An outpatient nursing work automation management method, system and terminal

Abstract

This invention belongs to the field of outpatient nursing technology, specifically relating to an automated management method, system, and terminal for outpatient nursing work. This invention acquires and integrates patient examination information, pre-examination information, and follow-up information. It then uses a validation structure to identify anomalies from the integrated information and uses these anomalies as input for matching processing to generate a nursing plan. The generated nursing plan directly addresses the specific problems identified from the multi-source information validation, ensuring high relevance and accuracy from the outset, thus overcoming the problem of mismatch between plans and actual needs in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of outpatient nursing technology, specifically relating to an automated management method, system, and terminal for outpatient nursing work. Background Technology

[0002] Outpatient services, as a core hub connecting patients and medical resources in the modern healthcare system, directly impact patients' medical experience and the overall efficiency of medical institutions. Outpatient nursing, a crucial component of the outpatient service system, encompasses multiple dimensions, including clinical care, process guidance, health education, and psychological support. Therefore, scientific and meticulous management of outpatient nursing is fundamental to improving medical service levels and ensuring medical safety.

[0003] Currently, the management of outpatient nursing work largely relies on traditional manual experience and post-event summary feedback. When faced with a large number of patients and complex nursing needs, the data collected mainly comes from nurses' subjective reporting and managers' rounds and observations. However, the data collected through this channel lacks objective quantitative standards, and due to the reliance on the subjective consciousness of managers and nurses, there is a time delay, which leads to distorted management decision-making and fails to truly reflect the real-time nursing status and difficulties.

[0004] Furthermore, existing management methods are unable to dynamically assess and predict potential risks and service efficiency in the nursing process, leading to a passive management approach where intervention is only implemented after problems occur, which affects the smoothness of the service process and patient safety.

[0005] To address the aforementioned issues, this invention provides an automated management method, system, and terminal for outpatient nursing work. Summary of the Invention

[0006] The purpose of this invention is to provide an automated management method, system, and terminal for outpatient nursing work, in order to solve the problem that the corresponding nursing plans in the prior art cannot be optimized quickly.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] An automated management method for outpatient nursing work includes the following steps:

[0009] Obtain examination information, pre-examination information, and follow-up information corresponding to the patient's identity, and identify abnormalities based on the examination information, pre-examination information, and follow-up information corresponding to the patient's identity;

[0010] Matching is performed based on anomalies to generate care plans;

[0011] When the execution status of the nursing plan is determined to be interrupted, corrective action is taken on the nursing plan.

[0012] The correction process includes: comparing the nursing plan with the patient's historical nursing records to determine the correction plan; and outputting the correction plan.

[0013] Among them, determining the execution status of the nursing plan as interrupted includes:

[0014] The system acquires execution records for the nursing project and generates feedback values ​​based on these records. When the feedback value is lower than the preset standard twice in a row, the execution status of the nursing plan is determined to be interrupted.

[0015] Preferably, determining the execution status of the care plan includes:

[0016] Obtain patient nursing satisfaction information; combine nursing satisfaction information and feedback values ​​to jointly determine the implementation status of the nursing plan.

[0017] Preferably, based on examination information, pre-examination information, and follow-up information corresponding to the patient's identity, the abnormal items identified include:

[0018] Integrate inspection information, pre-inspection information, and follow-up information to generate integrated information; construct a verification structure based on the integrated information, and use the verification structure to identify anomalies contained in the integrated information.

[0019] Preferably, the matching process based on anomalies to generate a care plan includes:

[0020] Perform a fuzzy matching operation on the anomalies and their supporting information to generate an initial matching node;

[0021] Calculate the matching score of the initial matching node according to the scoring rules;

[0022] When the matching score is lower than a preset threshold, a first care plan is generated and output.

[0023] When the matching score is greater than or equal to the preset threshold, the first nursing plan is generated and output, and the second nursing plan is generated and output based on the initial matching node.

[0024] Preferably, a second care plan is generated and output based on the initial matching node:

[0025] Based on the initial matching node, the execution time period of the composite nursing item is matched;

[0026] Calculate the time offset corresponding to the composite nursing care item; superimpose the time offset with the original planned time period in the first nursing care plan to generate the execution time period of the second nursing care plan.

[0027] This invention also discloses an automated management system for outpatient nursing work, comprising:

[0028] The plan generation module is used to obtain examination information, pre-examination information and follow-up information corresponding to the patient's identity, identify abnormal items, and perform matching processing based on the abnormal items to generate a nursing plan;

[0029] The plan execution module is used to output nursing items according to the nursing plan;

[0030] The status determination module is used to acquire execution records of the nursing project execution process, generate feedback values, and determine the execution status of the nursing plan based on the feedback values.

[0031] The plan correction module is used to compare the nursing plan with the patient's historical nursing records to determine a correction plan when the execution status of the nursing plan is determined to be interrupted, and then outputs the correction plan.

[0032] Preferably, the process involves acquiring examination information, pre-examination information, and follow-up information corresponding to the patient's identity, and identifying abnormal items, including:

[0033] Integrate inspection information, pre-inspection information, and follow-up information to generate integrated information;

[0034] A verification structure is constructed based on the integrated information, and the verification structure is used to identify anomalies contained in the integrated information.

[0035] Preferably, determining the execution status of the nursing plan based on feedback values ​​includes:

[0036] Obtain patient nursing satisfaction information;

[0037] By combining nursing satisfaction information and feedback values, the implementation status of the nursing plan can be jointly assessed.

[0038] Preferably, the execution status of the care plan is determined to be in an interrupted state, including:

[0039] The system acquires execution records for the nursing project and generates feedback values ​​based on these records. When the feedback value is lower than the preset standard twice in a row, the execution status of the nursing plan is determined to be interrupted.

[0040] This invention also discloses an automated management terminal for outpatient nursing work, comprising:

[0041] At least one processor; a memory communicatively connected to at least one processor;

[0042] The memory stores computer program instructions, which, when executed by at least one processor, cause the terminal to perform the aforementioned automated management method for outpatient nursing work.

[0043] Beneficial effects

[0044] 1. This invention acquires and integrates the patient's examination information, pre-examination information, and follow-up information, uses a verification structure to identify abnormal items from the integrated information, and uses these abnormal items as input for matching processing to generate a nursing plan. The generated nursing plan directly addresses the specific problems identified from the multi-source information, making the nursing plan highly targeted and accurate from the time of generation, overcoming the problem of mismatch between the plan and actual needs in the prior art.

[0045] 2. This invention employs fuzzy matching and calculates a matching score. When the matching score is lower than a preset threshold, a first nursing plan is generated. When the matching score is greater than or equal to the preset threshold, a second nursing plan for handling complex nursing items is generated based on the first nursing plan. Overlapping execution periods are avoided, thereby automatically adapting to and outputting nursing plans of different levels according to the complexity of abnormal items.

[0046] 3. This invention generates feedback values ​​by acquiring the execution records of nursing projects, and determines the execution status of nursing plans based on the feedback values. For nursing plans with an interrupted execution status, they are identified as correction targets. The correction plan is determined and output by comparing the results with the patient's historical nursing records. This automatically identifies and corrects ineffective or inefficient nursing plans. By introducing the patient's historical nursing records for iterative learning, the quality and adaptability of nursing strategies are continuously improved. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method provided by the present invention;

[0048] Figure 2 This is a system module diagram provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the scope of protection of the invention.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment provides an automated management method for outpatient nursing work, applicable to the automated management of patient care work in hospital outpatient departments through electronic systems, especially demonstrating advantages in efficiency and accuracy when carrying out large-scale, multi-batch patient care tasks. The specific steps include the following:

[0052] S1. Information acquisition and verification processing;

[0053] The examination information, pre-examination information, and follow-up information corresponding to the patient's identity are obtained from multiple heterogeneous information sources. The examination information comes from test reports or imaging diagnostic conclusions contained in laboratory information systems or image archiving and communication systems. The pre-examination information comes from vital sign data entered by nurses at their workstations, preferably body temperature, blood pressure, and heart rate. The follow-up information comes from rehabilitation logs or symptom reports submitted by patients through mobile applications.

[0054] The inspection, pre-inspection, and follow-up information are integrated to eliminate inconsistencies caused by heterogeneous data sources. Specifically, this involves data cleaning, format standardization, and unit of measurement unification to generate unified, structured integrated information that can be directly accessed in subsequent steps. Based on this integrated information, a logical construct for data verification and traceability—a verification structure—is built to screen for data integrity, consistency, and validity. This verification structure is a two-tiered architecture comprising a forward and a backward layer, separating the two different processing tasks of rapid rule judgment and precise traceability of abnormal data to improve overall processing efficiency.

[0055] Furthermore, the forward end determines the information attributes of the integrated information according to preset rules. The specific steps are as follows:

[0056] The forward end has multiple preset abnormal nodes. Each abnormal node is a preset logical judgment condition, which corresponds to a specific verification rule, such as "the systolic blood pressure value must not exceed 250 mmHg", which is used to automatically identify data items in the structured integrated information that do not conform to the preset rules.

[0057] When the integrated information flows through the forward end, if the content of a certain data triggers the judgment condition corresponding to an abnormal node, the forward end will identify the data as an abnormal item and immediately generate a verification instruction, i.e., a data packet; the judgment result of the forward end will form multiple verification records, each of which contains information such as abnormal content and discovery time.

[0058] The information attributes include whether the data type is correct, whether the value is within the normal physiological range, and whether there are logical contradictions between different pieces of information.

[0059] To facilitate tracking and management, the verification records are sorted in chronological order of occurrence, and each sorted verification record is assigned a unique unified number. At the same time, a mapping relationship is established between this unified number and the specific anomaly item identified, and this mapping relationship is included in the verification instruction to provide an index for precise operations at the back end, so as to facilitate tracking and management.

[0060] Furthermore, the backend is used to receive the verification command sent by the frontend. After receiving the verification command, the backend parses the mapping relationship contained therein and locates the specific abnormal item data. Based on the verification command, it extracts all context information related to the abnormal item from the original integrated information.

[0061] The backend further categorizes all extracted relevant information according to its source, forming a well-structured verification table to facilitate root cause analysis by nursing staff. Specifically, the information is divided into categories such as doctor's diagnosis, instrument testing, and patient self-report. While presenting the abnormal items themselves, the verification table also traces and displays their related contextual data, providing a data foundation for the subsequent development of targeted nursing plans.

[0062] S2, Matching and Plan Generation Processing;

[0063] After identifying anomalies and generating a verification table, the identified anomalies and their supporting information are input into the logical processing construct, i.e., the matching structure. The supporting information consists of pre-set structured nursing knowledge in the knowledge base, such as the clinical nursing practice guidelines issued by the National Health Commission, standardized nursing routines in hospitals, and preferred nursing pathways summarized based on a large number of historical successful cases.

[0064] The matching structure associates unstructured or semi-structured outlier descriptions with this accompanying information to recommend the most appropriate nursing interventions. Specific association matching processes include:

[0065] A fuzzy matching operation is performed on the anomalies and their accompanying information. Specifically, this involves using a specific text relevance calculation process to resolve inconsistencies between the colloquial or non-standard descriptions of the anomalies and the standard terminology in the accompanying information. In other words:

[0066] The text of anomalies such as "patients feel palpitations" is compared with the knowledge entries in the supporting information such as "nursing care for palpitations". By analyzing the keywords, word roots and the association weight of these words in the preset medical thesaurus, a quantitative association score is obtained. This association score is used to indicate the degree of matching between the anomaly description and a certain knowledge entry in the supporting information.

[0067] Fuzzy matching generates one or more initial matching nodes that represent a potential matching relationship between an anomaly and a certain set of information. Each node represents a potential matching result. It may also generate verification items, such as generating verification items that require manual confirmation by nursing staff when the calculated correlation score is not high.

[0068] According to the scoring rules, the final matching score of each initial matching node is calculated, which is used to finally assess the suitability of the initial matching node and serve as the basis for deciding what kind of care plan to generate.

[0069] The scoring rules are based on a pre-defined scoring method, which calculates the final score by weighting and summing indicators from multiple dimensions. These dimensions include the previously calculated text relevance score, the clinical urgency determined by the content of the anomaly, and the frequency of application of the matching item in historical successful cases.

[0070] Specifically, the scoring rule is a computational model used to comprehensively evaluate the suitability of the initial matching node, and is defined as follows:

[0071]

[0072] In the formula, This represents the final matching score, which is a quantitative score that comprehensively reflects the overall suitability of the matching item and is used for subsequent decision-making.

[0073] The text relevance score is a normalized score derived from the text relevance calculation process, reflecting the semantic similarity between the anomaly description and the knowledge base entry.

[0074] It indicates the clinical urgency level, which is a quantified value representing the urgency level matched in a preset rule base based on the content of the abnormal item.

[0075] This represents the historical application frequency, which is the frequency of application of the corresponding nursing measures in historical successful cases, after normalization.

[0076] The weight coefficients represent preset non-negative weight values ​​for text relevance, clinical urgency, and historical application frequency, respectively, and the sum of the three is 1.

[0077] Determine whether the final matching score is lower than a preset threshold, which is pre-set by the nursing expert group based on the clinical risk level of different abnormal items;

[0078] If the final matching score is lower than the preset threshold, it is determined that the matching result of the abnormal item is relatively clear and singular. At this time, only the first nursing plan needs to be generated and output. The first nursing plan is generated based on the matching item with the highest matching score between the abnormal item and multiple preset model templates, which constitutes a basic and highly targeted nursing plan.

[0079] If the final matching score is greater than or equal to the preset threshold, the anomaly is considered complex or related to multiple nursing points. In this case, the initial matching node with the highest score is converted into a secondary matching node, requiring a deeper level of planning. Specifically:

[0080] Similarly, the first nursing plan mentioned above is generated and output as a basic guarantee; based on this secondary matching node, a second nursing plan is generated and output, and the first and second nursing plans together constitute a combined nursing solution.

[0081] Furthermore, the process of generating a second care plan demonstrates the ability to handle complex situations, specifically:

[0082] When the abnormal item is that the patient's blood sugar is high and accompanied by mild anxiety, the first care plan may be for the high blood sugar, such as including insulin injection; while the second care plan is for the mild anxiety, based on the repeated matching node, matching composite care items from the knowledge base, such as "psychological counseling after blood sugar monitoring".

[0083] Furthermore, the time offset corresponding to the composite nursing item is calculated to avoid time conflicts with the items in the first nursing plan. For example, it is set to 15 minutes after the completion of the "insulin injection" item in the first nursing plan. This time offset is then superimposed on the original planned time period in the first nursing plan to generate the specific execution time period of the item in the second nursing plan.

[0084] In addition, conflict detection will be performed, which involves comparing the execution time of all nursing items in the combined nursing plan pairwise to determine whether there is any overlap or intersection in time. If there is an overlap in execution time, the time offset of one of the tasks will be recalculated according to the preset modification rules to ensure the overall feasibility and logic of the nursing plan.

[0085] The preferred modification rule is based on the preset priority of nursing items, which advances the lower priority items by one minimum time unit.

[0086] S3. Plan execution and feedback processing;

[0087] After receiving the output nursing plan, the nursing items are output to the execution terminal one by one according to the preset execution order in the plan. The execution terminal is the computer of the nurse workstation or a handheld PDA device.

[0088] During the execution of nursing projects, execution records are continuously acquired. These records include execution statuses marked by nursing staff as "completed," "incomplete," or "cancelled," as well as records of changes in patients' physiological parameters and adverse reactions after medication, which are automatically collected by IoT medical devices such as electronic blood pressure monitors and blood glucose meters. Based on these objective execution records, preliminary feedback data is generated.

[0089] Obtain patient satisfaction information, for example, after a nursing care program is completed, push a short satisfaction questionnaire to the patient's personal mobile device through the execution terminal, and use the patient's rating or selection results as nursing satisfaction information.

[0090] Based on preset scoring criteria, the objective execution results reflected in the execution records are weighted and combined with the obtained subjective nursing satisfaction information of patients to calculate a comprehensive quantitative feedback value. This feedback value serves as the direct basis for further judging the execution status of the nursing plan. Execution results include task completion rate and percentage improvement in physiological indicators, among others.

[0091] The preset scoring criteria are a calculation model used to quantitatively evaluate the overall effectiveness of a single nursing care plan execution cycle, and their specific definitions are as follows:

[0092]

[0093] In the formula, The feedback value represents a quantitative score that comprehensively reflects the overall effectiveness of the nursing plan within an execution cycle.

[0094] This represents the task completion rate, which is the ratio of the actual number of planned nursing items completed to the number that should have been completed, reflecting the degree of plan execution.

[0095] This indicates the improvement rate of key physiological indicators, which means the percentage of improvement of core physiological indicators related to abnormal items towards the target range, reflecting the objective efficacy of the plan;

[0096] The patient satisfaction score represents the patient's subjective evaluation of the nursing process. The score, after normalization, reflects the patient's subjective experience with the plan.

[0097] The satisfaction weight is a preset weighting coefficient used to balance the proportion of objective performance and subjective satisfaction in the total score, with a value range of [0,1].

[0098] The weights represent the objective indicators, which are the non-negative weights of the task completion rate and indicator improvement rate in the objective effect part, and the sum of the two is 1.

[0099] S4. Status judgment and correction processing;

[0100] Based on the feedback values ​​generated in S3, the execution status of the nursing plan is periodically determined. The specific judgment logic is as follows:

[0101] The system determines whether the latest feedback value associated with the nursing plan is lower than a preset standard, which is a quantitative baseline predefined by experts based on nursing quality control requirements. If the feedback value is lower than the preset standard, but it is the first time this has happened since the plan was implemented, or the feedback value in the previous cycle was up to standard, the implementation status of the nursing plan is set to "awaiting evaluation." Plans in the "awaiting evaluation" state will be highlighted to alert nursing staff, but the plan itself will continue to be implemented to avoid frequent changes to the nursing plan due to single accidental factors such as equipment reading errors. If the feedback value is lower than the preset standard twice or more consecutively, it is determined that there is a fundamental problem with the effectiveness or suitability of the plan, and the implementation status of the nursing plan is set to "interrupted." Once the implementation status of a nursing plan is set to "interrupted," it is automatically identified as a correction target.

[0102] The correction target is compared with the patient's historical nursing records to redefine the nursing plan. The historical nursing records contain a personalized database of the patient's past nursing plans, including the execution process, feedback values, and final results. The comparison process involves searching historical records for records containing the same or similar anomalies and selecting successful cases where the final feedback value consistently meets the target. One or more previously validated alternative nursing plans are then identified. The correction plan is then output and pushed to the execution terminal to replace the original interruption plan.

[0103] If the correction plan is generated based on historical successful cases, it is recommended to execute it directly. If no suitable successful cases are found in the historical nursing records after comparison, the matching structure is invoked, and a brand-new combination plan is generated by combining the system knowledge base. The new plan and its subsequent execution status are automatically stored in the patient's historical nursing records for future reference and retrieval.

[0104] The correction target refers to any nursing plan whose execution status is determined to be interrupted, which will be the target of subsequent correction processes.

[0105] Example 2

[0106] See Figure 2 This embodiment provides an automated management system for outpatient nursing work, including:

[0107] Plan generation module

[0108] It is configured to automatically generate one or more initial care plans based on the patient's specific situation. In practical application: it acquires multi-source information corresponding to a specific patient's identity, including examination information, triage information, and follow-up information. Examination information includes the patient's blood routine, urine routine, and imaging reports; triage information includes vital signs, chief complaints, and allergy history; follow-up information includes past recovery progress and medication feedback.

[0109] After acquiring the information, the examination, pre-examination, and follow-up information are integrated to generate structured integrated information. A validation structure is then constructed based on this integrated information. Using this validation structure, anomalies requiring attention are automatically identified from the integrated information, such as indicators exceeding normal ranges or symptom combinations inconsistent with standard care pathways. The validation structure is preferably based on a rule engine derived from a medical knowledge graph or a trained classification model.

[0110] The identified anomalies are matched to generate specific care plans. This matching process includes: performing a fuzzy matching operation between the anomalies and their supporting information to generate one or more initial matching nodes; each node represents a preliminary recommended care item. Based on a scoring rule, a final matching score is calculated for each initial matching node, reflecting the degree of fit between the recommended care item and the current patient's condition.

[0111] A care plan is generated based on the comparison between the final matching score and a preset threshold. Specifically:

[0112] When the final matching score of all initial matching nodes is lower than the preset threshold, it indicates that the anomaly is relatively simple or common, and a first care plan is generated and output.

[0113] When there is an initial matching node with a final matching score greater than or equal to a preset threshold, it indicates that there is a nursing need that requires special attention or is complex. At this time, a first nursing plan is generated and output, and a second nursing plan is generated and output based on the initial matching node with the high score.

[0114] Furthermore, during the generation of the second care plan, based on the initial matching node, the execution time periods of one or more composite care items are matched, and the time offset corresponding to the composite care item is calculated. This time offset is then superimposed on the original planned time period corresponding to the first care plan to generate the precise execution time period of the second care plan.

[0115] Plan Execution Module

[0116] Configured to output specific nursing items to nursing staff or relevant automated equipment based on the nursing plan generated by the plan generation module. When the plan generation module only outputs the first nursing plan, the nursing items are output according to the arrangement of the first nursing plan. When both the first and second nursing plans are generated, the contents of the two plans are integrated, and all nursing items are output in an orderly manner according to their respective determined execution periods.

[0117] The status determination module is configured to continuously monitor the execution effect of the care plan and determine its execution status.

[0118] During the implementation of the plan, execution records for each nursing item are continuously acquired. These records are derived from nurses' work entries, monitoring data from smart medical devices, or patient feedback. Based on these execution records, a scoring system is used to generate quantitative feedback values ​​that comprehensively reflect adherence to the nursing plan and improvement in patients' physiological indicators.

[0119] The real-time calculated feedback value is compared with the preset standard. When the feedback value is found to be lower than the preset standard twice in a row, it is determined that the current nursing plan can no longer achieve the expected effect, and the execution status of the nursing plan is determined to be interrupted.

[0120] Furthermore, the status determination module is also configured to simultaneously acquire the patient's nursing satisfaction information, combine the nursing satisfaction information with the calculated feedback value, and jointly determine the execution status of the nursing plan, so that the judgment result is closer to the patient's real feelings and the actual effect of nursing.

[0121] The plan correction module is configured to be activated when the status determination module determines the execution status of the care plan to be interrupted, in order to perform correction processing and generate a more targeted correction plan.

[0122] Upon receiving an interrupt status signal, the correction process is initiated, and the specific steps are as follows:

[0123] The current interrupted care plan is compared with the patient's historical care record, which includes the execution process, feedback values, and final adjustment plans of all previous care plans for the patient.

[0124] By comparing and analyzing the current plan, the reasons for its failure are identified, and historical records are used to identify successful nursing strategies or combinations of programs under similar circumstances. Based on the results of the comparative analysis, a new, optimized corrective plan is determined. This corrective plan includes replacing ineffective nursing programs, adjusting the frequency or intensity of program implementation, and adding new ancillary nursing interventions.

[0125] The output correction plan will be handed over to the plan execution module to take over the original correction target and implement it, so as to realize the dynamic adjustment and continuous optimization of nursing work.

[0126] Example 3

[0127] This embodiment provides an automated management terminal for outpatient nursing work, including:

[0128] At least one processor; a memory communicatively connected to at least one processor;

[0129] The memory stores computer program instructions, which, when executed by at least one processor, cause the terminal to perform an automated management method for outpatient nursing work as described in Embodiment 1.

[0130] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An automated management method for outpatient nursing work, characterized in that, Includes the following steps: Obtain examination information, pre-examination information, and follow-up information corresponding to the patient's identity, and identify abnormalities based on the examination information, pre-examination information, and follow-up information corresponding to the patient's identity; Matching is performed based on anomalies to generate care plans; When the execution status of the nursing plan is determined to be interrupted, corrective action is taken on the nursing plan. The correction process includes: comparing the nursing plan with the patient's historical nursing records to determine the correction plan; and outputting the correction plan. The process of determining the execution status of a nursing plan as interrupted includes: obtaining execution records of the nursing project execution process and generating feedback values ​​based on the execution records; when the feedback value is lower than a preset standard, the execution status of the nursing plan is determined to be in an evaluation state; when the feedback value is lower than the preset standard twice consecutively, the execution status of the nursing plan is determined to be interrupted. Among them, based on the examination information, pre-examination information and follow-up information corresponding to the patient's identity, the identification of abnormal items includes: integrating the examination information, pre-examination information and follow-up information to generate integrated information; constructing a verification structure based on the integrated information, and using the verification structure to identify abnormal items contained in the integrated information.

2. The automated management method for outpatient nursing work according to claim 1, characterized in that, Feedback values ​​are generated based on the execution records, including: Obtain patient nursing satisfaction information; based on preset scoring criteria, weight and combine the execution records and nursing satisfaction information to generate feedback values.

3. The automated management method for outpatient nursing work according to claim 1, characterized in that, The matching process based on outliers to generate a care plan includes: Perform a fuzzy matching operation on the anomalies and their supporting information to generate an initial matching node; According to the scoring rules, the matching score of the initial matching node is calculated; when the matching score is lower than the preset threshold, the first nursing plan is generated and output; when the matching score is greater than or equal to the preset threshold, the first nursing plan is generated and output, and the second nursing plan is generated and output based on the initial matching node.

4. The automated management method for outpatient nursing work according to claim 3, characterized in that, Generate and output a second care plan based on the initial matching nodes: Based on the initial matching node, the execution time period of the composite nursing item is matched; Calculate the time offset corresponding to the composite nursing care items; The time offset is superimposed on the corresponding original planned time period in the first nursing plan to generate the execution time period of the second nursing plan.

5. An automated management system for outpatient nursing work, characterized in that, include: The plan generation module is used to obtain examination information, pre-examination information and follow-up information corresponding to the patient's identity, identify abnormal items, and perform matching processing based on the abnormal items to generate a nursing plan; The plan execution module is used to output nursing items according to the nursing plan; The status determination module is used to acquire execution records of the nursing project execution process, generate feedback values, and determine the execution status of the nursing plan based on the feedback values. The plan correction module is used to compare the nursing plan with the patient's historical nursing records to determine the correction plan when the execution status of the nursing plan is determined to be interrupted, and then outputs the correction plan. This includes obtaining examination information, pre-examination information, and follow-up information corresponding to the patient's identity, and identifying abnormal items, including: Integrate inspection information, pre-inspection information, and follow-up information to generate integrated information; construct a verification structure based on the integrated information, and use the verification structure to identify anomalies contained in the integrated information; Among them, the nursing plan execution status is determined to be interrupted, including: The system acquires execution records of the nursing project and generates feedback values ​​based on these records. When the feedback value is lower than a preset standard, the execution status of the nursing plan is determined to be in an evaluation state. When the feedback value is lower than the preset standard twice in a row, the execution status of the nursing plan is determined to be in an interrupted state.

6. The automated management system for outpatient nursing work according to claim 5, characterized in that, Feedback values ​​are generated based on the execution records, including: Obtain patient nursing satisfaction information; based on preset scoring criteria, weight and combine the execution records and nursing satisfaction information to generate feedback values.

7. An automated management terminal for outpatient nursing work, characterized in that, include: At least one processor; Memory that is communicatively connected to at least one processor; The memory stores computer program instructions, which, when executed by at least one processor, cause the terminal to perform an automated management method for outpatient nursing work as described in any one of claims 1 to 4.

Images