Medical advice closed-loop management system and management method

Abstract

The invention relates to the technical field of medical informatization management, in particular to a medical advice closed-loop management system and management method.The system comprises an interaction module, a demand analysis module, an intelligent adaptation module and a monitoring feedback module, the problem that communication between an information department and a clinical department is not smooth is solved through the interaction module, and the demand analysis module evaluates and converts clinical demands; the intelligent adaptation module optimizes a system function interface, and the monitoring feedback module tracks medical advice execution. The management method comprises the steps of demand collection, analysis and evaluation, optimization and improvement, and monitoring and feedback. According to the invention, the problems of cognitive differences among departments and lack of communication feedback mechanisms are effectively solved, the accuracy and efficiency of medical advice execution are improved, and the medical service quality is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical information management, in particular to a medical order closed-loop management system and method. BACKGROUND

[0002] In the modern medical system, the accurate execution of medical orders is crucial for the treatment effect and safety of patients. With the development of medical informatization, a medical order closed-loop management system has emerged, aiming to realize the whole-process tracking and management of medical orders from issuance, review, execution to feedback through informatization means, and to improve medical quality and efficiency.

[0003] However, there are many problems in the construction and promotion of the medical order closed-loop management system.

[0004] First, there is a significant cognitive difference between the information department and the clinical department. The information department mainly focuses on the technical implementation and may not fully consider the actual clinical use in the closed-loop construction due to lack of in-depth understanding of the details of clinical work and actual needs. When designing the medical order entry interface, it is not optimized according to the daily diagnosis and treatment habits of clinical doctors, resulting in additional time and effort for doctors to adapt to the new system, thereby increasing the clinical workload. The clinical department, due to limited understanding of the principles and limitations of information technology, often proposes use requirements that are difficult to implement under the existing hardware and software conditions. For example, expecting the system to instantly process large-scale data and provide real-time feedback, or requiring the system to have special functions that do not conform to the current technical architecture, which makes it difficult for both parties to reach a consensus in the cooperation process and hinders the closed-loop promotion work.

[0005] Second, there is a lack of effective communication and feedback mechanism between departments. During system development and use, there is no convenient and efficient communication channel when problems arise, resulting in problems not being exposed and solved in a timely manner. The clinical department finds that the system has loopholes in handling medical orders for some complex conditions, but cannot accurately convey the problems to the information department in a timely manner; after the information department optimizes and upgrades the system, there is no effective way to inform the clinical department of the relevant changes and precautions, causing confusion in the clinical department during use, further affecting the promotion and use effect of the system.

[0006] According to relevant research statistics, due to the above problems, the medical error rate has not been significantly reduced after some hospitals introduced the medical order closed-loop management system, and even increased to a certain extent in the short term, while the satisfaction of medical staff with the system is also low, which seriously hinders the development of medical informatization and the improvement of medical service quality. SUMMARY

[0007] The application aims to provide a medical order closed-loop management system and a management method to solve the cooperation problem caused by the cognitive difference between the information department and the clinical department and the lack of communication feedback mechanism, and to realize efficient and accurate medical order closed-loop management.

[0008] The technical solution adopted by the application to solve its technical problems is: 1. An interaction module for providing a convenient communication interface between the clinical department and the information department, so that the clinical staff can submit use requirements and problem feedback in real time, and the information staff can reply and answer questions in time; 2. A requirement analysis module for in-depth analysis of the requirements submitted by the clinical department, combining with the technical feasibility evaluation, and converting reasonable requirements into technical implementation schemes; 3. An intelligent adaptation module for automatically optimizing system functions and interfaces according to clinical actual use data and feedback, so as to make them more suitable for clinical use habits; 4. A monitoring and feedback module for real-time monitoring of the whole process of medical order execution, timely warning of abnormal conditions, and feedback of execution results to relevant departments.

[0009] Technical architecture: 1. Hierarchical architecture Front-end interaction layer: based on WebSocket / Instant Messaging Protocol (such as MQTT) to realize real-time message push.

[0010] Business logic layer: (1) Requirement analysis module: integrated Scikit-learn / TensorFlow; (2) Intelligent adaptation module: user behavior point collection + behavior graph modeling.

[0011] Data service layer: (1) Medical data middleware (HL7 / FHIR standard interface); (2) Blockchain encryption storage (optional).

[0012] 2. Implementation route in stages Stage 1: basic capacity building (0-6 months) Referring to the following table:

[0013] Stage 2: construction of intelligent core (6-18 months) 1. Requirement analysis module Implementation path: (1) Build medical requirement feature engineering (TF-IDF+ICD coding mapping); (2) Develop multi-task learning model (medical order classification + urgency prediction); (3) Model distillation optimization (BERT→LightGBM).

[0014] Phase 3: System integration upgrade (18-30 months) 1. Monitoring feedback module: - Develop HL7 protocol converter; - Implement real-time stream processing (Flink+CDC); - Anomaly detection: application of isolation forest algorithm in order execution monitoring.

[0015] 2. Cross-module collaboration: User -> + Interaction module: submit requirements; Interaction module -> + Requirement analysis: real-time request; Requirement analysis -> - Intelligent adaptation: user portrait update; Intelligent adaptation -> + Interface: dynamic layout adjustment.

[0016] Four, verification index

[0017] Specifically, the interaction module uses instant messaging technology to realize real-time transmission of information between the two parties.

[0018] Specifically, the requirement analysis module uses data mining and machine learning algorithms to analyze and evaluate requirements.

[0019] Specifically, the intelligent adaptation module collects user operation behavior data, establishes a user behavior model, and realizes automatic optimization of system functions and interfaces.

[0020] Specifically, the monitoring feedback module interacts with each subsystem of the hospital information system to obtain detailed information of order execution.

[0021] Specificly, it also includes a security management module to ensure the security of system data and the privacy of user information.

[0022] Specifically, the security management module uses encryption technology to encrypt data storage and transmission, and sets up a user permission management mechanism.

[0023] Specifically, the system also has data backup and recovery functions, which regularly backup system data and can quickly recover when data is lost or damaged.

[0024] The management method based on the order closed-loop management system includes the following steps: S1, requirement collection, regularly collect the use requirements and problem feedback of clinical departments through the interaction module; S2, analysis and evaluation, the demand analysis module analyzes the collected information, evaluates the rationality and technical feasibility of the demand; S3, optimization and improvement, according to the analysis and evaluation results, the intelligent adaptation module synchronously adjusts the system settings; S4, monitoring and feedback, using the monitoring and feedback module to track the execution of medical orders in real time, and timely processing of exceptions and feedback results.

[0025] Specifically, in the S1 demand collection step, a special demand collection personnel is set up to organize and classify the feedback from the clinical department.

[0026] Specifically, in the S2 analysis and evaluation step, a demand evaluation index system is established to evaluate from multiple dimensions such as importance, urgency, and technical difficulty.

[0027] Specifically, in the S3 optimization and improvement step, agile development method is adopted to quickly iterate system functions.

[0028] Specifically, in the S4 monitoring and feedback step, an abnormal situation handling process is established to clarify the responsibilities and division of labor of each department in handling exceptions.

[0029] The highlights of the present application are: (1) The medical order closed-loop management system and management method of the present application effectively solves the problem of cognitive difference between the information department and the clinical department, promotes communication and cooperation between the two parties through the interaction module and the demand analysis module, and makes the system function more in line with the actual needs of the clinical department.

[0030] (2) The medical order closed-loop management system and management method of the present application establishes a perfect communication and feedback mechanism, discovers and solves problems in the use of the system in a timely manner, and improves the stability and reliability of the system.

[0031] (3) The medical order closed-loop management system and management method of the present application optimizes the system function and operation process through the application of the intelligent adaptation module and the monitoring and feedback module, improves the accuracy and efficiency of medical order execution, and reduces the medical error rate. BRIEF DESCRIPTION OF DRAWINGS

[0032] The present application will be further described below in conjunction with the drawings and examples.

[0033] Figure 1 The medical order closed-loop management system framework provided by the present application; Figure 2 The flowchart of the medical order closed-loop management method provided by the present application; DETAILED DESCRIPTION

[0034] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application is further described below in conjunction with specific embodiments.

[0035] As Figure 1 shown, the medical order closed-loop management system of the present application comprises: Interaction module: provides a convenient communication interface between the clinical department and the information department. Clinical staff can submit usage requirements and problem feedback in real time, and information staff can reply and answer questions in time.

[0036] Requirement analysis module: in-depth analysis of the requirements submitted by the clinical department, combined with technical feasibility evaluation, converts reasonable requirements into technical implementation schemes.

[0037] Intelligent adaptation module: automatically optimizes system functions and interfaces according to clinical actual use data and feedback, so that they are more in line with clinical use habits.

[0038] Monitoring feedback module: real-time monitoring of medical order execution whole process, timely warning of abnormal situation, and feedback of execution result to relevant departments.

[0039] Among them, the presentation layer: Clinical terminal: doctors / nurses access the system through PC, tablet or mobile device to realize medical order issuing, execution and feedback.

[0040] Management terminal: information department manages system configuration, receives demand feedback and monitors system status through a special interface.

[0041] Among them, the function layer: Interaction module: provides a two-way communication channel between the clinical and information departments (such as online feedback form, instant messaging).

[0042] Requirement analysis module: analyzes clinical requirements and generates technical solutions (including feasibility evaluation model).

[0043] Intelligent adaptation module: automatically optimizes interface layout and function priority based on user behavior data (such as AI algorithm driven).

[0044] Monitoring feedback module: real-time tracking of medical order execution status, triggering abnormal warning (such as automatic interception of dose overrun).

[0045] Safety management module: data encryption, permission grading, operation audit (such as RBAC permission model).

[0046] Data backup and recovery module: periodic backup of key data, supports disaster recovery.

[0047] Among them, the support layer: Middleware: supports cross-system data interaction (such as HL7 protocol interface).

[0048] Server cluster: Distributed deployment ensures system high availability.

[0049] Network infrastructure: Secure isolation of hospital intranet from the Internet.

[0050] Among them, the data layer: Prescription database: Store prescription content, execution status, timestamp, etc.

[0051] Patient information database: Associated with electronic medical record (EMR), laboratory report, etc.

[0052] Drug database: Record drug attributes, inventory, contraindications, etc.

[0053] Device information database: Interface with infusion pumps, monitors and other medical devices.

[0054] User permission database: Store role permissions (e.g. doctors can modify prescriptions, nurses only execute).

[0055] Among them, the key data flow between modules: Clinical terminal → interaction module → demand analysis module: After submitting clinical requirements, the demand analysis module generates technical solutions.

[0056] Intelligent adaptation module → clinical terminal: Optimize the interface according to behavior data (e.g. automatically sort frequently used prescriptions).

[0057] Monitoring feedback module → interaction module: Abnormal early warning information is pushed to clinical and information departments in real time.

[0058] Data layer ↔ function layer: Each module accesses the database through middleware (e.g. prescription execution record is written to the prescription database).

[0059] Among them, cross-department collaboration closed loop: Through the interaction module and demand analysis module, it is mandatory to require clinical and information departments to reach consensus in the demand confirmation stage (e.g. demand review electronic signature).

[0060] Among them, dynamic adaptation mechanism: Intelligent adaptation module uses reinforcement learning algorithm to continuously optimize system behavior.

[0061] Among them, abnormal handling linkage: When the monitoring feedback module triggers an alarm, it automatically freezes related prescriptions and notifies pharmacists for review.

[0062] The following is an explanation of the algorithms for each key module in the prescription closed-loop management system: Among them, the demand analysis module: Demand evaluation algorithm, in the demand analysis module, multi-dimensional evaluation is carried out on the demand put forward by the clinical department, here the weighted scoring method is used to determine the priority of the demand. Suppose there are Each requirement has an evaluation dimension (such as importance, urgency, technical difficulty, etc.). Suppose the score of the th requirement in the th dimension is , the weight of the th dimension is and . Then the comprehensive score of the th requirement is The calculation formula is: .

[0063] The scores of importance and urgency can use the 1-5 point system, with 1 being the lowest and 5 being the highest; the technical difficulty score is evaluated based on factors such as the technical complexity and resource input required to implement the requirement, also using the 1-5 point system.

[0064] Feasibility judgment algorithm, for each requirement, in addition to the comprehensive score evaluation, feasibility judgment is also needed. Feasibility judgment can be based on technical ability, resource constraints, etc. Let the technical feasibility index be and the resource feasibility index be , both using values between 0 and 1, with 0 indicating unfeasibility and 1 indicating complete feasibility. When , it is considered that the requirement is feasible in terms of technology and resources.

[0065] Among them, the intelligent adaptation module: User behavior modeling algorithm, the intelligent adaptation module collects user operation behavior data to establish a user behavior model to achieve automatic optimization of system functions and interfaces. Here, a Markov chain model can be used to predict the user's next operation.

[0066] Suppose the user's operation sequence is , where represents the th operation. The Markov chain model assumes that the user's next operation only depends on the current operation. Let the state transition probability matrix be , where represents the probability of transitioning from operation to operation . where represents the number of times operation transitions to operation , represents the total number of operations.

[0067] Function priority adjustment algorithm, according to the user behavior model and usage frequency, adjust the priority of system functions. Let the usage frequency of function be , the operation transition probability is (i.e., the sum of the probabilities of transitioning from other operations to this function operation), then the priority of the function The calculation formula is: (where, is a weight coefficient, with a value range of [0, 1], which can be adjusted according to actual conditions).

[0068] Among them, the monitoring feedback module: Abnormal early warning algorithm, the monitoring feedback module monitors the execution of medical orders in real time, and timely issues an early warning when an abnormality occurs. For abnormal drug dosage, set the drug dosage range specified by the medical order as , the actual execution dosage is When or , trigger an abnormal warning.

[0069] At the same time, the risk coefficient can be introduced to represent the risk level of the dosage abnormality, and the calculation formula is: When , it is considered that the risk is high and relevant personnel need to be notified immediately for processing.

[0070] Abnormality tracing algorithm, when an abnormality occurs, each link of the medical order execution needs to be traced back. The method of combining time stamp and event log can be used. Each medical order execution event records the time stamp and related operation information, when an abnormality is found, according to the time of the abnormality occurrence, find the relevant operation records from the event log, and gradually trace back to the source of the problem.

[0071] Among them, the safety management module: Permission management algorithm, the safety management module adopts the Role-Based Access Control (RBAC) model. Set the system to have roles, permissions, users. The role-permission assignment matrix represents whether the role has the permission , , 1 means having the permission, 0 means not having. The user-role assignment matrix represents whether the user belongs to the role , .

[0072] The judgment formula for whether the user has the right is: (where, represents logical or operation,​ Indicates a logical AND operation.

[0073] As shown in Figure 2 , the management method of the present application comprises the following steps: S1, demand collection, regularly collect the use demand and problem feedback of the clinical department through the interaction module; S2, analysis and evaluation, the demand analysis module analyzes the collected information, and evaluates the rationality and technical feasibility of the demand; S3, optimization and improvement, according to the analysis and evaluation results, the system is optimized and improved, and the intelligent adaptation module synchronously adjusts the system settings; S4, monitoring and feedback, the monitoring and feedback module is used to track the execution of medical orders in real time, and the abnormal situation is handled in time and the results are fed back.

[0074] Among them, in Figure 2 Step and logic: Start: the starting point of the whole process.

[0075] Demand collection: the clinical department submits the use demand and problem feedback through the interaction module, and the demand collection personnel sorts and classifies these feedbacks.

[0076] Analysis and evaluation: the demand analysis module receives the sorted demand information, evaluates it from multiple dimensions such as importance, urgency, technical difficulty, etc., and judges whether the demand is reasonable and technically feasible.

[0077] If feasible: enter the optimization and improvement stage.

[0078] If not feasible: communicate with the clinical department through the interaction module, adjust the demand or give an explanation, and then return to the demand collection step.

[0079] Optimization and improvement: system developers use agile development methods to optimize the system according to the analysis and evaluation results. The intelligent adaptation module synchronously adjusts the system settings such as interface layout, function priority, etc. according to the actual use data of the clinical department.

[0080] System deployment and testing: deploy the optimized system to the test environment for comprehensive testing, check whether it meets the demand and whether it introduces new problems.

[0081] If the test passes: enter the formal use stage.

[0082] If the test fails: return to the optimization and improvement step to adjust again.

[0083] Monitoring and feedback: the monitoring and feedback module tracks the execution of medical orders in real time, establishes an abnormal situation handling process, and clearly defines the responsibilities of each department. Once an abnormality is found, an early warning is sent out in time and relevant information is recorded.

[0084] Abnormality handling: relevant departments (such as clinicians, pharmacists, information departments, etc.) handle abnormal situations according to early warning information and job responsibilities. After handling, the results are fed back to the monitoring feedback module.

[0085] Result feedback and evaluation: the results of abnormality handling and order execution are fed back to the clinical department and information department. The entire process is evaluated to determine whether the expected goals are met.

[0086] If satisfied: continue with daily monitoring and feedback.

[0087] If not satisfied: enter the demand collection step and start a new round of optimization cycle.

[0088] Specific example one: demand collection and preliminary communication At the initial stage of introducing the medical order closed-loop management system in a certain hospital, the clinical department submitted a series of usage requirements through the interaction module. Clinicians complained that the existing medical order entry interface was cumbersome, and for commonly used combination orders, they had to choose and enter one by one each time, wasting a lot of time. After receiving the demand, the information department immediately communicated with the clinicians through the interaction module to understand the specific scenarios and frequency of using combination orders in their daily work. At the same time, the information department explained the basic architecture and technical limitations of the current system to the clinicians to avoid misunderstandings. Through this timely communication, both parties established initial trust, laying the foundation for future cooperation.

[0089] Specific example two: demand analysis and scheme development The information department summarized the clinical demands collected into the demand analysis module. Taking the combination order demand proposed by the clinicians as an example, the demand analysis module conducted in-depth analysis. First, it counted the frequency of use of different combination orders and the distribution of involved departments; then, it evaluated the feasibility of implementing the demand under the existing technical architecture. After analysis, it found that by adjusting the existing database structure and developing corresponding quick entry algorithms, the function of quick entry of combination orders could be realized. The information department developed a detailed technical implementation plan accordingly and fed it back to the clinical department through the interaction module to solicit their opinions. After the clinical department evaluated the plan, they made some suggestions on details, and the information department optimized again to finally determine the implementation plan.

[0090] Specific example three: system optimization and intelligent adaptation After the system is developed, it enters the actual use stage. The intelligent adaptation module begins to play a role, and it collects the operation data of the clinical doctors in real time, such as the time of order entry, the number of errors, the use frequency of common functions and the like. Through the analysis of these data, the intelligent adaptation module finds that, when the doctors use the drug selection function, due to the unreasonable sorting of the drug list, the time for searching for a specific drug is relatively long. Therefore, the intelligent adaptation module automatically adjusts the sorting mode of the drug list, places the commonly used drugs in the front row, and performs personalized sorting according to the use habits of the doctors. At the same time, for the functions frequently used by the doctors, the system automatically provides a shortcut entry on the interface. After a period of use, the clinical doctors feedback that the operation of the system becomes more convenient and efficient, and the work efficiency is significantly improved.

[0091] Specific embodiment four: monitoring feedback and abnormal processing The monitoring feedback module monitors the whole process of order execution in real time. In a certain order execution process, the system finds that the dosage of a patient's medication appears abnormal fluctuation. The monitoring feedback module immediately issues a warning information to notify the relevant clinical doctors and pharmacists. At the same time, the system automatically traces back to each link of the order issuing, auditing, execution and the like, to find the abnormal reason. After investigation, it is found that the dose input error is caused by the misoperation of the nurse when executing the order. The clinical doctors and pharmacists timely correct the error and closely observe the patient's condition. After the event, the information department optimizes the operation process of the system according to this abnormal event, and increases the dose rationality checking function to avoid similar errors from happening again.

[0092] Embodiment five: continuous improvement and department cooperation With the continuous use of the system, the clinical department and the information department maintain close communication through the interaction module. The clinical department regularly feeds back new requirements and problems found in the use process to the information department. The information department continuously optimizes and improves the system according to the feedback. The clinical department proposes that the system can be connected with other medical equipment to realize more comprehensive patient information collection. The information department successfully realizes the data connection function between the system and some key medical equipment after technical research and development. Through this continuous improvement and cooperation between departments, the order closed-loop management system is continuously improved, and better meets the needs of clinical work, and improves the quality of medical services.

[0093] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. The closed-loop medical order management system is characterized by: include: The interactive module is used to provide a convenient communication interface between the clinical department and the information department, allowing clinical staff to submit usage requirements and problem feedback in real time, and information staff can respond and answer questions in a timely manner; The demand analysis module is used to conduct in-depth analysis of the requirements submitted by clinical departments and, combined with technical feasibility assessment, convert reasonable requirements into technical implementation solutions; The intelligent adaptation module automatically optimizes system functions and interfaces based on actual clinical usage data and feedback to make them more in line with clinical usage habits; The monitoring and feedback module monitors the entire process of medical order execution in real time, issues timely warnings for abnormal situations, and feeds back the execution results to relevant departments.

2. The closed-loop medical order management system according to claim 1, characterized in that: The interactive module uses instant messaging technology to achieve real-time transmission of information between the two parties; The demand analysis module uses data mining and machine learning algorithms to analyze and evaluate demand; The intelligent adaptation module collects user operation behavior data, establishes a user behavior model, and realizes automatic optimization of system functions and interfaces; The monitoring feedback module exchanges data with various subsystems of the hospital information system to obtain detailed information on the execution of medical orders.

3. The closed-loop medical order management system according to claim 1, characterized in that: It also includes a security management module to ensure the security of system data and the privacy of user information.

4. The closed-loop medical order management system according to claim 3, characterized in that: The security management module uses encryption technology to encrypt data for storage and transmission, and sets up a user rights management mechanism.

5. The closed-loop medical order management system according to claim 1, characterized in that: The system also has data backup and recovery functions, which can back up system data regularly and quickly restore data when it is lost or damaged.

6. A management method based on a closed-loop medical order management system, characterized in that: The following steps are involved: S1. Demand collection: Regularly collect usage requirements and problem feedback from clinical departments through interactive modules; S2. Analysis and evaluation: the demand analysis module analyzes the collected information and evaluates the rationality and technical feasibility of the demand; S3, optimization and improvement: Based on the analysis and evaluation results, the system is optimized and improved in a targeted manner, and the intelligent adaptation module synchronously adjusts the system settings; S4. Monitoring and feedback: Use the monitoring and feedback module to track the execution of medical orders in real time, handle exceptions in a timely manner and provide feedback on the results.

7. The closed-loop management method for medical orders according to claim 6, characterized in that: In the S1 demand collection step, dedicated demand collection personnel are assigned to organize and classify the feedback from clinical departments.

8. The closed-loop management method for medical orders according to claim 6, characterized in that: In the S2 analysis and evaluation step, a demand assessment indicator system is established to evaluate the needs from multiple dimensions such as importance, urgency, and technical difficulty.

9. The closed-loop management method for medical orders according to claim 6, characterized in that: In the S3 optimization and improvement steps, agile development methods are adopted to quickly iterate system functions.

10. The closed-loop management method for medical orders according to claim 6, characterized in that: In the S4 monitoring and feedback step, establish an abnormal situation handling process and clarify the responsibilities and division of labor of each department in handling abnormal situations.

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