76 results about "Clinical pathway" patented technology

The invention provides a knowledge and data fused medical content image-text generation system and method. Relates to the technical field of digital medical treatment and artificial intelligence. The multi-modal feature extraction module is used for extracting multi-modal features from the multi-modal data; the multi-modal feature fusion module is used for carrying out cross-modal dynamic fusion on the multi-modal features to generate cross-modal high-consistency fusion features; the model training optimization module is used for configuring a soft and hard target loss function and a parameter optimizer; the information analysis and reasoning module is used for carrying out semantic matching on the fusion features, the disease classification and the clinical path based on a dynamic medical knowledge network, and generating disease prediction probability distribution and a semantic reasoning path; and the image-text report generation module is used for generating an image-text report containing a diagnosis conclusion and an interpretation basis according to the prediction probability and the semantic reasoning path. According to the invention, intelligent and precise generation of medical contents can be realized.

The invention discloses a knowledge graph-based cerebral hemorrhage clinical pathway intelligent recommendation method and system, and the method comprises the steps: constructing a pathway knowledge graph and an illness state grading system through analyzing a diagnosis and treatment guide, recognizing a key node of pathway execution and an illness state grade switching boundary, and configuring a differentiation decision rule for a single illness state region and a mixed illness state region; the method comprises the following steps of: performing dimensionalization acquisition on multi-source illness state data of a patient, extracting illness state characteristics, monitoring a dynamic index change trend, generating illness state description through weighted characteristic extraction and dynamic early warning analysis, and calculating a matching degree between a current illness state and a candidate path; when path execution deviation is detected, a deviation source node is positioned through cross-stage transitivity analysis and reverse tracing, and a targeted path correction instruction is generated; normativity of an adjustment scheme is ensured through node integrity verification and forced constraint correction, a clinical pathway recommendation result matched with the actual illness state of the patient is output, and adaptability and accuracy of clinical pathway recommendation are improved.

The invention discloses an auxiliary decision-making system and method based on a clinical pathway, and relates to the technical field of clinical medicines.The system comprises a patient indication collection module, a clinical pathway simulation module, a pathway diagnosis and treatment analysis module and a pathway auxiliary decision-making module, and the patient indication collection module and the clinical pathway simulation module are arranged. Multi-dimensional medical indication analysis and prediction are carried out on all patients in the early stage of clinical path making, so that a clinical path simulation model conforming to the actual diagnosis and treatment conditions of the patients is constructed, a path diagnosis and treatment analysis module and a path auxiliary decision module are arranged, deep analysis is carried out on a plurality of clinical simulation paths generated by the clinical path simulation model, and a path diagnosis and treatment result is obtained. Each clinical simulation path is subjected to multi-stage disassembly analysis from the two aspects of diagnosis and treatment payment and diagnosis and treatment expression, so that an auxiliary decision-making clinical path conforming to medical resource configuration and clinical diagnosis and treatment effects is selected, and the accuracy and personalization of medical services are guaranteed.

The invention relates to the technical field of natural language processing, and discloses a diagnosis and treatment path prediction method based on time sequence knowledge graph completion, which comprises the following steps: acquiring a diagnosis and treatment knowledge graph snapshot sequence; dividing the snapshot sequence of the diagnosis and treatment knowledge graph into long-term, middle-term and short-term diagnosis and treatment information sub-graphs according to the time sequence and the clinical path characteristics, and performing structure coding on the three types of diagnosis and treatment information sub-graphs to obtain diagnosis and treatment information graph structure representations of different time scales; performing weighted fusion on the diagnosis and treatment information graph structure representations of different time scales to obtain a diagnosis and treatment information query representation; and inputting the diagnosis and treatment information query representation into a scoring function to score and sort all candidate tail diagnosis and treatment information entities to obtain a diagnosis and treatment path prediction result. According to the invention, the accuracy and robustness of complementing the complex application task for the diagnosis and treatment path can be improved.

The invention belongs to the technical field of medical treatment, and discloses an auxiliary decision-making method for accelerating rehabilitation surgery clinical path multi-decision multi-objective optimization. The method comprises the following steps: constructing a data set of preoperative states and medical decisions of a patient; unsupervised clustering is adopted, the clustering method is beneficial to dividing the patients into different phenotypes and risk levels according to unique features of the patients, and the obtained clusters provide valuable insights for heterogeneity of patient groups. And then, according to the decision data of each clustering phenotype, constructing a Bayesian network model, and adding some regulations in the construction process to trim the network, so that the constructed Bayesian network model better accords with clinic. And finally, inferring the state posterior probability of each node by using the Bayesian network, and inferring a decision scheme of each risk group by using the support degree so as to accelerate the postoperative rehabilitation process of the patient, reduce the hospitalization time of the patient and reduce the hospitalization cost.

The invention discloses a clinical pathway monitoring method and system based on a DRG / DIP payment mode, and the method comprises the steps: determining the disease type of a target patient, and formulating a medical insurance version clinical pathway form for the target patient based on the disease type; determining a diagnosis and treatment item coefficient of each clinical pathway in the medical insurance version clinical pathway form through the historical treatment data, and optimizing the medical insurance version clinical pathway form according to the diagnosis and treatment item coefficients; obtaining related diagnosis and treatment opinions of professional doctors for the optimized medical insurance version clinical pathway form, and determining a final implementation clinical pathway according to the related diagnosis and treatment opinions; and acquiring a path progress monitoring index, a path quality monitoring index and a path cost monitoring index of the final implementation clinical path, and monitoring the medical process of the target patient according to the path progress monitoring index, the path quality monitoring index and the path cost monitoring index. Medical insurance cost index and clinical path management double-index system monitoring is created, hospitals are assisted in controlling unreasonable increase of medical cost, and medical quality is guaranteed.

The invention belongs to the technical field of artificial intelligence and medical information processing, and particularly relates to a medical text big data mining system based on natural language processing, which comprises a medical field pre-training module, a text structured extraction engine module, a knowledge graph guide reasoning module, a clinical path optimization module and a medical question and answer and literature mining platform module, according to the system, a self-adaptive hierarchical attention mechanism is adopted, and medical texts of any length are processed through dynamic fusion of local attention and global attention; constructing an entity dependency relationship graph and coding semantic association between entities by adopting a graph neural network; introducing a medical ontology knowledge graph to carry out conditional random field joint reasoning, and correcting unreasonable annotations; the optimal diagnosis and treatment mode is mined and recognized through the frequent mode, the entity recognition F1 value reaches 96.2%, the long-distance relation recognition accuracy reaches 91%, the structured data accuracy reaches 93.7%, the data preparation time is shortened by 85%, and the document review efficiency is improved by 320%.

The present disclosure introduces an AI-based system (100) and method for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease (COPD) over three months. The system interfaces with consumer devices through REST APIs, ensuring secure communication via HTTPS. Client modules collect and pre-process raw data for integrity and compatibility. This data is then sent to the server (104) which is then transmitted to Input module, the input module (112) authenticates and validates it. The module processes personal details, respiratory history, allergies, vaccination and medication history, and demographic data for air quality analysis. The prediction module (116) uses a CART Model with 90% accuracy to evaluate COPD risk. Based on predefined thresholds, the clinical pathway module (118) and risk prediction response module (120) offer personalized treatment protocols and clinical decision support. The system complies with ISO 13485 standards for safety and reliability.

The invention discloses a digital management system for the health state of a patient after an anorectal operation, and relates to the technical field of digital management of the health state after the operation. Comprising a data acquisition and processing module which is used for calling an anorectal postoperative clinical path rule engine based on real-time symptom data input by a patient and the number of postoperative days, and dynamically generating and pushing a corresponding structured interactive data acquisition sequence; and the feature extraction module is used for carrying out real-time feature extraction on a postoperative recovery related image provided by a patient through a preset image analysis model at a patient terminal side so as to generate a multi-modal structured data set containing time sequence physiological indexes and image feature vectors. According to the method, clinical priori knowledge is fused through the multi-modal neural network, a compound and atypical early complication mode is deeply recognized from time sequence physiological indexes and image features, the early discovery rate of complication is increased, and the false alarm rate caused by conventional recovery variation is reduced through explainable risk contribution factor output.

The invention relates to a flora transplantation clinical pathway standardization management method, device and equipment and a medium. The method comprises the following steps: on the basis of clinical data of a patient and screening data of a donor, executing flora suitability evaluation by using a deep learning model and selecting an adaptive donor; obtaining a flora sample of an adaptive donor, and carrying out high-throughput sequencing; generating a donor flora quality evaluation score according to a preset flora composition diversity index based on a sequencing result; screening the flora samples of which the quality evaluation scores are higher than a preset quality threshold value to prepare a flora preparation; detecting activity parameters and diversity parameters of the flora preparation, and performing colonization potential classification prediction by combining clinical data of the patient and adopting a support vector machine algorithm so as to output a potential colonization success rate; and when the potential colonization success rate exceeds a preset success rate threshold, outputting a clinical transplantation scheme through a preset transplantation scheme generation algorithm based on the flora preparation and the clinical data of the patient. According to the method, through multi-stage intelligent decision making, the safety and the effectiveness are improved.

The invention relates to the technical field of medical informatization, in particular to a clinical aid decision-making optimization method, device and equipment based on guide knowledge, and the method comprises the steps: obtaining electronic medical record data from a hospital information system and a laboratory information management system communication system, data standardization is achieved through the steps of duplicate removal, missing value processing, abnormal value processing, format standardization and the like; according to authoritative knowledge such as clinical pathways, diagnostic guidelines and disease consensus provided by doctors, key feature information is extracted by utilizing a natural language processing technology, structured data is extracted by adopting a regular expression, and unstructured texts are marked by pre-training a medical word vector dictionary, performing word segmentation by a classifier and performing part-of-speech tagging. The weight is optimized based on the loss function; an initial case knowledge base is constructed, and high-quality cases are screened out according to evaluation indexes such as knowledge richness and curative effect; dividing the high-quality cases into well-known physician cases and rare disease cases through syntactic analysis according to the classification indexes; case matching is carried out by adopting selective weighted heterogeneous value distance measurement, discrete attributes adopt value difference measurement, continuous attributes adopt standardized difference square, and attribute weights are optimized by means of a genetic algorithm, so that auxiliary clinical decision and knowledge services are realized. The invention can provide a scheme which can efficiently construct a high-quality case library, automatically extract key feature information, fuse guide knowledge for classification and screening, and introduce more accurate measurement and weight optimization in matching.

The invention discloses an accurate value incentive accounting method for a DIP disease category subdivision value portrait under DRG, and belongs to the technical field of medical payment and hospital management, and the method comprises the following steps: 1) a data fusion model: integrating DRG grouped data, a DIP score, a clinical path execution rate, cost accounting and patient satisfaction data, and constructing economic, clinical and social three-dimensional value portraits; 2) dynamic grouping optimization: introducing a machine learning algorithm, and dynamically subdividing DIP disease species into subgroups according to complication severity, treatment mode difference and resource consumption dispersion; after disease category subdivision, the matching degree of the DIP score and the actual cost is improved by 20%-30%, the medical insurance payment refusal risk is reduced, payment accuracy improvement is achieved, through strategic disease category support, the CMI value is averagely improved by 0.15, the difficult case recovery rate is increased by 12%, medical quality optimization is achieved, and through linkage excitation of the balance contribution rate and the patient satisfaction degree, the medical quality is improved. The medicine consumption ratio is reduced by 5-8%, the hospitalization day is shortened by 1.2 days, and the operation efficiency is improved.

The invention discloses a health-related information processing system applied to medical oncology, and relates to the technical field of health information processing. The system comprises a multi-source data acquisition module, an edge preprocessing module, a streaming data processing module, a cloud deep analysis module, a clinical decision support module and a dynamic feedback optimization module. The technical key points are as follows: the scheme realizes technical architecture innovation: a three-stage processing chain of edge screening-streaming computing-cloud optimization is adopted, and the technical barrier that real-time early warning and deep analysis cannot be achieved at the same time is broken through; innovation of an algorithm model: a dynamic weight adjustment algorithm is developed, and the problem of model generalization in individualized treatment is solved; application mode innovation: deep coupling of a natural language generation technology and clinical pathways is realized, and intelligent medical treatment is promoted to evolve from auxiliary analysis to autonomous decision making.

The invention relates to a clinical pathway intelligent management system, which comprises a sensing layer, an analysis layer and a decision-making layer, and is characterized in that the sensing layer is connected with medical Internet of Things equipment and is used for collecting vital sign information of a patient through the medical Internet of Things equipment and transmitting the vital sign information to the analysis layer; the analysis layer processes the vital sign information of the patient based on a complication risk prediction model under a graph neural network and a multi-objective optimization algorithm under resource constraints, so as to predict the complication risk of the patient; the decision-making layer comprises a dynamic path generation module and a collaborative decision-making module, the dynamic path generation module is used for displaying the prediction process of different complication risks of the patient, and the collaborative decision-making module is used for a doctor to collaboratively decide the prediction process. According to the invention, the vital sign information can be analyzed according to the personal condition of the patient, so that personalized complication risk prediction can be carried out on the patient. And doctors can participate in co-decision. Vital sign information of a patient can be collected in real time, and therefore body changes of the patient can be analyzed in real time.

The invention belongs to the technical field of intelligent management of hemp essence medicines, and discloses an intelligent hemp essence medicine management system and method, and the method comprises the steps: processing an operation context parameter, and calculating a risk score; secondly, a white-box rule engine is used for checking medicament metering specifications, and clinical pathway compliance and medicament interaction are analyzed; then, in combination with biological characteristics and geographical location information, evaluating risks in real time and taking multi-level intervention measures; then, generating a detailed visual auditing report according to the collected data; and finally, optimizing the system performance based on a feedback dynamic adjustment strategy. The safety and the accuracy of medical operation are improved, the intelligent level and the response efficiency of the system are remarkably improved, and a highly-safe operation verification process and an accurate prescription data verification capability are realized.

The invention relates to a traditional Chinese medicine medical insurance reimbursement process management method and device, equipment and a medium. According to the method, traditional Chinese medicine diagnosis and treatment feature data (including prescription composition, dose use and diagnosis and treatment item data) of a patient is acquired, compatibility analysis is performed by using a traditional Chinese medicine knowledge graph, and a dose deviation degree and therapy suitability are evaluated in combination with a clinical pathway specification database. And generating a comprehensive risk scoring level through a dynamic risk assessment model trained based on historical data. And when the score reaches a corresponding preset threshold value, generating a reimbursement decision instruction containing the risk level to guide the reimbursement process. By adopting the method, the efficiency and accuracy of traditional Chinese medicine medical insurance reimbursement are improved, the risk monitoring capability is enhanced, and safe use of medical insurance funds is effectively guaranteed.

This invention relates to the field of medical intelligent technology and discloses a method and system for intelligent recommendation of laboratory tests based on patient diagnosis and treatment information. The method includes: extracting clinical events from time-series diagnosis and treatment information to obtain standardized clinical events; determining the medical logical relationships of the standardized clinical events and labeling causal, temporal, and co-occurrence relationships to obtain labeled clinical events; constructing a disease evolution network with labeled clinical events as nodes and medical logical relationships as edges; analyzing the topology of the disease evolution network to obtain critical paths and hub nodes, and resolving the hub nodes as core clinical diagnosis and treatment intentions; associating and matching the core clinical diagnosis and treatment intentions with a pre-constructed dynamic correlation graph of test-intentions to obtain test item combinations; and ranking the test item combinations according to the clinical path fit based on the current diagnosis and treatment stage and individual characteristics to obtain a personalized test item recommendation list. This invention can improve the efficiency of test item recommendation.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

This invention relates to the field of medical information technology, and in particular to a method and system for generating clinical multi-agent task sequences. The method includes the following steps: acquiring and structuring core clinical objectives, which include patient basic information, clinical indicators, and disease management goals; decomposing the core clinical objectives into several structured sub-objectives with dependencies and execution order based on a clinical pathway template library and a medical knowledge graph; for each structured sub-objective, splitting it into multiple minimum executable clinical sub-tasks according to a clinical sub-task decomposition rule library, and defining the execution subject type, input / output, and dependencies for each clinical sub-task to construct a sub-task-level directed acyclic graph; integrating the sub-task-level directed acyclic graphs, identifying dependencies across structured sub-objectives, generating a global clinical task directed acyclic graph, and performing dependency verification and clinical logic consistency verification.

The invention belongs to the technical field of diabetic foot clinical path management, and discloses a diabetic foot patient path management method and system based on lower limb vasculopathy grading. Mapping the ulcer position to the corresponding perfusion area and the artery segment to generate ulcer space characteristics; calculating a blood supply risk score of the perfusion area and generating a space risk map by combining the blood vessel image detection data and the microcirculation detection data; then collecting time sequence data including an ulcer index, an infection index, a blood supply index and a space risk map, and predicting the progress speed and the risk level of the future ulcer course; and finally, according to the blood supply risk score and the disease course prediction result, generating an individualized path management scheme through a joint triggering rule. According to the invention, precise and dynamic management of the diabetic foot ulcer is realized, the healing rate is improved, and the recurrence rate and the medical cost are reduced.

The invention discloses an optimization method and device for medical large model auxiliary diagnosis and treatment and a computer program product, and the method comprises the steps: firstly carrying out the term standardization of target case information of a target patient, and keywords in a preliminary diagnosis and treatment scheme outputted by an LLM; after TF-IDF vectors and dense semantic vectors of a target keyword set composed of the keywords and the standard medical terms of the keywords are generated, fusion processing is carried out on the TF-IDF vectors and the dense semantic vectors, and mixed vectors are obtained; a clinical path knowledge graph conforming to personalized features of a target patient is dynamically constructed by using more accurate candidate knowledge texts retrieved from a medical knowledge base, and a preliminary diagnosis and treatment scheme output by a medical LLM is continuously and intelligently corrected and detected medical contradictory points are adjusted in combination with a portrait of the target patient, so that the clinical path knowledge graph is obtained. And finally, obtaining a final optimized diagnosis and treatment scheme. Therefore, closed-loop correction optimization of medical LLM auxiliary diagnosis and treatment results is realized, and an ideal optimization effect is achieved.

The application relates to the technical field of sales support, and discloses a medicine sales data analysis method, which comprises the following steps: constructing a cognitive map model; performing clinical path complexity grading; applying a clinical path complexity grading algorithm to analyze clinical decision process data of a medical institution, and calculating a clinical path complexity index; implementing cognitive-clinical path matching; adopting a cognitive-clinical path matching algorithm to process sales representative cognitive state data and clinical path complexity data, and calculating a matching degree score; performing hierarchical progressive information presentation; based on a hierarchical progressive information presentation algorithm, processing the matching degree score and the clinical path data, dividing information into different priority levels, and outputting an optimized information presentation sequence; and providing personalized clinical knowledge navigation; the application significantly improves the accuracy and effectiveness of professional information transmission by intelligently adapting the cognitive ability of a sales representative to the complexity of a clinical path.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

An AI-based system (100) designed to assess prediabetes risk in individuals. The system (100) integrates seamlessly with the user devices via rest APIs, ensuring secure communication through https. A data collection module (122) collects raw data forwards the data to standardization and pre-processing modules (124) for preparation. The pre-processed data is transmitted to the server (104) through a network (102) using an API integration module (126) and thereafter forwarded to the input module (112) that authenticates and validates patient data, which is transmitted to the processing module (114) that refines the data, while a prediction module (116) employs a deep learning model with an AUC of 0.87 to evaluate prediabetes risk. A clinical pathway (118) and risk prediction response (120) modules provide personalized protocols, risk status, and clinical recommendations. It (100) complements clinician decision-making, adheres to ISO 13485 standards, and serves as a cost-effective community-based prediabetes risk assessment.

This invention discloses a method, medium, and device for identifying DRG over-compensation based on invoice reverse engineering and causal imprinting. The method includes: acquiring detailed medical billing data from the medical records to be audited; extracting features of treatment items and mapping them to a medical knowledge graph to obtain a set of feature atoms; inputting the feature atom set into a large language model, outputting real disease phenotypes as pseudo-labels, and performing semantic comparison with the reported primary diagnosis to identify over-compensation of the primary diagnosis; classifying the sensitivity of other reported diagnoses by score, screening high-value complication entities, and verifying their resource consumption matching degree through a clinical pathway constraint library to identify fictitious complications; extracting the main billing items and their low-value billing imprint rule library, and identifying falsely inflated primary intervention items through counterfactual causal comparison; finally, calculating a comprehensive anomaly index based on deviation, matching degree, and divergence, and outputting an audit report. This invention automatically identifies DRG over-compensation anomalies from multiple dimensions, improving the accuracy and comprehensiveness of audits.

The invention discloses a fine-grained medical data link up-link and down-link cooperative interaction method and system, and is applied to a special disease clinical pathway supervision scene. The system comprises a plurality of medical institution nodes, each node is registered and added into a Fabric block chain to jointly maintain an abstract chain, and an inquiry module, an on-chain and off-chain storage module, an intelligent contract module, a supervision module, a shared fine granularity module and an on-chain and off-chain combined efficient inquiry and auditing module are deployed; the inquiry module is used for forming medical data generated during patient inquiry; the shared fine granularity module is used for realizing a shared fine granularity mechanism; the on-chain and off-chain storage module is used for storing data under the chain; the intelligent contract module is used for uploading and downloading the abstract; the on-chain and off-chain combined efficient query and audit module is used for selecting a proper shared fine-grained set and a query table according to a request; the auditing sub-module is used for auditing the data, and the supervision module supports a special disease clinical pathway supervision function and other safety operations.

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.

The invention provides a clinical path maintenance content recommendation method and device and electronic equipment, and the method comprises the steps: firstly obtaining an effective doctor advice set and an effective doctor seeing number of a patient in a preset time period of a clinical path, and then obtaining a doctor advice template set of the clinical path in the preset time period; determining a first execution rate of each doctor's advice template in the doctor's advice template set and a second execution rate of each new doctor's advice in the effective doctor's advice set based on the effective doctor's advice set, the effective doctor seeing number, the doctor's advice template set and preset treatment course stage division information; and finally, recommending to-be-maintained contents of the clinical path to a user corresponding to the clinical path based on the first execution rate of each doctor's advice template and the second execution rate of each new doctor's advice. By adopting the method and the device, related personnel can conveniently carry out targeted maintenance on the clinical pathway according to the recommended content, so that the maintenance of the clinical pathway is more objective and more efficient, and the maintained clinical pathway better fits the clinical actual condition.

The invention discloses a standardized consumption path management method and system based on disease categories, and belongs to the technical field of medical equipment and consumable management, and the method comprises the steps: obtaining related data of a target operation case, constructing an operation characteristic parameter set, and building a typical clinical path knowledge base matched with the corresponding disease category; historical operation video data of a target doctor are analyzed, a deviation value sequence of the actual consumable usage amount and a standard consumable baseline value is counted, and a dynamic skill score value of the target doctor is calculated in combination with the complication occurrence rate and the intraoperative operation fluency so as to correct an allowable fluctuation interval of the standard consumable baseline value; and dynamically associating the corrected interval with the knowledge base to form a personalized consumption guidance scheme. According to the method, a multi-modal data analysis and dynamic skill evaluation algorithm is adopted, a standard consumable baseline interval can be automatically corrected, a personalized consumption guide scheme can be generated, accurate balance between consumption and doctor skill adaptability is achieved, resource waste is reduced, and operation safety is improved.

This invention relates to the field of data analysis technology, specifically disclosing a method and system for secure sharing and exchange of ICU data. The method and system are characterized by the following steps: Patient status data is input into a pre-built clinical pathway prediction model to generate predicted pathway data; based on key department identifiers in the predicted pathway data, a pre-authorization token is generated for each key department identifier, and the generated pre-authorization token is sent to the corresponding department's server; according to the pre-authorized data range identifier, matching target patient data is filtered from the patient status data, and the target patient data is pre-fetched to the edge cache node corresponding to the department server; access intent data is extracted from data access requests; it is determined whether there is a target pre-authorization token that matches the department identifier and is in a valid state; and it is determined whether the data type identifier of the requested access falls within the pre-authorized data range identifier associated with the target pre-authorization token. The advantage is that it improves the efficiency of secure data sharing and exchange.