A medical clinical nursing decision-making assistance system and method

Abstract

The present invention relates to a medical clinical nursing decision-making support system and method, and belongs to the field of medical clinical nursing information technology. It includes: a screening module, which is used to obtain case information, extract case variable codes of case information, match case variable codes with pre-stored variable codes, and screen out matching pre-stored feature codes; a nursing assessment decision module, which is used to calculate the proportion of pre-stored feature codes, and judge whether the diagnosis and treatment plan and path service of nursing meet the nursing requirements; when the requirements are met, the characteristic factors of the case information are extracted to obtain the patient's nursing problems; according to the problems, nursing goals and plans are formulated; a nursing plan execution module, which is used to assign nursing staff according to nursing goals and plans; wherein the nursing staff updates the nursing problems according to the patient's current nursing status; a doctor's order execution module, which is used to automatically output drug use prompts according to the queried drug information. The present invention can achieve a balanced distribution of nursing staff workload and nursing tasks.

Description

Technical Field

[0001] The present invention relates to the field of medical clinical nursing information technology, and in particular to a medical clinical nursing decision-making assistance system and method. Background Art

[0002] In the field of medical care, after undergoing surgery or contracting a serious disease, patients often have a decline in their ability to take care of themselves for a period of time and are unable to complete daily activities, or they have a certain degree of self-care ability but need additional care due to physical weakness. In this case, it is particularly important to arrange professional nursing staff to provide nursing and care services.

[0003] However, the professional skills and experience levels of nursing staff vary significantly due to factors such as personal working time and training background, which directly affects the quality of nursing services. In addition, some patients will designate specific nursing staff to provide services based on their trust in certain experienced nursing staff, which further exacerbates the uneven distribution of nursing staff workload.

[0004] In the process of arranging nursing work, it is usually based on the patient's request that the assignor directly assigns the nursing staff to provide services. However, during the assignment process, it is often difficult for the assignor to fully grasp the patient's detailed situation and nursing needs, resulting in the assigned nursing staff being unable to perform specific nursing tasks. At the same time, the distribution of nursing tasks is also unbalanced. Some nursing staff may take on too many simple tasks and ignore the response to complex nursing needs, which not only affects the efficiency of nursing services, but also affects the patient's nursing experience and satisfaction. Summary of the invention

[0005] To this end, the technical problem to be solved by the present invention is to overcome the unbalanced distribution of nursing staff workload and nursing tasks in the prior art.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a medical clinical nursing decision-making support system, comprising:

[0007] A screening module is used to obtain case information of a patient, extract case variable codes of the case information, match the case variable codes with pre-stored variable codes, and screen out matching pre-stored feature codes; wherein the pre-stored variable codes are pre-stored in a database for storing nursing data information;

[0008] The nursing assessment decision module is used to calculate the proportion of the pre-stored feature code and determine whether the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements; when the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements, extract the characteristic factors of the case information and obtain the nursing problem of the patient; and formulate nursing goals and nursing plans according to the nursing problem;

[0009] A nursing plan execution module is used to automatically assign appropriate nursing staff according to the complexity of the nursing goal, the complexity of the nursing plan and the nursing level of the case information; wherein the nursing staff updates the nursing questions of the nursing assessment decision module according to the current nursing status of the patient, and formulates new nursing goals and nursing plans;

[0010] The doctor's order execution module is used to call the drug data in the database and output drug use rule prompts based on the drug information input by the nursing staff or the drug information in the executed doctor's order.

[0011] In one embodiment of the present invention, the nursing assessment decision module includes:

[0012] A nursing positive item module is used to extract characteristic factors of the case information, match the characteristic factors with characteristic factors in the database, and output matching characteristic factors;

[0013] A nursing diagnosis module, used for receiving the matching characteristic factors and outputting nursing questions, diagnosis names, definitions, diagnostic criteria and associated factors;

[0014] A nursing goal module, used for receiving the matching characteristic factors and the nursing problem, and outputting nursing goals, nursing measures and nursing plans;

[0015] The nursing record module is used to record the case information, the nursing staff's information, the nursing plan and the nursing medical history.

[0016] In one embodiment of the present invention, the nursing plan execution module includes:

[0017] A first input module, used to obtain nursing information input by the nursing staff;

[0018] A task generation module, used for receiving the nursing information, generating and outputting nursing tasks;

[0019] The task execution module is used to receive the nursing task and generate an intelligent task list; obtain the execution status and nursing effect evaluation input by the nursing staff, and record and save them;

[0020] The first output module is used to receive a retrieval request from the nursing staff or the patient and output the content of the retrieval request.

[0021] In one embodiment of the present invention, the doctor's order execution module includes:

[0022] A retrieval module, used to retrieve the medicines that may be used in the database according to the nursing plan;

[0023] The second output module is used to output usage instructions of the medicine based on the medicine.

[0024] In one embodiment of the present invention, a linkage module is further included, wherein the linkage module is used to obtain the inspection data sent by the detection equipment and dynamically adjust the nursing plan according to the inspection data.

[0025] In one embodiment of the present invention, the linkage module includes:

[0026] The temperature linkage module is used to obtain the body temperature data of the patient and determine whether to add cooling nursing measures to the nursing tasks of the nursing plan execution module according to a preset body temperature threshold;

[0027] The pressure sore linkage module is used to obtain the scores of the pressure sores from the detection equipment or the evaluation documents, and determine whether to add pressure sore treatment and early warning prevention measures to the nursing tasks of the nursing plan execution module according to the preset score threshold;

[0028] The fall linkage module is used to obtain the score of the fall by the detection equipment or the evaluation document, and obtain the evaluation data according to the preset fall threshold; the nursing plan execution module predicts potential safety hazards by analyzing the evaluation data, and adjusts the environment layout or increases the nursing measures in advance;

[0029] The pain linkage module is used to obtain the pain score of the detection equipment or the evaluation document, screen out the corresponding relief measures according to the preset pain level, and add the relief measures to the nursing tasks of the nursing plan execution module.

[0030] In one embodiment of the present invention, the nursing data information includes any one or more of a nursing diagnosis knowledge base, a relevant factor dictionary, a definitional feature dictionary, a relevant situation dictionary management, a nursing measure dictionary, an expected goal dictionary, a nursing evaluation library, a patient nursing diagnosis library and a commonly used nursing diagnosis library.

[0031] In a second aspect, in order to solve the above technical problems, the present invention provides a medical clinical nursing decision-making assistance method, comprising:

[0032] Obtaining the patient's case information and extracting the case variable code of the case information;

[0033] Matching the case variable code with the pre-stored variable code to filter out the matching pre-stored feature code; wherein the pre-stored variable code is pre-stored in a database for storing nursing data information;

[0034] Calculate the proportion of the pre-stored feature code to determine whether the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements;

[0035] When the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements, characteristic factors of the case information are extracted to obtain the nursing problem of the patient;

[0036] Develop nursing goals and nursing plans based on the nursing problems described;

[0037] According to the complexity of the nursing goal, the complexity of the nursing plan and the nursing level of the case information, the appropriate nursing staff is automatically assigned; wherein the nursing staff updates the nursing problem according to the current nursing status of the patient and formulates new nursing goals and nursing plans.

[0038] In one embodiment of the present invention, it also includes monitoring the execution status of the nursing staff, and the method is: collecting language data of the nursing staff during nursing; converting the language data into text data using speech recognition technology; preprocessing the text data to obtain preprocessed data; identifying keywords of the preprocessed data; monitoring the execution status of the nursing staff based on the keywords; establishing a feedback mechanism to provide real-time feedback of the execution status to the nursing staff.

[0039] In a third aspect, in order to solve the above-mentioned technical problems, the present invention provides an APP, including a medical clinical nursing decision support system as described in any one of the above items.

[0040] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0041] (1) The medical clinical nursing decision-making support system and method described in the present invention, through integrated modules and modular output and feedback mechanisms, enables the assigned personnel to quickly obtain a preliminary assessment of the patient's condition based on the nursing measures and nursing plans automatically generated by the system. This helps the assigned personnel to accurately select nursing staff with corresponding expertise and levels to provide personalized nursing services to patients. At the same time, the design of different modules also facilitates the assigned personnel to efficiently allocate human resources, optimize the allocation of nursing staff, achieve a balanced distribution of nursing staff workload and nursing tasks, and improve the overall work efficiency and service quality of the nursing team.

[0042] (2) The present invention provides reminders of drug use rules through the doctor's order execution module, and provides effective warnings to nursing staff for high-risk doctor's order execution, thereby improving medication safety. The present invention can also provide relevant warnings for specimen collection in various situations such as medication period and physiological cycle to avoid obtaining non-compliant specimens. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0044] Figure 1 This is a structural diagram of a medical clinical nursing decision-making support system in a preferred embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the structure of a database in a preferred embodiment of the present invention;

[0046] Figure 3 This is a structural diagram of a nursing assessment decision module in a preferred embodiment of the present invention;

[0047] Figure 4 This is a structural diagram of a nursing plan execution module in a preferred embodiment of the present invention;

[0048] Figure 5 It is a structural schematic diagram of a medical order execution module in a preferred embodiment of the present invention;

[0049] Figure 6 It is a structural diagram of a task list module in a preferred embodiment of the present invention;

[0050] Figure 7 A schematic diagram of the structure of the linkage module in a preferred embodiment of the present invention;

[0051] Figure 8 This is a flow chart of a medical clinical nursing decision-making assistance method in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention. Embodiment 1

[0053] Reference Figure 1 As shown, an embodiment of the present invention provides a medical clinical nursing decision-making support system, including:

[0054] A screening module is used to obtain the patient's case information, extract the case variable code of the case information, match the case variable code with the pre-stored variable code, and screen out the matching pre-stored feature code; wherein the pre-stored variable code is pre-stored in a database for storing nursing data information;

[0055] The nursing assessment decision module is used to calculate the proportion of pre-stored feature codes to determine whether the nursing diagnosis and treatment plan and nursing pathway services meet the nursing requirements; when the nursing diagnosis and treatment plan and nursing pathway services meet the nursing requirements, the feature factors of the case information are extracted to obtain the patient's nursing problems; according to the nursing problems, nursing goals and nursing plans are formulated;

[0056] The nursing plan execution module is used to automatically assign appropriate nursing staff according to the complexity of the nursing goals, the complexity of the nursing plan and the nursing level of the case information; among them, the nursing staff updates the nursing questions of the nursing assessment decision module according to the patient's current nursing status, and formulates new nursing goals and nursing plans;

[0057] The doctor's order execution module is used to call the drug data in the database and output drug use rule prompts based on the drug information input by the nursing staff or the drug information in the doctor's order.

[0058] The embodiment of the present invention provides a medical clinical nursing decision-making support system. After obtaining the patient's case information, the system can automatically analyze the case information and formulate nursing goals and nursing plans. The assigned personnel can arrange the corresponding nursing staff to care for the patient according to the nursing plan, thereby optimizing the scheduling of nursing staff. At the same time, the nursing problems of the nursing assessment decision module are updated. This design ensures that the nursing plan can be personalized according to the patient's latest condition to meet the patient's specific needs. In addition, the doctor's order execution module provides reminders of drug use rules, and provides effective warnings to nursing staff for high-risk doctor's order execution, thereby improving medication safety. The system described in the embodiment of the present invention enables the patient's condition to be understood as clearly as possible before assigning nursing staff, optimizes the scheduling of nursing staff, achieves a balanced distribution of nursing staff workload and nursing tasks, and improves nursing efficiency and quality.

[0059] Specifically, the database described in the embodiment of the present invention is used to store nursing data information, and the nursing data information includes any one or more of a nursing diagnosis knowledge base, a relevant factor dictionary, a definitional feature dictionary, a relevant situation dictionary management, a nursing problem library, a nursing measure dictionary, an expected goal dictionary, a nursing outcome library, a nursing evaluation library, a patient nursing diagnosis library, and a commonly used nursing diagnosis library, which can be referred to Figure 2. Among them, the nursing diagnosis knowledge base contains information such as diagnosis name, department information, field, classification, source, etc. The definitional feature dictionary contains words that can define patient symptoms, and it uses pre-stored variable codes to assign and save words that can define patient symptoms. The nursing measures dictionary contains all nursing measures. The expected goal dictionary contains nursing expected goals and related terms. The nursing evaluation library contains the storage of expected goal completion. The patient nursing diagnosis library not only stores detailed nursing diagnosis information, but also has the function of adjusting these diagnoses. In addition, it records the matching between the patient's assessment results and the corresponding nursing diagnoses, and can track and trace all the patient's nursing diagnoses. The common nursing diagnosis library stores the department's common nursing diagnoses and the default priority of nursing diagnosis settings. In addition to the nursing data information described above, the database may also store a medical order database, a rule engine library, and a national clinical pathway. Among them, the medical order database contains drug use rules. The rule engine library supports the modification and maintenance of logical rules, including the logical relationship operations of commonly used nursing risk assessments and nursing diagnoses, support for modification and improvement based on actual needs, support for logical processing of repeated diagnoses and mutual exclusion management of similar diagnoses, and support for the management of operational rules for diagnoses, related factors, measures, and evaluations.

[0060] Specifically, the screening module includes an input module and an analysis module. The input module is mainly used to obtain the patient's case information, while the analysis module is mainly used to extract case variable codes from the case information, call the pre-stored variable codes in the definitional feature dictionary for matching, and screen out all pre-stored feature codes that match the case variable codes.

[0061] Specifically, the nursing assessment decision module analyzes the pre-stored variable codes and pre-stored feature codes output by the screening module based on the condition that the nursing diagnosis and treatment plan and nursing pathway service can handle, and calculates the proportion of the pre-stored feature codes. Among them, the proportion is the proportion of the number of pre-stored variable codes that the nursing diagnosis and treatment plan and nursing pathway service can handle to all the pre-stored feature codes screened out. When the proportion is between 90% and 100% (including 90%), it indicates that the patient is very well matched with the nursing diagnosis and treatment plan and nursing pathway service and meets the nursing requirements; when the proportion is between 50% and 90% (including 50%), it indicates that the patient is relatively matched with the nursing diagnosis and treatment plan and nursing pathway service; when the proportion is between 0 and 50%, it indicates that the patient is not matched with the nursing diagnosis and treatment plan and nursing pathway service, and it is recommended to change the nursing diagnosis and treatment plan and nursing pathway service. The matching result is output to the patient by the result output module in the nursing assessment decision module for reference. Patients can decide whether to choose the nursing diagnosis and treatment plan and nursing pathway service based on their actual situation and needs. This process ensures patient autonomy in their choices while providing the necessary information support and best practice guidelines to help make more informed decisions.

[0062] Furthermore, the calculation formula for the percentage value is as follows:

[0063] ;

[0064] ;

[0065] ;

[0066] in, is the percentage value, is the ideal evaluation plan model value, To predict the approximate input parameter value, To code the measures, Encode the preset model, is the correlation factor, For learning frequency, It is a classification positive feature.

[0067] Specifically, the nursing assessment decision module includes a nursing positive item module, a nursing diagnosis module, a nursing goal module and a nursing record module. Among them, the positive items in the nursing positive item module are words, phrases, short sentences, etc. used to define the patient's symptoms in the case, that is, by understanding the patient's positive item content, the patient's condition symptoms can be understood. For example, if the positive item is: weakness, dyspnea after daily activities, it can be understood that the patient's existing problems are lack of daily physiological or psychological ability and lack of endurance. The nursing positive item module is mainly used to extract the characteristic factors of the patient's case information, match the characteristic factors with the characteristic factors in the database, and output the matching characteristic factors, wherein the characteristic factors in the database are the characteristic factors in the definitional feature dictionary stored in the database. The nursing diagnosis module is used to receive matching characteristic factors, call the data in the nursing diagnosis library, analyze nursing problems, diagnosis names, definitions, diagnostic criteria and related factors, and output them simultaneously. The nursing goal module is used to receive matching characteristic factors and nursing problems, and output nursing goals, nursing measures and nursing plans. In order to further obtain perfect nursing measures and nursing plans, the nursing module can be divided into nursing measures modules. The nursing measures module can be used to obtain matching characteristic factors and nursing problems, and then based on the nursing measures in the nursing measures dictionary, the nursing measures are screened and the nursing plans are arranged through the logic library according to the patient's condition and course of disease records, and the nursing measures, nursing plans, and nursing plan prompts are output. For nursing plans, patient assessments can be conducted based on diagnosis, department, gender, age, etc., and the plans can be formulated based on the assessment results. In addition, the nursing positive item module, nursing diagnosis module, nursing goal module, and nursing measures module can generate corresponding prompt information while providing output data. Based on the prompt information, the assigned personnel or nursing staff make necessary adjustments and updates to the nursing measures and plans according to their responsibilities and authority, thereby ensuring the real-time and adaptability of the nursing plan.

[0068] Further, refer to Figure 3 , the nursing assessment decision module may also include a nursing effect module, a nursing assessment module, a nursing document module and a nursing record module. The nursing effect module is used to obtain the patient's evaluation of the nursing staff's nursing effect and save it through the patient nursing diagnosis library. The nursing assessment module is used to obtain the nursing staff's evaluation of the nursing effect and write the evaluation into the nursing evaluation library. The nursing record module is used to record various data including patient case information, nursing staff information responsible for nursing, nursing medical records, nursing plans and nursing effect evaluation. This information is integrated and input into the nursing document module to generate detailed electronic medical records. Subsequently, the nursing document module archives these electronic medical record materials and stores them in the patient nursing diagnosis library to ensure that all records of relevant nursing activities are properly preserved and systematically managed. This process not only improves the efficiency and accuracy of nursing records, but also facilitates future query and analysis.

[0069] The nursing assessment decision module integrates various modules and modular output and feedback mechanisms, allowing the assignee to quickly obtain a preliminary assessment of the patient's condition based on the nursing measures and nursing plans automatically generated by the system. This helps the assignee to accurately select nurses with corresponding expertise and provide personalized nursing services to patients. At the same time, the design of this module also facilitates the assignee to efficiently allocate human resources, optimize the allocation of nursing staff, and improve the overall work efficiency and service quality of the nursing team.

[0070] Specifically, the nursing plan execution module automatically assigns appropriate nursing staff by comprehensively considering factors such as the complexity of the goal and plan, the nursing level of the case, etc. Figure 4 As shown, the module includes a first input module, a task generation module, a task execution module and a first output module. Among them, the first input module is used to obtain the nursing information input by the nursing staff, which includes a nursing target unit, a nursing measure unit, an execution frequency unit, an execution status unit, a nursing effect unit and a nursing plan query unit. The nursing staff can input corresponding parameters through the nursing target unit, the nursing measure unit, the execution frequency unit and the execution status unit according to the nursing diagnosis, so as to modify the nursing plan formulated for the patient. The task generation module is used to receive nursing information, generate and output nursing tasks, which can automatically generate nursing goals and nursing plans according to the nursing diagnosis output by the nursing diagnosis module. Specifically, when the task generation module obtains the determined nursing plan, it outputs nursing task prompts and nursing plans to the nursing staff through the first output module. The task execution module is used to receive nursing tasks, generate an intelligent task list according to the execution frequency input by the nursing staff or automatically calculated, and output it to the nursing staff through the first output module.

[0071] Specifically, after the nursing staff completes a single nursing service, the execution status of the nursing service is written through the execution status unit of the first input module, and the nursing effect of the nursing service is written through the nursing effect unit. The first input module sends a call request for nursing diagnosis, nursing goals, nursing measures, nursing activities, effect evaluation and other contents, and the first output module receives the call request of the nursing staff or the patient, and outputs the contents of the call request such as nursing diagnosis, nursing goals, nursing measures, nursing activities, effect evaluation and other contents. This design method not only makes it easier for patients and nursing staff to understand the progress and nursing status of nursing tasks, but also makes it easier to adjust and plan subsequent nursing services.

[0072] Specifically, the parameters Figure 5, for the doctor's order execution module, it includes a retrieval module and a second output module. The retrieval module is used to retrieve the drugs that may be used in the database according to the nursing plan output by the nursing measures module, and the search scope is mainly carried out in the doctor's order database of the database. The second output module is used to output the usage tips of the drug based on the retrieved drug. If the nursing staff adjusts the task plan during the execution of the nursing task, at this time, the retrieval module will search again in the doctor's order database and output the retrieved drug through the second output module. On the one hand, the doctor's order execution module can provide effective warnings for nurses for high-risk doctor's order execution and improve the safety of medication; on the other hand, it can also provide relevant warnings for specimen collection for various situations such as medication period and physiological cycle to avoid obtaining improper specimens. The intelligent warning function of this module not only improves the accuracy of nursing work, but also provides patients with safer and more meticulous nursing services.

[0073] Furthermore, the medical clinical nursing decision support system provided by the embodiment of the present invention may also include a task list module. The task list module includes a timer, a pending medical order query module, a pending physical sign reminder module, a pending nursing case module, a pending nursing measure module, a pending nursing treatment module, and a pending health education module. Figure 6 . Specifically, the timer counts the time in the task plan, and outputs the status of the pending medical orders through the pending medical order query module at the preset time, outputs the status of the pending physical signs and the measurement time through the pending physical sign reminder module, outputs the pending nursing measures through the pending nursing measures module, outputs the pending nursing measures through the pending nursing treatment module, and outputs the pending health education tasks through the pending health education module. The waiting nursing case module obtains the input electronic case from the document module, and performs a writing standard check on the electronic case. If there is an electronic case that is not written in a standard way or has not been input, a prompt of the incomplete nursing case is output to remind the nursing staff to complete the electronic case.

[0074] Furthermore, the medical clinical nursing decision support system provided by the embodiment of the present invention may also include a linkage module, which is used to obtain the test data sent by the detection equipment and dynamically adjust the nursing plan output by the nursing measure module according to the test data. Figure 7 The linkage module includes temperature linkage module, pressure sore linkage module, fall linkage module and pain linkage module. The linkage module can automatically calculate the corresponding physical sign test time point according to the hospital's physical sign test rules and provide nursing staff with physical sign test reminders.

[0075] Specifically, the temperature linkage module is used to obtain the patient's body temperature data and, based on the preset body temperature threshold, determine whether to add cooling nursing measures to the nursing tasks of the nursing plan execution module. The judgment method is: if the temperature data is greater than or equal to the preset body temperature threshold, it indicates that the patient's body temperature is high and cooling measures need to be taken, and cooling nursing measures are added to the nursing tasks.

[0076] Specifically, the pressure sore linkage module is used to obtain the score of the pressure sore from the detection equipment or evaluation documents, and judge whether to add pressure sore treatment and early warning prevention measures to the nursing tasks of the nursing plan execution module according to the preset scoring threshold. Among them, the scoring threshold includes a first scoring threshold and a second scoring threshold. The first scoring threshold is used for adult patients, and the second scoring threshold is used for pediatric patients. When the detection score of an adult patient exceeds the first scoring threshold, it indicates that the adult needs pressure sore treatment measures; when the detection score of a pediatric patient exceeds the second scoring threshold, it indicates that the child needs pressure sore treatment measures, and pressure sore treatment measures are added to the nursing tasks.

[0077] Specifically, the fall linkage module is used to obtain the score of the detection equipment or assessment documents on the fall, and obtain the assessment data according to the preset fall threshold; the nursing plan execution module predicts potential safety hazards by analyzing the assessment data, and adjusts the environment layout or increases nursing measures in advance. This process includes a judgment link, and the specific judgment method is: compare the fall score with the preset fall threshold. If the fall score is greater than or equal to the fall threshold, it indicates that nursing measures are needed and nursing measures need to be added to the nursing task.

[0078] Specifically, the pain linkage module is used to obtain the pain score of the detection equipment or the assessment document, screen out the corresponding relief measures according to the preset pain level, and add the relief measures to the nursing tasks of the nursing plan execution module.

[0079] Furthermore, to ensure the security and compliance of patient data, the medical clinical nursing decision support system provided by the embodiment of the present invention also includes a patient privacy management module. The module uses encryption technology to encrypt and store patient data and securely transmit it, thereby preventing any unauthorized access. At the same time, the module implements a refined permission allocation strategy, strictly restricts access rights to sensitive information, and only allows authorized users to access it. In addition, the patient privacy management module is also equipped with an uninterrupted monitoring mechanism and a rapid response process, which can promptly detect and respond to potential data security risks, thereby further enhancing the overall security and reliability of the system.

[0080] The patient privacy management module not only enhances patients’ trust in the system, but also provides audit and compliance proof for medical institutions, supports data sharing and cross-institutional collaboration, and ultimately improves the quality of care and patient satisfaction.

[0081] Furthermore, the medical clinical nursing decision-making support system provided by the embodiment of the present invention also has the function of generating a patient decision-making view, which can effectively integrate the patient's pathophysiological data and provide intelligent prompts. The system presents a comprehensive decision-making support integrated view to clinical nursing staff, so that the patient's pathophysiological condition is clearly displayed, thereby improving the efficiency and accuracy of nursing decisions. Embodiment 2

[0082] Based on the same inventive concept, this embodiment provides a medical clinical nursing decision-making support method, and its principle of solving the problem is similar to that of a medical clinical nursing decision-making support system provided in Example 1, and the repeated parts will not be repeated.

[0083] Reference Figure 8 As shown, this embodiment provides a medical clinical nursing decision-making assistance method, including:

[0084] Obtain the patient's case information and extract the case variable code of the case information;

[0085] Matching the case variable code with the pre-stored variable code to filter out the matching pre-stored feature code; wherein the pre-stored variable code is pre-stored in a database for storing nursing data information;

[0086] Calculate the percentage of pre-stored feature codes to determine whether the nursing diagnosis and treatment plan and nursing pathway services meet the nursing requirements;

[0087] When the nursing diagnosis and treatment plan and nursing pathway services meet the nursing requirements, the characteristic factors of the case information are extracted to obtain the patient's nursing problems;

[0088] Develop nursing goals and nursing plans based on nursing problems;

[0089] According to the complexity of the nursing goal, the complexity of the nursing plan and the nursing level of the case information, the appropriate nursing staff is automatically assigned; wherein the nursing staff updates the nursing problems and formulates new nursing goals and nursing plans according to the patient's current nursing status.

[0090] Specifically, the medical clinical nursing decision-making assistance method provided by this embodiment also includes monitoring the execution status of nursing staff, and the method is: collecting language data of nursing staff during nursing; using speech recognition technology to convert language data into text data; preprocessing the text data to obtain preprocessed data; identifying keywords of the preprocessed data; based on the keywords, monitoring the execution status of nursing staff; establishing a feedback mechanism to provide real-time feedback of the execution status to nursing staff.

[0091] This monitoring and feedback mechanism uses advanced speech recognition and text analysis technologies to provide an efficient, reliable and real-time auxiliary decision support for medical clinical care, helping to improve care quality, patient safety and care management efficiency. Embodiment 3

[0092] This embodiment provides an APP, including a medical clinical nursing decision support system as provided in Embodiment 1.

[0093] In the description of the present specification and the appended claims, the terms "first" and "second" etc. are only used to distinguish descriptions and should not be understood as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A medical clinical nursing decision-making support system, characterized in that: include: A screening module is used to obtain case information of a patient, extract case variable codes of the case information, match the case variable codes with pre-stored variable codes, and screen out matching pre-stored feature codes; wherein the pre-stored variable codes are pre-stored in a database for storing nursing data information; The nursing assessment decision module is used to calculate the proportion of the pre-stored feature code to determine whether the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements; when the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements, extract the characteristic factors of the case information to obtain the nursing problem of the patient; according to the nursing problem, formulate nursing goals and nursing plans; wherein the calculation formula of the proportion is: ； ； ； in, is the percentage value, is the ideal evaluation plan model value, To predict the approximate input parameter value, To code the measures, Encode the preset model, is the factor, For learning frequency, For classification positive characteristics; A nursing plan execution module is used to automatically assign appropriate nursing staff according to the complexity of the nursing goal, the complexity of the nursing plan and the nursing level of the case information; wherein the nursing staff updates the nursing questions of the nursing assessment decision module according to the current nursing status of the patient, and formulates new nursing goals and nursing plans; The doctor's order execution module is used to call the drug data in the database and output drug use rule prompts based on the drug information input by the nursing staff or the drug information in the executed doctor's order.

2. A medical clinical nursing decision support system according to claim 1, characterized in that: The nursing assessment decision module includes: A nursing positive item module is used to extract characteristic factors of the case information, match the characteristic factors with characteristic factors in the database, and output matching characteristic factors; A nursing diagnosis module, used for receiving the matching characteristic factors and outputting nursing questions, diagnosis names, definitions, diagnostic criteria and associated factors; A nursing goal module, used for receiving the matching characteristic factors and the nursing problem, and outputting nursing goals, nursing measures and nursing plans; The nursing record module is used to record the case information, the nursing staff's information, the nursing plan and the nursing medical history.

3. A medical clinical nursing decision support system according to claim 1, characterized in that: The nursing plan execution module includes: A first input module, used to obtain nursing information input by the nursing staff; A task generation module, used for receiving the nursing information, generating and outputting nursing tasks; The task execution module is used to receive the nursing task and generate an intelligent task list; obtain the execution status and nursing effect evaluation input by the nursing staff, and record and save them; The first output module is used to receive a retrieval request from the nursing staff or the patient and output the content of the retrieval request.

4. A medical clinical nursing decision support system according to claim 1, characterized in that: The doctor's order execution module includes: A retrieval module, used to retrieve the medicines that may be used in the database according to the nursing plan; The second output module is used to output usage instructions of the medicine based on the medicine.

5. A medical clinical nursing decision support system according to claim 1, characterized in that: It also includes a linkage module, which is used to obtain the inspection data sent by the detection equipment and dynamically adjust the nursing plan according to the inspection data.

6. A medical clinical nursing decision-making support system according to claim 5, characterized in that: The linkage module comprises: The temperature linkage module is used to obtain the body temperature data of the patient and determine whether to add cooling nursing measures to the nursing tasks of the nursing plan execution module according to a preset body temperature threshold; The pressure sore linkage module is used to obtain the scores of the pressure sores from the detection equipment or the evaluation documents, and determine whether to add pressure sore treatment and early warning prevention measures to the nursing tasks of the nursing plan execution module according to the preset score threshold; The fall linkage module is used to obtain the score of the fall by the detection equipment or the evaluation document, and obtain the evaluation data according to the preset fall threshold; the nursing plan execution module predicts potential safety hazards by analyzing the evaluation data, and adjusts the environment layout or increases the nursing measures in advance; The pain linkage module is used to obtain the pain score of the detection equipment or the evaluation document, screen out the corresponding relief measures according to the preset pain level, and add the relief measures to the nursing tasks of the nursing plan execution module.

7. A medical clinical nursing decision support system according to claim 1, characterized in that: The nursing data information includes any one or more of a nursing diagnosis knowledge base, a relevant factor dictionary, a definitional feature dictionary, a relevant situation dictionary management, a nursing measure dictionary, an expected goal dictionary, a nursing evaluation library, a patient nursing diagnosis library and a commonly used nursing diagnosis library.

8. A method for assisting decision making in clinical medical care, characterized in that: include: Obtaining the patient's case information and extracting the case variable code of the case information; Matching the case variable code with the pre-stored variable code to filter out the matching pre-stored feature code; wherein the pre-stored variable code is pre-stored in a database for storing nursing data information; Calculate the proportion of the pre-stored feature code to determine whether the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements; wherein the calculation formula of the proportion is: ； ； ； in, is the percentage value, is the ideal evaluation plan model value, To predict the approximate input parameter value, To code the measures, Encode the preset model, is the factor, For learning frequency, For classification positive characteristics; When the nursing diagnosis and treatment plan and nursing pathway service meet the nursing requirements, characteristic factors of the case information are extracted to obtain the nursing problem of the patient; Develop nursing goals and nursing plans based on the nursing problems described; According to the complexity of the nursing goal, the complexity of the nursing plan and the nursing level of the case information, the appropriate nursing staff is automatically assigned; wherein the nursing staff updates the nursing problem according to the current nursing status of the patient and formulates new nursing goals and nursing plans.

9. A medical clinical nursing decision-making assistance method according to claim 8, characterized in that: The method also includes monitoring the execution of the nursing staff, wherein the method comprises: collecting language data of the nursing staff during nursing; converting the language data into text data using speech recognition technology; preprocessing the text data to obtain preprocessed data; identifying keywords of the preprocessed data; and monitoring the execution of the nursing staff according to the keywords; A feedback mechanism is established to provide real-time feedback of the execution status to the nursing staff.

10. An APP, characterized in that: It comprises a medical clinical nursing decision support system as described in any one of claims 1 to 7.

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