required to be performed by a inpatient associated with each attending doctor to terminal equipment corresponding to the attending doctor according to the current diagnosis and treatment progress of the inpatient; the diagnosis and treatment project comprises a plurality of diagnosis and treatment project names, diagnosis and treatment expense, medicine names, medicine expense, consumable item names and consumable expense. That is, in the practical application process, after clicking the configured items and related fees of the target clinical path, the doctor can be docked to the workstation to provide prompts of daily diagnosis and treatment items, medicines, consumable items and corresponding fees.

In one example embodiment, after obtaining the target clinical path, the method of processing medical data further comprises: responding to a query request of a clinical path sent by terminal equipment, and analyzing the query request to obtain a DRG disease group to be queried; inquiring a target clinical path corresponding to the DRG disease group to be inquired in a preset clinical path storage database, and feeding back the target clinical path to the terminal equipment so that the terminal equipment displays the target clinical path. That is, in the course of actual application, when the configuration of the target clinical path is completed, a clinical manager or clinician can query or derive the clinical path that has been configured, and so on; wherein, the example diagram of the scene of the queried target clinical path can be referred to as shown in fig. 9.

Up to this point, the processing methods of medical data described in the exemplary embodiments of the present disclosure have been fully implemented. Further, after the target clinical path configuration is completed, a doctor can determine a clinical path required by a patient according to the actual condition of the patient in the diagnosis and treatment process and manage the patient based on the determined clinical path; meanwhile, in the process of managing the patient based on the determined clinical path, the following steps are required to be performed: selecting and starting a path, following and executing the path, and mutating or normally leaving the path, so as to realize accurate real-time management of the clinical path; for example, a clinical path scheme (target clinical path) achieving consensus can be embedded into a hospital informatization system, then project recommendation and cost prompt are displayed on a system interface, and meanwhile, an early warning function is set, so that reference is given for a clinician to implement next diagnosis and treatment measures, and the advance management and control of diagnosis and treatment standards and cost control are realized. Based on this, the exemplary embodiments of the present disclosure also provide a method of determining a clinical path. Specifically, referring to fig. 10, the method for determining a clinical path may include the steps of:

step S1010, responding to patient medical record information of a current patient input by an attending doctor, and matching whether a target clinical path matched with the current patient exists in a clinical path storage database according to the patient medical record information; wherein the target clinical path is obtained by the medical data processing method according to any one of the above;

Step S1020, when detecting that a target clinical path matched with the current patient exists, generating a sub-display interface and displaying the sub-display interface;

step S1030, in response to the selection operation of the attending physician with respect to the sub-display interface, determining whether the current patient needs to be managed based on the target clinical path;

step S1040, when it is determined that the current patient needs to be managed based on the target clinical path, of determining a patient clinical path required by the current patient, and of determining a current date on which the current patient entered the patient clinical path.

Hereinafter, step S1010 to step S1040 will be explained and explained. Specifically, in the practical application process, after the doctor inputs the main diagnosis of the patient, the doctor can match whether the clinical path is met according to the input diagnosis information (patient medical record information), if yes, the doctor pops up the "whether the current diagnosis of the patient meets the clinical path XXX and is included in the path" (sub-display interface) so as to select the clinical path by the doctor; then, the doctor can choose whether to incorporate clinical path management according to the condition of the patient; if the inclusion is selected, the specific list content of the clinical path and the corresponding path date are popped up; a specific example diagram of a scenario may be seen with reference to fig. 11. Meanwhile, in the scenario example diagram shown in fig. 11, a daily diagnosis and treatment item, a medicine, a consumable item, and a prompt for corresponding fees thereof may also be provided according to the generated clinical path.

Further, the clinical path may further include a portion of quality control of the clinical path during the actual application process. Specifically, in the process of quality control of the clinical path, the method specifically comprises the following two parts: one part is to carry out statistical analysis on the medical data of the entered path; the other part is to perform non-access analysis on medical data of non-access.

In one example embodiment, non-access analysis of non-access medical data may be accomplished by: when the fact that the current patient does not need to be managed based on the target clinical path is determined, determining a non-diameter-entering reason of the current patient and storing the non-diameter-entering reason; analyzing case data of a current patient without the entrance based on the reasons of the non-entrance to obtain characteristics of the patient without the entrance, and constructing a fingerprint map without the entrance based on the characteristics of the patient without the entrance; and analyzing the abnormal hospitalization cost of the current patient without the entrance based on the fingerprint map without the entrance to obtain an abnormal cost analysis result, and configuring the clinical path of the current patient without the entrance based on the abnormal cost analysis result. That is, in the practical application process, if the diameter is not to be entered, the reason of the non-entering diameter is required to be selected; an exemplary diagram of a specific scenario of the reason for no diameter entry may be shown with reference to fig. 12; and the reasons for the non-diameter entering are also required to be counted and analyzed; the example graph of the scenario of the specific statistical result of the non-invasive cause may refer to fig. 13, further, in the process of analyzing the case data of the current patient without invasive based on the non-invasive cause to obtain the characteristics of the non-invasive patient, the invasive case may be taken as a standard pole, and the detailed characteristics of the non-invasive case (age, number of hospitalization days, total hospitalization cost, main diagnosis, other diagnosis, main operation, and other operation) are analyzed by comparing and analyzing the non-invasive case monthly; wherein, the obtained scene exemplary diagram of the detailed features of the non-diameter medical record can be shown by referring to fig. 14 and 15; furthermore, after the non-invasive patient characteristics are obtained, a non-invasive fingerprint image can be constructed based on the non-invasive patient characteristics; specifically, the entering diameter case can be used as a marker post, the cost composition (major class, secondary class and cost detail) of the non-entering diameter case can be further compared and analyzed monthly, and the main cost abnormality of the non-entering diameter case can be analyzed; wherein, the scene example graph of the main cost abnormality of the obtained non-diameter case can be referred to as shown in fig. 16; and finally, configuring the clinical path of the current patient without the access based on the abnormal cost analysis result, so that the patient without the access can realize the access management.

Finally, the method of determining a clinical path may further include: judging whether the current treatment expense of the current patient which is included in the target clinical path exceeds the standard treatment expense in the target clinical path, and generating and displaying first early warning prompt information based on first personnel information exceeding the standard treatment expense when the current treatment expense is determined to exceed the standard treatment expense; and responding to the touch operation acted on the first early warning prompt information, and displaying the name of the sickbed and the expense information of the current patient associated with the first early warning prompt information. That is, during the practice, when a doctor opens a doctor's hospitalization workstation, a bullet window is provided that may include first personnel information exceeding the standard treatment cost (super DRG benchmarking) in the target clinical path; when the doctor clicks the first personnel information, the corresponding sickbed name and expense information can be displayed; the displayed interface of the patient bed name and the fee information may be shown with reference to fig. 17.

Meanwhile, the method for determining the clinical path further comprises the following steps: judging whether the current treatment expense of the current patient which is included in the target clinical path is lower than the standard treatment expense in the target clinical path, and generating and displaying second early warning prompt information based on second people information lower than the standard treatment expense when the current treatment expense is determined to be lower than the standard treatment expense; and responding to the touch operation acted on the second early warning prompt information, and displaying the name of the sickbed and the expense information of the current patient associated with the second early warning prompt information. That is, during the practice, when the doctor opens the doctor's hospitalization workstation, a pop-up window is provided, which may include a second population information below the standard treatment cost in the target clinical path (below the DRG payment standard); when the doctor clicks the second number of people information, the corresponding sickbed name and expense information can be displayed; the displayed interface of the patient bed name and the fee information may also be shown with reference to fig. 17.

In a possible example embodiment, during the actual application process, the primary physician may also adjust the target clinical path corresponding to the patient according to the actual diagnosis and treatment process of the patient, for example, add a diagnosis result or a surgical operation to the target clinical path according to the actual diagnosis and treatment process of the patient; meanwhile, index early warning indication can be carried out in the diagnosis and treatment process, namely, various costs actually generated by patients can be compared with the cost of the marker post, and the actual progress of the total cost is marked; the example diagram of the scene of the obtained index early warning indication can be shown by referring to fig. 18, and the obtained whole early warning indication of the department can be shown by referring to fig. 19.

In a possible example embodiment, when the patient needs to be discharged, on the patient discharge interface, a doctor needs to supplement and select diagnosis information and operation information according to the actual diagnosis and treatment process of the patient, and if the patient does not need to enter the patient, the patient needs to select a reason for the patient's discharge; a specific example diagram of a patient discharge interface may be seen with reference to fig. 20. In addition, the path establishment condition and the execution condition are counted according to the condition of constructing a clinical path in a hospital and the actual path entering and exiting conditions of cases, so that the execution condition counting result of the clinical path is obtained, and further the target clinical path is further adjusted based on the execution condition counting result; an exemplary view of the scenario of the obtained execution statistics may be specifically shown in fig. 21.

So far, the determination method of the clinical path described in the exemplary embodiments of the present disclosure has been fully implemented. As can be seen from the foregoing, the method according to the exemplary embodiments of the present disclosure has at least the following advantages: on the one hand, by implementing DRG clinical path management, medical institutions can optimize diagnosis and treatment processes, define the time nodes, responsibility main bodies, monitoring indexes and other contents of each link, and further achieve the purpose of avoiding unnecessary or repeated use of examination, inspection, medicines, consumable materials and the like; meanwhile, the hospitalization time and the complication occurrence rate can be reduced, and the service efficiency and the resource utilization rate are improved; on the other hand, in the practical application process, before the patient is in hospital, a doctor determines the clinical path to be executed through admission diagnosis and operation to be performed, and the system prompts the standard hospital stay, standard diagnosis and treatment, medicines and the like and the cost of the patient according to the clinical path; furthermore, in hospitalization, the system can automatically update DRG grouping results through definite diagnosis and specific operation or operation execution, and prompt the difference of clinical path execution of doctors so as to ensure medical quality; after discharge, the clinical route accurate intelligent management can be used for monitoring indexes such as hospital date and cost, comparing the indexes with target values, history synchronization, DRG (digital mark) marker post data and the like, and analyzing abnormal cases. And according to the research results of related documents, after clinical path management is implemented, the average hospitalization day of a hospital can be reduced by 10% -30%, the average hospitalization cost can be reduced by 10% -20%, and the average hospitalization mortality rate can be reduced by 10% -40%. The implementation of DRG clinical path management can also improve the service level and the management level of hospitals, so that the evaluation and assessment are more beneficial to the high-quality development of hospitals. The DRG clinical path management system promotes the hospital to implement standardized, normalized and programmed diagnosis and treatment processes, improves the medical quality and efficiency, and reduces the medical cost and risk. Meanwhile, the management level of the hospital is improved by promoting the aspects of optimizing resource allocation, strengthening risk control, enhancing innovation capability and the like of the hospital. These all contribute to the realization of sustainable development in DRG payment mode in hospitals and promote the advantages of hospitals in market competition.

Further, the processing method of medical data provided by the exemplary embodiments of the present disclosure combines the health and economic evaluation of evidence-based medicine and DRG data, which is not generally involved in clinical pathway systems on the market; meanwhile, it is very important to construct a clinical path combining evidence-based medicine and sanitary economy evaluation, which can help hospitals and departments to realize higher service quality, and can reduce medical cost and improve economic benefit; in order to fully combine evidence-based medicine and sanitary economics evaluation, firstly, evidence needs to be collected according to a common evidence-based flow of clinical paths so as to support the clinical paths to be built on the basis of optimal evidence-based practice; next, there is a need for a sanitary and economic evaluation of DRG cost data to understand the cost of clinical pathways and the relationship to quality of service and outcome. Finally, combining evidence-based medical evidence and DRG data economy evaluation to construct a clinical path which ensures the quality of service and simplifies the medical cost.

Furthermore, the medical data processing method provided by the exemplary embodiment of the present disclosure further characterizes the national unified clinical path as a specific diagnosis and treatment project, an actual charging project of medicines, etc., rather than a generalized medicine of a certain type, a surgical operation of a certain type, etc.; in the practical application process, the personalized treatment plan of the patient can be formulated according to the contents of the admission standard, the discharge standard, the treatment target, the time node, the key activities and the like specified in the clinical path, including diagnosis and treatment projects, medicines, nursing and the like. In the treatment process, a specific treatment plan can be timely adjusted according to the actual condition of a patient, and various data in the treatment process, such as an inspection result, a medication condition, complications and the like, are recorded.

In addition, the medical data processing method provided by the exemplary embodiment of the present disclosure may also adapt the contents of clinical paths, guidelines, etc. of countries to local hospitals, instead of a uniform recipe path; at the same time, the clinical path at the national level is a guideline that does not mandate that different hospitals perform a completely uniform clinical path. The clinical path of the hospital should be established based on the self situation and require the doctor to implement; in the practical application process, the hospital can also be used for preparing a clinical path which accords with the local policy and the self-hospital according to the medical characteristics such as key disciplines and dominant disciplines in the hospital, combining with the local common diseases and frequently encountered diseases of the hospital and implemented medical insurance policies, medical prices and DRG payment standards. Clinical paths which are suitable for local conditions and accord with the conditions of the hospital are formulated based on the DRG data of the hospital, so that the standardization and the uniformity of the clinical paths are maintained in the hospital, more flexible and innovative space is provided for the hospital, and the accurate application of the personalized clinical paths is realized.

The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.

Specifically, the example embodiment of the disclosure also provides a medical data processing device. Specifically, referring to fig. 22, the processing device of medical data may include a target DRG group determination module 2210, a group basic feature analysis module 2220, a candidate clinical path recommendation module 2230, and a target clinical path configuration module 2240. Wherein:

the target DRG disease group determination module 2210 may be used for screening candidate disease types from the historical medical records data, and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease types;

the disease group basic feature analysis module 2220 may be configured to determine the disease group basic feature of the target DRG disease group, and analyze the disease group basic feature of the target DRG disease group to obtain a feature analysis result;

the candidate clinical path recommending module 2230 may be configured to determine a group fingerprint of the target DRG group according to the feature analysis result, and recommend a candidate clinical path for the target DRG group based on the group fingerprint;

the target clinical path configuration module 2240 may be configured to configure the candidate clinical paths based on a preset standard medical rule, so as to obtain a target clinical path corresponding to the target DRG disease group.

In one exemplary embodiment of the present disclosure, screening candidate disease species from historical medical records data includes: acquiring historical medical record data from a hospital management system, and analyzing the historical medical record data to obtain disease type data and disease group data included in the historical medical record data; screening a first current disease seed included in the disease seed data to obtain a first screening result, and screening a second current disease seed included in the disease group data to obtain a second screening result; and obtaining the candidate disease seeds based on the first screening result and/or the second screening result.

In an exemplary embodiment of the present disclosure, screening the first current disease seed included in the disease seed data to obtain a first screening result includes: analyzing the disease data to obtain a first current disease and first disease attribute data of the first current disease, wherein the first current disease and the first disease attribute data are included in the disease data; the first disease attribute data comprises a first total cost of the first current disease, a first profit or first loss of the first current disease, and a first case number corresponding to the first current disease; according to the first total cost, the first profit margin or the first loss margin and the first case number, sequencing the first current disease type to obtain a first sequencing result; and screening the first current disease seeds arranged at the preset position from the first sorting result to serve as the first sorting result.

In one exemplary embodiment of the present disclosure, screening candidate disease species from historical medical records data includes: analyzing the historical medical records to obtain a first current disease type and first total cost of the first current disease type, a first current disease group and second total cost of the first current disease group, a first current department and third total cost of the first current department, which are included in the historical medical records; determining a first target disease type from the first current disease type based on the first total cost, and determining a second target disease group from the first current disease group based on the second total cost; determining a first target department from the first current department based on the third total cost, and determining a third target disease group and a second target disease species from the first target department; determining a second target department and a third target disease from the second target disease group, and determining a third target department and a fourth target disease from the first target disease; and cross screening is carried out on the second target department, the third target disease group, the fourth target disease group, the second target disease species and the third target disease species to obtain the candidate disease species.

In an exemplary embodiment of the present disclosure, determining a target DRG disease group based on a current diagnosis and treatment mode of the candidate disease species includes: acquiring a current diagnosis and treatment mode of the candidate disease, and judging whether the current diagnosis and treatment mode is a preset diagnosis and treatment mode or not; when the current diagnosis and treatment mode is determined to be a preset diagnosis and treatment mode, acquiring a current DRG disease group to which the candidate disease belongs in the current diagnosis and treatment mode, and taking the current DRG disease group as the target DRG disease group.

In one exemplary embodiment of the present disclosure, determining the group essential features of the target DRG group of diseases includes: acquiring second disease group attribute data of the target DRG disease group; wherein the second group attribute data comprises a plurality of historical hospital days, historical hospital costs, historical number of inpatients, patient age, patient gender, patient diagnostic information, patient diagnosis information, patient preference information for inpatients associated with the target DRG group; and determining the basic disease group characteristics of the target DRG disease group according to the second disease group attribute data.

In an exemplary embodiment of the present disclosure, analyzing the basic characteristics of the target DRG disease group to obtain the characteristic analysis result includes: determining average hospitalization days and distribution of hospitalization days according to the historical hospitalization days, and determining average hospitalization cost, hospitalization cost occupation ratio and total cost distribution of the disease group according to the historical hospitalization cost; determining the inpatient times ratio according to the historical inpatient times, and determining inpatient age distribution and inpatient sex distribution according to the patient age and the patient sex; determining a disease group diagnosis distribution and a disease group operation distribution according to the patient diagnosis information and the patient diagnosis information, and determining whether a disease group deviation or a disease group preference exists according to the patient preference information; determining the feature analysis result based on the average number of hospitalizations, average hospitalization cost, number of times of hospitalization duty, total hospitalization cost duty, hospitalization age distribution, hospitalization gender distribution, number of days of hospitalization distribution, total group cost distribution, group diagnosis distribution, group operation distribution, group offset, and group preference.

In an exemplary embodiment of the present disclosure, determining a group fingerprint map of the target DRG group according to the feature analysis result includes: determining a first-level fingerprint image according to the distribution information of the total cost of the disease group in the characteristic analysis result under the preset medical cost category, and determining a second-level fingerprint image according to the distribution information of the total cost of the disease group under the preset second-level medical cost; and determining tertiary catalog information based on the subsection information of the total cost distribution of the disease group under the preset tertiary medical catalog name, and determining the disease group fingerprint map of the target DRG disease group according to the primary fingerprint map, the secondary fingerprint map and the tertiary catalog information.

In one exemplary embodiment of the present disclosure, recommending candidate clinical paths for the target DRG group based on the group fingerprint map includes; acquiring a profit case and a loss case in the target DRG disease group, and analyzing the profit case and the loss case to obtain profit reasons and loss reasons; determining a profit case distribution of the profit cases and a loss case distribution of the loss cases, and determining a first average cost of the profit cases and a second average cost of the loss cases based on the group fingerprint map; and determining the average cost difference of the deficiency and the surplus times based on the average cost of the first time and the average cost of the second time, and recommending candidate clinical paths for the target DRG disease group based on the average cost difference of the deficiency and the surplus times, the surplus reasons, the deficiency reasons, the surplus case distribution and the deficiency case distribution.

In an exemplary embodiment of the present disclosure, configuring the candidate clinical paths based on a preset standard medical rule to obtain a target clinical path corresponding to a target DRG disease group includes: acquiring a preset standard medical rule; wherein the standard medical rules include medical evidence-based guidelines and/or clinical path high-quality system reviews; and configuring the patient diagnosis and treatment information included in the candidate clinical paths and project fees associated with the patient diagnosis and treatment information based on the standard medical rules to obtain target clinical paths corresponding to the target DRG disease groups.

In an exemplary embodiment of the present disclosure, the medical data processing apparatus further includes:

the target clinical path docking module can be used for docking the target clinical path to a doctor workstation so that the doctor workstation pushes a diagnosis and treatment project currently required to be performed by a inpatient associated with each attending doctor to terminal equipment corresponding to the attending doctor according to the current diagnosis and treatment progress of the inpatient; the diagnosis and treatment project comprises a plurality of diagnosis and treatment project names, diagnosis and treatment expense, medicine names, medicine expense, consumable item names and consumable expense.

the query request analysis module can be used for responding to a query request of a clinical path sent by the terminal equipment, analyzing the query request and obtaining a DRG disease group to be queried;

the target clinical path inquiring module can be used for inquiring a target clinical path corresponding to the DRG disease group to be inquired in a preset clinical path storage database and feeding the target clinical path back to the terminal equipment so that the terminal equipment displays the target clinical path.

The example embodiments of the present disclosure also provide a determination apparatus of a clinical path. Specifically, referring to fig. 23, the clinical path determination device may include a target clinical path matching module 2310, a first interface display module 2320, a current patient management module 2330, and a patient clinical path determination module 2340. Wherein:

a target clinical path matching module 2310, which is configured to, in response to patient medical record information of a current patient entered by an attending physician, match whether a target clinical path matching the current patient exists in a clinical path storage database according to the patient medical record information; wherein the target clinical path is obtained by the medical data processing method according to any one of the above;

A first interface display module 2320 that may be configured to generate and display a sub-display interface when it is detected that there is a target clinical path that matches the current patient;

a current patient management module 2330 operable to determine whether management of the current patient based on the target clinical path is required in response to a selection operation of the primary physician with respect to the sub-display interface;

the patient clinical path determination module 2340 may be configured to determine a patient clinical path required by the current patient and determine a current date the current patient entered the patient clinical path when it was determined that administration of the current patient based on the target clinical path was required.

In an exemplary embodiment of the present disclosure, the determining means of the clinical path further comprises:

the non-access cause determination module may be configured to determine a non-access cause of the current patient and save the non-access cause when it is determined that management of the current patient based on the target clinical path is not required;

the non-entrance fingerprint image construction module can be used for analyzing case data of a current non-entrance patient based on the non-entrance cause to obtain non-entrance patient characteristics and constructing a non-entrance fingerprint image based on the non-entrance patient characteristics;

The abnormal hospitalization cost analysis module can be used for analyzing the abnormal hospitalization cost of the current patient without the entrance based on the non-entrance fingerprint image to obtain an abnormal cost analysis result, and configuring the clinical path of the current patient without the entrance based on the abnormal cost analysis result.

The non-entrance cause determining module, the non-entrance fingerprint image constructing module and the abnormal hospitalization cost analyzing module described herein may be collectively referred to as a clinical path quality control module; that is, quality control of the clinical pathway may include, but is not limited to: determining the cause of the patient's non-approach, determining the fingerprint map of the non-approach, abnormal hospital cost analysis, and so forth.

the first expense judgment module can be used for judging whether the current treatment expense of the current patient which is included in the target clinical path exceeds the standard treatment expense in the target clinical path, and generating and displaying first early warning prompt information based on first personnel information exceeding the standard treatment expense when the current treatment expense is determined to exceed the standard treatment expense;

The first information display module can be used for responding to the touch operation acted on the first early warning prompt information and displaying the name of the sickbed and the expense information of the current patient associated with the first early warning prompt information.

the second expense judging module can be used for judging whether the current treatment expense of the current patient which is included in the target clinical path is lower than the standard treatment expense in the target clinical path, and generating and displaying second early warning prompt information based on second people number information lower than the standard treatment expense when the current treatment expense is determined to be lower than the standard treatment expense;

the second information display module can be used for responding to the touch operation acted on the second early warning prompt information and displaying the name and the expense information of the sickbed of the current patient associated with the second early warning prompt information.

The specific details of each module in the above-mentioned medical data processing device and clinical path determining device are described in detail in the corresponding medical data processing method and clinical path determining method, and therefore will not be described here again.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. The features and functions of one module or unit described in the foregoing may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

An electronic device 2400 according to such an embodiment of the present disclosure is described below with reference to fig. 24. The electronic device 2400 shown in fig. 24 is merely an example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments. As shown in fig. 24, the electronic device 2400 is presented in the form of a general purpose computing device. The components of the electronic device 2400 may include, but are not limited to: the at least one processing unit 2410, the at least one storage unit 2420, a bus 2430 connecting the different system components (including the storage unit 2420 and the processing unit 2410), and a display unit 2440.

Wherein the storage unit stores program code that is executable by the processing unit 2410 such that the processing unit 2410 performs steps according to various exemplary embodiments of the present disclosure described in the "exemplary method" section of the present specification. For example, the processing unit 2410 may perform step S110 shown in fig. 1: screening candidate disease seeds from the historical medical records data, and determining a target DRG disease group based on the current diagnosis and treatment mode of the candidate disease seeds; step S120: determining basic characteristics of the disease group of the target DRG disease group, and analyzing the basic characteristics of the disease group of the target DRG disease group to obtain a characteristic analysis result; step S130: determining a disease group fingerprint image of the target DRG disease group according to the feature analysis result, and recommending candidate clinical paths for the target DRG disease group based on the disease group fingerprint image; step S140: and configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group.

For another example, the processing unit 2410 may perform step S1010 shown in fig. 10: responding to patient medical record information of a current patient input by an attending doctor, and matching whether a target clinical path matched with the current patient exists in a clinical path storage database according to the patient medical record information; wherein the target clinical path is obtained by the medical data processing method according to any one of the above; step S1020: generating and displaying a sub-display interface when detecting that a target clinical path matched with the current patient exists; step S1030: responding to the selection operation of the main treating doctor aiming at the sub-display interface, judging whether the current patient needs to be managed based on the target clinical path; step S1040: upon determining that the current patient needs to be managed based on the target clinical path, a patient clinical path required by the current patient is determined, and a current date the current patient entered the patient clinical path is determined.

The storage unit 2420 may include readable media in the form of volatile storage units, such as a Random Access Memory (RAM) 24201 and/or a cache memory unit 24202, and may further include a Read Only Memory (ROM) 24203. The storage unit 2420 may also include a program/utility 24204 having a set (at least one) of program modules 24205, such program modules 24205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. The bus 2430 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 2400 may also communicate with one or more external devices 2500 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 2400, and/or with any device (e.g., router, modem, etc.) that enables the electronic device 2400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 2450. Also, electronic device 2400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet via network adapter 2460. As shown, the network adapter 2460 communicates with other modules of the electronic device 2400 over the bus 2430. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with the electronic device 2400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium having stored thereon a program product capable of implementing the method described above in the present specification is also provided. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.

A program product for implementing the above-described method according to an embodiment of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method of processing medical data, comprising:

2. The method of claim 1, wherein selecting candidate disease species from the historical medical records comprises:

obtaining the candidate disease seeds based on the first screening result and/or the second screening result;

wherein, based on the current diagnosis and treatment mode of the candidate disease, determining a target DRG disease group comprises:

3. The method of processing medical data according to claim 1, wherein determining the group essential features of the target DRG group of diseases comprises:

acquiring second disease group attribute data of the target DRG disease group, and determining disease group basic characteristics of the target DRG disease group according to the second disease group attribute data; wherein the second group attribute data comprises a plurality of historical hospital days, historical hospital costs, historical number of inpatients, patient age, patient gender, patient diagnostic information, patient diagnosis information, patient preference information for inpatients associated with the target DRG group;

analyzing the basic characteristics of the target DRG group to obtain characteristic analysis results, wherein the characteristic analysis results comprise:

4. The method of processing medical data according to claim 1, wherein determining a group fingerprint map of the target DRG group based on the feature analysis result comprises:

5. The method of processing medical data according to claim 1, wherein recommending candidate clinical paths for the target DRG group based on the group fingerprint map comprises;

6. The method of processing medical data according to claim 1, wherein configuring the candidate clinical paths based on preset standard medical rules to obtain target clinical paths corresponding to the target DRG disease group comprises:

and configuring the patient diagnosis and treatment information and project expense of the patient diagnosis and treatment information included in the candidate clinical paths based on the standard medical rules to obtain target clinical paths corresponding to the target DRG disease groups.

7. The method for processing medical data according to claim 1, characterized in that the method for processing medical data further comprises:

docking the target clinical path to a doctor workstation so that the doctor workstation pushes a diagnosis and treatment project currently required to be performed by a inpatient associated with each attending doctor to terminal equipment corresponding to the attending doctor according to the current diagnosis and treatment progress of the inpatient; the diagnosis and treatment project comprises a plurality of diagnosis and treatment project names, diagnosis and treatment expense, medicine names, medicine expense, consumable item names and consumable expense;

8. A medical data processing apparatus, comprising:

9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of processing medical data according to any one of claims 1-7.

10. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of processing medical data of any of claims 1-7 via execution of the executable instructions.