52 results about "Medical quality" patented technology

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention relates to a medical quality control dynamic scoring system, device and equipment based on multi-modal perception and federal evolution, and the method comprises the steps: obtaining original scene information through multi-modal input based on a medical equipment recognition model, carrying out the scene analysis and semantic fusion, generating structured medical scene data, and carrying out the dynamic scoring of the medical quality control according to the structured medical scene data. Multi-source evidence verification is carried out through a medical scoring rule base, a quality evaluation result is generated through weight dynamic adjustment, difference analysis is carried out through comparison with a manual correction result according to the quality evaluation result, and model parameters and scoring rule updating are generated. Multi-modal input is fused based on a medical special perception layer, anti-interference scene analysis and equipment semantic binding are achieved, multi-source evidences are verified in real time through a dynamic scoring engine, deduction weights are dynamically adjusted, subjective item scoring floating is compressed, an identification model is updated through federation, and a rule base is thermally loaded in real time, so that the anti-interference scene analysis and equipment semantic binding is achieved. And automatic closed-loop processing of problem items is realized.

The invention provides a medical data exception analysis processing method and system, a terminal and a medium, and belongs to the technical field of medical data processing, and the method specifically comprises the steps: obtaining and preprocessing medical original data, preliminarily screening abnormal data according to a preset rule, marking types, and storing the data in a cache region according to clinical priorities. And for the abnormal data in the cache region, matching historical cases, extracting clinical, equipment and time associated information, and outputting an analysis report. And generating an instruction packet containing an exception type, an interface parameter and an execution time limit, wherein the instruction packet is used for correcting data, calibrating equipment and rechecking a result, synchronously recording a log, triggering upgrading by a new exception and generating a new instruction packet. And when the exception appears repeatedly, generating a reverse verification instruction packet, performing sandbox reproduction, comparing differences, updating the knowledge base and the like, verifying the correction validity, and performing iteration if the correction is not passed. A medical data exception processing closed loop is formed, the processing efficiency and accuracy are improved, the medical quality and the patient safety are guaranteed, and the system adaptability is enhanced.

This invention discloses a method, apparatus, system, and storage medium for relocating hospital departments, including: step S1, data modeling and parameter initialization; step S2, constructing a multi-objective optimization model; step S3, constructing a constraint system; step S4, dynamic priority calculation and critical path identification; step S5, relocation scheduling optimization solution; and step S6, generating the optimal relocation plan. The technical solution of this invention enables systematic optimization under multi-objective constraints, simultaneously considering economic cost, continuity of medical services, and resource utilization efficiency, while meeting the requirements of medical quality and safety.

The invention provides a curative effect driving type medical performance and payment system oriented to active medical treatment. Active medical economics and DIKWP (data / information / knowledge / wisdom / intention) five-layer semantic modeling are fused. The system semantically converts diagnosis and treatment behaviors into a semantic bill, constructs a target chain by taking a patient purpose as a core, continuously tracks causal contribution of each content unit to a curative effect, and quantifies a doctor service value by a semantic value increment; therefore, payment according to effects and remuneration according to effects are realized through a fair payment engine, medical insurance and an intelligent contract are compatible, and automatic settlement and dispute arbitration are realized. The system can be used for internet hospitals, chronic disease management and medical insurance payment reform, the medical quality, transparency and fund efficiency are improved, and supervision and policy optimization are supported.

This invention discloses an automatic collection and analysis method for medical quality control indicators in critical care departments. The method includes: establishing a case feature database based on historical monitoring data; constructing and training a quality control indicator model based on the case feature database; establishing a model update database to store newly input cases; periodically inputting data from the model update database into the quality control indicator model; and determining whether the medical quality control indicators in critical care departments are within the acceptable range based on the output of the quality control indicator model display module and providing feedback to relevant personnel.

The present application relates to the medical care form technical field, propose a kind of medical care intelligent form generation method and system based on multimodal big model, comprising: collecting the physical examination of clinical business system in medical care patient, diagnosis record, diagnosis voice data and other multimodal data;Again, generate physical change sequence by multimodal big model in combination with physical examination data, construct diagnosis feature vector;Subsequently, the vector is mapped to medical quality index system, to generate visual form framework;Then, extract diagnosis record key elements and fill into framework, update form information in combination with diagnosis voice data;Finally, medical care intelligent form adapted to each system is output by multimodal big model, and is synchronized to medical care work terminal.The present application realizes the effective fusion of multimodal medical data, and the generated form can completely reflect the whole picture of diagnosis and treatment and the dynamic evolution of signs, improve the clinical work efficiency and medical quality.

The invention discloses a public institution internal data sharing method based on multi-source data fusion, and relates to the technical field of data sharing. According to the method, four-quadrant accurate division is carried out on multi-source fusion three-medicine structured data on the basis of sensitive risk scores and utility influence tolerance, differential noise adding strategies are matched in a targeted mode, and three kinds of deviation verification of medical quality, medical insurance fund risks and medicine supply and demand results and noise iterative optimization are combined, so that the multi-source fusion three-medicine structured data is obtained. And finally, through block chain intelligent contract encryption sharing, the problems that a traditional differential privacy technology is rigid in noise intensity setting and is difficult to adapt to data of different privacy levels are solved, dynamic balance between privacy protection strength and data availability is realized, meanwhile, the security and credibility of a data sharing process are guaranteed, and the data sharing efficiency is improved. On the premise that the multi-source heterogeneous public institution internal data meets the privacy security requirement, the practical value of supporting core services such as medical quality evaluation, medical insurance supervision and drug supply and demand allocation is reserved to the maximum extent.

The utility model belongs to the field of drainage tubes, and particularly relates to an abdominal cavity drainage tube capable of measuring pressure. A pressure measuring tube is arranged on the side wall of the drainage tube, a blocking piece and a compressible elastic piece are arranged in the pressure measuring tube, the blocking piece can move downwards along the inner wall of the pressure measuring tube, the elastic piece is connected below the blocking piece and connected with a pressure sensor at the lower end of the pressure measuring tube, and the pressure sensor is connected with a pressure value display screen through a wire. Pressure value scales are further arranged in the pressure measuring tube, a small hole is formed in the lower end of the pressure measuring tube, and the drainage tube is provided with a branch for a wire to penetrate out to be connected with a display screen. The intraperitoneal pressure measuring device can directly and accurately measure intraperitoneal pressure change, and provides reliable illness state judgment basis for medical staff. Scales inside the pressure measuring tube facilitate visual observation of pressure changes, the structure is simple, operation is convenient, complex operation and additional equipment are not needed, the workload of medical staff is reduced, the working efficiency is improved, and the medical quality and the rehabilitation effect of a patient can be improved.

The invention discloses a rib fracture intelligent auxiliary decision-making system based on multi-modal fusion, and the system comprises a database construction module which is used for obtaining a multi-dimensional special disease database, and a data processing and fusion module which is used for preprocessing multi-modal data and generating a structured data set. The intelligent detection module constructs a rib fracture intelligent detection network for fracture detection, the risk prediction module is used for constructing a vertical multi-modal network to output a risk probability and an intervention recommendation vector, and the scheme generation module generates an individual intervention path and dynamically updates a risk prediction and intervention scheme in combination with follow-up visit data; according to the method, the vertical multi-modal network for rib fracture is constructed, decision suggestions are given in combination with multi-modal data fusion, the early recognition and early warning capability of fracture of a fracture patient is improved, meanwhile, modeling is performed according to large samples and feature fusion which varies from person to person, the consistency of intervention path recommendation after fracture among different doctors is improved, and the accuracy of intervention path recommendation after fracture is improved. And the medical quality control capability is improved.

The application discloses a medical quality quantitative evaluation method and system based on a multi-dimensional symbiotic value network. The method obtains inpatient indication evaluation results, medical connotation quality evaluation results, doctor diagnosis and treatment level evaluation results and their converged evaluation results; generates three-party fusion value evaluation results based on patient result value indexes, medical insurance purchase value indexes and doctor professional value indexes; generates cross-dimension collaborative driving indexes and four-column collaborative quality indexes based on the differences between multi-dimensional evaluation results; and introduces patient five-dimensional values to generate five-dimensional symbiotic evaluation results. The application can further convert basic medical evaluation results into three-party value fusion, cross-dimension consistency diagnosis and patient real value symbiotic evaluation results, and improve the quantification degree, state positioning ability and interpretability of inpatient diagnosis and treatment whole-process evaluation.

A medical quality control index interpretation method, system, medium and program product, in the method, a hierarchical index dictionary is constructed; an index analysis rule library is established based on the hierarchical index dictionary; using a preset index name standardization algorithm, the index name is converted into a preset standardized format using the hierarchical index dictionary and the index analysis rule library, obtaining a plurality of standardized index names; an index semantic mapping matrix is established, and the core word, the modifier and the limiting word in the plurality of standardized index names are matched with the preset index connotation elements respectively, to obtain a hierarchical semantic mapping result; a hierarchical index interpretation template library is established; according to the hierarchical semantic mapping result, an initial template element is selected and combined in the hierarchical index interpretation template library to generate an interpretation text. The present application reduces the differences in the interpretation of index definitions between different medical institutions and regions, thereby improving the consistency of index interpretation between various medical institutions and regions.

The invention relates to the technical field of Internet of Things environment detection, in particular to a clean environment monitoring and early warning method and system based on the Internet of Things. The method comprises the following steps: acquiring a monitoring range, dividing the monitoring range into a plurality of sub-regions, deploying a multi-modal sensing node with an identifier in each sub-region, and collecting multi-dimensional environmental parameters of each sub-region; identifying a cross-region diffusion risk and a collaborative abnormal situation, and judging the cleanliness level of the current sub-region; a parameter multi-level threshold value is preset, multi-level early warning is divided in combination with a correlation analysis result, and corresponding sub-region equipment is linked to carry out differential regulation and control; the system comprises a sub-region multi-parameter acquisition module, a multi-sub-region association module and a cleanliness grading early warning module. By means of the mode, the multi-mode sensing nodes are dynamically deployed, a multi-level early warning and differentiation regulation and control mechanism is established, the accuracy and effectiveness of hospital environment infection control are improved, the medical quality and patient safety are guaranteed, the detection accuracy is improved, and the refined and real-time infection control requirement is met.

The application discloses a kind of based on visual guidance's mechanical arm mouthwash nursing, breath detection and holographic diagnosis and treatment auxiliary system and method, it is related to stomatology field, including: main control module, auxiliary mechanical arm module, gas detection module, visual sensing module, diagnosis and treatment intelligent body interaction module, audio and video acquisition communication module with main control module electricity connection, and multi-modal mobile nursing terminal.The application realizes the pose control of mechanical arm and the imaging visual angle adjustment of diagnosis and treatment intelligent body by visual sensing system, integrates supine position automatic mouthwash nursing, exhalation component disease screening, doctor-patient / holographic visualization communication, full-process audio and video diagnosis and treatment support, non-standard oral speech driven standard medical record automatic generation, automatically collects non-standard oral speech content in diagnosis and treatment full process, generates standardization medical record meeting medical quality control requirement, greatly improves diagnosis and treatment efficiency, patient comfort and diagnosis and treatment standardization, and can be widely integrated in dental treatment chair.

This application provides an analytical device for the combined Toric intraocular lens implantation effect, relating to the technical field of intraocular lens scanning. It includes an intraocular lens scanner with a display screen fixedly mounted on its top. Multiple detection modules can detect the Toric intraocular lens implantation effect from multiple dimensions. A data analysis module integrates and intelligently analyzes this data, achieving a comprehensive and accurate assessment of the implantation effect and providing doctors with more accurate diagnostic information. The device can provide diagnostic suggestions and treatment plan references based on the data analysis results, helping doctors develop more scientific and personalized treatment plans and improve medical quality. The human-machine interface display screen and voice prompt module make the device simple and convenient to operate, and the detection process efficient. It reduces the professional threshold for operators, shortens the detection time, improves medical efficiency, and provides patients with a better medical experience.

The invention belongs to the technical field of vascular access assessment, and relates to a vascular access assessment system and method based on machine learning, and the system employs a layered architecture, integrates follow-up management, postoperative maturity assessment and a machine learning decision support engine, achieves AVF maturity risk grading through multi-parameter adaptive threshold weighted scoring, and improves the assessment accuracy. A personalized training scheme is generated in combination with similar case matching, continuous self-learning is realized based on expert feedback, the problems of subjective evaluation, low efficiency, scheme homogenization and the like in internal arteriovenous fistula management are solved, patient data can be automatically processed, and structured reports and visual charts can be generated in batches; the method improves evaluation accuracy and clinical efficiency, improves patient prognosis, provides data support for medical quality control and scientific research, and is suitable for AVF full-cycle management of end-stage nephropathy hemodialysis patients.

The invention provides an active medical performance evaluation and payment system based on a DIKWP semantic hierarchical model (data-information-knowledge-intelligence-intention), and solves the problem that the traditional payment mode is difficult to reflect the medical quality. According to the system, medical services are decomposed into DIKWP five-layer semantic units, semantic association between doctor behaviors and patient curative effects is achieved, a semantic performance bill is generated, and payment is based on actual curative effects instead of the number of services. The system comprises a doctor behavior acquisition module, a patient feedback acquisition module, a semantic mapping module, a performance bill generation module and a payment matching module, and can be applied to hospitals, medical insurance and internet hospitals. The system quantifies the contribution of each medical behavior to the health target of the patient, promotes the medical service to change from'quantity-oriented 'to'quality-oriented', improves the medical quality, enhances the participation degree of the patient, realizes refined payment of medical insurance, and provides technical support for'value-type medical treatment '.

This invention discloses a method for tamper-proof verification of personal radiation dose logs from CT scans, comprising: real-time acquisition of radiation dose data and related operation information during CT scans to generate a structured raw dose log; generating an encrypted log string through asymmetric encryption signature and SHA-256 / SM3 hash operation, combining patient identifier, timestamp, and device number to generate a unique verification identifier and adding a blockchain calibration timestamp; synchronizing relevant data to a consortium blockchain node cluster and a third-party trusted server for distributed redundant storage; supporting verification triggered by multiple entities including patients, hospitals, and regulatory agencies, outputting results through encrypted string comparison, field-level consistency verification, and multi-dimensional verification of format / field / logic; accurately tracing the source of tampering based on stored data, and automatically sending warning information to relevant parties when warning conditions are met. This invention achieves transparent verification, efficient verification, and reliable traceability of dose logs, improving data credibility, reducing medical disputes, and ensuring the effectiveness of medical quality supervision.

The invention relates to the technical field of medical care training, and discloses a PTCD tube-carrying patient home care training method, which comprises the steps of obtaining illness state data and life data of a patient, generating and adjusting drainage liquid standard data, collecting actual drainage liquid data of the patient, comprehensively judging whether the drainage liquid of the patient is abnormal or not according to multiple judgment results, and if yes, judging whether the drainage liquid of the patient is abnormal or not. According to the PTCD tube-carrying patient home nursing training method, through multi-level data analysis and judgment, misjudgment is reduced, the accuracy of judgment on drainage liquid abnormity is improved, condition data and life data are combined, a personalized evaluation standard is provided for each patient, an algorithm is continuously optimized through the training result, and the training efficiency is improved. The reliability and accuracy of the system are improved, a scientific basis is provided for attending physicians, medical decision making is assisted, medical quality is improved, illness conditions and lifestyle factors are comprehensively considered, and the recovery condition of patients is comprehensively evaluated.

The invention provides an intelligent infusion management method and system based on dynamic identity verification. The system comprises an intelligent infusion support, a nurse station calling system and an HIS data interaction module. Before infusion, the intelligent infusion support carries out first-time identity verification by scanning a wrist strap of a patient and carries out second-time matching with an electronic doctor's advice in an HIS, and it is ensured that the identity of the patient, medicine information and infusion parameters are completely consistent. After verification is passed, the system automatically activates an intelligent infusion program and a mechanical execution mechanism and triggers acousto-optic prompts, and nurses are guided to complete infusion bag hanging and other related infusion support use operations. According to the scheme, wrist strap scanning and electronic doctor's advice comparison are combined, double identity safety verification is achieved, and the risk of medicine taking errors is reduced; the dripping speed and the remaining amount are intelligently and automatically monitored in real time, automatic alarm and adjustment are performed during abnormity, and the efficiency is improved; operation logs are electronically recorded, the whole process is traceable, and medical quality control standards are met; when infusion is completed or abnormal, a nurse station calling system is intelligently responded, and waiting anxiety of the patient is reduced.

The invention discloses a medical quality monitoring method and system based on artificial intelligence, particularly relates to the technical field of artificial intelligence assisted medical decision, and is used for solving the problem that the existing static early warning threshold cannot collaboratively optimize the adaptation of a conflict target and a dynamic resource. The method comprises the steps of obtaining a monitoring target set and a resource constraint set in real time; analyzing real-time cascade influence intensity and resource linkage fluctuation values between targets with medical relevance; generating a target conflict incidence matrix and identifying a strong conflict coupling target group; activating a high conflict-low resource pressure state when the cascade influence intensity is greater than a threshold value, the linkage fluctuation value is greater than a fluctuation risk threshold value and a strong conflict target group exists; under the state, differential adjustment coefficients for different conflict targets are dynamically calculated based on the conflict sensitive factor and the resource margin factor so as to correct an early warning threshold and trigger risk early warning, dynamic balance of conflict target optimization and resource constraint is realized, and the early warning sensitivity and the false alarm suppression capability are effectively improved.

The invention relates to an artificial intelligence device for acquiring health data, in particular to the field of medical artificial intelligence, realizes personalized and dynamic treatment optimization of deep venous thrombosis of lower limbs of a cerebral apoplexy patient by fusing deep reinforcement learning and human expert knowledge, and can analyze physiological state changes of the patient in real time, so that the system can be used for treating cerebral apoplexy. The dosage of the anticoagulant drug is automatically adjusted, insufficient or excessive treatment risks are avoided, treatment safety and effectiveness are remarkably improved, meanwhile, data redundancy is reduced through intelligent feature screening, model interpretability is enhanced, clinicians can understand and adopt AI decisions more easily, in the high-risk situation, the system is preferentially combined with expert experience, decision reliability is ensured, and the accuracy of the AI decisions is improved. Therefore, the survival rate of patients and the thrombus prevention effect are improved, and the medical quality and prognosis level are integrally improved.

The invention relates to the technical field of medical equipment quality control, in particular to a full-process quality control management system based on medical unit multi-source data fusion. The clinical multi-modal data module is used for acquiring an electronic medical record diagnosis and treatment text set, a medical equipment log set and an image focus slice set and outputting a clinical feature matrix set; the medical quality control network construction module is used for extracting equipment node state parameters and medical topological correlation parameters contained in the clinical feature matrix set, and outputting a medical quality control directed graph through topological projection connection; the reconstruction deviation module is used for performing neighborhood aggregation and inverse projection on the medical quality control directed graph, and outputting a reconstruction deviation set through deviation derivation; and the clinical anomaly identification blocking module is used for executing distribution fitting identification on the reconstruction deviation set, outputting medical anomaly identification parameters and executing port blocking. Through a strong coupling architecture of medical data perception, medical map deviation derivation and bottom layer terminal physical blocking, the probability of false triggering isolation is reduced, and the link time delay forbidden by an abnormal terminal is shortened.

This invention belongs to the field of medical quality control technology. It discloses a time-sharing quality control system and method for rapid intraoperative pathology diagnosis. By collecting historical time-series data from multiple intraoperative pathology diagnosis processes, a multi-dimensional time benchmark library containing a time window correlation matrix is ​​constructed. Multi-source sensor fusion technology is used to identify node switching events during operation periods. A time-factor coupling network is constructed based on environmental interference parameters and operational coherence parameters. Bottleneck nodes in the diagnostic process time chain are accurately identified, and their cascading delay risk levels are assessed. Finally, a traceable quality control report containing a time topology map is generated. This invention achieves precise time monitoring of the entire intraoperative pathology diagnosis process, scientifically quantifies the contribution intensity of multiple factors to time deviation, improves the timeliness and process stability of pathology diagnosis, reduces surgical waiting time, and provides timely and reliable pathological evidence for medical decision-making.

The invention provides a medical service semantic contribution degree calculation and curative effect causal chain modeling method based on a DIKWP semantic hierarchical model (data-information-knowledge-intelligence-intention), and precise quantification of a medical process is realized in combination with a'tension path 'theory. A semantic map is constructed, tension between a patient state and a target is defined, a semantic contribution degree is calculated by adopting a direct difference method, knowledge expectation calibration and a multi-factor apportionment algorithm, and a curative effect causal chain containing a causal relationship is constructed. The method can be applied to chronic disease management, rehabilitation medicine and doctor performance evaluation, so that the medical process can be quantified, traced and optimized, technical support is provided for payment according to the curative effect, medical service is promoted to be converted from'quantity-oriented 'to'quality-oriented', the medical quality and efficiency are improved, and the doctor-patient trust is enhanced.

The invention discloses a performance management method and system based on rehabilitation technology grading, and relates to the field of medical informatization and hospital performance management, and the method comprises the steps: S1, obtaining the performance related data of a rehabilitation therapist, which at least comprises rehabilitation items executed by the rehabilitation therapist; s2, on the basis of rehabilitation treatment items, the personal actual weighted workload of the rehabilitation therapists is calculated through a predefined rehabilitation treatment technology grading model, and the workload performance of the rehabilitation therapists is determined according to the proportion of the personal actual weighted workload and the total workload of the department; s3, basic performance is calculated according to basic information of the rehabilitation therapists, management performance is calculated according to management post information of the rehabilitation therapists, and scientific research reward performance and medical quality performance are calculated according to scientific research and service data of the rehabilitation therapists; s4, integrating the basic performance, the management performance, the scientific research reward performance, the medical quality performance and the workload performance, and generating the comprehensive performance of the rehabilitation therapist. And the fairness and incentive effect of performance allocation are improved.

This invention provides an AI-based de-identification and intelligent analysis management method for pet medical data, belonging to the field of medical data processing technology. It includes employing a multi-source data acquisition strategy to comprehensively acquire basic pet information, diagnostic and treatment data, and disease prevention and control data; preprocessing the collected data; classifying and labeling the preprocessed data based on privacy leakage risks and data structuring levels; dynamically matching the optimal de-identification strategy using AI algorithms to construct a hybrid de-identification model to complete data de-identification; constructing a secure management system encompassing hierarchical storage, secure transmission, and data backup to ensure the security and availability of data storage and transmission; and using intelligent analysis algorithms to predict disease trends, recommend treatment plans, assess medical quality, and identify abnormal behaviors from the de-identified data. This invention addresses the current challenges in data management by employing the aforementioned AI-based de-identification and intelligent analysis management method for pet medical data.

This invention relates to the field of abdominal drainage technology, and more particularly to an abdominal drainage system based on real-time hemoglobin monitoring and intelligent early warning. The technical solution includes an abdominal drainage tube, an optical sensing module, a microprocessor module, and a wireless communication module. The microprocessor module is configured to perform moving average filtering and dynamic trend modeling on hemoglobin concentration data; and to trigger early warnings based on preset multi-level alarm conditions. This invention, by real-time and accurate monitoring of hemoglobin concentration in abdominal drainage fluid, combined with intelligent early warning and personalized response mechanisms, overcomes the limitations of traditional manual observation, significantly improving the timeliness and accuracy of bleeding risk identification. Its closed design reduces the risk of infection, while its system integration capabilities optimize medical and nursing workflows, enabling data sharing and remote collaboration. This not only shortens the intervention time window and improves patient prognosis but also frees up nursing resources and improves medical quality, providing a comprehensive and efficient solution for postoperative monitoring.