Clinical Pathway-based Whole-process Monitoring and Management System for Rational Use of Antibacterial Agents

Abstract

The present invention relates to the field of software technology, and specifically discloses a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. The system includes a disease judgment module that obtains a disease judgment result by judging suspected infectious diseases; a drug treatment supervision module that selects the types of antibacterial drugs used by patients by using a multi-dimensional screening strategy based on the disease judgment result and assists in judging the treatment course of antibacterial drugs; and a perioperative drug prevention supervision module that intelligently supervises the preventive use of antibacterial drugs according to the monitoring information obtained from the whole process monitoring of perioperative patients. By selecting the types of antibacterial drugs used by patients by using a multi-dimensional screening strategy based on the disease judgment result and assisting in judging the treatment course of antibacterial drugs, and supervising the preventive use of antibacterial drugs according to the monitoring information obtained from the monitoring of perioperative patients, it is possible to intelligently assist clinical workers in the rational use of antibacterial drugs on the basis of the clinical treatment pathway.

Description

Technical Field

[0001] The present invention relates to the field of software technology, and particularly to a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway. Background Art

[0002] In recent years, the problem of microbial drug resistance evolution has become increasingly prominent, which has become a major hidden danger threatening public health and has attracted wide attention. However, it cannot be ignored that in medical institutions, the irrational use of antibacterial drugs is still common and remains one of the key factors promoting the deterioration of microbial drug resistance problems. At present, existing antibacterial drug management technologies have problems such as improper use of antibacterial drugs and insufficient monitoring, which directly lead to the overuse of antibacterial drugs, further exacerbating the development of microbial drug resistance and bringing challenges to clinical treatment.

[0003] Therefore, the present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway, which is used to intelligently assist clinical workers in the rational use of antibacterial drugs based on the clinical diagnosis and treatment pathway to improve the scientific management level of antibacterial drugs. Summary of the Invention

[0004] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway, which is used to perform personalized selection of the types of antibacterial drugs used by patients and assist in judging the treatment course of antibacterial drugs by adopting a multi-dimensional screening strategy that combines patient needs, drug interactions, and patient medication preferences based on the disease judgment results obtained by analyzing and judging suspected infectious diseases according to the patient's diagnosis and treatment data; and supervising the preventive use of antibacterial drugs according to the monitoring information obtained from the full-process monitoring of perioperative patients, which can intelligently assist clinical workers in the rational use of antibacterial drugs based on the clinical diagnosis and treatment pathway.

[0005] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway, including:

[0006] Disease Judgment Module: It is used to obtain the patient's diagnosis and treatment data, analyze and judge suspected infectious diseases, and obtain disease judgment results;

[0007] Drug Treatment Supervision Module: It is used to perform personalized selection of the types of antibacterial drugs used by patients and assist in judging the treatment course of antibacterial drugs by adopting a multi-dimensional screening strategy that combines patient needs, drug interactions, and patient medication preferences based on the disease judgment results;

[0008] Perioperative Drug Prevention Supervision Module: It is used to intelligently supervise the preventive use of antibacterial drugs according to the monitoring information obtained from the full-process monitoring of perioperative patients.

[0009] Preferably, the disease judgment module includes:

[0010] Data acquisition unit: used to acquire the patient's diagnosis and treatment data from the target medical system, and preprocess the patient's diagnosis and treatment data according to a preset disease diagnosis knowledge base to obtain target diagnosis and treatment data;

[0011] Diagnosis and analysis unit: used to input the target diagnosis and treatment data into a pre-established disease analysis model for diagnosis and analysis to obtain the disease judgment result of the current patient.

[0012] Preferably, the drug treatment supervision module includes:

[0013] Indication monitoring unit: used to divide patients into indication groups based on the disease judgment result, and evaluate the indication group and type of the patient to obtain an indication evaluation score;

[0014] Indication reminder unit: used to remind patients whose indication group is insufficient evidence of infection or no indication for antibacterial drug treatment but who have received antibacterial drug treatment;

[0015] Etiology monitoring unit: used to recommend to doctors the etiological examination items for the patient according to the disease judgment result; judge whether the etiological examination before the first use of antibacterial drugs is qualified by analyzing the etiological monitoring data;

[0016] Inpatient discussion unit: used to judge whether the management of the combined use of key antibacterial drugs for inpatients is qualified;

[0017] Antibacterial drug selection unit: used to intelligently assist in the rational selection of antibacterial drugs based on the pathogen-specific scenario and the patient's condition;

[0018] Antibacterial drug management unit: used to manage the drug grading, dosage and method of use, and discontinuation monitoring of the patient;

[0019] Treatment statistics unit: used to statistically analyze and visually display the monitoring data of the drug supervision and treatment module.

[0020] Preferably, the antibacterial drug selection unit includes:

[0021] Pathogen screening subunit: used to assist in determining the first drug selection list by using the etiological test results of specimens collected from the same infection site of the patient in the past and the infection treatment guidelines if the pathogen-specific scenario of the current patient is a non-etiological scenario or an etiological scenario without drug sensitivity;

[0022] If the pathogen-specific scenario of the current patient is an etiological scenario with drug sensitivity results, determine the first drug selection list according to the etiological and drug sensitivity results;

[0023] Functional screening subunit: used to obtain the physiological functions of drugs for the current patient, and determine the physiological adaptation scores between the physiological functions of drugs and each antibacterial drug in the first drug selection list by using a preset adaptation evaluation mechanism;

[0024] Delete the antibacterial drugs in the first drug selection list whose physiological adaptation scores with the physiological functions of drugs are less than the set adaptation threshold, and finally obtain the second drug selection list;

[0025] Single screening subunit: used to screen out the single most suitable antibacterial drug from the second drug selection list as the antibacterial drug actually used by the current patient when the drug requirement of the patient is single treatment;

[0026] Combined screening subunit: used to screen out multiple antibacterial drugs from the second drug selection list according to drug interactions and gather them to obtain the combination of antibacterial drugs actually used by the current patient when the drug requirement of the patient is combined treatment.

[0027] Preferably, the single screening subunit includes:

[0028] Expansion block: used to expand the physiological functions of drugs for the current patient according to a preset ratio to obtain the reference physiological function range;

[0029] Initial screening block: used to obtain the historical drug usage records of each type of antibacterial drug in the second drug selection list, and screen out the drug cost records from the historical drug usage records with the reference physiological function range as the screening condition;

[0030] Determine the reference average cost of each type of antibacterial drug in the second drug selection list according to the drug cost records;

[0031] If the current patient has no financial pressure, mark all antibacterial drugs in the second drug selection list as target candidate drugs;

[0032] If the current patient has financial pressure, mark the antibacterial drugs in the second drug selection list whose reference average cost does not exceed the maximum allowable cost of the patient as target candidate drugs;

[0033] Allocate economic weights to each target candidate drug according to the reference average cost;

[0034] Re-screening block: used to obtain historical reference patients whose drug biological functions belong to the reference physiological function range, and extract the historical patient drug usage records of all historical reference patients;

[0035] When the number of historical reference patients is less than the set number threshold, screen out the usage records of the patients to whom the target candidate drugs belong from the historical patient drug usage records;

[0036] Obtain and utilize the usage frequency, incidence rate of adverse reactions, and severity level of adverse reactions of the target candidate drug based on the usage records of the patient, and determine the functional safety score of the target candidate drug;

[0037] Combine the functional safety score with the economic weight to determine the comprehensive adaptation score corresponding to the target candidate drug;

[0038] Take the target candidate drug with the highest comprehensive adaptation score as the actual antibacterial drug used by the current patient;

[0039] When the number of historical reference patients is not less than the set number threshold, divide the historical reference patients according to the patient segmentation factors to obtain patient reference groups;

[0040] By assigning weights to the patient reference groups according to the patient segmentation factors, and comprehensively evaluating the functional safety situation and economic weight of the target candidate drugs within the patient reference groups, obtain the actual antibacterial drug used by the current patient.

[0041] Preferably, the re-screening block further includes:

[0042] According to the set assignment mechanism of different patient segmentation factors, combine the relevant segmentation information of the current patient, and sequentially assign group reference weights to all patient reference groups corresponding to the same patient segmentation factor;

[0043] According to the historical patient drug usage records, obtain the within-group patient usage records of the corresponding target candidate drugs in the current patient reference group;

[0044] According to the within-group patient usage records, obtain and analyze the within-group usage frequency, within-group incidence rate of adverse reactions, and within-group severity level of adverse reactions of the target candidate drug in combination with the group reference weight, and determine the within-group safety score of the target candidate drug in the current patient reference group;

[0045] Combine the within-group safety score with the economic weight to determine the within-group adaptation score corresponding to the target candidate drug;

[0046] Mark the target candidate drug with the highest within-group adaptation score in all patient reference groups corresponding to the same patient segmentation factor as the possible drug to be used;

[0047] If there is an overlap among the possible drugs to be used corresponding to all patient segmentation factors, take the possible drug with the highest overlap degree as the actual antibacterial drug used by the current patient;

[0048] If the possible drugs to be used corresponding to all patient segmentation factors are all inconsistent, then calculate by combining the preset priority weights of each patient segmentation factor with the within-group adaptation scores of the corresponding possible drugs to obtain a comprehensive score;

[0049] Use the drug with the highest comprehensive score as the actual antibacterial drug used for the current patient.

[0050] Preferably, the combined screening subunit includes:

[0051] Initial analysis block: used to screen the first combination of drugs for use from the second drug selection list according to drug indications;

[0052] Interaction analysis block: used to evaluate the interaction of each first combination of drugs for use by using a drug interaction database to obtain an interaction evaluation result;

[0053] According to the interaction evaluation result, represent the first combination of drugs for use without serious adverse interactions or those that may affect the treatment effect as the second combination of drugs for use;

[0054] Tolerance analysis block: used to input the basic patient information and tolerance analysis indicators of the current patient, and the second combination of drugs for use into a combination screening model to output the possible combination of drugs for use;

[0055] Patient preference block: used to use the current possible combination of drugs for use as the actual combination of antibacterial drugs used by the patient when there is only a single possible combination of drugs for use;

[0056] When there are multiple possible combinations of drugs for use, determine the overall drug use rules for each possible combination of drugs for use;

[0057] Obtain and, based on the patient's historical medication records within a preset time period, use preference evaluation features to evaluate and obtain the patient's true medication preference rules;

[0058] Use the corresponding available combination of drugs for use with the overall drug use rules that have the highest similarity to the true medication preference rules as the actual combination of antibacterial drugs used by the patient.

[0059] Preferably, the perioperative drug prophylaxis supervision module includes:

[0060] Surgical information acquisition unit: used to acquire the surgical monitoring information of surgical patients undergoing Class I surgical incision operations;

[0061] Surgical drug analysis unit: used to assist doctors in prescribing prophylactic use of antibacterial drugs, antibacterial drug selection, drug injection duration management, and discontinuation management based on the guiding principles for the use of prophylactic antibacterial drugs in surgery and the characteristics of surgical specialties;

[0062] Surgical statistics unit: used to statistically analyze and visualize the surgical volume of different incision amounts and the surgical-related data of Class I surgical incision operations.

[0063] The beneficial effects of the present invention compared with the prior art are as follows: Based on the disease judgment result obtained by analyzing and judging the suspected infectious disease according to the patient's diagnosis and treatment data, a multi-dimensional screening strategy that integrates patient needs, drug interactions, and patient medication preferences is adopted to individually select the types of antibacterial drugs used by the patient, and to assist in judging the treatment course of antibacterial drugs; The preventive use of antibacterial drugs is supervised according to the monitoring information obtained from the whole-process monitoring of perioperative patients, and on the basis of the clinical treatment path, it can intelligently assist clinicians in the rational use of antibacterial drugs.

[0064] Other features and advantages of the present invention will be described in the following description, and in part will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structure specifically pointed out in this application document.

[0065] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings are used to provide a further understanding of the present invention, and constitute a part of the description. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0067] Figure 1 It is a schematic diagram of a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] Embodiment 1:

[0070] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. Referring to Figure 1 , including:

[0071] Disease judgment module: used to obtain patient diagnosis and treatment data, analyze and judge suspected infectious diseases, and obtain disease judgment results;

[0072] Drug treatment supervision module: used to individually select the types of antibacterial drugs used by the patient based on the disease judgment result, adopting a multi-dimensional screening strategy that integrates patient needs, drug interactions, and patient medication preferences, and to assist in judging the treatment course of antibacterial drugs;

[0073] Perioperative drug prevention supervision module: used to intelligently supervise the preventive use of antibacterial drugs according to the monitoring information obtained from the whole-process monitoring of perioperative patients.

[0074] In this embodiment, the patient's diagnosis and treatment data refers to the patient's diagnosis and treatment data extracted from the target medical system by using natural language processing technologies, such as NLP, OCR, etc., such as electronic medical record data, inspection reports, and imaging reports, etc.; the target medical system includes multiple systems such as HIS (Hospital Information System), EMR (Electronic Medical Record System), LIS (Laboratory Information System), and PACS (Picture Archiving and Communication System), etc., which is the data source of the patient's diagnosis and treatment data; the multi-dimensional screening strategy refers to a comprehensive consideration method for personalized selection of the types of antibacterial drugs used by the patient, which integrates the dimensions of patient needs, drug interactions, and patient medication preferences. Among them, patient needs refer to the patient's economic needs and etiological scenarios, etc.

[0075] In this embodiment, suspected infectious diseases cover upper respiratory tract infections, lower respiratory tract infections, blood and circulatory system infections, urogenital tract infections, digestive system infections, central nervous system infections, skin and mucous membrane infections, skeletal and muscular system infections, and ophthalmic, otolaryngological, oral and maxillofacial infections; antibacterial drugs include carbapenems (imipenem, meropenem, panipenem, biapenem, and ertapenem), glycopeptides (vancomycin, teicoplanin), novel tetracyclines (tigecycline, omadacycline, eravacycline), and polymyxins (polymyxin B sulfate, colistin sulfate, polymyxin E methanesulfonate), etc.; surgical monitoring information includes surgical patients, surgical names, surgical start time (skin incision time), purpose of using antibacterial drugs (if used), time of intravenous infusion of antibacterial drugs into the body (if used), and surgical end time (time when the patient leaves the operating room).

[0076] The beneficial effects of the above technologies are as follows: based on the disease judgment results obtained by analyzing and judging suspected infectious diseases according to the patient's diagnosis and treatment data, a multi-dimensional screening strategy that integrates patient needs, drug interactions, and patient medication preferences is used to personalize the selection of the types of antibacterial drugs used by the patient, and to assist in judging the treatment course of antibacterial drugs; the preventive use of antibacterial drugs is supervised according to the monitoring information obtained from the whole-process monitoring of perioperative patients, which can intelligently assist clinicians in the rational use of antibacterial drugs on the basis of the clinical treatment path.

[0077] Embodiment 2:

[0078] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. The disease judgment module includes:

[0079] Data acquisition unit: used to acquire the patient's diagnosis and treatment data from the target medical system, and perform data preprocessing on the patient's diagnosis and treatment data according to a preset disease diagnosis knowledge base to obtain target diagnosis and treatment data;

[0080] Diagnosis and analysis unit: used to input the target diagnosis and treatment data into a pre-established disease analysis model for diagnosis and analysis to obtain the disease judgment result of the current patient.

[0081] In this embodiment, the disease judgment module further includes:

[0082] Monitoring and auxiliary judgment unit: used to establish a monitoring and auxiliary reminder framework based on the infectious syndrome, and conduct comprehensive information monitoring and auxiliary judgment on infectious diseases;

[0083] Disease statistics unit: used to statistically analyze and visually display the monitoring data of the disease judgment module.

[0084] Among them, the monitoring and auxiliary reminder framework based on the infectious syndrome includes a comprehensive monitoring and auxiliary judgment information framework based on the monitoring of infectious clinical syndromes, auxiliary examinations, tests, and etiological detection information, an infectious marker detection and imaging examination clinical information framework (the items can be adjusted and maintained), and an etiological detection clinical path based on the infectious syndrome, and monitors and gives auxiliary reminders on the rationality of the selection of etiological detection items.

[0085] Among them, the monitoring and auxiliary judgment scope of the comprehensive monitoring and auxiliary judgment information framework based on the monitoring of infectious clinical syndromes, auxiliary examinations, tests, and etiological detection information covers common types such as upper respiratory tract infection, lower respiratory tract infection, blood and circulatory system infection, urogenital tract infection, digestive system infection, and eye, ear, nose, throat, oral and maxillofacial regions.

[0086] Among them, in the etiological detection clinical path based on the infectious syndrome, for monitoring and giving auxiliary reminders on the rationality of the selection of etiological detection items, the etiological detection items cover currently known microbial morphology / component detection, culture and identification, immunological method detection, targeted nucleic acid and metagenomic sequencing methods; the clinical path therein can be adjusted and maintained according to the etiological detection method.

[0087] In this embodiment, the beneficial effect of the monitoring and auxiliary judgment unit is that by providing a clinical information framework with a covered monitoring scope, flexible adjustment, it realizes multi-dimensional information integration, precise monitoring and intelligent reminder, which is beneficial to reducing the risk of missed diagnosis / misdiagnosis, assisting in quickly locating the infection type clinically and improving the clinical diagnosis and treatment level;

[0088] In this embodiment, the beneficial effects of the disease statistics unit are as follows: By statistically analyzing the monitoring data of the module, the epidemic trend and distribution characteristics of infectious diseases can be accurately depicted, providing a quantitative basis for evaluating the quality of clinical tests. Through visual display, while improving the data analysis efficiency, it also provides strong data support for public health decision-making.

[0089] In this embodiment, the patient diagnosis and treatment data refers to the diagnosis and treatment data of patients extracted from the target medical system by using natural language processing technologies such as NLP and OCR, such as electronic medical record data, test reports, and imaging reports, etc.; data preprocessing includes data cleaning, data conversion, and data normalization processing, etc., providing an effective basis for the auxiliary judgment of subsequent disease diagnosis.

[0090] In this embodiment, the disease analysis model is a model obtained by training a neural network with a large amount of historical diagnosis and treatment data as the training set, which is used to determine the diagnosis name of the suspected infection of the patient and make a preliminary auxiliary judgment on infectious diseases; the disease judgment result includes the name or type of the suspected infectious disease and a preliminary auxiliary judgment on infectious diseases.

[0091] In this embodiment, the preset disease diagnosis knowledge base is a knowledge base pre-constructed based on theoretical knowledge, factual data, expert experience, etc. related to disease diagnosis; the purpose of preprocessing the patient diagnosis and treatment data according to the preset disease diagnosis knowledge base is to convert the unstructured or semi-structured patient diagnosis and treatment data into structured and standardized data for subsequent auxiliary judgment of disease diagnosis. The operation steps include matching the extracted patient diagnosis and treatment data with the disease diagnosis knowledge base to find the knowledge entries related to disease diagnosis, and processing and analyzing the patient diagnosis and treatment data according to the rules and logic in the knowledge base, such as data cleaning, data conversion, and data standardization, etc.

[0092] In this embodiment, the monitoring data statistics of the disease judgment module include: the number of discharged inpatients, the number of outpatient and emergency visits, the number of inpatients with infection diagnosis (admission diagnosis, discharge diagnosis), the number of outpatient and emergency visits with infection diagnosis, the detection rate and standardization rate of infection-related biological indicators, and the detection rate and standardization rate of infection-related etiology, etc. The monitoring scope covers inpatients and outpatient and emergency patients, and the statistical dimensions include time, department, specialty, infection site / infection type, and physician; the visual display can be achieved by using visualization tools or libraries (such as Matplotlib, Seaborn (Python), ECharts, D3.js (JavaScript)), etc.

[0093] In this embodiment, the disease statistics unit of the disease judgment module designs a statistical report related to the monitoring of infectious diseases, and the statistical parameters cover regions, departments / professions, time, types of infectious diseases, basic patient information (age, gender), and laboratory test-related information (biological index tests, etiological tests, etc.).

[0094] The beneficial effects of the above technologies are as follows: By extracting patient diagnosis and treatment data from the target medical system and performing data preprocessing, and then using the disease analysis model to judge the patient's disease based on the diagnosis and treatment data, the diagnostic accuracy and efficiency can be effectively improved.

[0095] Embodiment 3:

[0096] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway. The drug treatment supervision module includes:

[0097] The indication monitoring unit: used to divide patients into indication groups based on the disease judgment results, and evaluate the indication group and type of patients to obtain an indication evaluation score;

[0098] The indication reminder unit: used to remind patients whose indication group is insufficient evidence of infection or no indication for antibacterial drug treatment but who have received antibacterial drug treatment;

[0099] The etiological monitoring unit: used to recommend to doctors the etiological test items for patients according to the disease judgment results; judge whether the etiological test before the first use of antibacterial drugs is qualified by analyzing the etiological monitoring data;

[0100] The inpatient discussion unit: used to judge whether the management of the combined use of key antibacterial drugs for inpatients is qualified;

[0101] The antibacterial drug selection unit: used to intelligently assist in the rational selection of antibacterial drugs based on the specific pathogen scenario and the patient's condition;

[0102] The antibacterial drug management unit: used to manage the drug grading, dosage and method of use, and withdrawal monitoring of patients;

[0103] The treatment statistics unit: used to statistically analyze and visually display the monitoring data of the drug supervision and treatment module.

[0104] In this embodiment, the indication groups include three categories: the group with insufficient evidence of infection, the group with suspected infection but no indication for antimicrobial therapy, and the group with suspected infection and indication for antimicrobial therapy. These groups are obtained by dividing patients based on the disease judgment results using the built-in knowledge base model for antimicrobial therapy grouping. The knowledge base model for antimicrobial therapy grouping is an intelligent system constructed based on medical knowledge, clinical experience, and research data. It is used to divide patients into different indication groups according to the disease judgment results of patients. Specifically, a large amount of clinical case data (including patients' symptoms, signs, laboratory test results, imaging test results, etc.) is collected first. Then, features related to antimicrobial therapy grouping (such as infection indicators (such as white blood cell count, C-reactive protein level), pathogen type, disease severity, etc.) are extracted from the collected data as training data to train a neural network to obtain the model.

[0105] In this embodiment, the indication evaluation score is obtained by using the built-in antimicrobial therapy evaluation model to evaluate the indication group and type of patients, and is used to quantify the severity of different types of indication groups. For example, the value range of the indication evaluation score is 0 - 100 points, where 0 - 30 points represent mild, 31 - 60 points represent moderate, and 61 - 100 points represent severe. The antimicrobial therapy evaluation model is an intelligent system used to evaluate the indication group and type of patients and give the indication evaluation score. Specifically, first, according to the principles and guidelines of antimicrobial therapy, evaluation indicators (such as treatment effectiveness, safety, economy) are determined. Then, the treatment data of patients, including drug use, treatment response, laboratory test results, etc., are collected. Finally, after data preprocessing (data cleaning and normalization) of the collected data, it is constructed using the analytic hierarchy process.

[0106] In this embodiment, the etiological monitoring data includes the order for antimicrobial therapy, the order for targeted etiological testing items, the scanning time of the antimicrobial drug administration in the PDA system, and the scanning time of the etiological test specimen collection. Based on the etiological monitoring data, if a hospitalized patient does not undergo targeted etiological examination before the first systemic use of antimicrobial drugs, or the specimen collection time is after the use of antimicrobial drugs, it is judged that the etiological specimen submission before the first use of antimicrobial drugs is unqualified. Among them, the targeted etiological examination items refer to the morphological (staining), culture identification / susceptibility test, immunological detection technology, and nucleic acid detection items (including metagenomics) for bacteria, fungi, mycoplasma, and chlamydia.

[0107] In this embodiment, for inpatients who have been prescribed treatment with combined use of key antibiotics, a reminder will be given to consult the infectious disease department or discuss with the AMS. If the drugs are used without consulting the infectious disease department / discussing with the AMS, it will be judged that the management of combined use of key antibiotics is unqualified.

[0108] In this embodiment, the key types of antibacterial drugs include carbapenems (imipenem, meropenem, panipenem, biapenem and ertapenem), glycopeptides (vancomycin, teicoplanin), new tetracyclines (tigecycline, omadacycline, elacycline) and colistin (polymyxin B sulfate, colistin sulfate, polymyxin E mesylate), etc.

[0109] In this embodiment, pathogen-specific scenarios include three scenarios: empirical medication selection in the absence of etiology, antimicrobial drug selection in the presence of etiology but no drug sensitivity results, and antimicrobial drug selection in the presence of etiology and drug sensitivity results; patient condition refers to the patient's organ function, hematopoietic and coagulation function, allergic factors, etc.

[0110] In this embodiment, drug hierarchical management specifically means: if a physician prescribes antibiotics for use beyond the level of the physician, it is judged that the antibiotic hierarchical management is unqualified, and the antimicrobial drugs are classified into non-restricted, restricted, and special categories according to the current antibiotic classification standards (local).

[0111] In this embodiment, the dosage and method management specifically refers to determining the dosage and method for the patient in accordance with the current pharmacopoeia of antimicrobial drugs, combined with the patient's characteristics and treatment guidelines, and reminding the patient if there is any off-instruction use or the dosage exceeds the conventional dosage.

[0112] In this embodiment, drug discontinuation monitoring management specifically refers to assisting clinical judgment on the timing of drug discontinuation by monitoring changes in infection indicators and pathogen monitoring results, combined with comprehensive factors of the course of antimicrobial drug treatment for infectious diseases.

[0113] In this embodiment, the monitoring data of the drug supervision and treatment module include the percentage of antibiotic use during medical visits, the percentage of antibiotic use during medical visits to prescription drugs, the use rate of antibiotics for treatment without indications, the consultation and treatment rate of combined use of key antibiotics for inpatients, the execution rate of three-level management of therapeutic antibiotic use, and the timely discontinuation rate of antibiotic treatment, etc., among which, the statistical scope of antibiotics includes the number of inpatients discharged and / or outpatient and emergency, and the statistical dimensions include time, department, specialty, infection site / infection type, and physician; the visualization of the monitoring data of the drug supervision and treatment module can be achieved by using visualization tools or libraries (such as Matplotlib, Seaborn (Python), ECharts, D3.js (JavaScript)), etc.

[0114] The beneficial effects of the above technology are as follows: By providing indication monitoring and reminder, etiological monitoring and specimen submission, standardizing the use of antibacterial drugs for inpatients, intelligent antibacterial drug selection, comprehensive antibacterial drug management, and data monitoring and visualization functions, it helps doctors understand the pathogen situation of patients more accurately, so as to select more appropriate antibacterial drugs, improving the accuracy and efficiency of antibacterial drug selection and avoiding the abuse of antibacterial drugs.

[0115] Example 4:

[0116] The present invention provides a system for monitoring and managing the rational use of antibacterial drugs throughout the clinical pathway. The antibacterial drug selection unit includes:

[0117] Pathogen screening subunit: If the pathogen specific scenario of the current patient is a scenario without etiology or a scenario with etiology but without drug sensitivity, it uses the etiological test results of specimens collected from the same infected site of the patient in the past and the infection treatment guidelines to assist in determining the first drug selection list;

[0118] If the pathogen specific scenario of the current patient is a scenario with etiology and drug sensitivity results, it determines the first drug selection list according to the etiology and drug sensitivity results;

[0119] Function screening subunit: It is used to obtain the drug physiological function of the current patient and use a preset adaptation evaluation mechanism to determine the physiological adaptation scores of the drug physiological function and each antibacterial drug in the first drug selection list;

[0120] Delete the antibacterial drugs in the first drug selection list whose physiological adaptation scores with the drug physiological function are less than the set adaptation threshold, and finally obtain the second drug selection list;

[0121] Single screening subunit: When the drug requirement of the patient is single treatment, it screens out the single most suitable antibacterial drug from the second drug selection list as the antibacterial drug actually used by the current patient;

[0122] Combined screening subunit: When the drug requirement of the patient is combined treatment, it screens out multiple antibacterial drugs from the second drug selection list according to drug interactions, and aggregates them to obtain the combination of antibacterial drugs actually used by the current patient.

[0123] In this embodiment, the etiological test results of specimens collected from the same previous infection site refer to the results obtained by detecting pathogens through collecting specimens from the corresponding sites (such as sputum, urine, etc.) when the patient was infected at the same infection site (such as the lungs, urinary system, etc.) in the past; the infection treatment guidelines are formulated by professional medical organizations or institutions (such as the World Health Organization, national health departments, professional societies, etc.) and provide norms and suggestions for aspects such as the diagnosis, treatment, and prevention of specific infectious diseases; the first drug selection list includes antibacterial drugs that may be effective against the pathogens infecting the patient, and is determined by combining the etiological test results of specimens collected from the same previous infection site and the infection treatment guidelines in the absence of etiological scenarios or in the case of etiological results without drug sensitivity; in the case of etiological and drug sensitivity results, it is determined based on the etiological and drug sensitivity results; the physiological functions of drugs refer to the organ functions, hematopoietic functions, and coagulation functions of the patient.

[0124] In this embodiment, the preset adaptation evaluation mechanism specifically refers to: using the preset patient function performance parameters for each physiological function of the drug (for example, the patient function performance parameters for lung function are alanine aminotransferase and aspartate aminotransferase; the patient function performance parameters for coagulation function are prothrombin time (PT), activated partial thromboplastin time (APTT); the patient function performance parameters for hematopoietic function are white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), etc.), comparing with the corresponding allowable function performance parameter range of the antibacterial drug. If the patient function performance parameter of the patient's current physiological function of the drug does not belong to the allowable function performance parameter range of the antibacterial drug, the physiological adaptation score of the antibacterial drug and the current physiological function of the drug is marked as 0, otherwise, it is marked as 1; the set adaptation threshold is expressed as , where n represents the total number of patient function performance parameters corresponding to the physiological function of the drug.

[0125] In this embodiment, for example, there is a patient 1 who needs to use antibacterial drugs due to lung infection. The organ function of the patient, that is, the patient function performance parameters: alanine aminotransferase (ALT) is 120 U / L (normal range 0 - 40 U / L), aspartate aminotransferase (AST) is 100 U / L (normal range 0 - 40 U / L);

[0126] Information of antibacterial drug 1: When used by patients with liver function insufficiency, if ALT or AST exceeds 2 times the normal upper limit (that is, ALT > 80 U / L, AST > 80 U / L), it is not recommended to use this drug, that is, the allowable liver function performance parameter range of this drug is ALT ≤ 80 U / L and AST ≤ 80 U / L;

[0127] According to the preset adaptation evaluation mechanism, compare the liver function indicators of Patient 1 (ALT = 120 U / L, AST = 100 U / L) with the allowable functional performance parameter range of Antibacterial Drug 1 (ALT ≤ 80 U / L and AST ≤ 80 U / L);

[0128] Since both the ALT and AST of Patient 1 exceed the drug - allowed range, the physiological adaptation score of Antibacterial Drug 1 to the organ function of Patient 1, which is a physiological function of the drug, is marked as 0, less than the corresponding set adaptation threshold 2 (the number of current patient functional performance parameters is 2, and the set adaptation threshold is equal to ).

[0129] In this embodiment, the second drug selection list refers to a list formed by analyzing the physiological adaptability of the patient's drug physiological functions to each antibacterial drug in the first drug selection list on the basis of the first drug selection list; drug requirements include single - treatment and combination - treatment requirements, which are determined according to the patient's illness condition; the actually used antibacterial drug refers to a single antibacterial drug determined for the patient's treatment; the actually used antibacterial drug combination refers to a combination of multiple antibacterial drugs determined for the patient's treatment.

[0130] The beneficial effects of the above - mentioned technology are as follows: By assisting in the rational clinical selection of antibacterial drugs for specific etiological scenarios and patient conditions, the matching degree between antibacterial drugs and the patient's physiological condition can be improved, and the risk of treatment failure or adverse reactions caused by the mismatch between drugs and the patient's physiological functions can be reduced; according to the patient's drug requirements, single - drug screening and combination - drug screening are carried out to meet the treatment needs of different patients.

[0131] Example 5:

[0132] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. The single - screening sub - unit includes:

[0133] Expansion block: used to expand the data of the patient's current drug physiological functions according to a preset ratio to obtain a reference physiological function range;

[0134] Initial - screening block: used to obtain the historical drug usage records of each type of antibacterial drug in the second drug selection list, and use the reference physiological function range as the screening condition to screen drug cost records from the historical drug usage records;

[0135] According to the drug cost records, determine the reference average cost of each type of antibacterial drug in the second drug selection list;

[0136] If the current patient has no financial pressure, mark all antibacterial drugs in the second drug selection list as target candidate drugs;

[0137] If there is economic pressure on the current patient, then among the second drug selection list, the antibacterial drugs with a reference average cost not exceeding the maximum allowable cost of the patient are marked as target candidate drugs;

[0138] According to the reference average cost, an economic weight is assigned to each target candidate drug;

[0139] Re-screening block: used to obtain historical reference patients whose drug biological functions belong to the range of the reference physiological functions, and extract the historical patient drug use records of all historical reference patients;

[0140] When the number of historical reference patients is less than the set number threshold, from the historical patient drug use records, the patient use records of the target candidate drugs are screened out;

[0141] According to the patient use records, obtain and utilize the use frequency, adverse reaction incidence rate and adverse reaction severity level of the target candidate drugs to determine the functional safety score of the target candidate drugs;

[0142] Combine the functional safety score with the economic weight to determine the comprehensive adaptation score of the corresponding target candidate drug;

[0143] The target candidate drug with the highest comprehensive adaptation score is used as the actual antibacterial drug used by the current patient;

[0144] When the number of historical reference patients is not less than the set number threshold, the historical reference patients are divided according to the patient subdivision factors to obtain patient reference groups;

[0145] By assigning weights to the patient reference groups according to the patient subdivision factors, and comprehensively evaluating the functional safety situation and economic weight of the target candidate drugs within the patient reference groups, the actual antibacterial drug used by the current patient is obtained.

[0146] In this embodiment, the historical drug use record refers to the relevant records of past patients using various antibacterial drugs, usually including information such as drug name, dosage, usage duration, usage effect, adverse reactions, and costs; the drug cost record refers to the cost information screened out from the historical drug use records according to the reference physiological function range as the screening condition; economic pressure refers to the difficulties faced by the current patient in bearing the cost of antibacterial drugs.

[0147] In this embodiment, the reference average cost refers to the estimated cost of each type of antibacterial drug in the second drug selection list under the condition of the reference physiological function range, which is obtained by calculating the average cost of the drug cost records of each type of antibacterial drug screened out; the target candidate drug refers to any antibacterial drug in the second drug selection list (when the patient has no economic pressure), or the drug in the second drug selection list whose reference average cost does not exceed the maximum allowable cost of the patient (when the patient has economic pressure); the maximum allowable cost refers to the upper limit of the drug cost that the patient can bear; the economic weight v = ; where e represents the base of the natural logarithm, and its value is 2.7; represents the reference average cost of the patient.

[0148] In this embodiment, the preset ratio is predetermined, generally referring to 5%; the reference physiological function range is obtained by expanding the function performance parameters of the drug physiological function according to the preset ratio. For example, there are the function performance parameters of patient 2: alanine aminotransferase (ALT) is 100 U / L, and aspartate aminotransferase (AST) is 80 U / L. Then the reference physiological function range corresponding to alanine aminotransferase (ALT) is and the reference physiological function range of aspartate aminotransferase (AST) is .

[0149] In this embodiment, the historical reference patient refers to the historical patient whose drug biological function belongs to the current patient's reference physiological function range; the set quantity threshold is preset, such as 20; the historical patient drug use record refers to the drug use records of the historical reference patient in the past, including drug name, dosage, usage duration, usage effect, etc.; the drug use record of the belonging patient refers to the use record related to the target candidate drug screened out from the historical patient drug use record; the usage frequency refers to the frequency of the target candidate drug appearing in the drug use record of the belonging patient; the adverse reaction incidence rate refers to the proportion of adverse reactions caused by the target candidate drug in the drug use record of the belonging patient; the adverse reaction severity level is used to quantify the severity level of the adverse reactions caused by the target candidate drug. Different adverse reaction severity levels correspond to different adverse reaction severity scores (the value range is ).

[0150] In this embodiment, the function safety score is used to reflect the safety of the target candidate drug under specific physiological function conditions, and the calculation formula is as follows;

[0151]

[0152] In the formula, represents the function safety score of the current target candidate drug; represents the usage frequency of the current target candidate drug; It represents the influence weight of the drug usage situation on the safety of the candidate drug for the analysis target under specific physiological function conditions; It represents the incidence rate of adverse reactions of the current candidate drug for the target; It represents the severity score of the adverse reactions of the current candidate drug for the target; It represents the influence weight of the adverse drug reaction situation on the safety of the candidate drug for the analysis target under specific physiological function conditions.

[0153] In this embodiment, the value ranges of the weights assigned to the drug usage situation and the adverse drug reaction situation are both , which are obtained by solving the matrix constructed after pairwise comparison and scoring using the analytic hierarchy process.

[0154] In this embodiment, the comprehensive adaptation score is calculated by combining the functional safety score and the economic weight, and is used to reflect the adaptability of the candidate drug for the target after comprehensively considering safety and economy; among them, for example, if the functional safety score of candidate drug 1 is 0.7 and the economic weight is 0.3, then the comprehensive adaptation score of candidate drug 1 is equal to .

[0155] In this embodiment, the patient subdivision factors include the age range, gender, and the coincidence range of basic diseases. Among them, the age range refers to dividing patients into different age groups during patient subdivision, including five age ranges: 0 - 12 years old, 13 - 18 years old, 19 - 40 years old, 40 - 60 years old, and over 60 years old. The drug adaptation reference for different age ranges is different; the coincidence range of basic diseases refers to considering the similarity degree of the basic diseases among patients (the similarity degree is generally obtained by normalizing the calculation result after similarity calculation using the edit distance algorithm), including three coincidence ranges: complete coincidence (similarity degree not less than 0.9), partial coincidence (similarity degree less than 0.9 and greater than 0.3), and non - coincidence (similarity degree not greater than 0.3); the patient reference group refers to the group obtained by dividing the historical reference patients according to the patient subdivision factors when the number of historical reference patients is not less than the set number threshold.

[0156] The beneficial effects of the above - mentioned technology are as follows: By comprehensively considering various factors such as economy and physiological functions, the precise screening and comprehensive evaluation of antibacterial drugs are realized, which helps to improve the rationality, effectiveness, and safety of drug selection, and further optimize the allocation of medical resources.

[0157] Example 6:

[0158] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. The re - screening block further includes:

[0159] According to the setting and distribution mechanism of different patient segmentation factors, combined with the relevant segmentation information of the current patient, the corresponding grouping reference weights are sequentially assigned to all the reference groups of patients corresponding to the same patient segmentation factor;

[0160] According to the historical drug use records of patients, obtain the in-group patient use records of the corresponding target candidate drugs for the current patient's reference group;

[0161] According to the in-group patient use records, obtain and combine the in-group use frequency, in-group adverse reaction incidence rate, and in-group adverse reaction severity level of the target candidate drug, and analyze it in combination with the grouping reference weight to determine the in-group safety score of the target candidate drug within the current patient's reference group;

[0162] Combine the in-group safety score with the economic weight to determine the in-group adaptation score for the corresponding target candidate drug;

[0163] Mark the target candidate drug with the highest in-group adaptation score among all the reference groups of patients corresponding to the same patient segmentation factor as the likely-to-be-used drug;

[0164] If there is an overlap in the likely-to-be-used drugs corresponding to all patient segmentation factors, then take the likely-to-be-used drug with the highest overlap degree as the actual antibacterial drug used by the current patient;

[0165] If the likely-to-be-used drugs corresponding to all patient segmentation factors are all different, then calculate by combining the preset priority weights of each patient segmentation factor with the in-group adaptation scores of the corresponding likely-to-be-used drugs to obtain a comprehensive score;

[0166] Take the likely-to-be-used drug with the highest comprehensive score as the actual antibacterial drug used by the current patient.

[0167] In this embodiment, the setting and distribution mechanism of the age range specifically refers to: assigning the corresponding grouping reference weight with the highest age weight level to the reference group of patients corresponding to the age range to which the current patient belongs, and assigning lower age weight levels to the reference groups of patients corresponding to the age ranges that are farther away from the age range to which the current patient belongs. Among them, the age weight levels include three levels: high, medium, and low, and the corresponding grouping reference weights are preset, and the value range is .

[0168] In this embodiment, for example, there is a patient a1 whose age is 30 years old, belonging to the age range of 19 - 40 years old; for the patients in the age range of 19 - 40 years old, referring to the grouping, the grouping reference weight with a high age weight level is assigned as 0.6; for the patients in the age ranges of 13 - 18 years old and 40 - 60 years old, referring to the grouping, since they are relatively closer to the age range of 19 - 40 years old, the grouping reference weight with a medium age weight level is assigned as 0.4; for the patients in the age ranges of 0 - 12 years old and over 60 years old, referring to the grouping, the grouping reference weight with a low age weight level is assigned as 0.2.

[0169] In this embodiment, for example, there is a patient a1 whose age is 65 years old, belonging to the age range of over 60 years old; for the patients in the age range of over 60 years old, referring to the grouping, the grouping reference weight with a high age weight level is assigned as 0.6; for the patients in the age range of 40 - 60 years old, referring to the grouping, since they are relatively closer to the age range of over 60 years old, the grouping reference weight with a medium age weight level is assigned as 0.4; for the patients in the age ranges of 19 - 40 years old, 13 - 18 years old and 0 - 12 years old, referring to the grouping, the grouping reference weight with a low age weight level is assigned as 0.2.

[0170] In this embodiment, the setting and allocation mechanism of the gender range specifically refers to: for the patients in the reference grouping with the same gender as the current patient, the corresponding grouping reference weight with a high gender weight level is assigned (the value range is , and the gender weight levels include two levels, high and low, and the corresponding weights add up to 1), and for the patients in the reference grouping with a gender different from the current patient, the corresponding grouping reference weight with a low gender weight level (the value range is ) is assigned.

[0171] In this embodiment, for example, there is a patient a3 who is female; for the female patients in the reference grouping, the grouping reference weight with a high gender weight level is assigned as 0.7; for the male patients in the reference grouping, the grouping reference weight with a low gender weight level is assigned as 0.3.

[0172] In this embodiment, the setting and allocation mechanism of the basic disease overlap range specifically refers to: for the patients in the reference grouping with a completely overlapping basic disease range with the current patient, the corresponding grouping reference weight with the highest disease weight level is assigned, and for the patients in the reference grouping with a non - overlapping basic disease range with the current patient, the corresponding grouping reference weight with the lowest disease weight level is assigned, where the disease weight levels include three levels, high, medium and low, and the corresponding grouping reference weights are pre - set, and the value range is .

[0173] In this embodiment, for example, the overlap range of the basic diseases of patient a4 and patient reference group 1 is complete overlap, the overlap range with patient reference group 2 is non - overlap, and the overlap range with patient reference group 3 is partial overlap. Then, a grouping reference weight of 0.85 with a high disease weight level is assigned to patient reference group 1; a grouping reference weight of 0.1 with a low disease weight level is assigned to patient reference group 2; and a grouping reference weight of 0.4 with a medium disease weight level is assigned to patient reference group 3.

[0174] In this embodiment, the relevant detailed information includes the patient's gender, age, and basic diseases; the grouping reference weight is a value assigned to each patient reference group for the same patient segmentation factor according to the set assignment mechanism.

[0175] In this embodiment, the usage records of patients within the group refer to the records of patients who belong to the current patient reference group and use the target drug to be selected from the historical patient drug usage records; the usage frequency within the group refers to the frequency of use of the target drug to be selected in the usage records of patients within the group; the incidence rate of adverse reactions within the group refers to the ratio of the number of patients who experience adverse reactions after using the target drug to be selected to the total number of patients who use the drug in the usage records of patients within the group; the severity level of adverse reactions within the group is used to quantify the severity level of adverse reactions caused by the target drug to be selected. Different severity levels of adverse reactions correspond to different adverse reaction severity scores (the value range is ) and are different.

[0176] In this embodiment, the calculation formula for the safety score within the group is expressed as follows:

[0177]

[0178] In the formula, represents the safety score within the group for the current target drug to be selected; represents the usage frequency within the group for the current target drug to be selected; represents the influence weight of the drug usage situation on the safety of the target drug to be selected under specific physiological function conditions; represents the incidence rate of adverse reactions within the group for the current target drug to be selected; represents the adverse reaction severity score within the group for the current target drug to be selected; represents the influence weight of the drug adverse reaction situation on the safety of the target drug to be selected under specific physiological function conditions; represents the grouping reference weight of the corresponding patient reference group to which the current target drug to be selected belongs.

[0179] In this embodiment, the in-group adaptation score is calculated by combining the in-group safety score and the economic weight, and is used to reflect the adaptation degree of the target candidate drug in the current patient reference group after comprehensively considering safety and economy; for example, there is a target candidate drug 2 in patient reference group 1 with an in-group safety score of 0.8 and an economic weight of 0.1, then the in-group adaptation score is equal to .

[0180] In this embodiment, the likely-to-be-used drug refers to the target candidate drug with the highest in-group adaptation score among all patient reference groups of the same patient sub-factor; the preset priority weight is the priority weight preset for the patient sub-factor, and the value range is , which is obtained by solving the matrix constructed after pairwise comparison and scoring using the analytic hierarchy process, and the sum of the preset priority weights of the age range, gender, and basic disease overlap range is equal to 1, and the preset priority weight of the basic disease overlap range is the largest; the comprehensive score is calculated by multiplying the preset priority weight by the in-group adaptation score of the corresponding likely-to-be-used drug.

[0181] In this embodiment, for example, for patient a5, the likely-to-be-used drugs corresponding to the age range, gender, and basic disease overlap range of the patient sub-factor are drug d1, drug d1, and drug d2 respectively. At this time, the overlap degree of drug d1 is the highest, and it is used as the actual antibacterial drug for the current patient a5.

[0182] In this embodiment, for example, for patient a6, the likely-to-be-used drugs corresponding to the age range, gender, and basic disease overlap range of the patient sub-factor are drug f1, drug f2, and drug f3 respectively, that is, the corresponding likely-to-be-used drugs of all patient sub-factors are inconsistent. The comprehensive scores of drug f1, drug f2, and drug f3 are 0.7, 0.65, and 0.8 respectively. At this time, drug f3 is used as the actual antibacterial drug for the current patient a6.

[0183] The beneficial effects of the above technology are as follows: By adopting a multi-dimensional screening mechanism, from allocating grouping reference weights to patient reference groups divided according to different patient sub-factors of age range, gender, and basic disease overlap range, calculating the in-group safety score by comprehensively considering the in-group usage frequency and in-group adverse reaction status of the target candidate drug, combining the in-group safety score and the economic weight to determine the in-group adaptation score, to marking the likely-to-be-used drug, and finally determining the actual antibacterial drug used, it can help reduce the influence of subjective factors, make the selection of antibacterial drugs more scientific and reasonable, and effectively ensure the control of the rational use of antibacterial drugs.

[0184] Example 7:

[0185] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway. The combined screening subunit includes:

[0186] Initial analysis block: It is used to screen out the first drug combination for use from the second drug selection list according to the drug indications.

[0187] Interaction analysis block: It is used to utilize the drug interaction database to evaluate the interaction of each first drug combination for use, and obtain the effect evaluation result.

[0188] According to the effect evaluation result, the first drug combination for use without serious adverse interactions or those that may affect the treatment effect is represented as the second drug combination for use.

[0189] Tolerance analysis block: It is used to input the patient's basic information and tolerance analysis indicators of the current patient, as well as the second drug combination for use into the combination screening model, and output the possible drug combination for use.

[0190] Patient preference block: When there is only a single possible drug combination for use, the current possible drug combination for use is used as the actual antibacterial drug combination used by the patient.

[0191] When there are multiple possible drug combinations for use, determine the overall drug usage rules for each possible drug combination for use.

[0192] Obtain and based on the patient's historical medication records within a preset time period, use the preference evaluation features to evaluate and obtain the patient's true medication preference rules.

[0193] The corresponding available drug combination for use with the overall drug usage rules having the highest similarity to the true medication preference rules is used as the actual antibacterial drug combination used by the patient.

[0194] In this embodiment, the first drug combination for use is obtained by inputting the second drug selection list and the disease information of the current patient into a combination generation model established in advance based on the indication ranges of various antibacterial drugs. Among them, the combination generation model is first established by collecting the indication range information of various antibacterial drugs and the disease information of different patients, including the main disease, complications, disease severity, etc.; then extract the key features from the drug indications and disease information (such as the antibacterial spectrum, mechanism of action of the drug, pathogen type of the disease, symptom manifestations, etc.), perform quantitative processing on the key features and then train a neural network, and design the structure of the combination generation model according to the corresponding relationship between the drug indications and disease information.

[0195] In this embodiment, the drug interaction database refers to a database that stores a large amount of information on drug interactions, including detailed information such as the types of interactions (such as synergistic effects, antagonistic effects, etc.), severity, and mechanism of action that may occur when various drugs are used simultaneously; the action evaluation result is a conclusion obtained by evaluating the interaction of each first drug combination using the drug interaction database, such as a synergistic effect or a serious adverse interaction.

[0196] In this embodiment, the second drug combination refers to a drug combination selected from the first drug combinations based on the action evaluation result and having no serious adverse interactions or potential impacts on the treatment effect; the basic patient information refers to various basic data of the current patient, including age, gender, weight, height, allergy history, past medical history, etc.; the tolerance analysis indicators refer to relevant indicators used to evaluate the patient's drug tolerance ability, including liver and kidney function indicators (such as alanine aminotransferase, creatinine, etc.), blood routine indicators (such as white blood cell count, red blood cell count, etc.), and electrocardiogram indicators.

[0197] In this embodiment, the combination screening model is obtained by first collecting a large amount of patient data, drug information data, and treatment result data, then screening out the features that have important impacts on drug combination screening from all the collected data, quantifying the screened features as training data, and training a neural network. It is used to comprehensively evaluate the second drug combination. According to the evaluation result, the combination screening model can output a drug combination that may be suitable for the current patient, that is, the possible drug combination; the possible drug combination refers to the drug combination output after being evaluated by the combination screening model.

[0198] In this embodiment, the overall drug usage rules include the total number of drug categories within the possible drug combination, the dosage per use of each drug category, the number of daily uses of each drug category, and the dosage form of each drug category (such as tablets, capsules, oral liquids, etc.); the preset time period is a pre-set time range for obtaining the patient's historical medication records, such as half a year.

[0199] In this embodiment, the historical medication record refers to the detailed record of the patient's drug use within the preset time period, such as the medication frequency and the frequency of missed medications; the preference evaluation features refer to the characteristic indicators used to evaluate the patient's true medication preferences, including dosage form preference, medication frequency preference, and medication category quantity preference; the true medication preference rules refer to the actual drug use preference situation of the patient evaluated using the preference evaluation features based on the patient's historical medication records within the preset time period, reflecting the patient's personal tendency in drug selection, that is, dosage form preference, daily drug use frequency preference, and the number of drug categories used per time preference.

[0200] In this embodiment, the steps for obtaining the detailed rules of actual medication preferences specifically refer to: First, based on the patient's historical medication records within a preset time period, classify and summarize the data related to different preference evaluation features; perform quantization processing on each preference evaluation feature (for example, for the preference for drug dosage forms, assign different numerical codes to different dosage forms, and calculate the occurrence frequencies of different numerical codes within the preset time period; for the preference for medication frequency, calculate the occurrence frequencies of different numbers of times of taking medicine on time every day within the preset time period; for the preference for the number of medication categories per use, calculate the occurrence frequencies of different numbers of medication categories taken on time each time within the preset time period).

[0201] Then, by calculating the occurrence frequencies, select the specific manifestations with the highest occurrence frequencies in each preference evaluation feature for summarization to generate the detailed rules of actual medication preferences.

[0202] In this embodiment, for example, within the preset time period, the occurrence frequency of the tablet dosage form of patient 3 is 0.3, and the occurrence frequency of the capsule dosage form is 0.7; the occurrence frequency of taking medicine on time 2 times a day is 0.6, and the occurrence frequency of taking medicine on time 3 times a day is 0.4; the occurrence frequency of the number of medication categories per use being 1 category is 0.9, and the occurrence frequency of the number of medication categories per use being 2 categories is 0.1.

[0203] At this time, since the occurrence frequency of the capsule dosage form (0.7) is higher than that of the tablet (0.3), the occurrence frequency of taking medicine on time 2 times a day (0.6) is higher than that of 3 times (0.4), and the occurrence frequency of the number of medication categories per use being 1 category (0.9) is higher than that of 2 categories (0.1), the detailed rules of actual medication preferences are expressed as a preference for the capsule dosage form, hoping to take medicine 2 times a day, and the number of medication categories per use is 1 category.

[0204] In this embodiment, the similarity calculation between the detailed rules of actual medication preferences and the overall medication use rules is obtained by first extracting the key rule features (such as drug dosage forms (such as tablets, capsules), medication frequencies (such as once a day, twice a day), and the number of medication categories per use) from the detailed rules of actual medication preferences and the overall medication use rules, and then calculating using the cosine similarity algorithm after quantifying the key rule features.

[0205] The beneficial effects of the above technology are as follows: By accurately screening out the second combination of drugs to be used based on the indications and drug interactions of the drugs; then obtaining the possible combination of drugs to be used from the second combination of drugs to be used by considering the patient's tolerance, so as to fully consider individual differences and enhance the adaptability of medication; and when there are multiple possible combinations of drugs to be used, combining the detailed rules of the patient's actual medication preferences, select the corresponding combination with the highest similarity, thereby effectively realizing personalized and precise medication.

[0206] Example 8:

[0207] The present invention provides a monitoring and management system for the rational use of antibacterial drugs throughout the clinical pathway. The perioperative drug prophylaxis supervision module includes:

[0208] Surgical information acquisition unit: used to acquire the surgical monitoring information of surgical patients undergoing Class I surgical incision operations;

[0209] Surgical drug analysis unit: used to assist doctors in prescribing prophylactic use of antibacterial drugs, selecting antibacterial drugs, managing the drug injection duration, and discontinuing drug use according to the guiding principles for the use of prophylactic antibacterial drugs in surgery and the characteristics of surgical specialties;

[0210] Surgical statistics unit: used to statistically analyze and visualize the surgical volume with different incision amounts and the surgical-related data of Class I surgical incision operations.

[0211] In this embodiment, a Class I surgical incision operation refers to an operation that does not enter the inflammatory area, does not enter the respiratory tract, digestive tract, urogenital tract, or oropharyngeal area, and meets the conditions of a closed trauma operation. Common Class I surgical incision operations include thyroid surgery, breast surgery, and major vascular surgery, cardiac surgery, or lower extremity deep vein vascular surgery in cardiothoracic surgery, etc.; the surgical monitoring information includes the surgical patient, surgical name, surgical start time (skin incision time), purpose of using antibacterial drugs (if used), time of intravenous injection of antibacterial drugs into the body (if used), and surgical end time (time when the patient leaves the operating room).

[0212] In this embodiment, the guiding principles for the use of prophylactic antibacterial drugs in surgery include four guiding principles: according to the classification of surgical incisions (the requirements for prophylactic antibacterial drugs are different for different types of surgical incisions (such as Class I, Class II, and Class III)), selecting appropriate antibacterial drugs (the selection of prophylactic antibacterial drugs should be based on the possible types of contaminating bacteria and their sensitivity to antibacterial drugs; antibacterial drugs that are effective, safe, convenient to use, and of appropriate price against possible contaminating bacteria should be preferred), administration timing and method (prophylactic antibacterial drugs should be administered intravenously within 0.5 to 1 hour before the start of the operation or at the start of anesthesia; when the operation exceeds 3 hours or the blood loss is large, additional administration can be carried out during the operation), and medication course (the medication course of prophylactic antibacterial drugs should be as short as possible, generally not exceeding 24 hours, and can be extended to 48 hours in individual cases); the characteristics of surgical specialties include professionalism (operations in different specialties have their unique anatomical structures, physiological functions, and pathological changes), riskiness (patients need to be comprehensively evaluated before surgery to develop a personalized surgical plan), fineness, and cooperation.

[0213] In this embodiment, the specific prescription of prophylactic use of antibacterial drugs means that, in accordance with the guiding principles for the prophylactic use of antibacterial drugs in surgery, when a doctor prescribes prophylactic use of antibacterial drugs, whether there are indications for prophylactic use of antibacterial drugs is reminded according to the characteristics of each surgical specialty to assist in judgment.

[0214] In this embodiment, the specific selection of antibacterial drugs means that, according to the characteristics of each surgical specialty operation and in accordance with the guiding principles for the prophylactic use of antibacterial drugs in surgery, doctors are assisted in selecting antibacterial drug varieties.

[0215] In this embodiment, the specific management of the drug injection duration means that, in accordance with the guiding principles for the prophylactic use of antibacterial drugs in surgery, prophylactic use of antibacterial drugs should be administered within 0.5 - 1 hour before skin incision / before the start of anesthesia. Reminders are given when staff scan the code to execute the antibacterial drug infusion doctor's order, and the infusion time is recorded.

[0216] In this embodiment, the specific discontinuation management means that, in accordance with the guiding principles for the prophylactic use of antibacterial drugs in surgery, if the prophylactic use of antibacterial drugs exceeds 24 hours without discontinuation, reminders are given to evaluate the feasibility of timely discontinuation.

[0217] In this embodiment, the surgical - related data includes the prophylactic use rate of antibacterial drugs for class I incision surgeries, the qualification rate of the prophylactic use timing of antibacterial drugs, the qualification rate of the prophylactic use varieties of antibacterial drugs, the discontinuation rate within the monitoring time period of the prophylactic use of antibacterial drugs, the percentage of additional antibacterial drugs used, and the incidence of SSI for class I incision surgeries during hospitalization; the visual display of the surgical volume with different incision quantities and the surgical - related data of class I surgical incisions can be achieved by using visualization tools or libraries (such as Matplotlib, Seaborn (Python), ECharts, D3.js (JavaScript), etc.).

[0218] The beneficial effects of the above - mentioned technology are as follows: By obtaining monitoring data from monitored surgical patients and, in accordance with the guiding principles for the prophylactic use of antibacterial drugs in surgery and the characteristics of surgical specialty operations, assisting doctors in prescribing prophylactic use of antibacterial drugs, selecting antibacterial drugs, managing the drug injection duration, and discontinuing drugs, the pertinence and effectiveness of drug use can be improved, over - use and waste of antibacterial drugs can be avoided, and medical resources can be saved.

[0219] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A monitoring and management system for the rational use of antibacterial drugs throughout the entire clinical pathway, characterized in that, including: Disease Judgment Module: used to obtain the patient's diagnosis and treatment data, analyze and judge suspected infectious diseases, and obtain disease judgment results; Drug Treatment Supervision Module: used to, based on the disease judgment results, adopt a multi-dimensional screening strategy that integrates patient needs, drug interactions, and patient medication preferences to individually select the types of antibacterial drugs used by the patient, and assist in judging the treatment course of antibacterial drugs; Perioperative Drug Prophylaxis Supervision Module: used to intelligently supervise the prophylactic use of antibacterial drugs according to the monitoring information obtained from the full-process monitoring of perioperative patients; Among them, the Drug Treatment Supervision Module also includes: Indication Monitoring Unit: used to divide patients into indication groups based on the disease judgment results, evaluate the indication groups and types of patients, and obtain indication evaluation scores; Indication Reminder Unit: used to remind patients whose indication group is insufficient evidence of infection or without indication for antibacterial drug treatment but who have used antibacterial drug treatment; Etiology Monitoring Unit: used to, according to the disease judgment results, recommend that doctors prescribe etiological examination items for patients; judge whether the etiological examination before the first use of antibacterial drugs is qualified by analyzing etiological monitoring data; Inpatient Discussion Unit: used to judge whether the management of the combined use of key antibacterial drugs for inpatients is qualified; Antibacterial Drug Selection Unit: used to intelligently assist in the rational selection of antibacterial drugs clinically based on the specific pathogen scenario and patient condition; Antibacterial Drug Management Unit: used to manage the drug classification, dosage and method, and discontinuation monitoring of patients; Treatment Statistics Unit: used to statistically analyze and visually display the monitoring data of the drug supervision and treatment module.

2. The rational use monitoring and management system of antibacterial drugs based on the whole process of clinical pathway according to claim 1, characterized in that, The Disease Judgment Module includes: Data Acquisition Unit: used to obtain the patient's diagnosis and treatment data from the target medical system, and perform data preprocessing on the patient's diagnosis and treatment data according to the preset disease diagnosis knowledge base to obtain target diagnosis and treatment data; Diagnosis Analysis Unit: used to input the target diagnosis and treatment data into a pre-established disease analysis model for diagnosis and analysis to obtain the disease judgment results of the current patient.

3. The rational use monitoring and management system of antibacterial drugs based on the whole process of clinical pathway according to claim 1, wherein The Antibacterial Drug Selection Unit includes: Pathogen Screening Sub-unit: used to, if the specific pathogen scenario of the current patient is a scenario without etiology or a scenario with etiology but without drug sensitivity, use the etiological test results of specimens collected from the same infected site of the patient in the past and the infection treatment guidelines to assist in determining the first drug selection list; If the specific pathogen scenario of the current patient is a scenario with etiology and drug sensitivity results, determine the first drug selection list according to the etiology and drug sensitivity results; Function Screening Sub-unit: used to obtain the drug physiological functions of the current patient, and use a preset adaptation evaluation mechanism to determine the physiological adaptation scores of the drug physiological functions and each antibacterial drug in the first drug selection list; Delete the antibacterial drugs in the first drug selection list whose physiological adaptation scores to the drug physiological functions are less than the set adaptation threshold, and finally obtain the second drug selection list; Single Screening Sub-unit: used to, when the drug requirement of the patient is single treatment, screen out the single most suitable antibacterial drug from the second drug selection list as the actual antibacterial drug used by the current patient; Combined screening subunit: When the drug requirement for a patient is combined treatment, according to drug interactions, multiple antibacterial drugs are screened from the second drug selection list, and the actual combination of antibacterial drugs used by the current patient is obtained by aggregation.

4. The clinical pathway-based full-process monitoring and management system for rational use of antibacterial drugs according to claim 3, wherein The single screening subunit includes: Expansion block: used to expand the drug physiological functions of the current patient according to a preset ratio to obtain a reference physiological function range; Initial screening block: used to obtain the historical drug usage records of each type of antibacterial drug in the second drug selection list, and use the reference physiological function range as the screening condition to screen drug cost records from the historical drug usage records; According to the drug cost records, determine the reference average cost of each type of antibacterial drug in the second drug selection list; If the current patient has no financial pressure, all antibacterial drugs in the second drug selection list are marked as target candidate drugs; If the current patient has financial pressure, the antibacterial drugs in the second drug selection list whose reference average cost does not exceed the maximum allowable cost of the patient are marked as target candidate drugs; According to the reference average cost, an economic weight is assigned to each target candidate drug; Re-screening block: used to obtain historical reference patients whose drug biological functions belong to the reference physiological function range, and extract the historical patient drug usage records of all historical reference patients; When the number of historical reference patients is less than the set number threshold, from the historical patient drug usage records, screen the usage records of the patients to whom the target candidate drugs belong; According to the usage records of the patients to whom they belong, obtain and utilize the usage frequency, adverse reaction incidence rate, and adverse reaction severity level of the target candidate drugs to determine the functional safety score of the target candidate drugs; Combine the functional safety score with the economic weight to determine the comprehensive adaptation score of the corresponding target candidate drug; The target candidate drug with the highest comprehensive adaptation score is used as the actual antibacterial drug used by the current patient; When the number of historical reference patients is not less than the set number threshold, the historical reference patients are divided according to patient subdivision factors to obtain patient reference groups; By assigning weights to the patient reference groups according to patient subdivision factors, and comprehensively evaluating the functional safety and economic weight of the target candidate drugs within the patient reference groups, the actual antibacterial drug used by the current patient is obtained.

5. The clinical pathway-based full-process monitoring and management system for rational use of antibacterial drugs according to claim 4, wherein The re-screening block further includes: According to the set allocation mechanism of different patient subdivision factors, combined with the relevant subdivision information of the current patient, successively assign grouping reference weights to all corresponding patient reference groups of the same patient subdivision factor; According to the historical patient drug usage records, obtain the within-group patient usage records of the corresponding target candidate drugs in the current patient reference group; According to the within-group patient usage records, obtain and analyze the within-group usage frequency, within-group adverse reaction incidence rate, and within-group adverse reaction severity level of the target candidate drugs in combination with the grouping reference weights to determine the within-group safety score of the target candidate drugs in the current patient reference group; Combine the within-group safety score with the economic weight to determine the within-group adaptation score of the corresponding target candidate drug; Mark the target candidate drug with the highest within-group adaptation score in all patient reference groups of the same patient subdivision factor as the possible drug to be used; If there is an overlap in the possible drugs that can be used corresponding to all patient segmentation factors, then the drug with the highest overlap degree is used as the actually used antibacterial drug for the current patient; If the possible drugs that can be used corresponding to all patient segmentation factors are all different, then a combined score is calculated by combining the preset priority weights of each patient segmentation factor with the within-group adaptation scores of the corresponding possible drugs; The possible drug with the highest combined score is used as the actually used antibacterial drug for the current patient.

6. The monitoring and management system for rational use of antibacterial drugs throughout the entire clinical pathway according to claim 3, wherein The combined screening subunit includes: Initial analysis block: used to screen out the first drug usage combination from the second drug selection list according to the drug indications; Interaction analysis block: used to evaluate the interaction of each first drug usage combination by using the drug interaction database to obtain the interaction evaluation result; According to the interaction evaluation result, the first drug usage combination without serious adverse interactions or that may affect the treatment effect is represented as the second drug usage combination; Tolerance analysis block: used to input the basic patient information and tolerance analysis indicators of the current patient, and the second drug usage combination into the combination screening model to output the possible drug combination; Patient preference block: used to use the current possible drug combination as the actually used antibacterial drug combination for the patient when there is only a single possible drug combination; When there are multiple possible drug combinations, determine the overall drug usage rules for each possible drug combination; Obtain and, based on the patient's historical medication records within a preset time period, evaluate the patient's true medication preference rules by using the preference evaluation features; The available drug combination corresponding to the overall drug usage rules with the highest similarity to the true medication preference rules is used as the actually used antibacterial drug combination for the patient.

7. The rational use monitoring and management system for antibacterial drugs based on the full process of clinical pathways according to claim 1, characterized in that The perioperative drug prevention supervision module includes: Surgical information acquisition unit: used to acquire the surgical monitoring information of surgical patients undergoing Class I surgical incision operations; Surgical drug analysis unit: used to assist doctors in prescribing prophylactic use of antibacterial drugs, antibacterial drug selection, drug injection duration management, and discontinuation management based on the guiding principles for the use of prophylactic antibacterial drugs in surgery and the characteristics of surgical specialties; Surgical statistics unit: used to statistically analyze and visualize the surgical volume with different incision amounts and the surgical-related data of Class I surgical incision operations.

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