DRG paid drug therapy scheme analysis and evaluation method, equipment, system and medium

Abstract

The invention relates to the technical field of drug therapy, in particular to a DRG paid drug therapy scheme analysis and evaluation method, device and system and a medium, and the method comprises the steps: obtaining the medical record information of an inpatient, the medical record information comprises the basic information, diagnosis information and treatment scheme information of the patient; matching a corresponding treatment cost standard in a DRG cost library based on the medical record information; matching a corresponding treatment drug combination in a drug database based on the treatment cost standard and the treatment scheme information; obtaining validity data of each drug in the therapeutic drug combination, and calculating the validity of the drug combination; and generating a drug treatment scheme recommendation sequence based on the drug combination validity, then obtaining historical drug use data of a patient, and correcting the recommendation sequence based on the historical drug use data to obtain a final recommendation scheme. Therefore, the effect of the DRG pharmaceutical composition on the patient can be comprehensively evaluated, and the effectiveness of treatment is improved.

Description

Technical Field

[0001] The present invention relates to the field of drug therapy technology, and in particular to a DRG-paid drug therapy program analysis and evaluation method, device, system and medium. Background Art

[0002] Payment by Diagnosis Related Group (DRG) is a payment method that divides hospitalized patients into several groups based on the severity of the disease, treatment methods, and similarities in resource consumption, and then sets a fixed payment standard for each group. Under the DRG payment framework, the cost of drug treatment is included in the overall hospitalization cost, and the hospital receives a fixed fee for the disease group, rather than paying for drugs or other services separately.

[0003] Under the DRG payment model, some medical institutions may deviate from clinical guidelines in order to control costs and choose lower-cost drugs with uncertain efficacy. Although this controls the cost of treatment, it reduces the effectiveness of treatment. Summary of the invention

[0004] The purpose of the present invention is to provide a DRG-paid drug treatment plan analysis and evaluation method, equipment, system and medium, aiming to comprehensively evaluate the effects of DRG drug combinations on patients, thereby improving the effectiveness of treatment.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present invention provides a method for analyzing and evaluating a DRG-paid drug treatment plan, comprising obtaining medical record information of an inpatient, wherein the medical record information includes basic patient information, diagnosis information, and treatment plan information; Matching corresponding treatment cost standards in the DRG cost library based on the medical record information; Matching corresponding therapeutic drug combinations in a drug database based on the treatment cost standard and treatment plan information; Obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination; Based on the effectiveness of the drug combination, a recommended sequence of drug treatment plans is generated, and then the patient's historical medication data is obtained, and the recommended sequence is revised based on the historical medication data to obtain a final recommended plan.

[0006] The specific steps of obtaining the medical record information of the inpatient include: Use user credentials to authenticate in electronic health record systems; After verification, the inpatient treatment information is obtained; Clean and format the treatment information to obtain medical record information.

[0007] The specific steps of matching the corresponding treatment cost standard in the DRG cost library based on the medical record information include: Based on the diagnosis information in the medical record information, obtain the main disease keywords and the secondary disease keywords; Based on the primary disease keywords and secondary disease keywords, query in the DRG grouper to obtain the target group; Find the treatment cost standards for the target group in the DRG cost database.

[0008] The specific steps of querying in the DRG grouper based on the primary disease keywords and the secondary disease keywords to obtain the target group include: Obtain the patient's main diagnostic information and extract the main disease keywords; Secondary diagnosis information was collected based on complications and comorbidities and secondary disease keywords were extracted; Convert major disease keywords and minor disease keywords into corresponding International Classification of Disease codes; The DRG grouping algorithm was initiated to classify cases into target groups based on the International Classification of Disease codes.

[0009] The specific steps of searching the treatment cost standard of the target group in the DRG cost database include: Log in to the DRG cost database used by your medical institution; Enter the DRG code of the target group in the DRG cost library; Get the treatment cost standards associated with the DRG code.

[0010] The specific steps of matching the corresponding therapeutic drug combination in the drug database based on the treatment cost standard and treatment plan information include: Obtain information about the patient's treatment plan; Connect to a drug database, which should contain information on all available drugs; Screening a list of suitable drugs from a drug database based on treatment regimen information; Match all optional treatment drug combinations in the drug list based on treatment cost criteria.

[0011] The specific steps of obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination include: Setting drug effectiveness indicators, which include clinical cure rate, symptom relief rate and disease progression inhibition rate; Retrieve and extract data on drug effectiveness indicators for each drug in the target condition; Generate effectiveness scoring criteria for each drug based on effectiveness indicator data; The effectiveness of the drug combination was calculated based on the weighted effectiveness scoring criteria of each drug.

[0012] In a second aspect, the present invention also provides a DRG-paid drug treatment plan analysis and evaluation device, including a medical record information acquisition module, a treatment cost matching module, a drug combination matching module, a drug effectiveness calculation module, and a recommendation module; The medical record information acquisition module is used to acquire the medical record information of hospitalized patients, and the medical record information includes basic information of the patient, diagnosis information, treatment plan information and efficacy evaluation information; The treatment cost matching module is used to match the corresponding treatment drug combination in the drug database based on the treatment cost standard and treatment plan information; The drug combination matching module is used to match the corresponding therapeutic drug combination in the drug database based on the treatment cost standard; The drug effectiveness calculation module is used to obtain the effectiveness data of each drug in the therapeutic drug combination and calculate the effectiveness of the drug combination; The recommendation module is used to generate a recommended sequence of drug treatment plans based on the effectiveness of the drug combination, obtain the patient's historical medication data, and modify the recommended sequence based on the historical medication data to obtain a final recommended plan.

[0013] In a third aspect, the present invention also provides a DRG-paid drug treatment regimen analysis and evaluation system, including the aforementioned DRG-paid drug treatment regimen analysis and evaluation device.

[0014] In a fourth aspect, the present invention also provides a medium for storing an execution program corresponding to the DRG-paid drug treatment regimen analysis and evaluation method.

[0015] The DRG paid drug treatment plan analysis and evaluation method, device, system and medium of the present invention require obtaining detailed medical record information of hospitalized patients from the medical information system. These medical record information not only includes the patient's basic information such as age and gender, but also includes specific diagnosis information and previously implemented treatment plan information. Based on the collected medical record information, the corresponding treatment cost standard is searched and matched in the pre-built DRG cost library. This process takes into account factors such as the type, severity and treatment methods of the disease to ensure the accuracy and rationality of the selected cost standard.

[0016] According to the determined treatment cost standard and the treatment plan information in the medical record, the most suitable treatment drug combination is further screened in the drug database. The drug database contains a lot of detailed information about different drugs, such as ingredients, mechanisms of action, indications, side effects, etc., which provides a solid foundation for formulating effective drug treatment plans. For the selected treatment drug combination, the system will automatically retrieve the effectiveness data of each drug and calculate the effectiveness of the entire drug combination based on this. The effectiveness here refers to the expected effect of the drug combination in treating a specific disease. It comprehensively considers the efficacy of each drug and their synergistic or antagonistic effects.

[0017] Based on the effectiveness of the drug combination calculated above, the system will generate a preliminary sequence of recommended drug treatment plans. In order to make the recommendations more personalized and practical, the system will also obtain the patient's historical medication data, including but not limited to the types of drugs used in the past, dosages, efficacy feedback and other information. Using these historical data to modify the preliminary recommendation sequence can better avoid potential drug intolerance or adverse reactions. After the above series of meticulous analysis and adjustments, the system will output a final drug treatment recommendation tailored for the patient. This method not only helps to improve treatment outcomes, but also effectively controls medical costs, reflecting the dual pursuit of precision medicine and economy in modern medical services. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0019] Figure 1 It is a flow chart of the DRG-paid drug treatment plan analysis and evaluation method of the present invention.

[0020] Figure 2 It is a flow chart of obtaining medical record information of hospitalized patients according to the present invention.

[0021] Figure 3 It is a flowchart of the present invention for matching corresponding treatment cost standards in the DRG cost library based on the medical record information.

[0022] Figure 4 It is a flowchart of the present invention for obtaining a target group by querying in a DRG grouper based on major disease keywords and minor disease keywords.

[0023] Figure 5It is a flow chart of searching the treatment cost standard of the target group in the DRG cost library of the present invention.

[0024] Figure 6 It is a flow chart of the present invention for matching corresponding therapeutic drug combinations in a drug database based on the treatment cost standard and treatment plan information.

[0025] Figure 7 It is a flow chart of the present invention for obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination. DETAILED DESCRIPTION

[0026] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0027] First embodiment See also Figure 1 to Figure 7 The present invention provides a method for analyzing and evaluating a DRG-paid drug treatment regimen, comprising: S101 obtains medical records of hospitalized patients, wherein the medical records include basic information of the patient, diagnosis information, and treatment plan information; The specific steps include: S201 Use user credentials to authenticate in the electronic health record system; Medical staff need to prepare the corresponding access credentials, which usually include usernames and passwords, or more secure authentication methods such as biometrics (fingerprint or facial recognition). Using the above credentials, medical staff will log in to the hospital's electronic health record (EHR) system. This system is the core platform for storing all patient medical data. The system will verify the entered credentials to ensure that the person requesting access is authorized. This step is critical to protecting patient privacy and complying with relevant laws and regulations.

[0028] S202 obtains the inpatient treatment information after verification; Once authentication is successful, the system determines the scope of information that the user can access based on their permission level. Next, based on the patient identifier provided (such as hospitalization number), all treatment information for a specific patient is retrieved from the EHR system. This includes but is not limited to admission date, attending physician, current condition, tests performed and results, etc. Detailed information directly related to the patient's treatment is extracted, such as medication history, surgical records, doctor's orders, etc.

[0029] S203 cleans and formats the treatment information to obtain medical record information.

[0030] Since raw data may contain duplicates, errors, or incomplete entries, the collected information needs to be cleaned. This step is to ensure the accuracy and completeness of the data.

[0031] The cleaned data is organized in a predetermined standard format for easy reading and subsequent analysis. For example, the information can be classified into basic information (name, age, gender), diagnosis information (disease name, stage), and treatment options (drug therapy, physical therapy).

[0032] Finally, the sorted information is integrated into a complete medical record report, which not only helps the current medical team understand the patient's overall condition, but also provides an important reference for possible diagnosis and treatment in the future.

[0033] S102 matches the corresponding treatment cost standard in the DRG cost library based on the medical record information; The specific steps include: S301 obtains primary disease keywords and secondary disease keywords based on diagnosis information in the medical record information; First, conduct a comprehensive analysis of the patient's medical records, paying special attention to the diagnosis section to identify the patient's primary health problems. Identify and extract disease keywords related to the patient's primary diagnosis from the medical records. These keywords are the basis for understanding the condition and are key to subsequent steps. In addition to the primary diagnosis, any complications or comorbidities that may affect the treatment plan or resource utilization need to be identified and secondary disease keywords extracted from them.

[0034] S302 performs a query in a DRG grouper based on the primary disease keyword and the secondary disease keyword to obtain a target group; The specific steps include: S401 obtains the main diagnosis information of the patient and extracts the main disease keywords; Filter out detailed information about the patient's primary diagnosis from the medical record information. Use natural language processing technology or other methods to extract core keywords from the primary diagnosis description to ensure that they can accurately reflect the nature of the disease.

[0035] S402 collects secondary diagnosis information based on complications and comorbidities and extracts secondary disease keywords; The medical records were carefully reviewed to identify all complications and comorbidities related to the primary diagnosis. Based on the secondary health problems found, corresponding keywords were extracted, which were used to more precisely define the case characteristics.

[0036] S403 converts the major disease keywords and minor disease keywords into corresponding International Classification of Disease codes; Use standardized tools or databases to convert the extracted disease keywords into internationally accepted disease classification (such as ICD-10) codes. This step is crucial to ensure the consistency and comparability of the data.

[0037] S404 starts the DRG grouping algorithm to classify cases based on the International Classification of Disease codes to obtain target groups.

[0038] First, it is necessary to collect and organize the patient's medical record information, including but not limited to diagnostic information (ICD code), operation information (if any, usually expressed through specific codes), patient age, gender, and other factors that may affect treatment decisions or resource use. Use key information such as ICD codes to conduct preliminary screening of cases in order to quickly exclude cases that clearly do not meet the conditions of certain DRG groups. For example, based on the type and location of the disease, the scope of possible applicable DRG groups can be narrowed down, and various relevant information including ICD codes can be provided as input to the DRG grouping algorithm.

[0039] The algorithm has a set of rules within it that take into account multiple factors, such as the severity of the disease, whether there are complications or comorbidities, and the surgical or non-surgical treatment measures taken, to determine the most suitable DRG group.

[0040] Each DRG group is associated with a relative weight that reflects the resource consumption of that group relative to other groups. This step may also be included in the algorithm to calculate the corresponding DRG weight based on the specific circumstances of the case.

[0041] S303 searches for the treatment cost standard of the target group in the DRG cost database.

[0042] The specific steps include: S501 Log in to the DRG cost database used by the medical institution; Ensures secure login using correct credentials to the institution's designated cost database system, which stores treatment cost information for all relevant DRG groups.

[0043] S502 Enter the DRG code of the target group in the DRG cost library; Prepare the DRG codes of the target groups previously derived through the DRG grouping algorithm to facilitate quick and accurate location of relevant information in the cost library.

[0044] S503 obtains the treatment cost standard related to the DRG code.

[0045] After entering the DRG code, the system will return the treatment cost standards corresponding to the group. These cost standards reflect the average cost required to complete the treatment of similar cases, providing important budget planning reference for medical institutions.

[0046] Through the above steps, the most suitable treatment cost standard can be effectively found in the DRG cost library based on the patient's medical record information, thereby supporting the reasonable pricing of medical services and the effective allocation of resources.

[0047] S103 matches a corresponding therapeutic drug combination in a drug database based on the treatment cost standard and treatment plan information; The specific steps include: S601 obtains the patient's treatment plan information; Extract the latest treatment plan information from the patient's medical record, including the doctor's orders, recommended treatments (such as medication, physical therapy), disease stage, and any special instructions or precautions. Structure the information obtained to ensure that the subsequent steps can accurately use this data for analysis and decision-making.

[0048] S602 connects to a drug database, which should contain information on all available drugs; Determine which drug database or databases to use. These databases should be up-to-date and comprehensive, and provide information on drug ingredients, mechanisms of action, indications, side effects, interactions, and prices. Establish a connection with the selected drug database in a secure manner. This may involve technical implementation methods such as API interface calls and database query permission settings to ensure data security and access effectiveness.

[0049] S603: screening a suitable drug list from a drug database based on the treatment plan information; Based on the specific requirements of the treatment plan (e.g. treatment of target diseases, prevention of complications), the drugs in the drug database are preliminarily screened to identify those that are suitable for the current treatment scenario.

[0050] Further analysis is performed to determine whether each candidate drug meets the patient's specific needs, such as considering age restrictions, risk of allergic reactions, and whether there are contraindications, thereby narrowing the range of drug options.

[0051] S604 matches all optional therapeutic drug combinations in the drug list based on the treatment cost standard.

[0052] Combined with the treatment cost standards obtained previously, a cost-effectiveness analysis is performed on the screened drug list. Here, not only the price of a single drug should be considered, but also the cost of the entire course of treatment should be comprehensively evaluated, including the additional medical expenses caused by potential interactions between drugs.

[0053] Based on the above analysis results, one or more cost-effective therapeutic drug combinations are tailored for patients. These combinations must not only meet the requirements of therapeutic effects, but also be kept within the budget as much as possible to ensure economic feasibility.

[0054] The final drug combination recommendations are fed back to the medical team and necessary adjustments are made based on their opinions. In addition, machine learning algorithms can be used to continuously optimize the recommendation system and improve the accuracy of future predictions.

[0055] S104: obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination; The specific steps include: S701 sets drug effectiveness indicators, wherein the drug effectiveness indicators include clinical cure rate, symptom relief rate and disease progression inhibition rate; First, we will determine the core indicators for evaluating the effectiveness of the drug, including but not limited to clinical cure rate (i.e. the proportion of patients who have fully recovered after completing the entire course of treatment), symptom relief rate (the proportion of patients whose symptoms have significantly improved after treatment) and disease progression inhibition rate (the ability of the drug to prevent or slow the progression of the disease). These indicators will serve as the basis for measuring the effectiveness of the drug.

[0056] The weights of the above indicators should be assigned according to the nature of the specific disease and the treatment goals. For example, in some cases, controlling the progression of the disease may be more important than short-term symptom relief.

[0057] S702 retrieves and extracts drug effectiveness indicator data about each drug in the target disease; Use medical databases such as PubMed, Cochrane Library, etc. to search for research papers, clinical trial results, and other relevant information about each drug on specific diseases.

[0058] Extract data for the effectiveness indicators set above from the found literature. This step may require the use of text mining technology to automatically process large amounts of information to ensure the comprehensiveness and accuracy of the data.

[0059] The extracted data sources were assessed for quality, with preference given to those from high-quality journals or peer-reviewed research reports.

[0060] S703 generates an effectiveness scoring standard for each drug based on the effectiveness indicator data; Since different studies may use different evaluation systems, all collected data need to be converted into a unified standard format for easy comparison.

[0061] Based on the set effectiveness indicators and their weights, a scoring system is established for each drug. This system should be able to reflect the performance of the drug in various aspects and take into account its overall efficacy.

[0062] Taking into account the differences and potential biases between studies, a certain confidence interval or uncertainty analysis can be added to the scoring to reflect the reliability of the data.

[0063] S704 obtains the effectiveness of the drug combination by weighted calculation based on the effectiveness scoring standard of each drug.

[0064] For each drug in the treatment plan, the overall effectiveness score of the drug is calculated according to its score on each effectiveness indicator and the preset weight.

[0065] Considering that multiple drugs may have synergistic or mutually counteracting effects when used together, a special evaluation of the overall efficacy of the drug combination is required. This can be done by referring to existing studies on combined drug use or by using mathematical models to predict the effects of the combination.

[0066] The effectiveness score of each drug is combined with its role and expected contribution in the treatment plan, and the effectiveness of the entire drug combination is calculated through weighted average or other appropriate methods. This value can help the medical team better understand the actual potential of the selected treatment plan and make more informed decisions accordingly.

[0067] Through such a meticulous process, we can not only gain a deep understanding of the mechanism of action and efficacy of individual drugs, but also accurately evaluate the actual effects of the combined use of multiple drugs, thereby improving the design quality of treatment plans and patient treatment outcomes.

[0068] S105 generates a recommended sequence of drug treatment plans based on the effectiveness of the drug combination, then obtains the patient's historical medication data, and modifies the recommended sequence based on the historical medication data to obtain a final recommended plan.

[0069] Based on the previously calculated drug combination effectiveness, all possible drug combinations are first ranked from high to low according to their expected efficacy. This ranking provides a basic framework for subsequent adjustments based on other factors. In addition to efficacy, factors such as safety (risk of side effects), patient tolerance, and convenience of administration routes must be considered comprehensively to develop a comprehensive scoring system for each drug combination.

[0070] Taking into account the specific circumstances of different patients (such as age, gender, and health status), the above scoring system is personalized to ensure that the recommended treatment plan best suits the needs of specific patients.

[0071] Obtain the patient's historical medication data through the electronic health record system or other medical information system. This data includes but is not limited to the type of drugs used in the past, dosage, length of treatment, time of use, and corresponding treatment effects and any adverse reactions. Conduct in-depth analysis of the collected data to identify which drugs or drug combinations have been most effective for patients in the past, and also pay attention to whether there is a history of allergic reactions or other adverse reactions to certain drug ingredients.

[0072] The drug treatment regimen after preliminary sorting is matched and compared with the patient's historical medication data to find out whether there are repeated effective drug combinations or drugs that should be avoided. If it is found that a drug has caused serious side effects or poor effect in the patient's history, it will be removed from the recommended sequence or its priority will be reduced.

[0073] For those drugs or combinations that have shown good results in the past, their positions in the recommended sequence can be promoted. In addition, it is also possible to consider fine-tuning or enhancing these proven effective regimens in combination with the latest medical research results.

[0074] Finally, after fully considering the effectiveness of the drug combination, the patient's historical medication data and other relevant factors, a comprehensive list of recommended drug treatment options is formed. This list should clearly list the expected efficacy, potential risks and characteristics of the applicable population of each option.

[0075] After implementing the treatment plan, we continuously monitor the patient's response and collect feedback in a timely manner. This helps us evaluate the effectiveness of the treatment and make corresponding adjustments when necessary to ensure the best treatment results.

[0076] Second embodiment The present invention also provides a DRG-paid drug treatment plan analysis and evaluation device, including a medical record information acquisition module, a treatment cost matching module, a drug combination matching module, a drug effectiveness calculation module and a recommendation module; the medical record information acquisition module is used to obtain the medical record information of hospitalized patients, and the medical record information includes basic patient information, diagnosis information, treatment plan information and efficacy evaluation information; the treatment cost matching module is used to match the corresponding treatment drug combination in the drug database based on the treatment cost standard and treatment plan information; the drug combination matching module is used to match the corresponding treatment drug combination in the drug database based on the treatment cost standard; the drug effectiveness calculation module is used to obtain the effectiveness data of each drug in the treatment drug combination and calculate the effectiveness of the drug combination; the recommendation module is used to generate a drug treatment plan recommendation sequence based on the drug combination effectiveness, obtain the patient's historical medication data, and modify the recommendation sequence based on the historical medication data to obtain a final recommendation plan.

[0077] In this embodiment, the medical record information acquisition module collects various medical records of inpatients from the hospital's information system. These records include but are not limited to the patient's basic personal information (such as age, gender), disease diagnosis details, current or past treatment plans, and feedback on the efficacy of these treatment plans.

[0078] The treatment cost matching module and the drug combination matching module work together to search for suitable drug combinations in the huge drug database based on the preset treatment cost standards and the treatment plan information obtained from the medical record information acquisition module. It should be noted here that although the two modules have similar functions, they have different focuses: the treatment cost matching module focuses more on controlling costs while meeting treatment needs, while the drug combination matching module focuses on finding the most suitable drug combination for the patient's condition.

[0079] The drug effectiveness calculation module evaluates the effectiveness of the above selected drug combination. This module will deeply explore the mechanism of action of each drug, clinical trial data and its performance in similar cases, so as to accurately calculate the possible therapeutic effect of the entire drug combination for a specific patient.

[0080] The recommendation module integrates the work results of all previous modules and generates a series of preliminary drug treatment recommendations based on the effectiveness of drug combinations. In addition, this module will take into account the patient's medication history and use big data analysis technology to personalize the recommendation sequence to ensure that the drug treatment plan presented to the doctor is not only scientific and reasonable, but also fits the patient's actual situation and preferences to the greatest extent. This advanced equipment greatly improves the efficiency and accuracy of drug treatment plan formulation, helps to achieve more economical and effective allocation of medical resources, and also promotes the development of personalized medicine.

[0081] Third embodiment The present invention also provides a DRG-paid drug treatment plan analysis and evaluation system, including the DRG-paid drug treatment plan analysis and evaluation device.

[0082] The system integrates core components such as medical record information acquisition module, treatment cost matching module, drug combination matching module, drug effectiveness calculation module and recommendation module. These modules work together to develop the best drug treatment plan for the patient from the moment he or she is admitted to the hospital, and continuously monitor and adjust the plan throughout the hospitalization period to adapt to changes in the patient's condition.

[0083] In addition, this system further enhances data processing capabilities, and by connecting to larger databases, including but not limited to drug information databases, clinical guide databases, efficacy feedback databases, etc., the system can provide decision support based on the latest medical research results and clinical practice experience. This not only helps to discover more effective treatment strategies, but also can timely update drug safety and effectiveness information to ensure the scientific and cutting-edge nature of treatment plans.

[0084] The system is also equipped with a user-friendly interface and powerful data analysis tools, which allows medical staff to quickly understand the progress and effects of patients' treatment. It also supports in-depth mining of historical data and provides valuable information for scientific research and teaching. In this way, it can not only promote the development of individualized treatment, but also improve the diagnosis and treatment level of the entire medical institution.

[0085] In addition, considering the characteristics of the DRG payment model, this system places special emphasis on cost-effectiveness analysis, and through sophisticated cost accounting and cost control mechanisms, helps hospitals to minimize medical expenses while ensuring the quality of treatment. This is of great significance for improving the operational efficiency of hospitals and reducing the economic burden on patients.

[0086] Fourth embodiment The present invention also provides a medium for storing an execution program corresponding to the DRG-paid drug treatment plan analysis and evaluation method.

[0087] This medium plays a vital role in the development of modern medical technology. It not only guarantees the security and reliability of data, but also ensures the efficient execution and wide applicability of evaluation methods.

[0088] The medium can be in a variety of forms, including but not limited to hard disks, solid-state drives, optical disks, USB flash drives, or any other type of digital storage device. These media have high capacity, high-speed read and write capabilities, and long data retention periods, so that complex DRG-paid drug therapy program analysis and evaluation methods can be stably and reliably stored and ready to be executed at any time.

[0089] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for analyzing and evaluating DRG-paid drug treatment regimens, characterized in that: include: Obtaining medical records of hospitalized patients, including basic patient information, diagnosis information, and treatment plan information; Matching corresponding treatment cost standards in the DRG cost library based on the medical record information; Matching corresponding therapeutic drug combinations in a drug database based on the treatment cost standard and treatment plan information; Obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination; Based on the effectiveness of the drug combination, a recommended sequence of drug treatment plans is generated, and then the patient's historical medication data is obtained, and the recommended sequence is revised based on the historical medication data to obtain a final recommended plan.

2. The DRG-paid drug treatment program analysis and evaluation method according to claim 1, characterized in that: The specific steps of obtaining the medical record information of the inpatient include: Use user credentials to authenticate in electronic health record systems; After verification, the inpatient treatment information is obtained; Clean and format the treatment information to obtain medical record information.

3. The DRG-paid drug treatment plan analysis and evaluation method according to claim 2, characterized in that: The specific steps of matching the corresponding treatment cost standard in the DRG cost library based on the medical record information include: Based on the diagnosis information in the medical record information, obtain the main disease keywords and the secondary disease keywords; Based on the primary disease keywords and secondary disease keywords, query in the DRG grouper to obtain the target group; Find the treatment cost standards for the target group in the DRG cost database.

4. The DRG-paid drug treatment plan analysis and evaluation method according to claim 3, characterized in that: The specific steps of querying in the DRG grouper based on the primary disease keywords and the secondary disease keywords to obtain the target group include: Obtain the patient's main diagnostic information and extract the main disease keywords; Secondary diagnosis information was collected based on complications and comorbidities and secondary disease keywords were extracted; Convert major disease keywords and minor disease keywords into corresponding International Classification of Disease codes; The DRG grouping algorithm was initiated to classify cases into target groups based on the International Classification of Disease codes.

5. The DRG-paid drug treatment plan analysis and evaluation method according to claim 4, characterized in that: The specific steps of searching the treatment cost standard of the target group in the DRG cost database include: Log in to the DRG cost database used by your medical institution; Enter the DRG code of the target group in the DRG cost library; Get the treatment cost standards associated with the DRG code.

6. The DRG-paid drug treatment plan analysis and evaluation method according to claim 5, characterized in that: The specific steps of matching the corresponding therapeutic drug combination in the drug database based on the treatment cost standard and the treatment plan information include: Obtain information about the patient's treatment plan; Connect to a drug database, which should contain information on all available drugs; Screening a list of suitable drugs from a drug database based on treatment regimen information; Match all optional treatment drug combinations in the drug list based on treatment cost criteria.

7. The DRG-paid drug treatment plan analysis and evaluation method according to claim 6, characterized in that: The specific steps of obtaining the effectiveness data of each drug in the therapeutic drug combination and calculating the effectiveness of the drug combination include: Setting drug effectiveness indicators, which include clinical cure rate, symptom relief rate and disease progression inhibition rate; Retrieve and extract data on drug effectiveness indicators for each drug in the target condition; Generate effectiveness scoring criteria for each drug based on effectiveness indicator data; The effectiveness of the drug combination was calculated based on the weighted effectiveness scoring criteria of each drug.

8. A DRG-paid drug treatment plan analysis and evaluation device, applied to a DRG-paid drug treatment plan analysis and evaluation method according to any one of claims 1 to 7, characterized in that: It includes medical record information acquisition module, treatment cost matching module, drug combination matching module, drug effectiveness calculation module and recommendation module; The medical record information acquisition module is used to acquire the medical record information of hospitalized patients, and the medical record information includes basic information of the patient, diagnosis information, treatment plan information and efficacy evaluation information; The treatment cost matching module is used to match the corresponding treatment drug combination in the drug database based on the treatment cost standard and treatment plan information; The drug combination matching module is used to match the corresponding therapeutic drug combination in the drug database based on the treatment cost standard; The drug effectiveness calculation module is used to obtain the effectiveness data of each drug in the therapeutic drug combination and calculate the effectiveness of the drug combination; The recommendation module is used to generate a recommended sequence of drug treatment plans based on the effectiveness of the drug combination, obtain the patient's historical medication data, and modify the recommended sequence based on the historical medication data to obtain a final recommended plan.

9. A DRG-paid drug treatment plan analysis and evaluation system, characterized in that: Including a DRG-paid drug treatment plan analysis and evaluation device as described in claim 8.

10. A medium, characterized in that Used to store the execution program corresponding to the DRG payment drug treatment plan analysis and evaluation method as described in any one of claims 1 to 7.

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