51 results about "Nursing management" patented technology

The invention relates to the technical field of intelligent medical treatment, in particular to a urinary surgery postoperative risk layering intelligent nursing management system which comprises the steps of preprocessing dynamic sign flow data of a patient to obtain regular sign time sequence data; and separating a core vital sign track, a wound exudation quantitative sequence and an autonomous perception discomfort record through modal analysis. The feature calculation module compares the vital sign trajectory with a standard recovery trajectory library to generate a sign bias spectrum, analyzes the exudation sequence to obtain a wound humidity distribution vector, and calculates a time attenuation factor of the pain event according to the discomfort record. And the space construction and layering module combines the features to construct a multi-dimensional risk base space, and identifies boundary coordinates of high-risk, medium-risk and low-risk risk clusters through clustering. And the strategy generation module divides risk levels and allocates corresponding monitoring and intervention measures according to the risk levels. According to the system, dynamic and multi-dimensional quantitative evaluation of the postoperative recovery state and objective automatic layering of risks are achieved.

The invention relates to the technical field of medical information, in particular to an interventional operation patient perioperative period nursing information intelligent interaction and management system which comprises the steps that through an integrated data collection module, a timeline generation module, a risk identification module, a nursing intervention module and an interaction management module, time axis alignment and structured coding are conducted on scattered physiological data and diagnosis and treatment events; and a dynamic comprehensive nursing timeline is formed. And performing real-time analysis on the timeline based on a preset risk knowledge graph, identifying an abnormal data mode and generating an early warning. The system automatically matches a nursing rule base, pushes individualized intervention suggestions including specific operations, priorities and bases, synchronizes information and collects feedback through a multi-role interaction channel, and realizes closed-loop management. According to the system, fragmented nursing information can be integrated into a continuous dynamic view, and risk early warning and structured nursing actions are directly linked, so that the collaboration, accuracy and timeliness of nursing management are improved.

The invention belongs to the technical field of nursing management, and particularly relates to a remote nursing management method and system based on data analysis, which can make up for the defects of the prior art by integrating multi-dimensional nursing data, carrying out deep intelligent analysis and realizing accurate early warning and dynamic scheme adjustment based on comprehensive evaluation, quantify intraday blood pressure fluctuation and improve the nursing efficiency. According to the method, the deviation between the actual blood pressure and the target value is fused, symptom feeling risk calculation is introduced, subjective symptoms are objectively and quantitatively processed, health risks caused by symptom combinations or key symptoms can be effectively recognized, and the accuracy of the health risk is improved. Through multi-dimensional data fusion, innovative risk assessment, a comprehensive effect scoring mechanism and an intelligent closed-loop management process, the defects of fragmentation, one-sided and static states of an existing remote nursing technology are effectively overcome; a more comprehensive, more intelligent and more accurate remote nursing management solution is provided for chronic disease patients, especially crowds needing long-term management such as hypertension.

The invention discloses a traditional Chinese and western medicine cooperative emotion nursing management system and a physiotherapy robot applying the same, and aims to solve the technical problems in the aspects of traditional Chinese and western medicine theory deep cooperation and dynamic personalized nursing in the prior art. The system is realized through the following steps: collecting physiological, behavior, traditional Chinese medicine and other multi-modal data of a user, and constructing a unified traditional Chinese and western medicine cooperative emotional state vector; reasoning by using a traditional Chinese and western medicine fusion knowledge graph to generate an interpretable traditional Chinese medicine macroscopic treatment target; a double-layer hierarchical reinforcement learning model is adopted for decision making, a macroscopic intervention combination is generated through a high-layer strategy, and an accurate robot control instruction containing emotional tactile parameters is generated through a low-layer strategy; and finally, driving the physiotherapy robot to execute the instruction, and continuously optimizing the strategy through closed-loop feedback. According to the method, the artificial intelligence decision theory and the traditional Chinese medicine syndrome differentiation treatment thought are combined, and an automatic and emotional traditional Chinese and western medicine cooperative nursing solution is provided for mood-related functional disorders.

The application discloses a kind of management methods and systems to cope with nursing rehabilitation service, it is related to nursing management technical field;The method includes the steps of: collecting and maintaining the personal information of all users;Regular health assessment is carried out to target user, and obtains rehabilitation state data set;Its technical points are: the daily nursing time length fine tuning operation when the nursing level has reached the limit is constructed, and the ideal range deviation degree of Barthel index, PEI value and body state evaluation value is introduced therein, so that the overall scheme can flexibly adjust the nursing time length under the premise that the nursing level is unchanged or cannot continue to adjust, to adapt to the rehabilitation trend change, on the one hand, individualized nursing resource allocation is realized according to functional state difference, while maintaining the stability of nursing level, enhance service flexibility, on the other hand, avoid the nursing disjointedness phenomenon caused by level limit, complete the optimization management operation of nursing operation.

The present application relates to the technical field of Internet of Things, and particularly relates to an intelligent clinical nursing management and control method and system based on Internet of Things, which comprises the following steps: collecting multi-source data in a clinical nursing environment, and completing timestamp alignment and data encapsulation through an Internet of Things gateway to form multi-source data; after data cleaning, HL7 FHIR format unification and mapping based on an OMOP model, standard nursing data is generated; based on the standard nursing data, a pre-trained LSTM-Autoencoder-based time series anomaly detection model is used to identify collaborative anomaly patterns, and a risk assessment module combining a logistic regression classifier and a gradient boosting decision tree is used to generate a risk level and an occurrence probability; the system generates nursing early warning information and automatically generates nursing management and control instructions according to early warning rule templates, and sends the instructions to nursing equipment or nursing staff terminals through an MQTT network. The present application realizes intelligent clinical nursing management and control, and significantly improves the timeliness, accuracy and intelligent level of clinical nursing.

The invention discloses an intelligent monitoring and tracing system for intravenous medication allocation nursing quality, and relates to the technical field of intelligent nursing management, and the system comprises a time anchoring module, a conflict sensing module, a number fusing module, a dual-factor code forming module and an entropy gate regulation and control module. The time anchoring module is used for constructing a unified time anchoring chain and establishing a number pool monitoring system; and recording traceability code generation request frequency and request node distribution in a time slice corresponding to the unified time anchoring chain, and generating a high-concurrency interval list according to a recording result. Dynamic hierarchical management of traceability code generation is realized through time anchoring and a conflict perception mechanism, a high-concurrency area is identified in advance, dispersed scheduling is carried out, and code uniqueness and traceability chain continuity are ensured. In combination with serial number fusing, two-factor coding and entropy gate regulation and control mechanisms, cross-node non-repeated coding and adaptive load adjustment are realized, the collision risk is reduced, and the safety and stability of medication tracing are improved.

This item is a care management sheet designed to organize the user's medical and treatment history and ensure they receive appropriate care in nursing and medical settings. As shown in the front diagram illustrating the use of this item, the right half of the item contains sections for the user's specific information, emergency contact information, a visual representation of their treatment history, allergy information regarding food and drugs, regular medication information, other precautions, information on care management-related organizations, and the name of the issuing organization. The left half of the item also contains sections for the user's specific information, emergency contact information, medical and treatment history, regular medication information, information on care management-related organizations, and the name of the issuing organization.

The application discloses a rural elderly mild cognitive impairment risk prediction and stratified nursing management auxiliary system and method, and belongs to the technical field of intelligent nursing and mobile health auxiliary screening. The system comprises an application path selection module, a data acquisition module, a data preprocessing module, a nursing evaluation and scale automatic scoring module, a risk prediction module, a risk stratification module, an explanation output module, a nursing suggestion generation module and a mobile terminal display module. Through the rapid screening version, simple variables are collected; through the standard evaluation version, GDS-15, IADL, PSQI, SSRS and other scale items are embedded and automatically scored; a pre-trained machine learning model is called to output MCI risk prediction probability, low, medium and high risks are divided, and corresponding reevaluation frequency and stratified nursing management suggestions are generated. The application can display the results through a webpage calculator or a WeChat applet, is used for primary screening assistance and risk communication, and does not replace clinical diagnosis.

This invention relates to the field of urine glucose monitoring technology, and more particularly to a method and system for monitoring urine glucose data from adult diapers. The method first interacts with a nursing management system to obtain daily care milestones for the patient, and connects to a wearable terminal to generate a visual monitoring interface. Secondly, at preset milestones, multiple sensors integrated into the inner layer of the diaper collect data, combining health parameters to identify abnormalities and predict the level of blood glucose abnormalities. Subsequently, based on historical data from multiple milestones, a blood glucose fluctuation trend curve is generated on the interface. Finally, a personalized intervention plan is formulated based on the abnormality level and the trend curve, implementing targeted nursing care. This invention achieves non-invasive continuous monitoring through multi-source signal fusion to combat interference; it possesses the ability to predict blood glucose trends, transforming passive alarms into proactive prevention; and through a visual interface and intelligent decision-making closed loop, it significantly improves the accuracy, efficiency, and comfort of nursing care for diabetic patients.

The invention belongs to the technical field of patient nursing layered management, and provides a patient nursing layered intelligent management system based on drainage liquid data, which comprises the steps of performing real-time continuous monitoring on the drainage liquid data of a postoperative patient, establishing a drainage liquid amount prediction model, and taking the monitored drainage liquid data as input, outputting a drainage liquid amount predicted value in a future preset time period; performing drainage liquid trend analysis on the drainage liquid amount predicted value in the future preset time period, and judging whether the drainage liquid amount in the future preset time period shows a descending trend or not; targeted monitoring, evaluation and intervention schemes are provided for patients with different risk levels, the distribution efficiency of nursing resources is optimized, limited resources are preferentially inclined to high-risk patients, medical staff can be helped to timely discover postoperative abnormal conditions of the patients and take corresponding measures, dynamic optimal configuration of the nursing resources is achieved, and the nursing efficiency is improved. The scientificity and the refinement level of nursing management are improved, and the method has a remarkable management application value.

This invention relates to the field of data processing technology, specifically to an intelligent rehabilitation and nursing management system based on data analysis. The system includes: a motion state acquisition module that acquires raw electromyographic signals and joint torque and angle values; a physiological characteristic analysis module that extracts the median frequency sequence in the frequency domain and constructs an electromyographic fatigue gradient; a motion compensation calculation module that calculates the end-effector driving torque and generates a motion compensation coefficient; and an adaptive control execution module that calculates the resistance increment based on the fatigue gradient and calculates virtual wall parameters to construct a control strategy when the compensation coefficient exceeds the limit. In this invention, by combining frequency domain analysis to construct a fatigue gradient, using a dynamic model to calculate the compensation ratio, adjusting resistance based on the compensation coefficient and fatigue state, and constructing virtual wall constraints, quantitative analysis of physiological characteristics and motion states is achieved. This ensures adaptive matching of the optimal strategy while guaranteeing safety, thereby improving the intelligence and accuracy of training.

The application discloses a kind of chronic infectious disease digital people health management system construction method, it relates to AIDS nursing management technical field.Based on Triangle model, the management classification and characteristics of HIV patients are determined by latent profile and association rule analysis, and it is embedded into applet using agile development;Based on the basic nursing framework, the commonality and difference of patients in different levels are explored Care needs;Through the preliminary construction of AIDS hierarchical case management program by range review and focus group interview, the final draft is formed by revising and improving through written evaluation and field demonstration, and the well-constructed management program is embedded into a digital human interaction platform based on artificial intelligence.The application protects the privacy of patients, alleviates the shortage of medical human resources, meets the growing health needs of HIV patients, improves patient satisfaction and improves the quality of HIV care services.

This application provides a method for optimizing nursing tasks in a smart hospital, comprising: S1. Real-time acquisition of multidimensional physiological signals of patients and ward nursing workflow status data; inputting the multidimensional physiological signals of patients and the ward nursing workflow status data into a risk assessment model to generate a true risk value reflecting the patient's risk status; using the multidimensional physiological signals of patients, the ward nursing workflow status data, and the true risk value as initial state parameters, simulating and pre-visualizing the nursing task conflict state in future time periods through a ward digital twin, dynamically optimizing the nursing task queue according to the system entropy minimization criterion, and distributing the optimized task flow to the nurse's terminal. By introducing deep learning and multi-source data fusion technology into smart hospital nursing management, a proactive nursing management system with risk prediction, task conflict perception, and dynamic scheduling optimization capabilities is constructed, which can effectively improve the efficiency of nursing resource allocation.

The invention discloses an augmented reality thoracic surgery postoperative patient nursing management method, and relates to the technical field of postoperative nursing, and the technical scheme is characterized by comprising the following steps: collecting cognitive ability data, home nursing scene adaptation data and remote medical support data of a thoracic surgery postoperative patient; constructing a three-dimensional linkage model, inputting the cognitive competence data, the home nursing scene adaptation data and the remote medical support data into the three-dimensional linkage model, and performing score division through the adaptation degree of the three-dimensional linkage model to obtain a high-dependence nursing area, a collaborative nursing area and an autonomous nursing area; and judging the patient cognitive deviation data, the scene adaptation conflict data and the remote support response data of the high-dependence nursing area to obtain a first nursing feature set, so that the nursing support can be adjusted in real time along with the change of the cognition, scene and capability of the patient, and the accuracy and the flexibility of nursing management are improved; and finally, efficient rehabilitation of thoracic surgery postoperative patients is assisted, and the complication risk is reduced.

This invention discloses a method and system for constructing a disaster resilience classification training program for clinical nurses, belonging to the fields of disaster nursing management and computer image recognition technology. It involves acquiring facial micro-expression image sequences of nurses in standardized disaster simulation scenarios; extracting the activation frequency, activation duration, and left-right facial activation symmetry features of the zygomaticus major and orbicularis oculi muscles and inputting them into a preset resilience classifier; outputting subtype labels based on a fixed mapping relationship between the spatiotemporal features of muscle activity and resilience subtypes; retrieving the corresponding basic training framework based on the labels; comparing the activation duration features with a threshold; and generating reinforcement training labels on the framework using image overlay technology to form a personalized classification training program. This invention achieves objective and real-time subtype identification of clinical nurses' disaster resilience and can automatically generate differentiated training content targeting resource deficiencies in different subtypes, significantly improving the accuracy and automation level of disaster resilience training.

The invention relates to the technical field of intelligent nursing, and discloses a disease intelligent nursing auxiliary system based on evidence-based data, comprising: an evidence-based knowledge base module integrating multi-source evidence-based data to form a specialized knowledge graph; the model training optimization module carries out base model field fine tuning and multi-algorithm fusion according to the multi-source evidence-based data so as to carry out intelligent decision-making assistance and generate a personalized nursing scheme; the multi-scene intelligent interaction module carries out knowledge pushing, consultation and question answering through multi-terminal interaction; the full-period nursing management module performs staged nursing execution and dynamically adjusts a nursing plan; the quality control early warning module carries out risk early warning, medication verification and flow quality control; and the multi-dimensional effect evaluation module generates a multi-level evaluation report, and drives update of the evidence-based knowledge base and algorithm iteration. According to the invention, an accurate and adaptive whole-cycle nursing scheme and standardized guidance are provided, the nursing risk is reduced by means of real-time quality control early warning, and the treatment compliance and medical experience of a patient are improved.

The invention provides a PD classification identification optimization method based on I-III stage drug clinical test nursing, and relates to the technical field of drug clinical test nursing management. The method comprises the following steps: collecting I-III phase drug clinical test nursing related data, and screening PD data; dividing the PD into three categories, namely test drug configuration or use deviation, sample collection or treatment deviation and vital sign detection deviation, and judging the severity; the PD core root is analyzed in combination with test item types, medicine dosage forms and nursing working modes; the matching system comprises a data acquisition module, a classification identification module, a root analysis module, an optimization strategy output module and a dynamic monitoring module. According to the method, the defects of fuzzy PD classification, inaccurate root analysis and insufficient prevention and control strategy pertinence in the prior art are overcome, the PD risk point can be accurately positioned, the PD occurrence rate is effectively reduced, the clinical test nursing quality is improved, the test risk is reduced, and the method has good practicability and popularization value.

The invention discloses a psychological nursing management method and system based on electroencephalogram biofeedback, and belongs to the field of cognitive disorder nursing and rehabilitation management. According to the method, case basic information, cognitive function evaluation and electroencephalogram related data are obtained and are classified and divided into a joint intervention data set and a conventional intervention data set, individualized electroencephalogram biological feedback parameters and targeted psychological nursing schemes are matched for the joint intervention set, data are collected in stages to generate a comparison data set, and the comparison data set and the conventional intervention data set are matched. An analysis result is output after multi-dimensional analysis; the system comprises a data acquisition module, a data processing module, an intervention data generation module and a result analysis module which interact in sequence. According to the method and the system, the standardization of the whole process of nursing management is realized, the data quality and intervention suitability are guaranteed, the change trend of cognitive function indexes is accurately analyzed, reliable support is provided for intervention effect evaluation and scheme optimization, and the scientificity and normalization of nursing management are improved.

This patent application relates to the field of smart healthcare and discloses a smart nursing system, including a smart nursing platform. The smart nursing platform includes a document management subsystem and a nursing management subsystem. The document management subsystem is equipped with a dictionary, a document editor, and a file database. The dictionary is configured with data encoding. The document editor is used to analyze and process pre-input patient information and medical order information to generate data objects. It retrieves the data encoding corresponding to the data objects from the dictionary to generate metadata. Based on the metadata, it formulates nursing documents that conform to hospital usage habits and document writing standards and stores them in the file database. The nursing documents are configured with nursing plans, which include nursing categories. Each nursing category is configured with a nursing strategy. Nursing staff can obtain nursing documents from the smart nursing platform to execute nursing plans.

The invention discloses a physiotherapy robot device with a pain nursing management function and a sequential nursing method. The physiotherapy robot device comprises a data acquisition and evaluation module, an AI national medicine brain analysis module, a software and hardware collaborative execution module and an effect dynamic feedback and optimization module. The data acquisition and evaluation module is used for integrating infrared thermal imaging, tongue condition, pulse condition and other traditional Chinese medicine constitution data and imaging, biochemical index and other western medicine diagnosis data; the AI national medicine brain analysis module fuses traditional Chinese and western medicine data, judges the cold-dampness level and dialectical pain types in combination with the vicu defensive five-level cold-dampness theory, and generates a personalized recuperation scheme; the software and hardware collaborative execution module accurately executes a scheme through a mechanical arm of the AI physiotherapy robot main body and multiple types of physiotherapy probes; the effect dynamic feedback and optimization module collects intervention effect data in real time, dynamically optimizes a scheme and establishes double closed loops to guarantee safety, and the problems that traditional pain management evaluation is not accurate, scheme personalization is insufficient, physiotherapy precision is low, dynamic feedback is lacked and the like are solved.

The present application relates to the technical field of intelligent management of medical care, in particular to a working quality intelligent supervision system for medical care staff. The system comprises an environmental dynamic constraint analysis module, an action compliance analysis module, a physiological fluctuation analysis module, a behavior compliance analysis module, a fatigue risk analysis module, a historical performance attenuation analysis module, a core efficiency risk analysis module, a co-empathy communication ability analysis module and a research and judgment output module. The present application solves the technical problems of high artificial dependence, one-sided evaluation, high misjudgment rate and lack of closed-loop management of the existing nursing supervision, realizes full-dimensional, real-time and intelligent supervision of the working quality of the nursing staff, improves the nursing management efficiency and the nursing service quality, and reduces the nursing risk.

This invention discloses a computer vision-based fatigue detection system and trajectory analysis method for ICU nurses, relating to the field of medical and health monitoring. It aims to address the problems of subjective inaccuracy, interference with nursing operations, and lack of longitudinal trajectory analysis in existing detection methods. The system includes modules for image acquisition, preprocessing, eye feature extraction, fatigue assessment, data storage, and trajectory analysis. It acquires video streams via a USB camera and adaptive LED fill light, performs preprocessing such as HSV conversion and grayscale enhancement, extracts 12 eye feature points based on MediaPipe, and combines sliding window filtering and posture calibration to compensate for occlusion and posture errors. Three levels of fatigue are determined using EAR and PERCLOS values. The method collects data longitudinally at nine time points, uses an LGMM model to analyze fatigue trajectory heterogeneity, and combines Logistic regression to uncover key influencing factors. This invention features a non-invasive design, high detection accuracy in partially occluded scenarios, precise location of intervention targets, and convenient deployment, providing scientific support for nursing management.

A nursing care information system including mobile devices and hospital information computer devices; the mobile device includes: a user interface module electrically connected to the mobile device, for inputting user information; and six modules electrically connected to the user interface module, including a patient list module, for providing a list of patients cared for by nursing staff; a medical order confirmation module, for pushing and sound notifications, reminders and confirmations to nursing staff; a mobile drug delivery module to providing medical advice to patients under the care of nursing staff; an nursing record module to provide nursing staff with input of patient event records; a work list module to provide and remind nursing staff of to-do items; the upload photo module is used to provide nursing staff with the ability to take photos of patient wounds and image records of special events.

The invention belongs to the technical field of intelligent logistics, and relates to an automatic nursing method and system based on an inpatient area track system. The method comprises the following steps: setting a nursing task type, execution time and material information at a nursing management terminal; collecting vital sign data of bedside monitoring equipment and judging an abnormal event; the nursing central control platform generates an optimal path scheduling instruction based on the track topology, the transportation unit state and the traffic load; when an abnormal event exists, a high-priority task is automatically generated, and a way giving strategy is set; the rail transportation device completes distribution after receiving the instruction; and after the task is completed, returning the state and generating a visual record. The system comprises a nursing management terminal, a nursing central control platform, a rail transportation device and a rail network structure, wherein the nursing central control platform has the capability of abnormal recognition and emergency task distribution. According to the invention, the hospital nursing logistics efficiency can be improved, the linkage of vital sign monitoring and abnormal response is realized, and the intelligent nursing service capability is enhanced.

The invention provides a clinical closed-loop nursing management method based on hospital chronic disease nursing big data, and the method comprises the steps: constructing a closed-loop nursing management process of data collection-risk assessment-intervention generation-execution tracking-effect evaluation-loop optimization, and fusing the algorithms of time sequence prediction, real-time anomaly detection, causal inference, federated learning and the like. The chronic disease nursing data of the patient is comprehensively collected and preprocessed, so that the integrity and accuracy of the data are ensured. And by using the pre-trained LSTM risk prediction model, potential information in the data can be fully mined, and the chronic disease deterioration risk level of the patient can be accurately output. A propensity score matching (PSM) model is employed to evaluate the effectiveness of the intervention plan. The model can effectively control the influence of confounding factors and accurately evaluate the actual effect of the intervention plan by calculating the tendency score of the intervention accepted by the patient. Therefore, personalized, precise and dynamic chronic disease nursing management is realized.

The invention belongs to the technical field of robot collaboration, and particularly discloses a multi-brand robot collaborative nursing management system and method, which realizes compatible access and collaborative scheduling of different brands and types of robots by constructing a unified management framework, obtains patient demands based on human-computer interaction, dynamically generates and executes a collaborative nursing scheme, and improves the nursing efficiency. Advanced functions of all robots are integrated, comprehensive, efficient and personalized nursing services are provided for patients, the problem that existing robots of multiple brands cannot work cooperatively can be effectively solved, and the utilization efficiency of robot nursing resources and the patient satisfaction degree are improved. Through an innovative multi-brand robot collaborative management mechanism and deep man-machine interaction fusion, different brand robot resources can be effectively integrated, advantage complementation of all robots is achieved, high-quality and personalized robot collaborative nursing services are provided for patients, and the method has wide application value.

The invention relates to the technical field of medical care informatization, in particular to a grading nursing management early warning system based on ADL dynamic evaluation, which comprises a data acquisition module, an evaluation analysis module, a decision support module, an early warning processing module and a cross-system data interaction module, the method comprises the steps that ADL data (standardized scale + Internet of Things equipment + video analysis) and illness state data are acquired in a multi-mode mode, a two-dimensional (illness state severity + ADL score) evaluation model is constructed, and nursing level suggestions are generated in combination with AI aid decision making; based on triple triggering conditions of time, events and data, graded early warning (red / orange / yellow) is started, and medical care is linked for cooperative treatment; data communication with an HIS and an electronic medical record system is achieved through a standardized interface, and a closed loop of evaluation, decision making, execution, early warning and re-evaluation is formed. The nursing level matching accuracy and the doctor's advice adjustment timeliness rate are improved, and the nursing resource waste and the adverse event occurrence rate are reduced.

The invention relates to the technical field of medical care, and discloses a medical care cloud platform based on deep learning. The platform comprises a data acquisition module, a model analysis module, a scheme generation module and an abnormity early warning module. The data acquisition module is used for acquiring basic health data and real-time monitoring data of a patient; the model analysis module receives the nursing request, constructs a deep learning model to divide analysis time periods, and dynamically analyzes the health state of a patient to obtain nursing intervention demand indexes of all the time periods; the scheme generation module generates a personalized nursing scheme and continuously tracks the health indexes of the patient in the execution period; the abnormity early warning module monitors vital sign indexes, medication compliance and nursing operation standardability of a patient and carries out abnormity early warning. The platform integrates multi-source data of the patient, realizes dynamic analysis and personalized nursing, improves the nursing quality through continuous tracking and abnormal early warning, and is suitable for nursing management of various patients.

The invention discloses a ward nursing management method and system, a storage medium and a product, and the method comprises the steps: firstly obtaining an actual physiological parameter set, then setting an abnormal index calculation model, and carrying out the fitting of the actual physiological parameter set to generate a predicted physiological parameter set, generating an abnormal level according to the predicted physiological parameter set and the abnormal index calculation model, finally generating an alternative medical worker list, and generating an assignment list according to the abnormal level and the alternative medical worker list; compared with the prior art, the method has the advantages that the state of the patient in a certain time node or time period in the future is pre-judged through the prediction model, and the corresponding medical staff is allocated before the severe condition occurs, so that the timely nursing service is provided at the first time, meanwhile, the nursing intervention can be realized at the first time, and the ward nursing quality can be improved; meanwhile, medical staff can be allocated more leisurely, the adaptation degree of the medical staff and the field condition can be improved, and the ward nursing management efficiency is improved.