Intelligent management method for patient data based on general surgery department

Abstract

The invention discloses an intelligent patient data management method based on the general surgery department, and relates to the technical field of medical information processing, and the method comprises the steps: a multi-source patient data comprehensive collection step: collecting full-process multi-type data through four modes, and dynamically adjusting the collection frequency according to the data type; a data standardization preprocessing step: processing the data by adopting a cleaning algorithm; a structured storage and intelligent index construction step, wherein classified storage is carried out based on a mixed storage architecture; an intelligent data analysis step based on machine learning: constructing a multi-task model; a personalized medical intervention scheme generation step: generating a scheme in combination with an analysis result and a clinical guide; a data dynamic updating and real-time monitoring step: updating data in real time and monitoring key indexes; and a hierarchical authority management and data security guarantee step: distributing authorities according to roles. The method improves the integrity and efficiency of general surgery department patient data management, guarantees the data security and privacy, reduces the medical risk, and improves the rehabilitation effect of the patient.

Description

Technical Field

[0001] This invention relates to the field of medical information processing technology, and in particular to an intelligent management method for patient data based on general surgery. Background Technology

[0002] As one of the core clinical departments of the hospital, the Department of General Surgery encompasses multiple subspecialties such as gastrointestinal surgery, hepatobiliary surgery, and breast surgery. It treats a large number of patients with complex diseases and involves intricate treatment processes, generating a wealth of data in massive quantities. This data spans the entire process of patient admission, treatment, surgery, nursing, rehabilitation, and follow-up, including basic information, clinical records, laboratory test results, surgical parameters, nursing records, and rehabilitation data. It serves as a crucial basis for guiding clinical practice, evaluating treatment effectiveness, and optimizing medical services. However, current management of patient data in the Department of General Surgery still faces many pressing issues that need to be addressed.

[0003] Traditional patient data management relies heavily on manual entry and fragmented storage. Data collection channels are limited and lack standardization, resulting in insufficient data completeness and accuracy. Data from different sources varies in format, with clinical documents, lab reports, and imaging data stored in different systems without a unified integration mechanism, creating "data silos" that hinder comprehensive multi-dimensional data analysis and efficient retrieval. Data preprocessing is manual, with delayed removal of duplicate and outlier data, and simplistic handling of missing data, further impacting data quality and failing to provide reliable support for clinical decision-making.

[0004] Current data management methods lack intelligent analytical capabilities, often remaining at the level of data storage and simple statistics, failing to deeply explore the clinical value behind the data. Disease risk assessment and complication prediction rely heavily on physicians' subjective experience, lacking objective data support, leading to insufficient accuracy in assessment results. Treatment plans and rehabilitation programs often adopt a "one-size-fits-all" approach, failing to fully consider individual patient differences, resulting in limited specificity and effectiveness. Furthermore, patient data updates are not timely, and real-time monitoring of key indicators is lacking, leading to delayed warnings of postoperative changes and complications, increasing medical risks. In addition, medical data security and patient privacy protection face severe challenges. Unclear data access permissions and inadequate encryption measures increase the risk of data leakage. Cross-hospital and cross-regional data collaboration and sharing mechanisms have not yet been established, requiring repeated examinations during patient referrals, increasing patient burden, wasting medical resources, and hindering the homogenization of regional treatment levels. These problems severely restrict the efficiency and quality of general surgical medical services, urgently requiring an intelligent, standardized, and end-to-end patient data management method. Summary of the Invention

[0005] This invention proposes an intelligent management method based on patient data from general surgery to address the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent management method based on patient data from general surgery, comprising the following steps: Comprehensive collection of multi-source patient data is achieved through four methods: hospital information system interface, smart terminal input, data transmission from laboratory equipment, and synchronization with wearable devices. This includes collecting basic patient information, clinical diagnosis and treatment data, laboratory examination data, surgery-related data, nursing record data, rehabilitation follow-up data, and lifestyle data. The collection frequency is dynamically adjusted according to the data type. Data standardization preprocessing involves using data cleaning algorithms to remove redundant data, identifying outliers using the Laida criterion, filling missing numerical and categorical data with the median and mode respectively, and performing unified standardization transformation on multi-source heterogeneous data. Structured storage and intelligent index building adopt a hybrid storage architecture of relational and non-relational databases, classifies and stores data according to data type, and builds multi-dimensional data indexes; Intelligent data analysis based on machine learning constructs a multi-task machine learning model for disease risk assessment, complication prediction, rehabilitation trend analysis, and treatment effect evaluation. Personalized medical intervention plans are generated by combining intelligent analysis results, clinical pathway guidelines, and physician experience to produce treatment plans, nursing plans, rehabilitation training plans, and follow-up plans. Dynamic data updates and real-time monitoring: Establish a dynamic data update mechanism to collect and update new data in real time, and monitor key indicators in real time. Hierarchical access control and data security are ensured by adopting a role-based access control policy, assigning different access permissions to five types of roles, using the AES-256 encryption algorithm to ensure the security of data storage and transmission, and establishing an access log auditing mechanism.

[0007] Furthermore, it also includes a dynamic risk assessment optimization step, introducing a dynamic cumulative assessment mechanism based on the risk assessment model, through formulas. Calculate the patient's real-time disease risk value during the diagnosis and treatment process, among which... The risk of illness at time t. for Risk weighting coefficients for each clinical indicator at any given time. for Standardized values ​​of each clinical indicator at any given time, where C is the patient's baseline risk value determined based on past medical history and age, and t is the cumulative time of the diagnosis and treatment process.

[0008] Furthermore, it includes multi-dimensional data quality assessment steps, constructing a data quality assessment index system that includes five dimensions: data completeness, accuracy, consistency, timeliness, and usability. Data completeness is calculated by the ratio of the actual data items collected to the data items that should be collected; data accuracy is verified by manually checking the consistency between the sample and the system data; data consistency is confirmed by cross-system data comparison; data timeliness is measured by the time difference between the data collection time and the data generation time; and data usability is determined by whether the data meets the requirements for clinical use. A data quality scoring mechanism is established, and the overall data quality score is given by comprehensively evaluating the results of the five dimensions.

[0009] Furthermore, it also includes a personalized rehabilitation progress prediction step, which constructs a rehabilitation progress prediction model based on the patient's postoperative rehabilitation data and historical rehabilitation cases, and uses formulas to predict the progress. Calculate the rate of change in rehabilitation progress, where Let be the rate of change in rehabilitation progress at time t. The natural recovery coefficient varies depending on the type of surgery. Let t represent the recovery progress. To achieve the target recovery progress, The coefficient representing the impact of adverse factors. This represents the combined value of unfavorable factors at time t.

[0010] Furthermore, it includes enhanced patient privacy protection measures, integrating privacy protection mechanisms into the entire process of data collection, storage, transmission, and use. Sensitive patient information is anonymized using irreversible encryption algorithms, generating unique anonymous identifiers for data management. Data transmission is encrypted using SSL / TLS protocols, and the entire access process is traced. For data involving research purposes, data aggregation and perturbation are further performed on top of the anonymization process, and privacy protection agreements are signed with data users.

[0011] Furthermore, it also includes steps to optimize the allocation of medical resources, based on the patient's disease risk level, rehabilitation needs, and the real-time status of hospital medical resources, using formulas. Calculate the rationality score of medical resource allocation, where S is the rationality score of resource allocation, and n is the number of medical resource types, including five categories: beds, nurses, rehabilitation therapists, medical equipment, and medicines (n=5). Let be the weight coefficient of the i-th type of medical resources. Let t be the patient's access to the i-th type of medical resource. Let t be the i-th type of medical resource at time t, and T be the resource allocation period. The degree of matching between patient demand and resource supply is quantified, and bed allocation, medical staff scheduling, and medical equipment dispatching plans are dynamically adjusted based on the scoring results.

[0012] Furthermore, it also includes multi-center data collaboration and sharing steps, establishing a cross-hospital and cross-regional general surgery patient data collaboration and sharing platform, adopting unified data standards and interface specifications, so that when patients are referred or treated across centers, the relevant medical institutions can obtain the patient's complete medical data through the platform after authorization, and at the same time establish a data sharing permission management mechanism.

[0013] Furthermore, it includes emergency early warning and rapid response procedures. Based on real-time monitoring of patients' vital signs, test indicators, and disease risk assessment results, multi-level emergency early warning thresholds are set. When a patient experiences life-threatening abnormalities or a rapid deterioration of their condition, a level-one warning is triggered, and the system automatically initiates the emergency response process, quickly pushing the warning information to the responsible physician, nurse, department director, and hospital emergency department. At the same time, the system displays the patient's current condition, past medical records, and emergency treatment plan. When general abnormalities occur, a level-two warning is triggered, reminding medical staff to pay attention and intervene in a timely manner. The emergency early warning mechanism covers three common critical situations: postoperative bleeding, septic shock, and respiratory failure.

[0014] Furthermore, it also includes intelligent generation and execution tracking steps for follow-up plans. Based on the patient's disease type, surgical method, and rehabilitation progress prediction results, combined with clinical follow-up guidelines, a personalized follow-up plan is automatically generated. The follow-up plan is dynamically adjusted according to the patient's rehabilitation status. Before the follow-up is carried out, the patient and medical staff are notified through three methods: SMS, APP push, and telephone reminder. During the follow-up, the follow-up data is recorded through a smart terminal and synchronized to the system, and the difference between the follow-up data and the rehabilitation goals is automatically compared.

[0015] Furthermore, it also includes clinical decision support steps. Based on the intelligent analysis results of patient data, combined with massive general surgery clinical case data and the latest clinical guidelines, a clinical decision support model is constructed. When physicians formulate treatment plans, the system automatically pushes treatment experience of similar cases, relevant clinical guideline recommendations and potential risk warnings, and provides comparative analysis of multiple treatment plans. Physicians choose the optimal plan based on the patient's specific situation and their own experience. At the same time, the system supports physicians to provide feedback on decision suggestions.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The comprehensiveness and accuracy of data management have been significantly improved. A multi-source data acquisition mechanism covers the entire patient diagnosis and treatment process, integrating multiple acquisition channels to ensure data integrity. Standardized preprocessing workflows use scientific algorithms to remove outlier data and fill in missing data, achieving unified conversion of data from different formats and providing a high-quality data foundation for subsequent analysis. Structured storage and multi-dimensional indexing design break down data silos, enabling rapid data retrieval and efficient access, greatly improving data management efficiency.

[0017] The intelligent analysis and personalized service capabilities have been significantly enhanced. Multi-task machine learning models deeply mine the clinical value of data, accurately completing disease risk assessment, complication prediction, rehabilitation trend analysis, and treatment effect evaluation, providing objective data support for clinical decision-making. Personalized treatment, nursing, rehabilitation, and follow-up plans generated based on the analysis results are fully tailored to individual patient differences, improving the pertinence and effectiveness of treatment and rehabilitation, and enhancing the patient's treatment experience and rehabilitation outcomes.

[0018] Real-time monitoring and early warning mechanisms enable proactive prevention and control of medical risks. Continuous monitoring of key postoperative indicators promptly detects abnormal changes and triggers alerts, providing ample intervention time for medical staff, reducing the probability of adverse events, and improving medical safety. Hierarchical access control and end-to-end encryption measures strictly define data access permissions for different roles, comprehensively protecting patient privacy and data security, and complying with relevant medical data management regulations.

[0019] The efficiency of medical resource allocation and regional collaboration has been significantly optimized. By quantifying the matching degree between patient needs and resource supply, resource allocation plans are dynamically adjusted to improve the utilization rate of medical resources and ensure that high-risk patients receive priority treatment. A multi-center data collaboration and sharing platform breaks down geographical barriers, enabling cross-institutional data interconnection, reducing redundant examinations, alleviating the burden on patients, and promoting the homogenization of regional medical standards. Clinical decision support functions integrate massive amounts of case data and clinical guidelines, providing physicians with scientific references, reducing medical errors, and improving the accuracy and efficiency of clinical decision-making.

[0020] Overall, this invention enables intelligent management of the entire process of general surgery patient data, from collection, preprocessing, storage, analysis to application. It not only improves the standardization and efficiency of data management, but also enhances the personalization and security of clinical services. At the same time, it optimizes the allocation of medical resources and the level of regional collaboration, providing strong support for the high-quality development of general surgery medical services. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of an intelligent management method for patient data based on general surgery proposed in this invention; Figure 2 A bar chart comparing key data management metrics; Figure 3 A dynamic line graph showing the accuracy of complication prediction; Figure 4 This is a radar diagram illustrating the multi-center collaborative effectiveness. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 4 A smart management method based on general surgery patient data includes the following steps: The comprehensive collection of multi-source patient data involves four methods: hospital information system interface, smart terminal input, data transmission from laboratory equipment, and synchronization with wearable devices. This process collects basic information, clinical diagnosis and treatment data, laboratory examination data, surgical-related data, nursing records, rehabilitation follow-up data, and lifestyle data from general surgery patients. Basic information includes name, age, gender, height, weight, past medical history, and allergy history. Clinical diagnosis and treatment data includes chief complaint, present illness, physical examination results, diagnosis, and medical orders. Laboratory examination data includes complete blood count, liver and kidney function tests, electrolytes, coagulation function tests, imaging reports, and pathology results. Surgical data includes surgery name, operation duration, intraoperative blood loss, anesthesia method, intraoperative complications, and postoperative drainage volume; nursing record data includes vital sign monitoring values, wound care status, medication administration records, and pain scores; rehabilitation follow-up data includes postoperative examination results, symptom improvement, and functional recovery status; lifestyle data includes dietary structure, daily routine, and exercise frequency. The collection frequency is dynamically adjusted according to the data type. Real-time monitoring data is collected every 5 minutes, routine medical data is entered in real time, laboratory test data is transmitted synchronously, and rehabilitation follow-up data is collected according to the follow-up period. The data collection integrity is ≥99.5%. The data standardization preprocessing steps employ data cleaning algorithms to remove duplicate, outlier, and invalid data; identify outliers exceeding three standard deviations from the mean using the Laida criterion; handle missing numerical data using median imputation; handle missing categorical data using mode imputation; and perform unified standardization transformation on data from different sources and formats. Textual data is converted into structured data using natural language processing technology, and numerical data is converted to the same order of magnitude using Z-score standardization. The data preprocessing accuracy is ≥99%. The structured storage and intelligent indexing steps utilize a hybrid storage architecture of relational and non-relational databases. Patient data is stored according to data type: basic information and clinical diagnosis data are stored in the relational database, while unstructured data such as imaging data and pathological slide images are stored in the non-relational database. Multi-dimensional data indexes are constructed, including patient ID indexes, treatment time indexes, disease type indexes, surgery type indexes, and examination item indexes. This supports rapid data queries based on single or combined conditions, with a query response time ≤1 second. Data storage security complies with the relevant requirements of the "Medical Data Security Guidelines" and the "Personal Information Protection Law." Based on machine learning, an intelligent data analysis process was implemented, constructing a multi-task machine learning model, including a disease risk assessment model, a complication prediction model, a rehabilitation trend analysis model, and a treatment effectiveness evaluation model. The disease risk assessment model uses a random forest algorithm, taking into account the patient's preoperative baseline data, laboratory test indicators, and surgical parameters, and outputs the postoperative disease deterioration risk level. The complication prediction model uses a logistic regression algorithm, trained based on historical complication data, to predict the probability of three common postoperative complications: incision infection, bleeding, and intestinal obstruction. The rehabilitation trend analysis model uses a long short-term memory neural network to predict the patient's functional recovery trajectory by analyzing rehabilitation data at different postoperative stages. The treatment effectiveness evaluation model quantifies the effectiveness of the treatment plan by comparing changes in clinical indicators before and after treatment; the model's prediction accuracy is ≥95%. The personalized medical intervention plan generation process combines intelligent data analysis results, clinical pathway guidelines, and physician experience to generate personalized treatment plans, nursing plans, rehabilitation training plans, and follow-up plans for patients. The treatment plan includes the type, dosage, route of administration, and frequency of medication. The nursing plan includes four specific measures: wound care, drainage tube care, pain management, and nutritional support. The rehabilitation training plan is based on the patient's postoperative recovery and develops progressive training content, duration, and frequency. The follow-up plan specifies the follow-up time, method, items, and recommendations for handling abnormal situations. The plan generation time is ≤3 minutes. The system implements dynamic data updates and real-time monitoring. It establishes a dynamic data update mechanism to collect new data during the patient's diagnosis and treatment process and update it to the database in real time. It monitors key indicators such as postoperative vital signs, drainage volume, and pain scores in real time, sets normal range thresholds for each indicator, and automatically triggers an early warning when an indicator exceeds the threshold or shows an abnormal trend. The early warning information is synchronized to the responsible physician and nurse through three methods: system messages, SMS, and APP push. The early warning response delay is ≤30 seconds. The system employs tiered access control and data security measures, using a role-based access control strategy to assign different data access permissions to five different roles: physicians, nurses, patients, administrators, and researchers. Physicians can view all data of their patients and perform treatment-related operations; nurses can view patient care-related data and enter nursing records; patients can view their own treatment plans, examination results, and rehabilitation plans; administrators are responsible for system maintenance and access control; and researchers can only access anonymized data. Patient data is encrypted during storage and transmission using the AES-256 encryption algorithm. Regular data backups are performed, and a data access log auditing mechanism is established to record all data access and operation behaviors, ensuring data security and patient privacy.

[0026] This invention also includes a dynamic risk assessment and optimization step, which introduces a dynamic cumulative assessment mechanism based on the disease risk assessment model, using formulas... Calculate the patient's real-time disease risk value during the diagnosis and treatment process, among which... The risk value for the condition at time t ranges from 0 to 100. for The risk weight coefficients for each clinical indicator at any given time are dynamically adjusted based on the indicator's importance, with a range of 0.1-0.8. for The standardized values ​​of each clinical indicator at any given time range from 0 to 1. C is the patient's baseline risk value, which is determined based on past medical history and age, and ranges from 5 to 20. t is the cumulative time of the diagnosis and treatment process in hours. Real-time integration accumulates the impact of clinical indicators on the risk of the disease at each time period, and the risk assessment results are updated in real time, making the risk assessment more consistent with changes in the patient's condition and providing timely and accurate risk references for clinical intervention.

[0027] This invention also includes a multi-dimensional data quality assessment step, constructing a data quality assessment index system encompassing five dimensions: data integrity, accuracy, consistency, timeliness, and usability. Data integrity is calculated by the ratio of actual collected data items to the required collected data items; data accuracy is verified by manually checking the consistency between the sample and the system data; data consistency is confirmed by cross-system data comparison; data timeliness is measured by the time difference between data collection time and data generation time; and data usability is determined by whether the data meets clinical usage requirements. A data quality scoring mechanism is established, and a total data quality score is given based on the assessment results of the five dimensions. When the total data quality score is below 80, a data supplementation or correction prompt is automatically triggered, and the type and cause of the data quality problem are recorded, providing a basis for optimizing the data collection process. The data quality assessment cycle is synchronized with the data collection cycle to ensure that patient data always meets the needs of clinical application and analysis.

[0028] This invention also includes a personalized rehabilitation progress prediction step, which constructs a rehabilitation progress prediction model based on the patient's postoperative rehabilitation data and historical rehabilitation cases, and uses a formula... Calculate the rate of change in rehabilitation progress, where The rate of change in rehabilitation progress at time t ranges from 0 to 0.1. The natural recovery coefficient is determined based on the type of surgery and ranges from 0.01 to 0.05. The value of the recovery progress at time t ranges from 0 to 1. The target recovery progress is set to a value of 1. The influence coefficient for adverse factors ranges from 0.005 to 0.02. The comprehensive value of adverse factors at time t ranges from 0 to 1. Taking into account the patient's own recovery ability and the impact of various adverse factors, the system accurately predicts the time nodes when the patient reaches each stage of rehabilitation. Based on the prediction results, the rehabilitation training program and follow-up plan are dynamically adjusted to improve the pertinence and effectiveness of rehabilitation intervention.

[0029] This invention also includes enhanced patient privacy protection steps, integrating privacy protection mechanisms into the entire process of data collection, storage, transmission, and use. Sensitive information such as patients' ID numbers, mobile phone numbers, and home addresses are de-identified using irreversible encryption algorithms to generate unique anonymous identifiers for data management. Data transmission is encrypted using SSL / TLS protocols to prevent data leakage. A data access whitelist system is established, allowing only authorized personnel to access patient data, with the entire access process being traced. For data involving research purposes, further data aggregation and perturbation processing are performed on top of the de-identification to ensure that the identity of individual patients cannot be identified. At the same time, privacy protection agreements are signed with data users to clarify the scope of data use and responsibilities, comprehensively protecting patient privacy and security.

[0030] This invention also includes a medical resource allocation optimization step, which, based on the patient's disease risk level, rehabilitation needs, and the real-time status of hospital medical resources, uses a formula... Calculate the rationality score of medical resource allocation, where S is the resource allocation rationality score ranging from 0 to 100, and n is the number of medical resource types, including five categories: beds, nurses, rehabilitation therapists, medical equipment, and medicines, with n=5. The weight coefficient for the i-th type of medical resource is determined based on its importance, ranging from 0.1 to 0.3, and summing to 1. The intensity of a patient's demand for the i-th type of medical resources at time t ranges from 0 to 1. The supply intensity of the i-th type of medical resources at time t ranges from 0 to 1, and T is the resource allocation cycle in days. This quantifies the matching degree between patient demand and resource supply. Based on the scoring results, the allocation of beds, the scheduling of medical staff, and the dispatching of medical equipment are dynamically adjusted to improve the utilization rate of medical resources, ensure that high-risk patients and critically ill patients receive priority treatment, and improve the overall efficiency of medical services.

[0031] This invention also includes a multi-center data collaborative sharing step, establishing a cross-hospital and cross-regional general surgery patient data collaborative sharing platform. It adopts unified data standards and interface specifications to achieve interconnection and interoperability of patient data between different medical institutions. When patients are referred or receive cross-center treatment, authorized medical institutions can obtain complete patient treatment data through the platform, reducing redundant examinations and information gaps. Simultaneously, a data sharing permission management mechanism is established to clarify the data access scope of different institutions. Blockchain technology is used to record the data sharing trajectory, making the data sharing process traceable and auditable. The data collaborative sharing process strictly follows the "Medical Data Security Guidelines" and the "Medical Institution Data Security Management Measures" to ensure the security and compliance of data sharing and improve the homogenization of general surgery treatment levels within the region.

[0032] This invention also includes emergency early warning and rapid response steps. Based on real-time monitoring of patient vital signs, test indicators, and disease risk assessment results, multi-level emergency early warning thresholds are set. When a patient experiences life-threatening abnormalities or a rapid deterioration of their condition, a level-one early warning is triggered, and the system automatically initiates the emergency response process, quickly pushing early warning information to the responsible physician, nurse, department director, and hospital emergency department. Simultaneously, the system displays the patient's current condition, past medical records, and emergency treatment plan, automatically reserving emergency resources including operating rooms, emergency equipment, and emergency medications to shorten emergency response time. When general indicators are abnormal, a level-two early warning is triggered, reminding medical staff to pay attention and intervene in a timely manner. The emergency early warning mechanism covers three common critical situations: postoperative bleeding, septic shock, and respiratory failure, reducing patient treatment risks and improving medical safety.

[0033] This invention also includes intelligent generation and execution tracking steps for follow-up plans. Based on the patient's disease type, surgical method, and rehabilitation progress prediction results, combined with clinical follow-up guidelines, a personalized follow-up plan is automatically generated, specifying follow-up time points, follow-up methods including outpatient follow-up, telephone follow-up, and online follow-up, and follow-up items including physical examination, laboratory examination, imaging review, and functional assessment. The follow-up plan can be dynamically adjusted according to the patient's rehabilitation status. Before the follow-up is executed, the patient and medical staff are notified through three methods: SMS, APP push, and telephone reminder. During the follow-up, follow-up data is recorded through a smart terminal and synchronized to the system. The difference between the follow-up data and the rehabilitation goals is automatically compared. When the rehabilitation progress is found to be lagging or abnormal, the follow-up frequency and intervention plan are adjusted in a timely manner. At the same time, the execution of the follow-up plan is tracked, and reminders and supervision are given for follow-ups that are not completed on time, ensuring that the follow-up work is carried out in a standardized and orderly manner and improving the patient's rehabilitation effect.

[0034] This invention also includes a clinical decision support step. Based on the intelligent analysis results of patient data, combined with massive general surgery clinical case data and the latest clinical guidelines, a clinical decision support model is constructed. When physicians formulate treatment plans, the system automatically pushes treatment experience of similar cases, relevant clinical guideline recommendations, and potential risk warnings, providing comparative analysis of multiple treatment plans, covering quantitative comparisons of four dimensions: expected efficacy, risk of complications, treatment costs, and recovery period. Physicians can choose the optimal plan based on the patient's specific situation and their own experience. At the same time, the system supports physicians to provide feedback on decision suggestions, continuously optimizing the decision support model. For complex and difficult cases, the system can generate case summaries and analysis reports to assist physicians in multidisciplinary consultations and discussions, improving the scientific nature, accuracy, and efficiency of clinical decision-making and reducing medical errors.

[0035] The following two examples further illustrate the specific implementation of this system: Example 1: Intelligent management of end-to-end data for gastrointestinal surgery patients in a tertiary hospital's general surgery department This embodiment targets the gastrointestinal surgery subspecialty of general surgery in a tertiary hospital, focusing on the whole process of data management for patients undergoing radical gastrectomy for gastric cancer, including preoperative assessment, intraoperative monitoring, postoperative care, and rehabilitation follow-up. It involves 50 patients aged 45-75 years, covering different levels of surgical difficulty and basic health conditions. This method achieves standardized collection, intelligent analysis, and personalized intervention of patient data, thereby improving diagnostic and treatment efficiency and medical safety.

[0036] I. Implementation Process Details Comprehensive multi-source patient data collection employs four methods to integrate patient data throughout the entire process: Synchronizing basic patient information and past medical records via the hospital information system interface, including name, age, gender, height, weight, history of gastrointestinal diseases, history of hypertension and diabetes, and history of drug allergies; Real-time input of clinical medical data by medical staff via smart terminals, including chief complaints such as upper abdominal pain and loss of appetite, detailed records of the onset and progression of symptoms in the present medical history, physical examination results including the location of abdominal tenderness and bowel sounds, a clear diagnosis of gastric cancer stage, and medical orders covering preoperative medication and examination appointments; Data transmission from laboratory equipment to obtain preoperative and postoperative laboratory test data, including complete blood count (white blood cell count, hemoglobin, platelets, etc.), liver and kidney function tests (alanine aminotransferase, creatinine, blood urea nitrogen, etc.), electrolytes (potassium, sodium, chloride), coagulation function tests (prothrombin time, activated partial thromboplastin time), imaging reports (gastroscopy, abdominal CT results), and pathological examination results (clear tumor differentiation); Surgical-related data input via smart terminals, including surgical data... The procedure was named laparoscopic radical gastrectomy for gastric cancer. The operation time was recorded from the start of anesthesia to the end of suturing. Intraoperative blood loss was calculated by combining suction measurement and gauze weighing. General anesthesia was used. Intraoperative complications were recorded, including whether there was massive bleeding or organ damage. Postoperative drainage volume was recorded hourly. Nursing records were entered in real-time through a nursing terminal. Vital signs monitoring values ​​included temperature, heart rate, respiratory rate, and blood pressure, collected every 5 minutes. Wound care records included dressing exudate and the presence of redness and swelling. Medication administration records included the time and dosage of antibiotics and analgesics. Pain scoring used the NRS method. Follow-up data was collected synchronously through outpatient visits and an online platform. Postoperative examination results included complete blood count, tumor markers, and abdominal ultrasound results. Symptom improvement records included the degree of pain relief and food intake. Functional recovery status was assessed by evaluating gastrointestinal motility. Lifestyle data was collected through a patient-side app. Dietary structure records the proportion of staple food, protein, and vegetables consumed. Sleep patterns recorded sleep duration and bedtime. Exercise frequency recorded the number of postoperative walks and rehabilitation training sessions.

[0037] Data standardization preprocessing employed data cleaning algorithms to process the collected data, identified outliers using the Laida criterion, and calculated the historical mean of white blood cell counts to be 6.5 × 10⁻⁶. / L, standard deviation is 1.2× / L, which will exceed 6.5±3×1.2, or 2.9-10.1× Values ​​within the / L range were identified as outliers and removed. Missing hemoglobin values ​​were filled using the median imputation method, using the median hemoglobin value of patients of the same age and surgical type. Missing allergy history data were filled using the mode imputation method, using the mode of allergy history statistics for patients in the department as the imputation value. Data of different formats were standardized and converted. Natural language processing technology was used to extract key information from textual data such as gastroscopy reports and pathology diagnoses, transforming them into structured data such as fields for tumor location, size, and differentiation degree. Numerical data were processed using the Z-score standardization method, calculated using the following formula: Where X is the original data, This is the average of the indicator. For standard deviation, blood pressure, heart rate, and test results are converted to the same order of magnitude.

[0038] The structured storage and intelligent indexing system employs a hybrid storage architecture based on relational and non-relational databases. Structured data such as patient basic information, clinical diagnosis and treatment data, and nursing records are stored in the relational database, with each data table linked by patient ID. Unstructured data such as gastroscopy images, abdominal CT images, and pathological slide images are stored in the non-relational database, using distributed storage to ensure fast access. A multi-dimensional data index is constructed: the patient ID index uses a unique identifier to link all data; the treatment time index categorizes data by admission time, surgery time, and follow-up time; the disease type index categorizes data by gastric cancer, gastric ulcer, and intestinal obstruction; the surgery type index categorizes data by laparoscopic surgery and open surgery; and the examination item index categorizes data by blood routine, imaging examination, and pathological examination. Querying is supported using combined conditions such as "radical gastrectomy + 3 days post-surgery + abnormal heart rate." Data storage uses the AES-256 encryption algorithm, complying with the relevant requirements of the "Medical Data Security Guidelines" and the "Personal Information Protection Law."

[0039] Based on machine learning, intelligent data analysis constructs multi-task machine learning models. The disease risk assessment model uses a random forest algorithm, inputting 12 parameters such as the patient's preoperative age, underlying diseases, tumor stage, liver and kidney function indicators, and operation duration. It is trained using data from 500 historical cases and outputs three risk levels: high, medium, and low. The complication prediction model uses a logistic regression algorithm, trained based on historical complication data from 300 gastric cancer surgery patients. It inputs indicators such as postoperative white blood cell count, body temperature, drainage volume, and pain score to predict the probability of incision infection, bleeding, and intestinal obstruction. The rehabilitation trend analysis model uses a long short-term memory neural network, inputting data such as vital signs, diet, and activity level from 1 to 14 days postoperatively to predict the patient's gastrointestinal function recovery trajectory. The treatment effect evaluation model quantifies the effectiveness of the treatment plan by comparing preoperative and postoperative tumor marker levels, symptom improvement, and quality of life scores.

[0040] Dynamic risk assessment of disease and generation of personalized medical intervention plans introduce a dynamic cumulative assessment mechanism based on the disease risk assessment model, through formulas. Calculate the real-time risk value of the disease. Taking a 60-year-old gastric cancer patient as an example, t=24 hours, i.e., 1 day post-surgery. Risk weighting coefficients of various clinical indicators at different times In the mean time, heart rate k=0.7, body temperature k=0.6, drainage volume k=0.5, and white blood cell count k=0.8. Standardized values ​​were set for each indicator: heart rate 0.8, body temperature 0.7, drainage volume 0.6, and white blood cell count 0.9. The baseline risk value (C) was set to 15 due to the patient's history of hypertension. Integral calculations were then performed. =(0.7×0.8+0.6×0.7+0.5×0.6+0.8×0.9)×6=(0.56+0.42+0.3+0.72)×6=2.0×6=12, therefore R(24)=12+15=27, the risk level is low risk. Based on model analysis results, gastric cancer diagnosis and treatment guidelines, and the experience of the chief physician, a personalized treatment plan was generated for the patient: The treatment plan included postoperative intravenous infusion of cephalosporin antibiotics, 2g twice daily, administered via intravenous drip for 5 consecutive days; the nursing plan included wound care (daily dressing changes), drainage tube maintenance (keeping it patent and secured), pain management (analgesics every 6 hours for the first 48 hours postoperatively), and nutritional support (parenteral nutrition for days 1-3 postoperatively); the rehabilitation training plan included bed turning on day 1, standing at the bedside on day 3, and indoor walking for 10 minutes on day 5, gradually increasing the duration; the follow-up plan included outpatient follow-up one month postoperatively, with repeat blood tests and tumor markers, and an abdominal CT scan three months postoperatively, with outpatient follow-up as the confirmed method. For abnormal situations, such as persistent high fever, immediate medical attention was advised. The plan was generated in 2.2 minutes, meeting the requirements.

[0041] A dynamic data update mechanism has been established for real-time monitoring and emergency early warning. New data such as postoperative vital signs, drainage volume, and pain scores are entered into the system in real time and synchronized to the database, with key indicators updated every 5 minutes. Normal range thresholds are set for key indicators such as heart rate, blood pressure, body temperature, and drainage volume. The normal range for heart rate is 60-100 beats / minute, blood pressure is 90-140 / 60-90 mmHg, body temperature is 36.0-37.5℃, and drainage volume is ≤100ml per hour within 24 hours postoperatively. When a patient's body temperature reaches 38.2℃ 6 hours postoperatively, exceeding the normal threshold, the system automatically triggers a level-two early warning. The warning information is synchronized to the responsible physician and nurse via system messages, SMS, and APP push notifications, with a 22-second response delay. Upon receiving the warning, medical staff promptly assess the situation. If increased exudation from the incision dressing is detected, it is considered a precursor to incision infection. The antibiotic regimen is adjusted promptly, and wound care is strengthened to prevent the condition from worsening.

[0042] Hierarchical access control and data security are implemented through a role-based access control policy. Physicians can view all data of their patients, including medical records, test results, and imaging data, and can enter medical orders and treatment opinions. Nurses can view patient care-related data, including vital signs and wound care records, and can enter nursing records and medication administration status. Patients can view their own treatment plans, test results, and rehabilitation plans through the app, but cannot view other patients' data. Administrators are responsible for system user management, access control, and system maintenance. Researchers can only access anonymized data, where sensitive information such as patient names, ID numbers, and mobile phone numbers have been replaced with anonymous identifiers. AES-256 encryption is used for data storage and transmission encryption. A full data backup is performed daily at midnight, and a weekly off-site backup is conducted. A data access log auditing mechanism is established to record all users' login times, accessed data, and operational behaviors, including data queries, modifications, and exports. Audit logs are retained for 6 months to ensure data security and patient privacy.

[0043] II. Data Characterization for Effectiveness Verification Table 1: Comparison of indicators before and after the implementation of data management for patients undergoing gastrointestinal surgery in tertiary hospitals Table 1 clearly demonstrates the significant advantages of this method in the management of gastrointestinal surgery patients in the general surgery department of a tertiary hospital. Data collection completeness improved from 92% to 99.7%, thanks to a multi-source data collection mechanism and standardized processes, integrating various channels such as hospital information systems, smart terminals, and testing equipment to ensure no data omissions throughout the process. Data query response time was reduced from 8.5 seconds to 0.7 seconds, attributed to structured storage and multi-dimensional index design, breaking down traditional "data silos," enabling efficient data retrieval, and significantly improving the work efficiency of medical staff. Complication prediction accuracy improved from 76% to 95.6%, reflecting the intelligent analytical capabilities of the machine learning model. By deeply mining multi-dimensional data, it provides objective support for complication prediction, helping medical staff intervene early. Early warning response delay was reduced from 5.2 minutes to 22 seconds; real-time monitoring and rapid early warning mechanisms enabled timely detection of abnormal conditions, buying time for clinical intervention. The patient's recovery period was shortened from 21 days to 16 days, thanks to the precise implementation of personalized diagnosis and treatment and rehabilitation plans. This approach fully considers individual patient differences, improves treatment and rehabilitation outcomes, and verifies the core value of this method in enhancing the quality and efficiency of medical services.

[0044] Example 2: Data Management and Multicenter Collaboration for Patients Undergoing Laparoscopic Cholecystectomy in General Surgery Department of Primary Hospitals This embodiment focuses on the management of diagnostic and treatment data for patients undergoing laparoscopic cholecystectomy in the general surgery department of a primary hospital. It involves 40 patients aged 30-65 years, all diagnosed with gallstones and cholecystitis. At the same time, it connects two tertiary hospitals in the region to build a multi-center data collaboration and sharing platform to achieve interconnection of patient data and optimized allocation of medical resources, thereby improving the diagnostic and treatment level of primary hospitals and the efficiency of regional medical collaboration.

[0045] I. Implementation Process Details Comprehensive patient data collection from multiple sources involves four methods to collect patient data throughout the entire process: First, the hospital information system interface synchronizes basic patient information, including name, age, gender, height, weight, history of gallbladder disease, surgical history, and allergy history. Second, smart terminals input clinical diagnosis and treatment data, such as chief complaint of right upper quadrant pain, nausea, and vomiting; current medical history records the frequency and triggers of pain attacks; physical examination results include whether Murphy's sign is positive; the diagnosis is gallstones with cholecystitis; and medical orders include preoperative examination appointments and medications. Third, laboratory equipment data is transmitted to obtain laboratory test data, including essential preoperative tests such as complete blood count, liver and kidney function, electrolytes, and coagulation function; and abdominal ultrasound reports clarify gallbladder size, number, and location of stones. Fourth, smart terminals input surgical data, such as the surgical name (laparoscopic cholecystectomy) and the surgical duration (from anesthesia). From the start of anesthesia to the end of the operation, intraoperative blood loss was measured using a suction device. The anesthesia method was either general or epidural. Intraoperative complications were recorded, including bile leakage and bleeding. Postoperative drainage volume was recorded hourly. Nursing records were entered into a nursing terminal, with vital signs monitored every 5 minutes. Wound care was recorded for any bleeding or exudation. Medication administration records included the use of antibiotics and antispasmodics. Pain was assessed using the NRS score. Rehabilitation follow-up data was collected through outpatient visits and an online platform. Postoperative examination results included abdominal ultrasound and complete blood count. Symptom improvement was recorded, including the degree of pain relief. Functional recovery was assessed, including dietary and digestive function. A patient-side app collected lifestyle data, including the proportion of fatty foods in the diet, the frequency of staying up late, and postoperative activity levels. Data collection completeness reached 99.6%, meeting clinical needs.

[0046] Data standardization preprocessing and structured storage employ data cleaning algorithms to process collected data, and the Raida criterion to identify outliers. The mean alanine aminotransferase (ALT) level is calculated to be 40 U / L with a standard deviation of 10 U / L; outliers are defined as 10-70 U / L, and data exceeding this range are discarded. Median imputation is used to handle missing platelet counts, and mode imputation is used to handle missing surgical history classification data. Textual data is processed using natural language processing to extract key information and transform it into structured data, such as extracting fields like gallbladder wall thickness and stone size from abdominal ultrasound reports. Numerical data is standardized using Z-scores to convert it to the same order of magnitude. The data preprocessing accuracy reaches 99.2%. In the hybrid storage architecture, structured data is stored in a relational database, while unstructured data, such as abdominal ultrasound images and surgical videos, is stored in a non-relational database. Multi-dimensional indexes are constructed for patient ID, treatment time, disease type, surgical type, and examination items, supporting combined condition queries with a query response time of 0.8 seconds. Data storage uses AES-256 encryption, complying with medical data security standards.

[0047] A multi-task machine learning model was constructed for intelligent data analysis and rehabilitation progress prediction based on machine learning. The disease risk assessment model takes into account parameters such as patient age, underlying diseases, degree of gallbladder inflammation, and liver and kidney function, and outputs the postoperative disease risk level. The complication prediction model, trained on historical data, predicts the probability of incision infection, bile leakage, and bleeding. The rehabilitation trend analysis model analyzes postoperative rehabilitation data to predict the functional recovery trajectory. The treatment effect evaluation model compares changes in symptoms and examination indicators before and after treatment. The model's prediction accuracy reached 95.3%. In the personalized rehabilitation progress prediction step, a formula was used... Calculate the rate of change in rehabilitation progress. Taking a 45-year-old patient as an example, t=7 days, i.e., 1 week post-surgery. The natural recovery coefficient was determined to be 0.03 for laparoscopic surgery. The current recovery progress is 0.6. Target recovery progress 1, The influence coefficient for adverse factors is set to 0.01. The composite value of adverse factors is taken as 0.3 due to the patient's reduced postoperative activity. The calculation yields... (7) = 0.03 × 0.6 × (1 - 0.6 / 1) - 0.01 × 0.3 = 0.03 × 0.6 × 0.4 - 0.003 = 0.0072 - 0.003 = 0.0042, that is, the rate of change in rehabilitation progress is 0.0042. Based on this result, it is predicted that the patient will need 10 days to achieve full recovery. The rehabilitation training program is dynamically adjusted, the indoor walking time on the 8th day after surgery is increased to 15 minutes, and mild abdominal massage is added on the 10th day after surgery to improve the targeted nature of rehabilitation intervention.

[0048] Personalized medical intervention plans and follow-up tracking are generated by combining intelligent data analysis results, clinical pathway guidelines for gallbladder disease, and physician experience. The treatment plan includes postoperative intravenous antibiotics (1.5g twice daily for 3 days) and analgesics if the pain score is ≥4. The nursing plan includes wound care (daily observation of drainage), drainage tube maintenance (keeping it fixed and patent), pain management (administering medication as needed), and nutritional support (gradual transition to a normal diet after 6 hours of fasting). The rehabilitation training plan includes bed activity on day 1, standing at the bedside on day 2, and indoor walking for 5 minutes on day 3, gradually increasing the duration. The follow-up plan includes outpatient follow-up one week post-surgery, abdominal ultrasound, and online follow-up one month post-surgery to assess digestive function. Before the follow-up plan is implemented, patients and medical staff are notified via SMS, app push notifications, and telephone reminders. During the follow-up, data is recorded via smart terminals and synchronized to the system, automatically comparing the follow-up data with the rehabilitation goals. If a patient still has significant abdominal pain one week post-surgery, the follow-up frequency is adjusted to once every 3 days, the frequency of analgesic use is increased, and the patient is encouraged to strengthen rehabilitation training.

[0049] A multi-center data collaboration and sharing platform for optimizing medical resource allocation has been established across hospitals. This platform adopts unified data standards and interface specifications, enabling data interconnection between primary hospitals and two tertiary hospitals within the region. When a patient needs to be transferred to a tertiary hospital due to postoperative bile leakage, the tertiary hospital physicians, with the patient's authorization, can access the patient's complete medical data through the platform, including preoperative examination results, surgical records, postoperative nursing records, and follow-up reports. This eliminates the need for repeated abdominal CT scans and blood tests, allowing for the direct development of treatment plans based on existing data. The data sharing process utilizes blockchain technology to record the sharing trajectory, clearly identifying the accessing institutions and personnel, ensuring traceability and auditability. In the medical resource allocation optimization step, a formula is used... Calculate a score for the rationality of resource allocation. n=5, representing beds, nurses, rehabilitation therapists, medical equipment, and medicines. The breakdown is as follows: bed 0.3, nurses 0.25, rehabilitation therapists 0.2, medical equipment 0.15, medications 0.1; T=3 days. The patient's demand intensity for each resource is as follows: beds 0.9, nurses 0.8, rehabilitation therapists 0.6, medical equipment 0.7, and medicines 0.8. Resource supply intensity is calculated as follows: beds 0.8, nurses 0.9, rehabilitation therapists 0.7, medical equipment 0.8, and medicines 0.9. (Integrated calculation) The breakdown is as follows: bed 2.16, nurses 2.16, rehabilitation therapists 1.26, medical equipment 1.68, and medications 2.16. S = 0.3 × 2.16 + 0.25 × 2.16 + 0.2 × 1.26 + 0.15 × 1.68 + 0.1 × 2.16 = 0.648 + 0.54 + 0.252 + 0.252 + 0.216 = 1.908, resulting in a standardized score of 95.4. Based on the score, resource allocation was adjusted, prioritizing a single-room ward for this patient and increasing the frequency of rounds by the responsible nurse to ensure their treatment needs were met.

[0050] Tiered access control and enhanced privacy protection are implemented based on role-based data access permissions, with clear divisions of authority among physicians, nurses, patients, administrators, and researchers. In the enhanced patient privacy protection process, sensitive information such as ID numbers, mobile phone numbers, and home addresses are anonymized using irreversible encryption algorithms to generate unique anonymous identifiers; data transmission is encrypted using SSL / TLS protocols; a data access whitelist system is established, authorizing only medical personnel to access patient data, with the entire access process logged; research data undergoes aggregation and perturbation processing after anonymization, making it impossible to identify individual patients, and privacy protection agreements are signed with research institutions, clearly defining the scope of data use and responsibilities.

[0051] The clinical decision support and emergency early warning model integrates data from 1000 laparoscopic cholecystectomy cases within the region with the latest clinical guidelines. When physicians in primary care hospitals develop treatment plans for complex cases, the system automatically pushes the treatment experience of three similar cases, including surgical techniques, complication management methods, relevant clinical guideline recommendations such as postoperative antibiotic use duration, and potential risk warnings such as high-risk factors for bile leakage. It provides comparative analysis of two treatment options, covering quantitative data across four dimensions: expected efficacy, complication risk, treatment cost, and recovery period. Physicians can choose the optimal plan based on the patient's specific situation. The system supports physician feedback on decision-making suggestions, continuously optimizing the model. The emergency warning mechanism sets multiple thresholds. When a patient experiences life-threatening abnormalities such as a heart rate >120 beats / minute, blood pressure <90 / 60 mmHg, or drainage volume >200 ml per hour after surgery, a Level 1 warning is triggered. The system automatically initiates the emergency response process, pushes warning information to the responsible physician, nurse, department director, and hospital emergency department, displays the patient's current condition, previous medical records, and emergency treatment plan, and automatically reserves emergency equipment and medications to shorten the emergency response time.

[0052] II. Data Characterization for Effectiveness Verification Table 2: Comparison of indicators before and after the implementation of data management for patients undergoing laparoscopic cholecystectomy in primary hospitals. Table 2 highlights the application value of this method in general surgery departments of primary hospitals and in multi-center collaborative scenarios. The data preprocessing accuracy improved from 88% to 99.2%, thanks to a standardized preprocessing process. Scientific algorithms removed outliers and filled in missing data, achieving data format uniformity and providing a high-quality data foundation for clinical analysis. The rate of duplicate examinations in inter-hospital referrals decreased from 75% to 12%, primarily due to the construction of a multi-center data collaboration and sharing platform. This broke down the "data silos" between hospitals within the region, allowing patients to directly share complete diagnostic and treatment data during referrals, avoiding duplicate examinations, reducing the economic burden on patients, and saving medical resources. The utilization rate of medical resources increased from 65% to 89%, stemming from an optimized medical resource allocation mechanism. By quantifying the matching degree between patient needs and resource supply, the resource allocation plan is dynamically adjusted to ensure resources are tilted towards patients with high needs, improving resource utilization efficiency. Clinical decision-making efficiency decreased from 45 minutes / case to 15 minutes / case, demonstrating the advantages of the clinical decision support function. Integrating massive case data and clinical guidelines provides physicians with scientific references, reducing decision-making time and errors. Patient satisfaction increased from 72 to 94 points, which comprehensively reflects the effectiveness of this method in improving the accuracy of diagnosis and treatment, reducing the medical burden, and improving rehabilitation outcomes, and verifies the significant role of this method in improving the quality of medical services in primary hospitals.

[0053] Reference Figure 2 This diagram visually illustrates the advantages of this invention across the entire data management process. Data collection integrity is improved to 99.7%, thanks to a multi-source collection mechanism integrating hospital information systems, smart terminals, and other channels, eliminating data omissions. Query response time is reduced from 8.5 seconds to 0.7 seconds, benefiting from structured storage and multi-dimensional index design, breaking down "data silos." Complication prediction accuracy improves by 19.6 percentage points, demonstrating the machine learning model's ability to deeply mine multi-dimensional data. Early warning response latency is reduced from 312 seconds to 22 seconds, with real-time monitoring and tiered early warning mechanisms enabling rapid detection of abnormal conditions. The average recovery period is shortened by 5 days, attributed to the precise implementation of personalized treatment and rehabilitation plans, fully adapting to individual patient differences, validating the comprehensive value of this method in improving medical efficiency and quality.

[0054] Reference Figure 3This figure illustrates the dynamic changes in the accuracy of predicting postoperative complications from day 1 to 10, highlighting the sustained advantages of this invention. Traditional methods show a slow increase in accuracy over time, from 72% to 82%, but due to reliance on accumulated human experience and limited statistical data, they struggle to capture dynamic changes in the patient's condition. The accuracy of this invention consistently remains between 93% and 97%, steadily increasing over time. This is primarily due to the combination of a dynamic cumulative assessment mechanism and a machine learning model. By integrating real-time dynamic data such as postoperative vital signs and laboratory indicators, and quantifying the risk weights for each time period using formulas, the predictive model is dynamically optimized. Particularly in days 5-10 postoperatively, the accuracy gap between traditional methods and this invention widens to 17-19 percentage points, demonstrating the accuracy of this method in early warning of complications and providing healthcare professionals with ample time for intervention.

[0055] Reference Figure 4 This diagram assesses the multi-center collaborative efficiency from five dimensions, fully demonstrating the breakthroughs of this invention. In the traditional model, data sharing timeliness is only 35%, and cross-hospital data flow is difficult due to the lack of unified standards and platforms; the rate of duplicate examinations is as high as 75%, increasing the burden on patients and wasting resources. This invention, through a multi-center data collaborative sharing platform, achieves 98% real-time data interoperability, reducing the duplicate examination rate to 12%, and ensuring data security and standardized sharing by relying on unified data standards and blockchain traceability technology. Resource allocation rationality increases from 52% to 89%, dynamically optimizing the allocation of resources such as beds and medical staff through a formula that quantifies the matching degree of demand and supply. The consistency of treatment plans and referral efficiency increase by 52 and 45 percentage points respectively, stemming from standardized treatment pathways and complete data support, promoting the homogenization of medical services within the region, which is particularly significant for improving the treatment level of primary hospitals.

[0056] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An intelligent management method based on patient data from general surgery, characterized in that, Includes the following steps: Comprehensive collection of multi-source patient data is achieved through four methods: hospital information system interface, smart terminal input, data transmission from laboratory equipment, and synchronization with wearable devices. This includes collecting basic patient information, clinical diagnosis and treatment data, laboratory examination data, surgery-related data, nursing record data, rehabilitation follow-up data, and lifestyle data. The collection frequency is dynamically adjusted according to the data type. Data standardization preprocessing involves using data cleaning algorithms to remove redundant data, identifying outliers using the Laida criterion, filling missing numerical and categorical data with the median and mode respectively, and performing unified standardization transformation on multi-source heterogeneous data. Structured storage and intelligent index building adopt a hybrid storage architecture of relational and non-relational databases, classifies and stores data according to data type, and builds multi-dimensional data indexes; Intelligent data analysis based on machine learning constructs a multi-task machine learning model for disease risk assessment, complication prediction, rehabilitation trend analysis, and treatment effect evaluation. Personalized medical intervention plans are generated by combining intelligent analysis results, clinical pathway guidelines, and physician experience to produce treatment plans, nursing plans, rehabilitation training plans, and follow-up plans. Dynamic data updates and real-time monitoring: Establish a dynamic data update mechanism to collect and update new data in real time, and monitor key indicators in real time. Hierarchical access control and data security are ensured by adopting a role-based access control policy, assigning different access permissions to five types of roles, using the AES-256 encryption algorithm to ensure the security of data storage and transmission, and establishing an access log auditing mechanism.

2. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes a dynamic risk assessment optimization step, introducing a dynamic cumulative assessment mechanism based on the risk assessment model, through formulas. Calculate the patient's real-time disease risk value during the diagnosis and treatment process, among which... The risk of illness at time t. for Risk weighting coefficients for each clinical indicator at any given time. for Standardized values ​​of each clinical indicator at any given time, where C is the patient's baseline risk value determined based on past medical history and age, and t is the cumulative time of the diagnosis and treatment process.

3. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes multi-dimensional data quality assessment steps, constructing a data quality assessment index system that includes five dimensions: data completeness, accuracy, consistency, timeliness, and usability. Data completeness is calculated by the ratio of the actual data items collected to the data items that should be collected; data accuracy is verified by manually checking the consistency between the sample and the system data; data consistency is confirmed by cross-system data comparison; data timeliness is measured by the time difference between the data collection time and the data generation time; and data usability is determined by whether the data meets the requirements for clinical use. A data quality scoring mechanism is established, and the overall data quality score is given by comprehensively evaluating the results of the five dimensions.

4. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes a personalized rehabilitation progress prediction step, which constructs a rehabilitation progress prediction model based on the patient's postoperative rehabilitation data and historical rehabilitation cases, and uses formulas... Calculate the rate of change in rehabilitation progress, where Let be the rate of change in rehabilitation progress at time t. The natural recovery coefficient varies depending on the type of surgery. Let t represent the recovery progress. To achieve the target recovery progress, The coefficient representing the impact of adverse factors. This represents the combined value of unfavorable factors at time t.

5. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes enhanced patient privacy protection measures, integrating privacy protection mechanisms into the entire process of data collection, storage, transmission, and use. Sensitive patient information is desensitized using irreversible encryption algorithms, generating a unique anonymous identifier for data management. Data transmission is encrypted using SSL / TLS protocols, and the entire access process is traced. For data involving scientific research purposes, data aggregation and perturbation are further performed on the basis of desensitization. At the same time, privacy protection agreements are signed with data users.

6. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes steps for optimizing the allocation of medical resources, based on the patient's disease risk level, rehabilitation needs, and the real-time status of hospital medical resources, using formulas. Calculate the rationality score of medical resource allocation, where S is the rationality score of resource allocation, and n is the number of medical resource types, including five categories: beds, nurses, rehabilitation therapists, medical equipment, and medicines (n=5). Let be the weight coefficient of the i-th type of medical resources. Let t be the patient's access to the i-th type of medical resource. Let t be the i-th type of medical resource at time t, and T be the resource allocation period. The degree of matching between patient demand and resource supply is quantified, and bed allocation, medical staff scheduling, and medical equipment dispatching plans are dynamically adjusted based on the scoring results.

7. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes multi-center data collaboration and sharing steps, establishing a cross-hospital and cross-regional general surgery patient data collaboration and sharing platform, adopting unified data standards and interface specifications, and allowing relevant medical institutions to obtain complete patient diagnosis and treatment data through the platform after authorization when patients are referred or treated across centers, while establishing a data sharing permission management mechanism.

8. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes emergency early warning and rapid response steps. Based on real-time monitoring of patients' vital signs, test indicators, and disease risk assessment results, it sets multi-level emergency early warning thresholds. When a patient's life-threatening indicators are abnormal or their condition deteriorates rapidly, a level one early warning is triggered, and the system automatically initiates the emergency response process, quickly pushing the early warning information to the responsible physician, nurse, department director, and hospital emergency department. At the same time, the system displays the patient's current condition, previous medical records, and emergency treatment plan. When general indicators are abnormal, a level two early warning is triggered, reminding medical staff to pay attention and intervene in a timely manner. The emergency early warning mechanism covers three common critical situations: postoperative bleeding, septic shock, and respiratory failure.

9. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes intelligent generation and execution tracking steps for follow-up plans. Based on the patient's disease type, surgical method, and recovery progress prediction results, combined with clinical follow-up guidelines, it automatically generates personalized follow-up plans. The follow-up plans are dynamically adjusted according to the patient's recovery status. Before the follow-up is carried out, patients and medical staff are notified through three methods: SMS, APP push, and telephone reminder. During the follow-up, follow-up data is recorded through smart terminals and synchronized to the system, and the difference between the follow-up data and the recovery goals is automatically compared.

10. The intelligent management method for patient data based on general surgery according to claim 1, characterized in that, It also includes clinical decision support steps. Based on the intelligent analysis results of patient data, combined with massive general surgery clinical case data and the latest clinical guidelines, a clinical decision support model is constructed. When physicians formulate treatment plans, the system automatically pushes treatment experience of similar cases, relevant clinical guideline recommendations and potential risk warnings, and provides comparative analysis of multiple treatment plans. Physicians choose the optimal plan based on the patient's specific situation and their own experience. At the same time, the system supports physicians to provide feedback on decision suggestions.

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