Auxiliary analysis method and system for precise drug delivery of drug-resistant bacteria

Abstract

The invention belongs to the technical field of medical information, and discloses a drug-resistant bacterium precise drug delivery auxiliary analysis method and system.The method comprises the steps that a microorganism submission event is responded, clinical data of a patient and a pathogen drug sensitivity report are automatically collected and integrated, and a structured electronic tracking intervention record chart is generated; based on a preset distributed node rule, multi-role real-time cooperative tracking and authority controlled interaction of pharmacists, clinicians and hospital infection specialists are realized; by integrating a standardized auxiliary evaluation panel and a pharmacy knowledge base, a pharmacist is guided to perform structured and standardized evaluation on pathogenicity and medication rationality; and performing batch analysis on archived cases by means of a preset intelligent contract rule, automatically generating a standardized report and a medication warning, and forming a management closed loop through directional pushing. According to the invention, standardization and intellectualization of the whole process from information acquisition, collaborative intervention to data analysis and utilization are realized, and the efficiency and quality of scientific management of antibacterial drugs are significantly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of medical information technology, and discloses a precision drug delivery assisting analysis method and system for drug-resistant bacteria. BACKGROUND

[0002] Currently, in medical institutions at all levels, relevant information is usually recorded by pharmacists manually or using basic electronic spreadsheets, and the pharmacy department, the clinical laboratory, the infection control department and the clinical department are coordinated for communication and management, aiming to monitor the medication process, intervene in unreasonable use and prevent treatment-related adverse events.

[0003] However, the existing practice mode faces many technical challenges and bottlenecks. First, the recording tools relied on for tracking intervention are often poorly designed and not deeply integrated with the hospital information system, resulting in the need for manual entry of key information such as patient infection indicators and medication records, which is inefficient and prone to errors; the table columns lack standardized design, making it difficult to clearly reflect the dynamic changes in treatment and unable to effectively assist pharmacists in quickly identifying pathogenic bacteria, judging the rationality of medication and the safety of treatment. Second, the multi-disciplinary collaboration process is loose, and drug-resistant bacteria information is transmitted through non-instant methods such as office systems, resulting in delayed pharmacist intervention; the rights and responsibilities between departments are not clear, and there is a lack of institutionalized coordination and supervision mechanisms, affecting the timeliness and execution of intervention. Finally, the analysis and utilization of accumulated intervention record data are in the early stages, analysis reports are prepared arbitrarily, medication alerts are issued through a single channel with low awareness, and it is difficult to form an effective management loop to continuously improve clinical practice and improve related quality control indicators.

[0004] Therefore, it is urgent to build a drug-resistant bacteria medication tracking intervention platform that is deeply integrated with the hospital information system, standardized in process, and can effectively support real-time multi-disciplinary collaboration. This platform needs to address the problems of information fragmentation, low efficiency, and extensive analysis in the existing mode, and achieve automatic data collection, intelligent assistance in judgment, standardized collaborative intervention and deep data utilization through technical means, thereby systematically and standardized improving the quality and efficiency of pharmacist tracking intervention, and truly ensuring the scientific management level of antibacterial drugs. SUMMARY

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: According to the first aspect of the present application, a precision drug delivery assisting analysis method for drug-resistant bacteria is claimed, the method comprising the following steps: S1, in response to the submission operation of a microbiological specimen of an inpatient, acquiring the test application sheet identifier and the patient identifier of the patient in real time; S2, based on the patient identifier, synchronously extracting the clinical data set of the patient; S3, when a pathogen drug sensitivity report containing drug resistance or special drug resistance markers is generated, a pre-warning event is triggered according to the test application form, and the pathogen drug sensitivity report is associated and bound with the clinical data set to generate an initial intervention case data package; S4, a standardized tracking intervention record table template is called, and each data item in the initial intervention case data package is automatically filled into the pre-defined field of the standardized tracking intervention record table template to generate an electronic tracking intervention record table; S5, the electronic tracking intervention record table is assigned to the work queue of the pharmacist, and the access and editing authority of the electronic tracking intervention record table is synchronously granted to the designated physician nodes of the clinical department to which the patient belongs and the designated hospital infection management department specialist nodes based on the preset distributed node rules; S6, after the electronic tracking intervention record table enters an editable state, a multi-role collaborative tracking phase is started; S7, after the patient's infection treatment period ends, the pharmacist triggers a closing operation in the electronic tracking intervention record table, marks the treatment outcome, and changes the state of the electronic tracking intervention record table to a read-only archived state, and stores it in the tracking intervention case database; S8, batch analysis is performed on the archived record table in the tracking intervention case database, and key analysis dimension data is extracted from the archived record table through a preset smart contract rule, and the smart contract rule defines the field, format and logical judgment condition of data extraction.

[0006] Further, the design and application of the standardized tracking intervention record table template in step S4 includes: The standardized tracking intervention record table template adopts an electronic form, and the interface is divided into a plurality of logically interlocked regions; The patient basic information data area includes ward, hospitalization number, name, age, and diagnosis information fields; The drug-resistant bacteria detection detail data area includes submission date, specimen type, pathogen name, drug resistance mechanism, and key drug sensitivity result fields, and is provided with a pathogenicity preliminary judgment drop-down selection field with options of suspected pathogenic bacteria, colonized bacteria or contaminated bacteria; The infection dynamic indicator data area dynamically links the patient's time series data through an interface to display the temperature, white blood cell count, C-reactive protein, and procalcitonin change curves over time in the form of a chart, and provides a text field for linking or summarizing infection-related imaging and symptom descriptions; The antibacterial drug use detail data area extracts from the medical order system, and lists the current and historical use of antibacterial drug names, doses, frequencies, administration routes, and time periods, and is provided with a drug rationality preliminary judgment field with options of reasonable, adjustment required or unreasonable; The pharmacist evaluation and suggestion data area includes several structured input components for recording the pharmacist's analysis of the infection and medication matching, specific medication adjustment suggestions, adjustment basis, and medication education points; The treatment process tracking log data area is a log panel arranged in reverse chronological order, which automatically records and displays the key operations and text notes of the patient's condition and treatment by pharmacists, doctors, and infection control officers, forming a complete audit tracking of the treatment process.

[0007] Further, step S6 further comprises: S61a. The pharmacist activates the pathogenicity auxiliary judgment panel linked with the drug-resistant bacteria detection details data area and the infection dynamic indication data area in the interface of the electronic tracking intervention record table; S61b. The pathogenicity auxiliary judgment panel first calls the preset pathogen knowledge base, automatically loads the microorganism as the background probability information of common pathogenic bacteria, conditional pathogenic bacteria or common contaminating bacteria according to the microorganism name in the pathogen drug sensitivity report, and displays it in the reference information bar of the panel; S61c. The pathogenicity auxiliary judgment panel provides standardized judgment factor checkboxes for the pharmacist to check, and the judgment factors at least include that the patient has clear infection site symptoms or signs, the pathogen is consistent with the common pathogenic bacteria spectrum of the infection site, the inflammatory markers show significant increase before and after detection or change synchronously with the infection process, the imaging examination results support active infection, and the patient has not used effective antibiotics against the bacteria before this detection; S61d. The pathogenicity auxiliary judgment panel has a rule-based logical judgment engine built-in, which runs a predefined set of judgment rules according to the combination of judgment factors checked by the pharmacist, and the set of judgment rules is defined in the form of if factor combination, then judgment tendency; S61e. The logical judgment engine outputs the running result in the form of non-mandatory suggestion, which is displayed as a system auxiliary judgment prompt; S61f. The pharmacist refers to the system auxiliary judgment prompt, combines his own professional judgment, selects one of the suspected pathogenic bacteria, colonizing bacteria or contaminating bacteria in the pathogenicity preliminary judgment drop-down selection field, and completes manual confirmation and entry.

[0008] Further, in step S6, further comprising: S62a. The pharmacist activates the medication rationality auxiliary judgment panel linked with the antibacterial drug use details data area and the drug sensitivity result in the interface of the electronic tracking intervention record table; S62b, the medication rationality auxiliary judgment panel automatically compares the variety of the currently used antibacterial drugs with the drug sensitivity result in the pathogen drug sensitivity report, generates an initial drug sensitivity matching degree report, and identifies sensitive, intermediate, drug-resistant, or untested; S62c, the medication rationality auxiliary judgment panel accesses the integrated pharmacy knowledge base to obtain the standard administration scheme information of the currently used drugs, including the recommended dose range, administration interval, infusion requirements, and adjustment strategies for major organ dysfunction, and compares and displays the adjustment strategies with the extracted actual medical order data; S62d, the medication rationality auxiliary judgment panel guides pharmacists to perform step-by-step judgment: first, for drug selection, providing selection items to evaluate the matching degree of the current medication and the drug sensitivity result; second, for the administration scheme, providing selection items to evaluate the suitability of the dose, route, and frequency; third, comprehensive evaluation, providing selection items to evaluate the rationality of the overall treatment scheme; S62e, the medication rationality auxiliary judgment panel automatically generates an evaluation draft based on the selection of the pharmacist in the aforementioned step-by-step judgment and the recent inflammation index trend read from the infection dynamic indicator data area, the content including: drug selection based on / deviating from drug sensitivity results, the current administration scheme compared with the standard, and recent infection indicators showing effective / ineffective / uncertain treatment response; S62f, the pharmacist reviews and modifies the evaluation draft to form a final pharmacist evaluation text, fills in the pharmacist evaluation and suggestion data area, and synchronously selects the corresponding overall evaluation grade in the medication rationality preliminary judgment field.

[0009] Further, in step S5, the specific implementation is: In the permission management module of the medical information system, an independent application role matrix is defined for the drug-resistant bacteria tracking intervention platform; The application role matrix at least includes pharmacists, supervising physicians, and infection control officers, and each role is configured with read and write permissions for different data areas in the electronic tracking intervention record table; When the initial intervention case data package is generated, the system automatically instantiates the application role matrix according to the patient-physician association relationship and the rules for the infection control officers to manage the area, and binds the electronic tracking intervention record table with the instantiated role permissions.

[0010] Further, the method guarantees privacy and security during data flow, which is specifically embodied in: In steps S1 to S3, the patient identity identifier obtained is transmitted through a secure data bus in the hospital intranet, and the secure data bus uses a transport layer encryption protocol; The electronic tracking intervention record table generated in step S4 is stored in a logically independent database partition, access to the partition is controlled by the application role matrix, and the partition is physically or logically isolated from the production database; The multi-role collaborative tracking phase in step S6, all the add, delete and modify operations on the record table are recorded in real time into the operation audit log which is tamper-proof, and the log records the operator identity, time, and the state before and after the modification.

[0011] Further, in step S8, specifically comprising: The smart contract rules are defined in the form of executable scripts and deployed on the server side of the tracking intervention case database; Rule script one: define the data fields and aggregation calculation methods required for the extraction of pathogen distribution, specimen source composition, and drug resistance rate statistics in a specific period from the drug-resistant bacteria detection detail data area of the archived record table; Rule script two: define the logic of extracting and correlating the treatment outcome data of unreasonable drug use cases from the drug rationality preliminary judgment field and the treatment outcome field; Rule script three: define the fixed template for generating report documents, including cover, abstract, data analysis charts, typical case analysis, core drug warning points, and improvement suggestions format and content filling rules; When the periodic analysis task is triggered, the system executes the rule scripts one, two, and three in sequence, extracts the standardized data set after rule processing from the database, and fills it into the fixed template to finally generate a composite document containing text, tables, and charts.

[0012] Further, the method realizes the standardization and real-time sharing of pharmacist drug use guidance through the cooperation and data integration of the system, which is specifically embodied in: The electronic tracking intervention record table generated in step S4 serves as the unique and synchronized information carrier for all participating nodes, ensuring that pharmacists, physicians, and infection control officers make decisions and communicate based on real-time updated data; The standardized evaluation options and structured text input in the pharmacist evaluation and suggestion data area, as well as the suggestions generated through the auxiliary judgment panel, form a unified evaluation discourse system; In the multi-role collaborative phase of step S6, the pharmacist's evaluation and suggestions are presented to the supervising physician node in real time, and the physician's feedback and subsequent medical order adjustments are also recorded in real time; All archived record tables constitute a searchable case knowledge base, and other pharmacists can refer to the evaluation and suggestion logic in historical cases when dealing with the same drug-resistant bacteria or clinical situation.

[0013] Further, for the comparative evaluation of the system's running effect, the following evaluation dimensions are implemented: Operation evaluation step: After the design deployment, a structured questionnaire is issued to pharmacists and physicians using the system, focusing on the clarity of the system interface logic, the convenience of data acquisition, the efficiency of record filling, and the smoothness of multi-role collaborative processes, feedback is collected and the overall ease of use score is calculated; Effectiveness evaluation step: From the tracking intervention case database, the treatment outcome data of all closed records within a certain period after the application of the system is extracted, and compared with the historical data of cases tracked by traditional methods in the same period before the application of the system, the difference in the proportion of cases with infection treatment marked as cured or effective in the two groups of data is calculated and compared; Safety evaluation step: From the treatment process tracking log of the electronic tracking intervention record table, the number of adverse drug reactions or treatment-related adverse events recorded is identified and counted by text mining technology; the incidence of the above events in the same period before and after the application of the system is compared; Efficiency evaluation step: Obtain groups of drug-resistant bacteria infection patient cases before and after the application of the system, and compare and analyze the median changes of key efficiency indicators such as average hospitalization days and average antibiotic use course; Quality control index evaluation step: Retrieve hospital pharmacy quality control data, compare the numerical trends of hospital-wide antibiotic use intensity, microbiological submission rate, and antibiotic prescription pass rate in consecutive statistical periods before and after the application of the system, and analyze the change direction and amplitude.

[0014] According to the second aspect of the present application, the present application claims to protect a precision drug-resistant bacteria administration assistance analysis system, comprising: One or more processors; A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the precision drug-resistant bacteria administration assistance analysis method.

[0015] The present application discloses a precision drug-resistant bacteria administration assistance analysis method and system, which automatically collects and integrates patient clinical data and pathogen drug sensitivity reports by responding to microbiological submission events, generates a structured electronic tracking intervention record table; based on the pre-set distributed node rules, realize the multi-role real-time collaborative tracking and permission-controlled interaction of pharmacists, clinicians and hospital infection specialists; by integrating a standardized auxiliary judgment panel and a pharmacy knowledge base, guiding pharmacists to make structured and standardized judgments on pathogenicity and drug rationality; with the help of pre-set smart contract rules, batch analyze the archived cases, automatically generate standardized reports and drug warnings, and form a management closed loop through targeted pushing. The present application realizes the standardization and intelligentization of the whole process from information acquisition, collaborative intervention to data analysis and utilization, significantly improves the efficiency and quality of scientific management of antibacterial drugs. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A work flow chart of a precision drug-resistant bacteria administration assisting analysis method claimed by the embodiments of the present application; Figure 2 A second work flow chart of a precision drug-resistant bacteria administration assisting analysis method claimed by the embodiments of the present application; Figure 3 A third work flow chart of a precision drug-resistant bacteria administration assisting analysis method claimed by the embodiments of the present application; Figure 4 A fourth work flow chart of a precision drug-resistant bacteria administration assisting analysis method claimed by the embodiments of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0018] The terms first, second and third in the present application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with first, second and third can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of multiple is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited. All directional indications such as up, down, left, right, front, back, etc. in the embodiments of the present application are only used to explain the relative positional relationship, movement condition, etc. between the components in a certain posture, such as shown in the drawings, if the certain posture changes, the directional indications also change accordingly. In addition, the terms include and have as well as any transformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units not listed, or optionally includes other steps or units inherent to the process, method, product or device.

[0019] In this document, the term "comprising" is intended to mean that the listed features are present, but other features can also be present. The term "comprising" is intended to cover the embodiments "including", "including but not limited to", "having", "including at least", "including at least one of", "including one of", "including some of", and "including, but not limited to", as well as other variations thereof. The term "comprising" is intended to cover the embodiments "consisting of", "consisting essentially of", and "consisting of", as well as other variations thereof. The term "comprising" is intended to cover the embodiments "including", "including but not limited to", "having", "including at least", "including at least one of", "including one of", "including some of", and "including, but not limited to", as well as other variations thereof. The term "comprising" is intended to cover the embodiments "consisting of", "consisting essentially of", and "consisting of", as well as other variations thereof.

[0020] According to the first embodiment of the present application, with reference to Figure 1 The present application claims a precision drug-resistant bacteria administration assisting analysis method, comprising the following steps: S1, in response to the submission operation of the patient's microbial specimen, the test application form identification and patient identification of the patient are obtained in real time; S2, based on the patient identification, the patient's clinical data set is synchronously extracted; S3, when generating a pathogen drug sensitivity report containing drug resistance or special drug resistance markers, triggering an early warning event according to the test application form identification, and associating and binding the pathogen drug sensitivity report with the clinical data set, an initial intervention case data package is generated; S4, calling a standardized tracking intervention record table template, automatically filling each data item in the initial intervention case data package into the predefined field of the standardized tracking intervention record table template, and generating an electronic tracking intervention record table; S5, distributing the electronic tracking intervention record table to the work queue of the pharmacist, and based on the preset distributed node rule, synchronously granting the access and editing permission of the electronic tracking intervention record table to the designated physician node of the clinical department to which the patient belongs and the designated hospital infection management department specialist node; S6, after the electronic tracking intervention record table enters the editable state, the multi-role collaborative tracking stage is started; S7, after the patient's infection treatment cycle ends, the pharmacist triggers the closing operation in the electronic tracking intervention record table, marks the treatment outcome, changes the state of the electronic tracking intervention record table to the read-only archiving state, and stores it in the tracking intervention case database; S8, batch analyzing the archived record table in the tracking intervention case database, extracting key analysis dimension data from the archived record table through the preset smart contract rule, and the smart contract rule defines the field, format and logical judgment condition of data extraction.

[0021] The embodiment is applied to a specific application panorama of a tertiary hospital infectious disease department.

[0022] A patient Chen with cirrhosis with ascites was admitted to hospital due to fever and abdominal pain, and was clinically suspected of having spontaneous bacterial peritonitis. The attending physician issued an ascites culture + drug sensitivity test application through the hospital information system HIS. After receiving the specimen, the laboratory information system LIS generated a unique test application form identification and associated it with the patient's hospital number patient identification.

[0023] When LIS completes the culture and identifies E. coli 3GCR-EC resistant to the third-generation cephalosporin, and the drug sensitivity report is automatically marked as resistant, the built-in warning rule of the system is triggered. The HIS-LIS hospital information system-laboratory information system data interface captures this warning event accurately according to the test application sheet identification, and the system automatically extracts the complete clinical data set of Chen since his admission from HIS with the patient's hospital number as the index, including: demographic information, diagnosis of cirrhosis, daily body temperature records, white blood cell count, neutrophil percentage, inflammation marker C-reactive protein, procalcitonin sequence, abdominal ultrasound report abstract, and course records about abdominal pain and abdominal distension.

[0024] The system intelligently associates and binds the drug sensitivity report of 3GCR-EC with this clinical data set, packages an initial structured intervention case data package, and sends it to the drug-resistant bacteria tracking intervention platform. The platform calls the pre-defined standardized tracking intervention record table electronic template, automatically fills each item in the data package such as patient information, drug sensitivity results, and inflammation indicator data points into the corresponding preset fields of the template, and forms an initialized electronic tracking intervention record table.

[0025] According to the preset rules, the system automatically assigns this record table to the work queue of clinical pharmacist Zhang responsible for the infectious disease department on that day, and based on the distributed node rules, the system automatically identifies the supervisor of Chen as Li and the specialist of the infection control department responsible for this area as Wang, and synchronously grants these two nodes restricted access and editing permissions of the record table.

[0026] Pharmacist Zhang opens the record table and enters the multi-role collaborative tracking phase. She reviews the integrated data and enters the preliminary evaluation in the record table, while the computer of physician Li receives a prompt and can immediately view the pharmacist's evaluation and provide feedback, and the infection control specialist Wang can also view the whole process synchronously.

[0027] After a week of treatment and tracking, Chen's infection is controlled, pharmacist Zhang marks the treatment outcome as cured in the record table, and performs the closing operation, and the record table becomes read-only and is archived in the dedicated tracking intervention case database.

[0028] Every other quarter, the database automatically starts the analysis task, and the pre-set smart contract script starts running, extracting key analysis dimension data from all archived record tables according to the script-defined rules such as extracting all E. coli detection departments, specimen sources, and resistance rates to ceftriaxone, and calculating logic, to prepare for subsequent management report generation.

[0029] Further, the design and application of the standardized tracking intervention record table template in step S4 include: The standardized tracking intervention record table template adopts the form of an electronic table, and the interface is divided into a plurality of logical areas that are interlocked and associated; The patient basic information data area contains a ward, a hospital number, a name, an age, and a diagnosis information field. The drug-resistant bacteria detection details data area contains a submission date, a specimen type, a pathogen name, a drug resistance mechanism, and a key drug sensitivity result field, and is provided with a pathogenicity preliminary judgment drop-down selection field with options of suspected pathogenic bacteria, colonized bacteria, or contaminated bacteria. The infection dynamic indicator data area dynamically links the patient's time series data through an interface to display the change curves of body temperature, white blood cell count, C-reactive protein, and procalcitonin over time in the form of a chart, and provides a text field for linking or summarizing infection-related imaging and symptom descriptions. The antibacterial drug use details data area extracts from the medical order system, and lists the current and historical use of antibacterial drug names, doses, frequencies, administration routes, and time periods, and is provided with a drug rationality preliminary judgment field with options of reasonable, needing adjustment, or unreasonable. The pharmacist evaluation and suggestion data area contains a plurality of structured input components for recording the pharmacist's analysis of the matching degree of infection and drug use, specific drug adjustment suggestions, adjustment basis, and drug education points. The treatment process tracking log data area is a log panel arranged in reverse chronological order, which automatically records and displays the key operations and text notes of the patient's condition and treatment by pharmacists, doctors, and infection control officers, forming a complete audit tracking of the treatment process.

[0030] In this embodiment, the template is presented in the form of an interactive web form, and each area has clear logic and data linkage.

[0031] Patient basic information data area: located at the top of the form, it is an automatically filled read-only area that automatically displays the ward: infectious disease department, hospital number, name: Chen, main diagnosis: decompensated cirrhosis, spontaneous bacterial peritonitis, and other information, providing a background anchor point for the entire record.

[0032] Drug-resistant bacteria detection details data area: including LIS data automatically filled in submission date, specimen: ascites, pathogen: Escherichia coli, special drug resistance mechanism: ESBL positive, key drug sensitivity: ceftriaxone resistance, piperacillin-tazobactam sensitivity, ertapenem sensitivity. This area is provided with a pathogenicity preliminary judgment drop-down selection box with options of suspected pathogenic bacteria, colonized bacteria, and contaminated bacteria, waiting for the pharmacist to select manually in the follow-up.

[0033] Infection dynamic indicator data area: This area is designed as a dynamic visualization panel. The left side automatically draws the body temperature change curve and C-reactive protein trend graph by real-time calling the time series data in the patient's HIS through the API interface. The chart supports mouse hovering to view specific values. The right side has a text field that automatically links to the latest image examination report such as abdominal ultrasound: moderate amount of ascites and abstract key symptom description such as abdominal pain, rebound tenderness positive.

[0034] Antibacterial drug use details data area: dynamically lists all antibacterial drug orders since the submission date in table form, including drug name such as ceftriaxone, dose, administration frequency, route of intravenous infusion, start and stop time. The table also has a medication rationality preliminary judgment drop-down menu for pharmacists to choose reasonable, need to adjust or unreasonable.

[0035] Pharmacist evaluation and suggestion data area: This is the core work area of pharmacists, which includes multiple structured components: a multi-line text input box for infection and medication matching analysis; a group of check boxes for specific intervention suggestion options such as step-up therapy, step-down therapy, dose adjustment, and consultation suggestion; a text input box for adjustment basis such as drug sensitivity, guidelines, and consensus; and a text box for recording key points of medication education for patients.

[0036] Treatment process tracking log data area: Located at the bottom of the form, it is an automatically refreshed log window in reverse chronological order, each record contains timestamp, operation role and specific content, for example, the system automatically records the system date and time, automatically loads the patient's admission body temperature and CRP data, pharmacist Zhang: evaluates that the current ceftriaxone treatment may be ineffective, and suggests replacing it; physician Li: adopts the suggestion and has stopped using ceftriaxone, and replaces it with piperacillin-tazobactam. This log constitutes a complete and tamper-proof intervention audit tracking chain.

[0037] Further, with reference to Figure 2 , step S6 further comprises: S61a. The pharmacist activates the pathogenicity auxiliary judgment panel linked to the drug-resistant bacteria detection details data area and the infection dynamic indicator data area in the interface of the electronic tracking intervention record table; S61b. The panel first calls the pre-set pathogen knowledge base, automatically loads the microorganism as a common pathogenic bacteria, conditional pathogenic bacteria or common contaminant bacteria according to the microorganism name in the pathogen drug sensitivity report, and displays it in the reference information bar of the panel; S61c. The panel provides standardized evaluation factor checkboxes for the pharmacist to check, including at least that the patient has clear infection site symptoms or signs, the pathogen is consistent with the common pathogenic bacteria spectrum of the infection site, the inflammatory markers show significant increase before and after detection or change synchronously with the infection process, imaging examination results support active infection, and the patient has not used effective antibiotics against the bacteria before this detection; S61d. The panel has a rule-based logical judgment engine, which runs a predefined set of judgment rules according to the combination of evaluation factors checked by the pharmacist, and the judgment rules are defined in the form of if factor combination, then evaluation tendency; S61e. The logical judgment engine outputs the results in the form of non-mandatory suggestions, which are displayed as system-assisted evaluation prompts; S61f. The pharmacist refers to the system-assisted evaluation prompts, combines his own professional judgment, and selects one of the suspected pathogenic bacteria, colonizing bacteria or contaminating bacteria in the pathogenicity preliminary judgment drop-down selection field to complete manual confirmation and entry.

[0038] In this embodiment, it is shown how pharmacist Zhang uses the system to evaluate pathogenicity after receiving the report that Chen's ascitic fluid culture has 3GCR-EC.

[0039] Zhang clicks the pathogenicity auxiliary judgment button next to the resistant bacteria detection details area of the electronic record table to activate the special panel.

[0040] Knowledge base information loading: The panel sidebar first displays background information retrieved from the integrated knowledge base: Escherichia coli: normal flora in the intestine, also a common pathogenic bacteria of community and hospital acquired infections, especially abdominal and urinary system infections.

[0041] Standardized factor checking: The panel main body lists the evaluation factor checkboxes. Zhang checks the following according to Chen's condition: Patient has clear infection site symptoms or signs abdominal pain, fever, peritonitis signs.

[0042] The pathogen is consistent with the common pathogenic bacteria spectrum of the infection site Escherichia coli is the most common pathogen of spontaneous bacterial peritonitis.

[0043] Inflammatory markers show significant increase before and after detection or change synchronously with the infection process CRP is significantly elevated at admission.

[0044] Imaging examination results support active infection Ascitic fluid suggests the presence of an infectious basis.

[0045] The patient has not used effective antibiotics against the bacteria before this detection The patient has used ceftriaxone after admission, so this factor is not satisfied.

[0046] Rule engine inference and prompt: After checking, Zhang clicks analysis. The rule engine built-in panel runs the pre-defined logic, for example, a rule is triggered: if factors A, B, C, D are checked, and the infection site is a sterile site such as ascites, even if factor E does not meet the invalid drug used, it is still highly prompted as a pathogenic bacteria, and the engine outputs a non-mandatory suggestion: system assisted judgment prompt: based on the selected clinical indications, the 'Escherichia coli' is highly likely to be the 'pathogenic bacteria' of this abdominal infection.

[0047] The pharmacist finally confirms: Zhang refers to this prompt, and combines the key points of ascites being a sterile body fluid and single bacteria being detected, and finally manually selects and confirms the suspected pathogenic bacteria in the pathogenicity preliminary judgment drop-down menu of the record table main interface, which makes the pharmacist's implicit clinical thinking process explicit and structured.

[0048] Further, referring to Figure 3 , in step S6, further comprising: S62a, the pharmacist activates the medication rationality auxiliary judgment panel linked with the antibacterial drug use details data area and the drug sensitivity result in the interface of the electronic tracking intervention record table; S62b, the panel automatically compares the variety of the currently used antibacterial drug with the drug sensitivity result in the drug sensitivity report, generates an initial drug sensitivity matching degree report, and identifies sensitive, intermediate, resistant, or not tested; S62c, the panel accesses the integrated pharmaceutical knowledge base to obtain the standard dosing regimen information of the currently used drug, including the recommended dose range, dosing interval, infusion requirements, and adjustment strategies for major organ dysfunction, and compares and displays the adjustment strategies with the extracted actual medical order data; S62d, the panel guides the pharmacist to perform step-by-step evaluation: first, for drug selection, provide selection items to evaluate the matching degree of the current drug and the drug sensitivity result; second, for the dosing regimen, provide selection items to evaluate the appropriateness of the dose, route, and frequency; third, comprehensive evaluation, provide selection items to evaluate the rationality of the overall treatment regimen; S62e, the panel automatically generates an evaluation draft based on the pharmacist's selection in the aforementioned step-by-step evaluation and the recent inflammation index trend read from the infection dynamic indication data area, the content includes: drug selection based on / deviating from drug sensitivity result, current dosing regimen compared with standard, recent infection index showing effective / ineffective / uncertain treatment response; S62f, the pharmacist reviews and modifies the evaluation draft to form the final pharmacist evaluation text, fills in the pharmacist evaluation and suggestion data area, and synchronously selects the corresponding overall evaluation grade in the medication rationality preliminary judgment field.

[0049] Wherein, in this embodiment, after completing the pathogenicity evaluation, Zhang needs to make a rationality evaluation on the current ceftriaxone treatment plan. She clicks the drug rationality auxiliary evaluation button next to the drug details area.

[0050] The panel first displays the comparison results, with the drug ceftriaxone on the left side and the drug sensitivity results on the right side: drug resistance is usually identified in red, and other sensitive drug options are listed.

[0051] The panel retrieves the standard regimen of ceftriaxone for abdominal infection in the pharmacy knowledge base, such as dose range and dosing interval, and displays it side by side with the actual medical order extracted from the HIS, prompting whether the dose is within the conventional range.

[0052] The panel guides the three-step evaluation: The first step is drug selection: for ceftriaxone, it does not match the 3GCR-EC drug sensitivity results. She selects not matching; The second step is dosing regimen: assuming the dose is standard, she selects appropriate; The third step is comprehensive evaluation: given the clear pathogenic bacteria and drug sensitivity mismatch, she selects not reasonable and needs to be adjusted immediately; The evaluation draft is generated: the panel generates a draft based on the above choices and the trend of high CRP in the infection indicator area: the drug selection deviates from the drug sensitivity results, ceftriaxone is resistant, the current dosing regimen is appropriate. The recent infection indicator CRP has not decreased, indicating that the current treatment is ineffective.

[0053] The pharmacist refines and confirms: Zhang refines this draft into the final evaluation: ESBL-positive E. coli is detected, which is resistant to the currently used ceftriaxone, which is the main reason for the initial treatment failure. It is recommended to replace it with a sensitive drug such as piperacillin / tazobactam or ertapenem based on the drug sensitivity results. She fills this text into the pharmacist's evaluation and recommendation area of the main record table, and the system synchronously updates the drug rationality initial judgment field to unreasonable.

[0054] Further, referring to Figure 4 , in step S5, the specific implementation is: In the permission management module of the medical information system, an independent application role matrix is defined for the drug-resistant bacteria tracking intervention platform; The application role matrix at least includes three roles: pharmacist, supervisor physician, and infection control specialist. Each role is configured with read and write permissions for different data areas in the electronic tracking intervention record table; When the initial intervention case data package is generated, the system automatically instantiates the application role matrix based on the patient-physician association and the rules for the infection control specialist to manage the area, and binds the electronic tracking intervention record table with the instantiated role permissions.

[0055] In this embodiment, in the authority management background of the platform, an application role matrix named drug-resistant bacteria tracking intervention is defined by the administrator in advance.

[0056] The role and authority configuration includes: The pharmacist role: has read and write permissions for all data areas of the record table, including basic information, detection details, infection indications, medication details, evaluation suggestions, and tracking logs, and exclusively occupies the case closing operation button.

[0057] The supervising physician role: the authority is limited, and only read-only access to the pharmacist evaluation and suggestion data area is allowed. However, it has read and write permissions for a dedicated physician feedback field for responding to suggestions and can view the tracking log.

[0058] The infection control officer role: has read-only access to all data areas and read and write permissions for a dedicated infection marker field for marking infection types and whether it is clustered.

[0059] Automatic instantiation and binding: when the initial data packet of Chen is generated, the system automatically performs: a) According to the information of Chen's attending physician in the HIS, the physician account Li is instantiated as the supervising physician role.

[0060] b) According to the department-officer responsibility system rules preset by the infection control department, the officer account Wang is instantiated as the infection control officer role.

[0061] c) According to the pharmacist scheduling or professional grouping, the pharmacist Zhang is instantiated as the pharmacist role.

[0062] Subsequently, the system creates a permission binding record to associate the electronic tracking intervention record table of Chen with the three instantiated role instances. Thereafter, any access request must pass through this matrix verification to ensure data security and clear responsibilities.

[0063] Further, the method guarantees privacy security during data flow, which is embodied in: In steps S1 to S3, the patient identity obtained is transmitted through a secure data bus in the hospital intranet, and the secure data bus uses a transmission layer encryption protocol; The electronic tracking intervention record table generated in step S4 is stored in a logically independent database partition, and the access to this partition is controlled by the application role matrix and is physically or logically isolated from the production database; In the multi-role collaborative tracking phase in step S6, all add, delete, and modify operations on the record table are recorded in real time in an operation audit log that cannot be tampered with, which records the operator's identity, time, and the state before and after the modification content.

[0064] In this embodiment, when transmitting the patient identity from the HIS, LIS system to the tracking intervention platform server, a special medical information exchange bus of the hospital internal network is used. This bus compulsorily enables the transmission layer encryption protocol, and encrypts all the transmitted data packets to prevent eavesdropping or interception during transmission.

[0065] The generated electronic tracking intervention record table is not stored in the business core database of the HIS or LIS, but is saved in a physically independent or logically strictly partitioned clinical decision support database. The access entry of the database is unique, and is compulsorily verified through the application role matrix described in the embodiment. This achieves effective isolation from the production business system, and reduces the risk of irregular access to core data.

[0066] The platform records all user operations in full. When the pharmacist Zhang chooses the suspected pathogenic bacteria as the pathogenic preliminary judgment, or the physician Li inputs text in the physician feedback field, the operation audit log table in the system background immediately adds a new record. The log entry includes: accurate timestamp, unique login ID of the operator, record table ID being operated, field name being modified, value before modification, and value after modification. This log adopts an append-only design, and anyone cannot delete or modify it, forming an unalterable evidence chain for subsequent tracing, quality review, and dispute clarification.

[0067] Further, in step S8, specifically comprising: The smart contract rule is defined in the form of an executable script, and is deployed on the server side of the tracking intervention case database; Rule script one: defining the data fields and aggregation calculation methods required for extracting the pathogen distribution, specimen source composition, and drug resistance rate statistics in a specific period from the drug-resistant bacteria detection detail data area of the archived record table; Rule script two: defining the logic of extracting and correlatively analyzing the treatment effect outcome data of the unreasonable drug use cases from the drug use rationality preliminary judgment field and the treatment outcome field; Rule script three: defining the fixed template of the report document, including the format and content filling rules of the cover, abstract, data analysis chart, typical case analysis, core drug warning points, and improvement suggestions; When the periodic analysis task is triggered, the system sequentially executes the rule scripts one, two, and three, extracts the standardized data set after rule processing from the database, and fills it into the fixed template, to finally generate a composite document containing text, tables, and charts.

[0068] In this embodiment, a plurality of executable scripts as smart contracts are deployed on the server of the tracking intervention platform.

[0069] Rule script one: data extraction and aggregation. This script defines how to extract the last quarter's data from the archived record table's drug-resistant bacteria detection details data area. For example, it instructs the database to find all records with pathogen names containing Escherichia coli and drug resistance mechanisms containing ESBLs; count the number of detections in each department according to the ward field; and calculate the drug resistance rates for drugs such as ceftriaxone, piperacillin-tazobactam, and carbapenems. The results are output as standardized data objects.

[0070] Rule script two: association analysis. This script performs in-depth analysis by associating drug use rationality preliminary judgment and treatment outcome fields. For example, it finds all cases where the preliminary judgment is unreasonable and analyzes the distribution proportion of these cases in terms of final cure, invalidity, or death. It calculates the clinical consequences of unreasonable drug use. The script may also analyze the rate at which physicians adopt pharmacists' recommendations, and the analysis results are incorporated into data objects.

[0071] Rule script three: report synthesis. This script connects a pre-made Word report template. The template has fixed-format sections and placeholders, such as {esbl_ecoli_distribution_by_ward}, {antibiotic_resistance_trend_chart}, and {typical_case_summary}. The script reads the aforementioned data objects, converts the data into corresponding charts and tables, selects 1-2 desensitized cases with educational significance, and writes a brief analysis. Finally, the script replaces all placeholders in the template with these contents, automatically synthesizing a quarterly ESBL Escherichia coli monitoring and clinical intervention analysis report with graphs and tables.

[0072] Further, the method realizes the standardization and real-time sharing of pharmacists' medication guidance through the synergy and data integration of the system, which is specifically embodied in: The electronic tracking intervention record table generated in step S4 serves as the unique and synchronized information carrier for all participating nodes, ensuring that pharmacists, physicians, and infection control officers make decisions and communicate based on real-time updated data; The standardized evaluation options and structured text input in the pharmacist evaluation and recommendation data area, as well as the recommendations generated through the medication rationality auxiliary judgment panel, form a unified evaluation discourse system; In the multi-role collaboration phase of step S6, the pharmacist's evaluation and recommendations are presented to the supervising physician node in real time, and the physician's feedback and subsequent medication adjustments are also recorded in real time; All archived record tables constitute a searchable case knowledge base, which other pharmacists can refer to when dealing with the same drug-resistant bacteria or clinical situations.

[0073] In this embodiment, during Chen's treatment process, the same real-time updated electronic record table is viewed and discussed by pharmacist Zhang, physician Li, and infection control officer Wang. The suggestions just entered by Zhang are immediately visible on Li's computer; Li's feedback and medical order changes are also fed back to the log of the record table in real time. This eliminates the delay and ambiguity of information transmission.

[0074] By assisting the evaluation panel and structured input fields such as reasonable / need adjustment / irrational, the pharmacists are guided to use standardized clinical thinking paths and concluding language. Regardless of which pharmacist handles Chen's case, the analysis process will cover core links such as pathogenicity judgment, drug sensitivity matching, and efficacy evaluation under the guidance of the system, and the structure and key language of the output evaluation tend to be consistent, reducing fluctuations in work quality caused by individual habits and seniority differences.

[0075] The traditional pharmaceutical intervention mode may have a lag. In this system, after Zhang submits the evaluation suggestions, the HIS interface of physician Li receives a prompt in real time, realizing instant communication between pharmacists and physicians. This close collaboration enables the pharmacist's suggestions to directly and quickly influence the physician's immediate decision-making, moving the intervention window forward and improving the timeliness and effectiveness of the intervention.

[0076] All record tables that are closed and archived like Chen's case constitute a valuable real-world case library. When new or rotating pharmacists encounter a case of 3GCR-EC with cirrhosis and ascites, they can search for similar historical cases in the system. They can directly see how their predecessors analyzed and judged, what suggestions they made, how physicians responded, and the final efficacy. This structured case-based learning greatly promotes the efficient and homogeneous inheritance of knowledge and experience.

[0077] Further, the comparative evaluation of the system operation effect includes the following implementation steps of evaluation dimensions: Operational evaluation steps: After design and deployment, structured questionnaires are sent to the pharmacist and physician groups using the system, focusing on the clarity of the system interface logic, the convenience of data acquisition, the efficiency of record filling, and the smoothness of multi-role collaboration processes. Feedback is collected and the overall ease of use score is calculated; Effectiveness evaluation steps: From the tracked intervention case database, extract the treatment outcome data of all closed records in a specific period after the system is applied, and compare it with the historical data of cases tracked by traditional methods in the same period before the system is applied. The difference in the proportion of cases marked as cured or effective in the two groups of data is calculated and compared. Safety evaluation step: From the treatment process tracking log of the electronic tracking intervention record form, identify and count the number of mentions of drug adverse reactions or treatment-related adverse events recorded by text mining technology; compare the incidence of the above events before and after the application of the system in the same period; Efficiency evaluation step: Obtain comparable drug-resistant bacteria infection patient case groups before and after the application of the system, and compare and analyze the median changes of key efficiency indicators such as average hospitalization days and average antibiotic use course; Quality control index evaluation step: Retrieve hospital pharmaceutical quality control data, compare the numerical trends of hospital-wide antibiotic use density, microbiological submission rate, and antibiotic prescription pass rate in consecutive statistical periods before and after the application of the system, and analyze the direction and amplitude of the changes.

[0078] In this embodiment, after the system has been fully online for one year, how does the hospital management department scientifically and multi-dimensionally evaluate its effectiveness?

[0079] The hospital information department and the pharmacy department jointly design an online questionnaire. The questionnaire is distributed to all clinical pharmacists who have used the system and clinical physicians who frequently interact. The questionnaire focuses on user experience, such as including statements that it is more convenient to obtain drug-resistant bacteria patient information through the system than in the past, the automatic filling function of the record form significantly reduces my clerical work time, and the efficiency of collaborative work with pharmacists / doctors through the system is higher, etc. The feedback is collected using an agreement scale. Statistical analysis is performed on all returned questionnaires to obtain a subjective evaluation report on the ease of use and efficiency improvement of the system.

[0080] The research team sets clear inclusion and exclusion criteria. From the tracking intervention case database, all drug-resistant bacteria infection case records that meet the criteria and have been closed within one year after the system goes online are exported. At the same time, from the historical medical records archives, cases that meet the same criteria within one year before the system goes online are collected manually and retrospectively as a control group. The core efficacy observation indicator is the clinical treatment success rate, which is calculated based on the cured and effective outcomes marked in the record form. Using standardized statistical methods, compare whether there is a significant difference in the treatment success rate between the two groups of cases to objectively evaluate whether the intervention assisted by the system has improved patient outcomes.

[0081] The evaluators use natural language processing tools to text mine the treatment process tracking logs of all archived record forms within one year after the system goes online, screen for keywords related to drug adverse reactions such as skin rash, liver function abnormalities, kidney function impairment, and allergic reactions, and manually review and confirm the exact number of drug adverse events. The same method is used to retrospectively analyze the course of disease records, nursing records, and other documents of the control group. Compare the incidence of drug adverse events between the two groups of cases to evaluate the impact of the system on medication safety after strengthening the whole process of monitoring.

[0082] From the hospital medical record system, the hospitalization information of drug-resistant bacteria infection patients in the two control groups before and after the application of the system is obtained respectively. The total days of antibacterial drug use and the total days of hospitalization are extracted as two key efficiency indicators. Since such data usually do not conform to normal distribution, median and interquartile range are used for descriptive statistics, and appropriate non-parametric test methods are used to compare whether the two medians change significantly before and after the application of the system, so as to analyze whether the system saves medical resources and time cost by optimizing the treatment path.

[0083] From the annual quality control report of the hospital pharmacy management and pharmacotherapy committee, the hospital-wide antibacterial drug management core indicators in the consecutive quarters before and after the system goes online are extracted, for example, the four quarters before the system goes online and the four quarters after the system goes online. Mainly include: antibacterial drug use intensity, antibacterial drug use rate of inpatients, and pathogen detection rate before therapeutic use of antibacterial drugs in inpatients. These indicators are plotted into a continuous trend line chart according to the time sequence, which intuitively shows the change trajectory and fluctuation of the indicators before and after the introduction of the system, and comprehensively evaluates the long-term contribution of the system to improving the overall scientific management level of antibacterial drugs in the hospital.

[0084] According to the second embodiment of the present application, a drug-resistant bacteria precise drug administration auxiliary analysis system is claimed, comprising: one or more processors; a memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, so that the one or more processors implement the drug-resistant bacteria precise drug administration auxiliary analysis method.

[0085] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the contents of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0086] The specific embodiments of the application are described in detail above, but they are only examples. The present application is not limited to the above described specific embodiments. Any equivalent modification or substitution of the present application by those skilled in the art is also within the scope of the present application, therefore, any equivalent transformation and modification, improvement, etc. made without departing from the spirit and principle range of the present application should be covered within the scope of the present application.

Claims

1. A precision drug-resistant bacteria administration assistance analysis method, characterized in that, The method comprises the following steps: S1, in response to the submission operation of the inpatient microbial specimen, the test application form identification and patient identification of the patient are acquired in real time; S2, based on the patient identification, the clinical data set of the patient is synchronously extracted; S3, when a pathogen drug sensitivity report containing drug resistance or special drug resistance markers is generated, a warning event is triggered according to the test application form identification, the pathogen drug sensitivity report is associated and bound with the clinical data set, and an initial intervention case data package is generated; S4, a standardized tracking intervention record table template is called, each data item in the initial intervention case data package is automatically filled into the pre-defined field of the standardized tracking intervention record table template, and an electronic tracking intervention record table is generated; S5, the electronic tracking intervention record table is distributed to the work queue of the pharmacist, and based on the preset distributed node rule, the access and editing permission of the electronic tracking intervention record table is synchronously granted to the designated doctor node of the clinical department to which the patient belongs and the designated hospital infection management department specialist node; S6, after the electronic tracking intervention record table enters the editable state, the multi-role collaborative tracking stage is started; S7, after the infection treatment cycle of the patient ends, the pharmacist triggers the closing operation in the electronic tracking intervention record table, marks the treatment outcome, changes the state of the electronic tracking intervention record table to the read-only archiving state, and stores it into the tracking intervention case database; S8, the archived record table in the tracking intervention case database is analyzed in batches, and the key analysis dimension data is extracted from the archived record table through the preset smart contract rule, and the smart contract rule defines the field, format and logical judgment condition of data extraction.

2. The method of claim 1, wherein, The design and application of the standardized tracking intervention record table template in step S4 comprises: The standardized tracking intervention record table template adopts an electronic form, and the interface is divided into a plurality of interlocked and associated logical areas; The patient basic information data area includes ward, hospitalization number, name, age, and diagnosis information fields; The drug-resistant bacteria detection detail data area includes submission date, specimen type, pathogen name, drug resistance mechanism, and key drug sensitivity result fields, and is provided with a pathogenicity preliminary judgment drop-down selection field with options of suspected pathogenic bacteria, colonized bacteria or contaminated bacteria; The infection dynamic indication data area dynamically links the patient time series data through an interface, displays the temperature, white blood cell count, C-reactive protein and procalcitonin change curves with time in a chart form, and provides a text field for linking or summarizing infection related imaging and symptom description; The antibacterial drug use detail data area is extracted from the medical order system, and the current and historical antibacterial drug names, doses, frequencies, administration routes, start and end times are displayed in a list, and a drug rationality preliminary judgment field is provided, with options of reasonable, need to adjust or unreasonable; The pharmacist evaluation and suggestion data area comprises a plurality of structured input components for recording the pharmacist's analysis of the matching degree of infection and drug use, specific drug adjustment suggestions, adjustment basis and drug education points. The treatment process tracking log data area is a log panel arranged in reverse chronological order, which automatically records and displays the key operations and text notes on the patient's condition and treatment by pharmacists, doctors, and infection control officers, forming a complete audit tracking of the treatment process.

3. The method of claim 2, wherein, Step S6 further comprises: S61a. The pharmacist activates the pathogenicity auxiliary judgment panel linked to the drug-resistant bacteria detection details data area and the infection dynamic indication data area in the interface of the electronic tracking intervention record table; S61b. The pathogenicity auxiliary judgment panel first calls the preset pathogen knowledge base, automatically loads the microorganism as a common pathogenic bacteria, conditional pathogenic bacteria or common contaminant bacteria according to the microorganism name in the pathogen drug sensitivity report, and displays it in the reference information bar of the panel; S61c. The pathogenicity auxiliary judgment panel provides a standardized evaluation factor checkbox for the pharmacist to check, and the evaluation factor at least includes that the patient has a clear infection site symptom or sign, the pathogen is consistent with the common pathogenic bacteria spectrum of the infection site, the inflammatory markers show an increase before and after detection or change synchronously with the infection process, the imaging examination result supports active infection, and the patient has not used effective antibiotics against the bacteria before this detection; S61d. The pathogenicity auxiliary judgment panel has a rule-based logical judgment engine built-in, which runs a predefined set of judgment rules according to the combination of evaluation factors selected by the pharmacist, and the set of judgment rules is defined in the form of if factor combination, then evaluation tendency; S61e. The logical judgment engine outputs the running result in the form of non-mandatory suggestion, which is displayed as a system auxiliary judgment prompt; S61f. The pharmacist refers to the system auxiliary judgment prompt, combines his own professional judgment, selects one of the suspected pathogenic bacteria, colonizing bacteria or contaminant bacteria in the pathogenicity preliminary judgment drop-down selection field, and completes manual confirmation and entry.

4. The method of claim 2, wherein, In step S6, further comprising: S62a. The pharmacist activates the medication rationality auxiliary judgment panel linked to the antibacterial drug use details data area and the drug sensitivity result in the interface of the electronic tracking intervention record table; S62b. The medication rationality auxiliary judgment panel automatically compares the variety of the currently used antibacterial drug with the drug sensitivity result in the pathogen drug sensitivity report, generates an initial drug sensitivity matching degree report, and identifies sensitive, intermediate, resistant or not tested; S62c. The medication rationality auxiliary judgment panel accesses the integrated pharmaceutical knowledge base to obtain the standard drug administration scheme information of the currently used drug, including the recommended dose range, drug administration interval, infusion requirements and adjustment strategies for major organ dysfunction, and displays the adjustment strategies and the extracted actual medical order data side by side; S62d. The medication rationality auxiliary judgment panel guides the pharmacist to make a step-by-step evaluation: first, for drug selection, it provides selection items to evaluate the matching degree of the current medication and the drug sensitivity result; second, for drug administration scheme, it provides selection items to evaluate the suitability of dose, route and frequency; third, comprehensive evaluation, it provides selection items to evaluate the rationality of the overall treatment scheme; S62e, the drug rationality auxiliary evaluation panel automatically generates an evaluation draft based on the choices of the pharmacist in the previous step-by-step evaluation and the recent inflammation indicator trend read from the infection dynamic indicator data area, including: drug selection based on / deviating from drug sensitivity results, current drug regimen compared to standards, and recent infection indicators showing effective / ineffective / uncertain treatment response; S62f, the pharmacist reviews and modifies the evaluation draft to form a final pharmacist evaluation text, fills in the pharmacist evaluation and suggestion data area, and synchronously selects the corresponding overall evaluation level in the drug rationality preliminary judgment field.

5. The method of claim 1, wherein, In step S5, the specific implementation is: In the permission management module of the medical information system, an independent application role matrix is defined for the drug-resistant bacteria tracking intervention platform; The application role matrix at least includes three roles of pharmacist, supervising physician, and infection control officer, and each role is configured with read and write permissions for different data areas in the electronic tracking intervention record table; When the initial intervention case data package is generated, the system automatically instantiates the application role matrix based on the patient-physician association and the rules for the division of the infection control officer's area of responsibility, and binds the electronic tracking intervention record table with the instantiated role permissions.

6. The method of claim 5, wherein, The method guarantees privacy and security during data flow, which is specifically embodied in: In steps S1 to S3, the patient's identity is transmitted through a secure data bus within the hospital network, and the secure data bus uses a transport layer encryption protocol; The electronic tracking intervention record table generated in step S4 is stored in a logically independent database partition, and the access to this partition is controlled by the application role matrix and is physically or logically isolated from the production database; In the multi-role collaborative tracking phase in step S6, all operations of adding, deleting, and modifying the record table are recorded in real time in the operation audit log, which records the operator's identity, time, and the state before and after the modification.

7. The method of claim 1, wherein, In step S8, specifically includes: The smart contract rules are defined in the form of executable scripts and deployed on the server side of the tracking intervention case database; Rule script one: define the data fields and aggregation calculation methods required for pathogen distribution, specimen source composition, and drug resistance rate statistics within a specific period from the drug-resistant bacteria detection details data area of the archived record table; Rule script two: define the logic for extracting and correlatively analyzing the treatment outcome data of unreasonable drug use cases from the drug rationality preliminary judgment field and the treatment outcome field; Rule script three: define the fixed template for generating report documents, including cover, abstract, data analysis charts, typical case analysis, core drug warning points, and improvement suggestions in terms of format and content filling rules; When the periodic analysis task is triggered, the system executes the rule scripts one, two, and three in sequence to extract the standardized data set after rule processing from the database, and fills it into the fixed template to finally generate a composite document containing text, tables, and charts.

8. The method of claim 4, wherein, The method realizes the standardization and real-time sharing of pharmacist drug guidance through system collaboration and data integration, which is specifically embodied in: The electronic tracking intervention record table generated in step S4 is a unique and synchronized information carrier for all participating nodes, ensuring that pharmacists, physicians, and infection control officers make decisions and communicate based on real-time updated data; The standardized evaluation options and structured text input in the pharmacist evaluation and suggestion data area, as well as the suggestions generated by the drug use rationality auxiliary judgment panel, form a unified evaluation discourse system; In the multi-role collaboration phase of step S6, the pharmacist's evaluation and suggestion is presented to the supervising physician node in real time, and the physician's feedback and subsequent order adjustment is also recorded in real time; All archived record tables constitute a searchable case knowledge base, which can be referred to by other pharmacists when dealing with the same drug-resistant bacteria or clinical situation.

9. The method of claim 1, wherein, For comparative evaluation of system operation effect, the following evaluation steps are included: Operational evaluation step: After design and deployment, a structured questionnaire is sent to the pharmacist and physician groups using the system, focusing on the clarity of the system interface logic, the convenience of data acquisition, the efficiency of record filling, and the smoothness of the multi-role collaboration process. Collect feedback and calculate the overall ease of use score; Effectiveness evaluation step: From the tracking intervention case database, extract the treatment outcome data of all closed records in a specific period after the system is applied, and compare it with the historical data of cases tracked by traditional methods in the same period before the system is applied. Calculate and compare the proportion of cases marked as cured or effective in the two groups of data; Safety evaluation step: From the treatment process tracking log of the electronic tracking intervention record table, identify and count the number of adverse drug reactions or treatment-related adverse events recorded in the text mining technology; Compare the incidence of the above events in the same period before and after the application of the system; Efficiency evaluation step: Obtain groups of drug-resistant bacteria infection patient cases before and after the application of the system, and compare the median changes of key efficiency indicators such as average hospitalization days and average antibiotic use course; Quality control index evaluation step: Retrieve hospital pharmacy quality control data and compare the numerical trends of hospital-wide antibiotic use intensity, microbiological submission rate, and antibiotic prescription pass rate in consecutive statistical periods before and after the application of the system. Analyze the change direction and amplitude.

10. A precision drug-resistant bacteria administration assistance analysis system, characterized in that, Comprise: One or more processors; A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, so that the one or more processors implement a drug-resistant bacteria precise drug administration auxiliary analysis method according to any one of claims 1 to 9.

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