113 results about "Nursing care quality" patented technology

The invention discloses a critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

The invention relates to the field of medical informatization, in particular to a nursing risk assessment system and method based on multi-modal data. According to the nursing risk assessment system, data acquisition, preprocessing, multi-modal fusion, risk assessment and early warning are integrated, and comprehensive, accurate, timely and personalized nursing assessment is realized. The system collects physiological signals, nursing behaviors and environment data, the data are preprocessed and fused to generate feature vectors, a risk assessment matrix is constructed, and risk indexes are calculated. The risk early warning module sends early warning information to the nurse work station in real time, and nursing safety is ensured. Innovative technologies such as multi-modal data fusion, real-time dynamic evaluation, personalized adjustment and the like greatly improve the nursing quality, reduce the occurrence rate of adverse events, relieve the burden of medical staff and improve the utilization efficiency of medical resources. Long-term application is hopeful to significantly improve patient prognosis and reduce medical cost, and makes an important contribution to development of intelligent medical treatment and precise nursing.

The invention discloses a patient rehabilitation monitoring method and system based on nursing data, and particularly relates to the technical field of rehabilitation monitoring. Core rehabilitation indexes are extracted by acquiring and classifying nursing data of a patient; analyzing the nursing data, identifying the current rehabilitation stage of the patient and evaluating the matching degree of the rehabilitation target and the core rehabilitation index; performing trend analysis on the time sequence of the nursing data, identifying abnormal data and evaluating the interference degree on the rehabilitation process; judging whether the rehabilitation progress of the patient meets the expectation or not according to the evaluation results of the matching degree and the interference degree; when the rehabilitation progress does not conform to the expectation, analyzing the demand change of the patient and evaluating the help effect of the nursing scheme; by comprehensively analyzing the matching degree, the interference degree and the nursing effect, the rehabilitation effect of the patient is evaluated, whether the nursing scheme needs to be adjusted or not is judged, individuation, accuracy and effectiveness of the nursing scheme are improved, the rehabilitation process of the patient can be accelerated, and the nursing quality is improved.

The invention discloses a PICC catheter real-time data acquisition-based nursing quality monitoring system, and relates to the technical field of nursing quality monitoring, the system comprises a data acquisition module, a real-time analysis module, a long-term tracking module, a risk dynamic evaluation module, a nursing intervention module and a patient monitoring module, and the modules are in electric signal connection. According to the nursing quality monitoring system based on PICC real-time data collection, by continuously monitoring physiological parameters of a patient and PICC related data, nursing personnel can respond to demand changes of the patient in time, PICC related complications can be effectively recognized and prevented, abnormal conditions can be found in time, intervention measures are taken, and the nursing quality is improved. Therefore, the risk of complications is reduced, the safety of the patient is improved, nursing personnel can make a personalized nursing plan more accurately, the pertinence and effectiveness of nursing are improved, and the treatment effect of the patient is improved.

The invention, which relates to the technical field of nursing quality monitoring, discloses a nursing quality monitoring and feedback system comprising a data acquisition monitoring module, a nursing index extraction module, a nursing quality evaluation module, an intelligent analysis early warning module, a nursing quality feedback module and a nursing process optimization module. Wherein the modules are connected through electric signals. According to the nursing quality monitoring and feedback system, through real-time collection and analysis of physiological data of a patient, operation records of nursing personnel and feedback suggestions of the patient and family members, potential problems and risks in the nursing process are found, improvement suggestions are generated, the nursing personnel are guided to adjust a nursing plan, the operation process is optimized, and therefore the nursing quality is remarkably improved; and operation of nursing personnel can be monitored and fed back in real time, improper behaviors can be found and corrected, and standardization and accuracy of nursing operation are improved.

The invention discloses a medical care data management system based on big data calculation, and relates to the technical field of data management, and the system comprises a data collection module, a data security module, a data storage module, a care quality analysis module, a care aid decision support module and a data visualization module. According to the medical care data management system based on big data calculation, by integrating and analyzing massive medical care data, more scientific and accurate decision support is provided for nursing personnel, multi-dimensional nursing data of a patient is collected and is compared and analyzed with a standardized nursing knowledge base, differences and defects in a nursing plan are quickly identified, and the medical care data management system is suitable for medical care. And the deviation of the nursing plan of the patient is determined through data analysis, and prospective decision-making suggestions are provided for nursing personnel, so that the subjectivity and uncertainty of manual judgment are reduced, and the efficiency and accuracy of nursing decision-making are improved.

The invention discloses a safety monitoring system for a nursing sickbed. The safety monitoring system comprises a sensing module, a data transmission module, a data processing module, an alarm module and a display and interaction module. The sensing module collects physiological data and environmental data of a patient on a sickbed, the data transmission module transmits the data to the data processing module through wireless communication, and the data processing module performs anomaly detection and trend prediction based on the collected data and calculates a safety risk score of the patient. When the safety risk score exceeds a preset threshold value, the alarm module sends out an alarm signal. And the display and interaction module displays the real-time data, the risk score and the prediction result of the patient to a nurse, and supports the nurse to carry out interaction operation through a terminal. According to the system, by integrating various sensors and an advanced data processing technology, comprehensive monitoring and accurate early warning on the safety of a patient are achieved, the falling risk of the patient is effectively reduced, the nursing quality is improved, the workload of nursing personnel is relieved, and the medical cost is reduced.

The invention relates to the technical field of medical care, in particular to a real-time nursing quality monitoring and alarm system which comprises a data acquisition module, a real-time monitoring module, an intelligent analysis module, an alarm generation module, a decision support module and a visualization module. The real-time monitoring module monitors acquired nursing quality data in real time, the intelligent analysis module processes and analyzes the data and identifies potential problems and trends, the alarm generation module generates alarms and reminds nursing staff to take measures in time, and the decision support module provides suggestions and decision support for optimizing the nursing process. And the visual module provides a user interface, so that nursing personnel can check monitoring data, alarm information and decision suggestions conveniently. Through comprehensive data collection and an intelligent analysis and early warning mechanism, the nursing quality monitoring and management level is improved, potential problems are found and processed in time, and the overall level of nursing service and the patient satisfaction degree are improved.

The invention discloses a pediatric nursing quality prediction and optimization method based on transfer learning, and the method comprises the following steps: S1, collecting target pediatric nursing data, and carrying out the preprocessing; s2, selecting source data, and extracting source data features; s3, constructing and training a nursing quality prediction model; s4, carrying out migration and fine tuning on the nursing quality prediction model by adopting an improved fine tuning method; s5, inputting target pediatric nursing data to the fine-adjusted nursing quality prediction model, calculating a nursing quality index, and evaluating a prediction result; s6, generating a nursing scheme based on the prediction result, and optimizing the nursing scheme according to the execution effect; and S7, applying the optimized nursing scheme to actual nursing work, and establishing an updating mechanism, so that the nursing quality prediction model is combined with new data to carry out iterative optimization. The pediatric nursing quality prediction and optimization are realized by adopting transfer learning and deep learning methods, and the pediatric nursing quality prediction and optimization method has the advantages of high data adaptability, high prediction accuracy and intelligent optimization of the nursing scheme.

The embodiment of the invention provides a nurse station ward information collection method and system based on a wireless network. A flexible electronic skin network is integrated in a patient mattress, pressure and temperature data are captured through distributed piezoresistive units, a multi-modal thermodynamic diagram is generated, and data synchronization is achieved through a wireless ad hoc network protocol. And driving an air cushion bed actuator to perform gradient compensation according to a matching result of the pressure gradient track and the physiological parameters, transmitting a compressed hierarchical data packet to a nurse station gateway by adopting a remote wide area network protocol, reconstructing a three-dimensional pressure model and generating an early warning instruction. According to the technical scheme provided by the embodiment of the invention, the occurrence probability of health problems such as bedsore caused by long-time bed lying is remarkably reduced, and the nursing quality of the patient is improved.

The invention relates to the technical field of medical care, in particular to a pediatric care quality evaluation method based on multi-modal data interaction. The method comprises the steps of multi-modal data acquisition, data preprocessing, nursing behavior feature analysis, infant state evaluation, and nursing quality scoring and feedback. According to the scheme, the multi-source heterogeneous data in the nursing process is collected, the ResNet50 model, the Glove model and the Mel-frequency cepstral coefficient are used for preprocessing and feature extraction of the multi-modal data of the image, the audio and the text, and the integrity and the analysis efficiency of feature fusion are improved; according to the scheme, a multi-granularity feature interactive fusion method is adopted, key features in the nursing process are extracted and fused from multiple dimensions, and the nursing behavior features and the infant state features are comprehensively obtained, so that the accuracy and the intelligent level of nursing quality evaluation are remarkably improved, and real-time monitoring and scientific feedback support of the nursing process are achieved.

The invention relates to the technical field of data feature mining, in particular to a remote nursing management method based on data feature mining. The method comprises the following steps: acquiring specific conditions and demand data of a patient; performing data source selection and configuration according to the specific condition and demand data of the patient to obtain a data source configuration table; performing multi-dimensional data acquisition according to the data source configuration table, and performing multi-dimensional data preprocessing to obtain patient comprehensive data; carrying out individualized baseline modeling on the patient comprehensive data to obtain an individual baseline model; performing real-time data standardization on the patient comprehensive data to obtain standardized real-time data; performing deviation calculation according to the standardized real-time data and the individual baseline model to obtain a deviation value sequence; and performing index correlation analysis on the deviation value sequence to obtain an index correlation matrix. According to the invention, the utilization of data feature mining is improved, so that remote nursing management is more accurate and personalized, and finally the nursing quality and efficiency are improved.

The invention discloses a medical care data intelligent evaluation system based on multi-modal data fusion, and belongs to the technical field of medical care. The problems that subjective deviation exists in the prior art, evaluation results of different medical workers on the same patient may be different, the medical data source is single, and patient information cannot be comprehensively reflected are solved. Through real-time feedback of nursing personnel, the frequency of abnormal data in a feedback result within a period of time is analyzed, so that the updating frequency of the model is dynamically adjusted, and misjudgment caused by model deviation is reduced; according to the method, the abnormal data in the multi-modal data are identified by calculating Z-score, the time sequence data are set to be high priority, and the abnormal data at the high priority are preferentially fed back to the nursing personnel, so that the response speed of the nursing personnel to abnormal conditions is increased, the distribution of nursing resources is optimized, and the nursing efficiency is improved. Therefore, nursing personnel can deal with emergencies in time, and nursing quality and patient safety are improved.

The invention discloses a nursing decision support system and a nursing decision support method, and relates to the field of health care, in the system, a data acquisition module is used for acquiring multi-source data in real time; the data processing module is used for integrating multi-source data by adopting a natural language processing technology and a data mining technology to obtain a unified data set; the graph construction module is used for taking each type of data in the unified data set as a knowledge node, analyzing association relationships among the knowledge nodes by adopting a data mining technology, endowing each association relationship with a weight value, and constructing a knowledge graph according to the knowledge nodes, the association relationships and the weight values; the dynamic updating module is used for regularly updating the knowledge graph according to the currently acquired multi-source data; the decision support module is used for calling the current knowledge graph to generate nursing measures of the patient, comprehensive, accurate and real-time nursing decision support can be provided, and the nursing experience and nursing quality of the patient are improved.

The invention discloses a hospital nursing quality evaluation system based on hospital information terminal data, and relates to the technical field of medical information, and the system comprises a data acquisition module which extracts index data based on a hospital terminal, and the index data comprises patient satisfaction subjective index data, nursing personnel working life data, performance data and adverse event occurrence probability; after preprocessing the data, constructing a data set; and the analysis module performs correlation analysis on the data of the data set based on the Pearson's correlation coefficient, constructs a nursing quality evaluation model through a neural network, and performs training optimization on the quality evaluation model. The comprehensive evaluation level is efficiently and objectively output through the evaluation module, the result is presented through a visual chart, managers can conveniently master the comprehensive evaluation level, hospitals accurately position nursing service short boards according to the evaluation result, targeted optimization is implemented, the nursing system is continuously improved, the requirements of patients are met, and it is ensured that the nursing quality is kept at a high level; and data in a hospital can be fully utilized.

The invention discloses an anesthesia awakening state prediction method and system, and the method comprises the steps: firstly constructing a standard prediction model, then obtaining an actual physiological parameter set, generating an actual recovery state parameter set according to the actual physiological parameter set, and generating an initial recovery curve in the standard prediction model; generating a minimum verification circle, and performing primary correction on the initial recovery curve through the minimum verification circle to obtain a corrected recovery curve; according to the correction recovery curve and a standard prediction model, initial prediction waking time is calculated, according to the actual recovery state parameter set and the correction recovery curve, a composite deviation rate is generated, according to the composite deviation rate, secondary correction is carried out on the initial prediction waking time, and prediction waking time is obtained. According to the method, accurate positioning of the wake-up point is realized by correcting the intersection point of the recovery curve and the standard prediction model, and early warning is performed in time, so that the work pressure of medical staff is reduced, the orderly development of medical work can be ensured, and the nursing quality and the operation efficiency of a wake-up ward can be improved.

The invention discloses a multiple myeloma patient nursing management system and method, and relates to the technical field of medical nursing, and the method comprises the steps: obtaining the medical data of a multiple myeloma patient through a data fusion platform, and integrating the medical data through a multi-source heterogeneous data set; making a medical plan for the multiple myeloma patient based on the preprocessing result of the medical data; and performing intervention suggestion on the medical plan according to the medical treatment decision model to complete nursing management of the multiple myeloma patient. According to the method, efficient integration and high-quality processing of medical data are achieved, abnormal data can be recognized and repaired in time, the accuracy and safety of a medical plan are ensured, and the nursing quality and the treatment effect of multiple myeloma patients are remarkably improved in combination with intervention suggestions of a medical treatment decision model.

The invention discloses an emergency rescue room nursing resource dynamic scheduling method and system based on demand prediction, and relates to the technical field of resource dynamic scheduling. The method comprises the following steps: collecting and preprocessing historical emergency room data, and constructing a multi-dimensional feature data set; respectively training an improved GRU model and a Transform model for high-precision prediction of patient inflow and triage level distribution in a future time period, and quantifying a prediction result as standard nursing time; based on real-time nurse data, a multi-target optimization model with the purposes of minimizing patient waiting time, balancing nurse loads, controlling cost and meeting preferences is built, a Pareto optimal scheduling scheme set is solved and generated through a genetic algorithm, and a manager selects a final scheme according to the actual situation; according to the invention, the conversion from passive response to active accurate scheduling is realized, and the operation efficiency, the resource utilization rate and the nursing quality of the emergency rescue room are effectively improved.

The invention relates to a clinical symptom nursing strategy intelligent recommendation system and method based on artificial intelligence, and the method combines historical clinical symptoms and nursing big data to construct a corresponding nursing strategy intelligent recommendation model, thereby enabling the nursing strategy intelligent recommendation model to recognize the feature relationship between different symptom features and corresponding nursing strategies. And enabling the model to recognize the symptom characteristics of a clinical patient and intelligently recommend a corresponding nursing strategy. The system can also combine model output and nurse feedback, fuse and recommend a clinical symptom nursing strategy most related to patient symptoms, so that nurses can make preliminary assessment and deal with the appearing symptoms to confirm nursing problems, sort critical and urgent conditions, develop nursing measures, highlight nursing key points and improve the nursing efficiency. And potential complications can be prevented or treated in time. In clinical nursing work, clinical nurses can be effectively guided to perform judgment thinking and carry out predictive nursing, so that the nursing work is homogenized, and the nursing quality and safety are improved.

A system and method for facilitating a member journey through healthcare service by augmenting stewardship workflow across multiple service areas is disclosed. The system and method includes augmenting the healthcare stewardship workflow by standardizing the healthcare service practices, increasing the quality of care for patients, improving the workflow efficiency of healthcare personnel, reducing the costs associated with providing healthcare services, and enabling the ease of regulatory compliance using machine learning techniques. The system and method provides the healthcare personnel with access to healthcare-relevant and fact-based artificial intelligence powered by machine learning for decision support opportunities to drive recommended care pathways.

The invention discloses a postoperative chest cold and hot compress integrated nursing pad for a thoracic surgery patient. The postoperative chest cold and hot compress integrated nursing pad comprises a heating layer, a refrigerating layer, a nursing pad sensor, a physiological sensor, an intelligent control unit and a man-machine interaction system. The heating layer is composed of a liquid circulation heating cushion layer and a small heating pump, the refrigerating layer is composed of a liquid circulation refrigerating cushion layer and a miniature refrigerating pump, the nursing pad sensor layer is located between the heating layer and the refrigerating layer, and the physiological sensor is used for collecting the physiological state of a patient. The intelligent control unit integrates an artificial intelligence algorithm, intelligently regulates and controls work of the heating layer and the refrigerating layer according to feedback signals of the sensor, and achieves automatic switching and precise adjustment of the cold and hot compress modes. And the man-machine interaction system is convenient for medical personnel and patients to operate and feed back. Through multi-sensor data acquisition and intelligent algorithm analysis and in combination with a remote monitoring function, a personalized and intelligent chest cold and hot compress nursing scheme is provided for a patient after the thoracic surgery, recovery of the patient is effectively promoted, and the nursing quality is improved.

The invention provides a multi-mode intelligent analysis internal fistula postoperative rehabilitation nursing method and system, and belongs to the technical field of medical nursing and artificial intelligence crossing. The application aims to solve the problems of single monitoring dimension, subjective risk judgment and static homogenization of the nursing scheme in the existing internal arteriovenous fistula postoperative nursing. The method comprises the following steps: acquiring multi-modal data related to a wound state and a hemodynamic state of an internal arteriovenous fistula; processing the multi-modal data to generate standardized feature data which can be analyzed; generating a risk assessment result about the health condition of the internal arteriovenous fistula; and a preset nursing scheme is dynamically adjusted based on a risk assessment result. According to the method and the device, multi-modal data are fused and an intelligent algorithm is used for collaborative analysis, so that the accuracy and early stage of risk identification can be realized, the individuation and high efficiency of a nursing scheme are realized, and the nursing quality is improved.

The invention relates to the technical field of data processing, in particular to an intelligent data processing method and system based on surgical nursing, and the method comprises the steps: firstly obtaining food intake and physiological indexes of a patient within a preset time, comparing the food intake and physiological indexes with a standard food intake set, and generating a food intake standard score; in conjunction with the physiological metric, the system determines a real-time feeding score and a total feeding score. By comparing the real-time and total feeding scores at different time points, the system finally generates a feeding balance score of the patient. According to the method, food intake and physiological data are effectively integrated, a scientific and quantitative food intake evaluation tool is provided for surgical nursing, nutrition management of a patient is optimized, nursing quality is improved, the absolute value of the food intake is considered in the evaluation, the relevance between time distribution of food intake and physiological indexes is concerned, and the evaluation accuracy is improved. Therefore, more comprehensive and in-depth patient state information is provided for nursing personnel. Especially, due to the introduction of a feeding balance degree score, an important basis is provided for formulating a more reasonable nutrition intervention scheme.

The invention relates to a stress injury assessment opportunity prediction method based on a Braden scoring system and an RNN model. The method comprises the following steps: collecting a Braden index and other related data of a patient; preprocessing the data; performing feature extraction on the collected data, and constructing a feature vector; an RNN model is trained through the feature vectors, and a stress damage risk prediction model is formed; inputting the real-time data of the patient into the trained stress injury risk prediction model, predicting the risk probability of the patient, and automatically determining the optimal time of the next evaluation according to a preset threshold value; and result output and feedback: feeding back the prediction result and the suggested evaluation opportunity to nursing personnel, and providing personalized nursing suggestions. According to the method, the risk change of the pressure injury of the patient can be automatically judged, the optimal evaluation and intervention opportunity is determined, the nursing quality is improved, and the occurrence rate of the pressure injury is reduced.

The invention provides an acupuncture level quantification standard and device based on the acupuncture process of a nurse, and aims to establish an objective and scientific evaluation system by performing quantitative analysis on key data of the acupuncture process so as to improve the nursing quality. The method comprises three main steps of data collection and preprocessing, evaluation standard establishment and acupuncture process scoring. Acceleration data in the acupuncture process are collected in real time through a three-axis accelerometer, a speed and displacement sequence is obtained through filtering and integral calculation, the operation process is divided into three stages of skin contact, subcutaneous insertion and target depth approaching, and statistical characteristics of all the stages are extracted to serve as quantitative indexes. The evaluation standard covers key indexes such as speed stability, sudden stop times and insertion time, and quantitative evaluation of the needle insertion level of the nurse is achieved in combination with the segmentation scoring weight and the comprehensive scoring rule. The device consists of a hardware module (a sensor, a microprocessor, a wireless communication module and the like) and a software module (a data processing and analysis algorithm), supports real-time data transmission and feedback, and has the characteristics of portability, automation and high precision. Scientific basis is provided for continuous improvement of the nursing level, and more comfortable and safer medical experience is created for a patient.

The invention relates to a nursing home priority service response method, system and device based on digital twinning, and the method comprises the following steps: collecting health data of old people and state data of nursing personnel, and carrying out the preprocessing of the data; constructing an old people health state dynamic model, a nursing resource dynamic model and an environment path model; coupling the three models to form a digital twin model; calculating a service response priority based on the health score, the waiting time attenuation and the medical history characteristics of the elderly; according to the service response priority, scheduling the matched nursing personnel to carry out service response; and collecting a service execution result after the nursing personnel completes the task, updating the digital twinborn model based on the service execution result, and dynamically adjusting a scheduling strategy. According to the invention, real-time mapping and dynamic optimization of the health state of the elderly, the position of the nursing personnel and the environment path information in the nursing home are realized, and the service response efficiency, the resource scheduling accuracy and the nursing quality are remarkably improved.

The invention relates to the technical field of extubation action recognition and early warning systems, and discloses a patient unplanned extubation action recognition and early warning system based on wrist strap equipment, the system comprises the wrist strap equipment worn on the wrist of a patient, and the equipment is internally provided with an action sensor, a processor and a communication module. The action sensor is used for detecting hand actions of a patient, the processor analyzes data of the sensor to recognize non-planned extubation actions, and once potential extubation actions are recognized, the system immediately gives out early warning to medical staff in a sound-light alarm and wireless communication mode. The method has the advantages of high recognition accuracy, real-time early warning, protection of patient privacy, reduction of burden of medical staff, improvement of medical safety and the like, is suitable for various patients in hospitals, particularly patients in intensive care units, is beneficial to reduction of the incidence rate of non-planned tube drawing events, reduction of adverse nursing events, reduction of harm to the patients, and is beneficial to popularization and application. The nursing quality is improved and the satisfaction degree is improved.

The invention provides a nursing quality management and control system based on multi-modal data, relates to the field of intelligent nursing, and adopts a data processing technology based on artificial intelligence to perform comprehensive analysis on a set described by acquired nursing feedback texts and an environment state image of a target inpatient area. Therefore, nursing feedback text association semantics related to a plurality of target patient objects and environment state characteristics of the target inpatient area are captured, and nursing quality management suggestions are intelligently generated based on remarkable interaction representation information between two cross-modal data including the nursing feedback semantics and the environment state of the inpatient area. Therefore, more accurate and effective nursing quality management suggestions can be provided, and more scientific and reasonable nursing quality management can be realized.

The invention relates to an intelligent nursing and monitoring device used after glaucoma filtration, and belongs to the technical field of medical instruments, the intelligent nursing and monitoring device comprises a rigid main frame, the inner side of the rigid main frame is provided with a flexible sealing gasket, the upper portion of the rigid main frame is integrated with a self-adaptive positioning system, and the left side and the right side of the rigid main frame are connected with directional physical nursing execution systems; a microenvironment humidity monitoring system is embedded in the ball head, and the rear side of the frame is connected with an intelligent central control system. The self-adaptive positioning system accurately recognizes eyeball and filtered bubble areas, the directional physical nursing execution system reproduces standardized nursing operation, the microenvironment humidity monitoring system verifies the nursing effect in real time, and the intelligent central control system overall plans all the systems to cooperate and supports remote data transmission. The device can solve the problems of inaccurate postoperative nursing, lagged effect feedback and poor patient compliance, improves the nursing quality, assists in improving the success rate of an operation, and is suitable for families and clinical nursing scenes.

The application discloses a medical care data intelligent evaluation system based on multi-modal data fusion and belongs to the technical field of medical care. The application solves the problems that the existing technology has subjective bias, the evaluation results of different medical staffs for the same patient can be different, and the medical data source is single and cannot comprehensively reflect the patient information. Through the real-time feedback of nursing staff, the frequency of model update is dynamically adjusted by combining the frequency of abnormal data in the feedback results in a period of time, so as to reduce the misjudgment caused by model bias; the abnormal data in the multi-modal data is identified by calculating Z-score, the time series data is set as high priority, and the abnormal data in the high priority is preferentially fed back to the nursing staff, which not only improves the response speed of the nursing staff in the face of abnormal conditions, but also optimizes the allocation of nursing resources, so that the nursing staff can more timely handle the emergency, thereby improving the nursing quality and patient safety.