52 results about "Patient feedback" patented technology

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention provides a head and neck cancer patient symptom management method and system based on multi-source data fusion and AI driving, and the method comprises the steps: integrating electronic medical records, wearable equipment, questionnaire survey, popular science videos and other multi-modal data, employing an attention mechanism to dynamically fuse features, and constructing unified knowledge representation. A dynamically extended symptom-risk-intervention knowledge graph is established based on a Bayesian network and a graph neural network (GNN), and cross-layer symptom recommendation is realized in combination with a collaborative filtering algorithm. The symptom severity is evaluated in real time through an XGBoost algorithm, three-color early warning is output, and finally the knowledge graph is continuously optimized through reinforcement learning based on patient feedback data. According to the method, accurate and personalized symptom management of the patient with the head and neck cancer is realized.

The invention relates to a hospital nursing resource intelligent allocation method and device, equipment and a medium. The method comprises the following steps: generating a nurse skill assessment report based on nursing operation records, training data and patient feedback; secondly, analyzing a nursing demand dependency relationship and a time constraint in the electronic medical record, and constructing a patient demand constraint graph; further quantitatively matching the nurse skill items with the patient demand items, and generating a nurse-demand sorting result sorted according to the matching degree; and finally, through deviation analysis of actual execution data and a prediction result, continuously optimizing the nurse skill evaluation model. According to the method, the technical defects that dynamic changes of nurse skills are not captured in real time, a complex nursing demand dependency relationship is not systematically analyzed, man-post matching lacks a quantitative basis and execution feedback does not drive model optimization in traditional manual distribution are overcome, and the nursing resource utilization rate and the clinical demand response accuracy are remarkably improved.

The invention relates to the technical field of telemedicine, and particularly discloses an online inquiry method and device, computer equipment and a storage medium. According to the method, the doctor can be accurately matched according to the inquiry information and the historical medical data of the patient, the inquiry information and the historical medical data are analyzed, the to-be-examined part and the examination prompt of the patient are determined, effective reference is provided for diagnosis and treatment, manual analysis is not needed, the virtual consulting room is constructed through the augmented reality equipment, and the diagnosis efficiency is improved. Patient feedback can be obtained in time according to the patient virtual image, and the diagnosis efficiency is improved. The method is applied to an online inquiry system in the medical field, inquiry information and historical medical data of a patient can be accurately analyzed, and a doctor is effectively assisted in quickly and accurately obtaining a diagnosis result.

The invention relates to a respiration guiding system for image examination, which is applied to the technical field of image examination and comprises a respiration monitoring module, a patient feedback module, a control processing module and a physician interaction terminal, a plurality of different target breathing waveforms are pre-stored in the doctor interaction terminal, the control processing module generates real-time breathing waveforms based on breathing waveform signals, time synchronization is conducted on the target breathing waveforms and the real-time breathing waveforms, then the patient feedback module and the doctor interaction terminal are controlled to be displayed in a parallel comparison mode, and a patient is guided to adjust breathing. According to the invention, through ''physician-system-patient'' three-element closed-loop interaction, respiratory training and pre-judgment before scanning and continuous guidance and stable maintenance during scanning are realized, so that the success rate of first scanning is improved, the number of times of repeated scanning is reduced, and the radiation dose risk and medical cost of a patient are reduced.

The invention discloses a disease control state evaluation system and method based on psoriasis patient feedback, subjective feelings of patients on disease control can be captured in clinical and non-clinical scenes, and a concept framework is based on literature and doctor-patient feedback. Different from the past psoriasis PROM, the invention provides a method for comprehensively, standardly and quickly collecting'disease control 'information, and the structure content has value for clinical and scientific research. Statistical analysis shows that good clinical benefits can be obtained when the model is applied clinically, and an effective basis for individualized precise treatment is provided for improvement and self-management of psoriasis, especially chronic diseases in China.

The present invention belongs to the field of medical simulation and education technology. The present invention discloses a method and system for building an interactive medical simulation scene based on AI artificial intelligence, including collecting patient physiological data, medical information data and patient feedback data; preprocessing the patient physiological data and medical information data to obtain a physiological characteristic data set and a medical information characteristic data set; fusing the patient feedback data into the physiological characteristic data set and the medical information characteristic data set to obtain a medical comprehensive characteristic data set; training a medical scene prediction model, inputting the medical comprehensive characteristic data set into the medical scene prediction model, and predicting medical scene simulation data; processing the predicted medical scene simulation data to obtain data simulation similarity error; providing more realistic data support for medical training, research and decision-making, and improving the effect and reliability of medical simulation.

This invention provides a basic jaw pad, a dental instrument, and a preparation method. The basic jaw pad includes a first support portion and a second support portion connected to each other. After the basic jaw pad is installed in the through hole of an orthodontic appliance, at least a portion of the first support portion is located within the cavity of the orthodontic appliance for accommodating the dentition. The first support portion mates with the occlusal surface of the dentition on which the orthodontic appliance is worn. At least a portion of the second support portion is located outside the orthodontic appliance and protrudes towards the occlusal surface of the opposing dentition. The basic jaw pad has a preset basic shape, and at least the material of the second support portion of the basic jaw pad is a polished material. This invention improves the versatility of the basic jaw pad, enabling mass production. Furthermore, clinicians can make real-time adjustments and optimizations based on the target positions of the upper and lower jaws in the patient's occlusal state, the specific situation during actual wear, and patient feedback, thereby improving the orthodontic effect.

The invention belongs to the field of auxiliary medical treatment, and provides a personalized electrical stimulation treatment scheme generation system and method, and the method comprises the steps: obtaining a voice signal of a patient, and extracting voice dynamic change information; acquiring neuromyoelectric signals, heart rate signals and motion information of the patient, and extracting physiological change information; performing association modeling on the voice change information and the physiological change information to generate fusion features for representing a multi-part tremor mode of the patient; performing dynamic generation of personalized electrical stimulation parameters based on the fused features; constraining the dynamic change of the current amplitude, the stimulation frequency and the pulse width within an individually set safety threshold range, and performing protective adjustment in combination with real-time feedback of a patient; and the electrical stimulation module is used for implementing electrical stimulation on a target nerve part in the radial nerve, the median nerve or the ulnar nerve according to the parameters issued by the decision module.

This invention provides an intelligent pre-diagnosis medical history collection system for anesthesia clinics, characterized by: a patient terminal, a physician terminal, and a control terminal; the control terminal includes a consultation module and an analysis module; the consultation module stores consultation templates for the patient terminal to access; the analysis module identifies and analyzes patient feedback information and adds intelligent tags to specific information in the patient feedback information; the patient terminal sequentially calls each sub-module, displays relevant content from the consultation template on the front end of the patient's client, and guides the patient to fill in self-reported information; the physician terminal obtains the patient form with added intelligent tags from the control terminal. This system significantly reduces the interview time for information collection, improves the efficiency and accuracy of anesthesia clinic diagnosis and treatment, and reduces the error rate caused by insufficient experience.

The invention belongs to the technical field of big data medical health, and particularly relates to a maternal and fetal congenital anomaly case tracking system based on regional big data, which comprises a data acquisition module, a data transmission and storage module, a big data analysis module, a case tracking and early warning module, a patient feedback and intervention module and a case database and cloud management module. The system can monitor the health conditions of pregnant women and fetuses in real time, generate abnormal early warning in time, effectively reduce the missed diagnosis rate and misdiagnosis rate of maternal and fetal congenital abnormalities, and perform intelligent analysis on large-scale maternal and fetal health data through the big data analysis module to find potential disease risks, thereby providing decision support for doctors, and improving the diagnosis efficiency of the maternal and fetal congenital abnormalities. Meanwhile, the system not only can monitor health data of individuals, but also can realize large-scale maternal and fetal congenital abnormality case tracking, promote reasonable configuration and allocation of regional health resources, improve the working efficiency of doctors, reduce errors of manual diagnosis and ensure early intervention of maternal and fetal health.

The invention discloses an electronic medical record analysis and personalized rehabilitation recommendation method and system based on a large model, and relates to the technical field of medical information processing and artificial intelligence. Comprising the steps of collecting multi-source medical data of a patient from a hospital information system; preprocessing the multi-source medical data; fusing the preprocessed multi-source medical data, and constructing a multi-modal patient portrait; inputting the constructed patient portrait into a pre-trained artificial intelligence analysis model, and performing multi-dimensional analysis to obtain an analysis result; constructing a personalized rehabilitation recommendation engine; based on the analysis result, generating a rehabilitation recommendation scheme through a personalized rehabilitation recommendation engine; in the execution process of the rehabilitation recommendation scheme, continuously collecting feedback data of the patient, and performing incremental learning and retraining on the artificial intelligence analysis model. According to the method, the electronic medical record can be automatically and accurately analyzed, and the personalized rehabilitation recommendation scheme is generated based on the analysis result.

The invention discloses a medical insurance management system and management method, and belongs to the field of medical insurance management, and the method comprises the following steps: collecting all pharmacy sales data, patient medical insurance medicine purchasing records, patient feedback data and medicine enterprise circulation data of a region, and carrying out the correlation integration processing of the data; and analyzing a price strategy and a prescription recommendation behavior of drug sales of each pharmacy, evaluating an influence degree of the pharmacy on a drug purchase decision of the patient in combination with a drug purchase record of the patient, and finally generating an individual behavior portrait of each pharmacy. According to the method, the multi-source data is collected, associated and integrated, the pharmacy behavior portraits are generated, the pharmacy portraits of the same specification are compared to find the abnormal points, the abnormal standards are formulated according to the abnormal points, regulation and settlement are performed on the illegal pharmacies, and the suspected pharmacies are manually screened, so that the medicine purchasing behavior can be accurately identified and induced, and the loss of medical insurance funds is reduced.

This invention discloses a multimodal health management method and system for TIPS postoperative care, specifically relating to the field of medical and health management technology. It involves constructing a postoperative multimodal follow-up dataset by collecting patient health consultation records, dietary protein intake records, oral medication adherence records, and doctor-patient feedback records. Through consultation topic classification, protein intake deviation identification, medication adherence breakpoint detection, and feedback tendency determination, a set of behavioral events is extracted. Within the same follow-up period, co-occurrence associations of consultation, diet, medication, and feedback events are established to generate a risk trigger chain for hepatic encephalopathy. Based on the risk trigger chain, the intervention type is determined, generating individualized intervention instructions such as dietary guidance, medication reminders, and health consultation responses. These intervention instructions are then pushed to both the patient and healthcare professionals, collecting execution and treatment feedback and writing it back into the dataset to form a closed-loop follow-up update. Finally, the updated data is used to correct the risk trigger chain and automatically generate intervention instructions for the next cycle.

The invention discloses a lung function rehabilitation device capable of conveniently adjusting respiratory resistance, relates to the technical field of medical auxiliary devices, and aims to solve the problems that in the prior art, respiratory resistance adjustment is inconvenient, and training feedback cannot be provided for a patient, the lung function rehabilitation device comprises a shell, and the shell is internally provided with a head end channel and a tail end channel which are communicated with each other; a breathing resistance adjusting assembly used for adjusting breathing resistance is arranged in the head end channel and the tail end channel, and an effect feedback assembly is further arranged in the tail end channel. Adjusting knobs on the two sides of a shell are rotated to drive a fan-shaped baffle to rotate so as to adjust a shielding area of an air conveying hole in an adjusting plate, then breathing resistance is adjusted, a testing ball moves upwards through the air pressure difference generated in a circulation pipeline and an adjusting pipe when a patient breathes, and the breathing resistance is adjusted by combining monitoring of a position sensor. Transmitting to a display device to feed back a respiratory training effect for the patient; the device has the advantages of comprehensive functions, convenience in use, good rehabilitation effect and simple structure.

The invention discloses a risk layering and decision-making method based on interaction of a large language model and a risk model, and the method comprises the steps: obtaining and processing multi-modal clinical data of a patient, generating a structured feature matrix, and inputting a risk prediction model to obtain a multi-dimensional risk spectrum; retrieving evidence-based evidence from the medical knowledge base based on the risk probability exceeding the threshold; combining the feature matrix, the risk spectrum and the evidence, and generating an explanatory report through a large language model and cue word engineering; the model weight or retrieval strategy is dynamically adjusted by comparing the consistency of the intervention priority and the risk probability; performing risk layering according to the adjusted risk spectrum and generating personalized intervention suggestions; and continuously optimizing the model according to a medical intervention result and patient feedback data to form closed-loop iterative updating. According to the method, dynamic risk prediction, explanatory report generation and personalized intervention suggestion are realized, and meanwhile, the clinical application value and decision accuracy are improved based on a doctor feedback and patient data closed-loop optimization model.

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

The invention relates to a pelvic floor function screening and rehabilitation integrated system, which belongs to the technical field of medical instruments and comprises an integrated probe assembly, a handheld operation controller, a main control processing and display unit and a patient feedback terminal. The integrated probe assembly integrates a high-definition image system, a self-adaptive cleaning mechanism, an intelligent constraint skeleton and a multi-cavity biomechanical training unit, and pelvic floor morphological observation, electromyographic signal detection and three-dimensional vectorization resistance training can be synchronously achieved. The handheld operation controller is provided with multiple sensors and a humanized operation interface, and accurate and stable operation is ensured. The system fuses multi-modal physiological data in real time through the main control unit, generates a personalized rehabilitation scheme, and provides intuitive biological feedback guidance at a patient terminal. According to the device, seamless connection of pelvic floor function evaluation and rehabilitation treatment is achieved, screening accuracy, training pertinence and operation safety are remarkably improved, an integrated solution is provided for postpartum pelvic floor rehabilitation, and the rehabilitation effect and diagnosis and treatment efficiency are effectively improved.

The invention discloses a diagnosis and treatment rule auditing method based on real-time confidence threshold judgment, and the method comprises the steps: obtaining diagnosis and treatment data, and building a game model in which multiple parties participate according to a doctor diagnosis record, patient feedback information and a supervision auditing record in the diagnosis and treatment data; establishing a causal relationship graph model used for representing the causal relationship among the diagnosis confidence coefficient, the misdiagnosis probability and the audit result, and introducing an intervention variable representing intervention on a confidence coefficient threshold value into the causal relationship graph model; based on the estimation of the influence corresponding to the intervention variable, constructing a correction revenue function containing the influence estimation item; and inputting the corrected revenue function into the game model, determining a strategy combination of each participant by adopting equilibrium solution, and determining a Pareto optimal confidence threshold. The confidence coefficient threshold value can be dynamically adjusted according to the change of the real-time diagnosis and treatment data, so that the examination is remarkably improved. The flexibility and the accuracy are considered, and complex and changeable diagnosis and treatment scenes are effectively dealt with.

The invention relates to the technical field of exercise prescription recommendation, in particular to an intelligent exercise prescription recommendation system and method for different chronic disease patients, and the system comprises a data collection module, a data preprocessing module, a dynamic disease interaction module, a real-time closed-loop risk monitoring module, a patient feedback and long-term optimization module and a central control management module. Through a multi-source data fusion mode, exercise prescription recommendation can be conveniently carried out on patients suffering from various chronic diseases, the reasonability of a recommended scheme is improved, then the adoption rate of the scheme is improved, through a real-time dynamic risk management and control mode, the exercise intensity and the exercise time of the patients are adjusted, the exercise safety of the patients is guaranteed, and the exercise efficiency of the patients is improved. Through a personalized feedback mechanism, the recommendation precision of the system for the patient each time is improved, long-term exercise prescription recommendation is provided for the patient, and the treatment effect of the patient is improved.

The application discloses a brain-computer interface interaction system and method for cerebral infarction rehabilitation training, relates to the technical field of electroencephalogram feature processing, and comprises the following steps: feature extraction is performed on electroencephalogram signal data, comprehensive electroencephalogram feature fusion coefficients are obtained through feature weighting fusion calculation based on the electroencephalogram feature data, feedback signal comprehensive calculation is performed based on patient feedback data, comprehensive signal feedback coefficients are obtained, comprehensive rehabilitation evaluation processing is performed according to the comprehensive signal feedback coefficients and the comprehensive electroencephalogram feature fusion coefficients, comprehensive rehabilitation evaluation coefficients are obtained, and the most suitable rehabilitation processing plan for the patient is finally determined based on the comprehensive rehabilitation evaluation coefficients, so that the rehabilitation process is more suitable for the actual situation of the patient itself, and the rehabilitation effect is improved.

With the above in mind, embodiments of the present invention are related to a method including the steps of: providing a graphical user interface to a medical provider, wherein the graphical user interface displays a first dashboard or a second dashboard based on a criteria, wherein the graphical user interface automatically displays the first dashboard having a patient rounding list when a device is located within a hospital geofence and automatically displays the second dashboard having remote monitoring data when the device is located outside the hospital geofence; sending a secure clinical message to the medical provider when a trigger is met; forming a care team by automatically and dynamically selecting a plurality of care team members from a user database based on a location of each user, shift data of each user, and role data of each user; sending a discharge notification to the provider; and remotely monitoring the patient after discharge via at least one remote monitoring device and patient feedback.

The invention discloses an orthopedic postoperative rehabilitation monitoring system based on intelligent analysis, and relates to the technical field of rehabilitation monitoring, and the orthopedic postoperative rehabilitation monitoring system comprises a data acquisition module, a data analysis module, a rehabilitation training module, a rehabilitation monitoring module, an early warning terminal and a database. Analyzing the probability of each complication caused by each training action from the information, training action combination and complication detection report of each historical patient in the database, setting the training action combination for the patient, and acquiring each motion parameter and each feedback of the patient in each rehabilitation training stage at the same time; the training effect of the patient in each rehabilitation training stage and the credibility fed back by the patient are analyzed, and whether the patient has complications in each rehabilitation training stage is judged, so that a doctor can adjust the rehabilitation training of the patient in time, and the effectiveness of training actions and the probability of complications are guaranteed; and the timeliness of training result analysis and the accuracy of complication monitoring are ensured.

The utility model provides a novel brachial artery fixing component which comprises a fixing supporting plate and a fixing belt, the fixing belt is connected with the bottom face of the fixing supporting plate, the fixing plate comprises a middle layer and an outer layer, the outer layer is made of skin-friendly materials, the inner layer comprises a fixing plate body and a reinforcing piece, and the fixing plate body is made of carbon fiber materials. According to the fixing assembly, the fixing plate is made of light materials such as a carbon fiber plate, under the condition that it is guaranteed that the arms of a patient are fixed, more comfortable feedback force is provided for the patient, and acrylic or a steel plate serves as a reinforcing piece to provide more stable supporting; a unique locking structure is convenient for doctors to observe and patients to operate; the arm of a patient can be effectively fixed, stimulation to a puncture point is reduced, and the occurrence risk of complications is reduced; binding and fixing are carried out through the multiple fixing bands, the working efficiency of medical workers can be improved, and doctors can conveniently observe puncture points and find and handle problems in time. The patient experience can be improved, the activity limitation is reduced, and the comfort of the patient is improved.

The invention provides an implantable closed-loop nerve stimulation system and a control method thereof.The implantable closed-loop nerve stimulation system comprises a local real-time control device, a local optimization control device and a cloud server which are connected in sequence, and a patient feedback device connected with the local optimization control device, the local real-time control device is implanted into the brain and is configured to calculate the signal confidence coefficient of the electroencephalogram signal in the first time window to detect an abnormal electroencephalogram signal, and adjust the length of the first time window based on the first instruction; the local optimization control device is an extracranial portable device configured to: process a patient feedback event within a second time window greater than the first time window, determine a first control policy based on at least one of an electrode impedance change rate, a signal confidence, and the patient feedback event; and the cloud server is configured to analyze the first data in a third time window greater than the second time window and generate an alarm message and / or a second control strategy.

The invention discloses a chronic disease follow-up visit and management system, and relates to the technical field of medical health management, and the technical key points are that the system comprises the following modules: an information data collection and transmission module, which is used for collecting and transmitting health data of a patient; the health risk assessment module is used for assessing the health risk of the patient according to the collected data; the comprehensive intervention scheme making module is used for making a personalized intervention scheme according to the evaluation result; the follow-up scheme module is used for planning and executing a follow-up plan of the patient; and the patient feedback module is used for collecting feedback information of the patient on the follow-up visit process. By constructing a general outpatient chronic disease follow-up safety net mode based on active participation of the patient, the system can remarkably improve the participation sense and self-management ability of the patient and enhance the participation sense and self-management ability of the patient, so that the follow-up quality and the blood pressure / blood sugar control standard-reaching rate are improved, unnecessary medical resource waste is reduced, and the patient experience is improved. And the utilization efficiency of medical resources is improved.