Ear-nose-throat head and neck surgery nursing quality intelligent management and control system

Abstract

The application discloses an ear-nose-throat-head-neck surgery nursing quality intelligent management and control system and relates to the technical field of nursing quality management and control. The system comprises the following steps: processing operation mode information, doctor's advice information, vital sign information, pipeline information, drainage information, symptom information, nursing record information and handover record information, and generating a nursing event sequence; identifying a current risk stage based on the nursing event sequence, generating a current stage nursing obligation set, and performing closed loop verification, stage switching re-authentication and nursing quality state output. The system can adjust the nursing strategy according to the change of the patient risk stage, and realize nursing abnormality identification and nursing breakpoint positioning.

Description

Technical Field

[0001] This invention relates to the field of nursing quality management technology, and more specifically, to an intelligent control system for the nursing quality of otolaryngology-head and neck surgery. Background Technology

[0002] In the context of otolaryngology-head and neck surgery nursing, the patient's postoperative or post-treatment risk status does not change linearly along a fixed timeline, but rather dynamically shifts between different risk stages as airway conditions, swallowing function, changes in local wounds, drainage status, and related symptoms evolve. Existing nursing management and quality control techniques typically rely on fixed rounds, fixed nursing checklists, fixed scoring thresholds, or predetermined task processes. The evaluation of nursing behavior often focuses on whether records are kept on time, whether predetermined operations are completed, and whether relevant indicators meet preset requirements. While this type of technical approach can achieve process standardization and record keeping in routine nursing management, in the specialized setting of otolaryngology-head and neck surgery, patient risk often exhibits stages, concealment, and rapid transformation. That is, although the patient may not yet have obvious abnormal vital signs exceeding the threshold, and the nursing records may appear complete on the surface, the patient may have actually transitioned from a routine recovery state to an airway vulnerable state, an aspiration-sensitive state, or a state before occult bleeding. At this point, the focus of nursing observation, verification methods, handover content, and intervention priorities should be adjusted simultaneously.

[0003] However, existing technologies lack the ability to continuously identify the patient's risk stage transition process, and still use the nursing standards and quality control logic corresponding to the previous stage. As a result, nursing actions may conform to the established process in form, but in substance they have deviated from the current risk state. This causes the critical window that most needs to be strengthened in identification and timely switching of nursing strategies to be covered by routine processes.

[0004] Based on this, the following issues can be identified: existing ENT head and neck surgery nursing quality control technology mainly focuses on managing static process compliance, lacking a mechanism for identifying and controlling the dynamic migration of patient risk phases, and therefore cannot promptly promote the synchronous switching of nursing strategies when risk phases change. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, the present invention provides an intelligent management and control system for nursing quality in otolaryngology-head and neck surgery. This system solves the problems mentioned in the background art by forming a nursing event sequence and a continuous processing chain based on risk stage identification, nursing obligation generation, closed-loop verification, stage switching re-authentication, and nursing quality status output.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The intelligent management and control system for nursing quality in otolaryngology-head and neck surgery includes: The data processing module is used to collect information on the target patient's surgical procedure, medical orders, vital signs, tubing, drainage, symptoms, nursing records, and handover records. It performs time alignment, field standardization, and patient association processing on the collected information and outputs a sequence of nursing events for the target patient. The stage identification module is used to input the nursing event sequence into a pre-established set of rules for determining the nursing stage in ENT head and neck surgery. Based on the surgical method, postoperative time, tube status, drainage changes, symptom combinations and vital sign changes, it identifies the current risk stage of the target patient and outputs the current risk stage identifier. The obligation generation module is used to match the current risk stage identifier with a pre-established stage nursing requirement mapping table, extract nursing observation items, nursing execution items, nursing verification items, execution time windows and stage switching re-authentication items corresponding to the current risk stage, and output the current stage nursing obligation set; The closed-loop verification module is used to compare the current set of nursing obligations with the executed nursing behaviors in the nursing event sequence item by item, and determine whether the pre-existing evidence for each nursing obligation is complete, whether the execution time falls within the execution time window, and whether the corresponding nursing verification result is formed after execution. Nursing obligations that do not meet the conditions of complete pre-existing evidence, matching execution time, and completion of post-execution verification are identified as invalid closed-loop obligations, and the nursing obligation closed-loop judgment result is output. The recertification module is used to set the nursing conclusions that have been completed in the previous risk stage and belong to the recertification items of the stage switch to the state of pending recertification when the current risk stage is determined to be different from the previous risk stage. It also regenerates the corresponding nursing observation tasks, nursing execution tasks and nursing verification tasks based on the current stage nursing obligation set, and outputs the recertification nursing task set after the stage switch. The quality output module is used to determine the nursing quality status of the target patient based on the nursing obligation closure judgment result and the set of recertification nursing tasks. Target patients with invalid closure obligations or uncompleted recertification nursing tasks are identified as patients with abnormal nursing quality, and the module outputs the corresponding abnormality type, abnormality occurrence stage, and corresponding nursing breakpoint information.

[0007] By adopting the above technical solutions, surgical procedure information, medical order information, vital sign information, tubing information, drainage information, symptom information, nursing record information, and handover record information can be uniformly processed into a nursing event sequence. Based on risk stage identification, nursing obligation generation, closed-loop verification, re-authentication, and quality output, dynamic and coordinated management of the ENT head and neck surgery nursing process can be achieved.

[0008] In a preferred embodiment, the data processing module is used to extract the record text, record time, recorder identifier and patient identifier from nursing record information and handover record information, perform semantic segmentation on the record text according to preset sentence segmentation rules and nursing action vocabulary, and split the segmentation results into nursing observation fragments, nursing execution fragments, nursing verification fragments and handover prompt fragments according to the action target object and the result target object, and output a fragmented record set. The system reads the nursing action words, status description words, result description words, and risk warning words corresponding to each segment from the fragmented record set. According to the rules of matching observation actions with observation objects to nursing observation segments, execution actions with execution objects to nursing execution segments, verification actions with verification results to nursing verification segments, and handover warning words with matters to be concerned to handover warning segments, the system determines the type of each segment and extracts the corresponding event attributes, event values, and event status, and outputs a standard nursing event item set. The system reads patient identification, recording time, event type, event attributes, and event status from the standard nursing event entry set. It then associates nursing observation events, nursing execution events, nursing verification events, and handover prompt events with the same patient identification and whose recording time is within a preset continuous time window. When there is a chain of observation followed by execution and then verification, or a chain of handover prompt and subsequent observation, it generates an association marker and outputs a nursing event sequence with the association marker.

[0009] By adopting the above technical solution, nursing record information and handover record information can be separated into nursing observation events, nursing execution events, nursing verification events and handover prompt events, thereby improving the structuring degree of nursing event sequences and the availability of subsequent stage identification and closed-loop verification.

[0010] In a preferred embodiment, the stage identification module is used to extract surgical method, surgical end time, tubing-related events, drainage-related events, symptom-related events, vital sign-related events, nursing closed-loop markers and handover continuation markers from the nursing event sequence, and determine the postoperative time period according to the time difference between the target patient's surgical end time and the recording time of each event, and output the stage determination input set. The stage identification module is also used to read tubing-related events, drainage-related events, symptom-related events and vital sign-related events from the stage determination input set, extract tubing status, drainage changes, symptom combinations and vital sign changes features respectively, and combine nursing closed-loop markers and handover continuation markers to determine the validity of each feature, and output the stage feature set; The stage identification module is also used to write the surgical method, postoperative time period, tube status, drainage changes, symptom combinations and vital sign changes into the stage feature set, forming a stage judgment feature group for matching the ENT head and neck surgery nursing stage judgment rule set.

[0011] By adopting the above technical solutions, it is possible to extract surgical methods, postoperative time periods, tubing status, drainage changes, symptom combinations, and vital sign changes from the nursing event sequence. Combined with nursing closed-loop markers and handover continuation markers, a stage determination feature group is formed, thereby improving the continuity and pertinence of risk stage identification.

[0012] In a preferred embodiment, the stage identification module is used to input the stage determination feature group into the ENT head and neck surgery nursing stage determination rule set, and match them item by item according to the basic nursing stage corresponding to the surgical method, the stage entry condition corresponding to the postoperative period, the stage maintenance condition corresponding to the tube status, the stage upgrade condition corresponding to the drainage change, and the stage adjustment condition corresponding to the symptom combination and vital sign change characteristics, and output a candidate risk stage set. The stage identification module is also used to determine the candidate risk stage with the highest priority as the current risk stage when the candidate risk stage set contains multiple candidate risk stages, according to the number of nursing observation items, nursing execution items and nursing verification items from most to least, and output the current risk stage identifier; The phase identification module is also used to generate a phase change marker when the current risk phase identifier is inconsistent with the previous risk phase identifier, and output the current risk phase identifier and the phase change marker to the obligation generation module.

[0013] By adopting the above technical solutions, candidate risk stages can be matched and prioritized based on surgical methods, postoperative time periods, tube status, drainage changes, symptom combinations, and vital sign changes, thereby improving the accuracy of current risk stage determination and providing a clear basis for the generation of subsequent nursing obligations.

[0014] In a preferred embodiment, the obligation generation module is used to input the current risk stage identifier into the stage nursing requirement mapping table, read the nursing observation items, nursing execution items, nursing verification items, execution time windows and stage switching re-authentication items corresponding to the current risk stage identifier, generate stage nursing requirement groups according to the same nursing object merging and the same nursing purpose association, and output the current stage nursing requirement group. The obligation generation module is also used to read each nursing observation item, nursing execution item, and nursing verification item from the current stage nursing requirement group, establish obligation association relationships in the order of nursing observation items first, nursing execution items last, and nursing verification items last, configure the execution time window to the corresponding nursing observation item, nursing execution item, and nursing verification item, and output a set of nursing obligation items; The obligation generation module is also used to read phase switching re-authentication items from the set of nursing obligation items, mark the nursing obligation items corresponding to the phase switching re-authentication items as re-authentication obligations, mark the remaining nursing obligation items as regular obligations, and output the current phase nursing obligation set containing regular obligations and re-authentication obligations.

[0015] By adopting the above technical solution, a set of current stage nursing obligations, including routine obligations and recertification obligations, can be generated from the stage nursing requirement mapping table based on the current risk stage identifier, so that nursing observation items, nursing execution items, and nursing verification items correspond to the current risk stage.

[0016] In a preferred embodiment, the closed-loop verification module is used to read nursing observation items, nursing execution items, nursing verification items and execution time windows one by one from the current stage nursing obligation set, and extract nursing observation events, nursing execution events and nursing verification events corresponding to each nursing observation item, nursing execution item and nursing verification item from the nursing event sequence, establish the obligation event correspondence relationship according to the same patient, the same nursing object and the same risk stage, and output the obligation set to be verified. The closed-loop verification module is also used to read nursing observation events, nursing execution events and nursing verification events one by one from the set of obligations to be verified, determine whether the nursing observation event serves as preliminary evidence for the nursing execution event, whether the occurrence time of the nursing execution event falls within the execution time window, and whether the nursing verification event forms a corresponding nursing verification result after the nursing execution event. When the preliminary evidence exists, the execution time matches and the post-execution verification is completed simultaneously, a valid closed-loop marker is generated and the set of obligation verification results is output. The closed-loop verification module is also used to read nursing obligations that have not generated valid closed-loop markers from the obligation verification result set, identify them as invalid closed-loop obligations, and classify and mark them according to the lack of prior evidence, mismatch of execution time and failure to complete post-execution verification, and output the nursing obligation closed-loop judgment result.

[0017] By adopting the above technical solutions, it is possible to conduct closed-loop verification of nursing obligations around nursing observation events, nursing execution events, and nursing verification events, and identify invalid closed-loop obligations and their specific reasons for invalidity, thereby improving the timeliness of abnormality detection in the nursing process and the accuracy of closed-loop determination.

[0018] In a preferred embodiment, the re-authentication module is used to, after receiving the current risk stage identifier, the previous risk stage identifier, and the current stage nursing obligation set, first determine whether the current risk stage identifier is consistent with the previous risk stage identifier. If they are inconsistent, extract the nursing conclusions corresponding to the stage switching re-authentication items from the completed nursing obligations corresponding to the previous risk stage, and output the set of nursing conclusions to be re-authenticated. The re-certification module is also used to read the nursing object, nursing result and completion time corresponding to each nursing conclusion from the set of nursing conclusions to be recertified, and match the nursing object with the nursing observation items, nursing execution items and nursing verification items in the current stage nursing obligation set. When a corresponding relationship exists, the corresponding nursing conclusion is set to the state to be recertified and the set of states to be recertified is output. The recertification module is also used to read the nursing subjects and nursing results corresponding to each nursing conclusion to be recertified from the set of states to be recertified, remove the nursing conclusions with the state to be recertified from the valid nursing conclusions of the current risk stage, and output the set of nursing conclusions to be recertified after the stage switch.

[0019] By adopting the above technical solution, when the current risk stage changes relative to the previous risk stage, the nursing conclusions corresponding to the stage switch recertification items can be extracted and set as pending recertification status, thereby avoiding the improper application of nursing conclusions formed under the previous risk stage to the current risk stage.

[0020] In a preferred embodiment, the re-authentication module is used to read the nursing object and the status to be re-authenticated from the set of nursing conclusions to be re-authenticated after the stage switch, and extract the nursing observation items, nursing execution items, nursing verification items and execution time windows corresponding to the nursing object from the current stage nursing obligation set, and regenerate the re-authentication nursing task items in the order of nursing observation items first, nursing execution items second, and nursing verification items last, and output the re-authentication nursing task set. The re-certification module is also used to read the nursing observation items, nursing execution items, nursing verification items and execution time windows corresponding to each re-certification nursing task item from the re-certification nursing task set, and to add a stage switching mark and task generation time to each re-certification nursing task item, and output the re-certification nursing task set after the stage switching. The recertification module is also used to keep the corresponding nursing conclusions to be recertified in an invalid state before all nursing observation tasks, nursing execution tasks and nursing verification tasks corresponding to the recertification nursing task set are completed, and to restore the corresponding nursing conclusions to be recertified to valid nursing conclusions of the current risk stage after all tasks are completed.

[0021] By adopting the above technical solution, a set of recertification nursing tasks can be regenerated based on the nursing conclusions to be recertified, and the relevant nursing conclusions can be kept invalid until the corresponding nursing observation tasks, nursing execution tasks and nursing verification tasks are completed, thereby promoting the synchronous switching of nursing strategies as the risk stage changes.

[0022] In a preferred embodiment, the quality output module is used to extract the nursing obligation type, invalid reason, risk stage and occurrence time corresponding to the invalid closed-loop obligation from the nursing obligation closed-loop judgment result, and to extract the nursing object, task type, risk stage and task status corresponding to the uncompleted recertification nursing task from the recertification nursing task set, and to merge them according to the same patient, the same risk stage and the same nursing object, and output the abnormal judgment data set. The quality output module is also used to determine whether the target patient has an invalid closed-loop obligation or has not completed the recertification nursing task from the abnormal judgment data set. When there is an invalid closed-loop obligation, the corresponding target patient is identified as a closed-loop abnormal patient. When there is a recertification nursing task that has not been completed, the corresponding target patient is identified as a recertification abnormal patient. When there are both invalid closed-loop obligations and recertification nursing tasks that have not been completed, the corresponding target patient is identified as a compound abnormal patient, and the nursing quality status judgment result is output. The quality output module is also used to generate information on the abnormality type, abnormality occurrence stage and nursing breakpoint corresponding to closed-loop abnormal patients, recertified abnormal patients or compound abnormal patients based on the nursing quality status judgment results, and output the nursing quality status of the target patient. By adopting the above technical solutions, it is possible to classify target patients abnormally and output their nursing quality status based on invalid closed-loop obligations and incomplete recertification nursing tasks, thereby improving the clarity of nursing quality abnormality identification and classification management capabilities.

[0023] In a preferred embodiment, the quality output module is used to extract the abnormality type, abnormality occurrence stage, nursing obligation type, nursing object, occurrence time and task status from the nursing quality status judgment result, determine the nursing obligation that first fails to meet the closure condition as the breakpoint obligation among the nursing observation items, nursing execution items and nursing verification items corresponding to the invalid closed-loop obligation, and determine the recertification nursing task that first fails to be completed as the breakpoint task among the nursing observation tasks, nursing execution tasks and nursing verification tasks corresponding to the incomplete recertification nursing task, and output the breakpoint location result; The quality output module is also used to generate pre-evidence breakpoint information when the pre-evidence corresponding to the breakpoint obligation is missing, to generate time window breakpoint information when the execution time corresponding to the breakpoint obligation is mismatched, to generate verification result breakpoint information when the verification after execution corresponding to the breakpoint obligation is not completed, and to generate re-authentication task breakpoint information when the task status corresponding to the breakpoint task is not completed, based on the breakpoint location results, and output a set of nursing breakpoint information. The quality output module is also used to write the abnormality type, abnormality occurrence stage and nursing breakpoint information set into the nursing quality status record corresponding to the target patient, and output the nursing quality status record to the nursing terminal, nurse station terminal or quality control management terminal.

[0024] By adopting the above technical solution, it is possible to locate breakpoint obligations or tasks in invalid closed-loop obligations and incomplete recertification nursing tasks, and generate corresponding nursing breakpoint information and output it to the corresponding terminal, thereby improving the clarity of nursing quality abnormality location and the pertinence of quality control measures.

[0025] The technical effects and advantages of this invention are as follows: By identifying risk phases in nursing event sequences and triggering nursing obligation reconstruction and nursing conclusion recertification when phases change, nursing observation, nursing execution, and nursing verification can be dynamically adjusted in tandem with the patient's risk phase, thereby relatively improving the problem of untimely switching of nursing strategies under static process management. By breaking down nursing records and handover records into events and conducting closed-loop verification in conjunction with prior evidence, execution time windows, and post-execution verification results, invalid closed-loop obligations that are formally completed but substantially mismatched can be identified, thereby relatively improving the timeliness of nursing anomaly identification and the clarity of nursing breakpoint location. Attached Figure Description

[0026] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Refer to the instruction manual appendix Figure 1 The intelligent management and control system for the quality of nursing care in otolaryngology-head and neck surgery includes: The data processing module is used to collect information on the target patient's surgical procedure, medical orders, vital signs, tubing, drainage, symptoms, nursing records, and handover records. It performs time alignment, field standardization, and patient association processing on the collected information and outputs a sequence of nursing events for the target patient. First, the data processing module extracts the record text, record time, recorder identifier, and patient identifier from nursing record information and handover record information. When a patient identifier is missing, it retrieves the corresponding inpatient patient based on bed information, department information, and record time, and then adds the patient identifier. Subsequently, the record text is segmented according to preset sentence segmentation rules and a nursing action vocabulary list. The preset sentence segmentation rules include periods, semicolons, line breaks, and conjunctions. The nursing action vocabulary list is stored in observation, execution, verification, and prompt categories. Observation category terms include "observation," "assessment," and "monitoring," execution category terms include "suctioning," "fixation," and "replacement," and prompt category terms include "attention," "vigilance," and "handover." Then, the module reads the noun phrases following the actions and the noun phrases following the results in each segment, using them as the action target and result target, respectively. The segmentation results are then divided into nursing observation segments, nursing execution segments, nursing verification segments, and handover prompt segments, outputting a fragmented record set. Then, the data processing module reads action words, status words, result words, and prompt words from the fragmented record set, and combines them with nursing object words to determine the type of each fragment. Among them, fragments that contain both observation-type action words and observation objects are identified as nursing observation fragments; fragments that contain both execution-type action words and execution objects are identified as nursing execution fragments; fragments that contain both verification-type action words and verification results are identified as nursing verification fragments; and fragments that contain both prompt words and matters to be concerned about are identified as handover prompt fragments. After completing the type determination, the module further extracts the event attributes, event values, and event states corresponding to each fragment to generate a standard nursing event item set. Among them, the event attributes represent nursing items, the event values ​​represent the recorded content, and the event states represent the status of observation, execution, verification, or handover prompts. Finally, the data processing module reads patient identification, recording time, event type, event attributes, and event status from the standard nursing event entry set. It then performs sequential association on nursing observation events, nursing execution events, nursing verification events, and handover prompt events with the same patient identification and recording times within a preset continuous time window. For nursing observation events, nursing execution events, and nursing verification events with the same event attributes or the same nursing subjects, if the time sequence of observation, execution, and verification is met, a nursing closed-loop association marker is generated. For handover prompt events, if a corresponding subsequent nursing observation event exists within a continuous time window after the handover, a handover continuation association marker is generated. After completing the association, the nursing event sequence with association markers is output in chronological order of recording time. Through the above implementation process, nursing record information and handover record information can be converted into nursing events with clear types, unified fields and related relationships, thereby providing a continuous event basis for subsequent risk stage identification, nursing obligation generation and closed-loop verification, and relatively improving the continuity of nursing process identification and the accuracy of anomaly judgment. In practical applications: For postoperative nursing texts that record "observe low drainage volume and light red color; fix drainage tube; recheck drainage patency; pay attention to incision bleeding during shift handover", the data processing module first breaks down the nursing observation segment, nursing execution segment, nursing verification segment, and handover prompt segment, and then establishes the "observe first, then execute, then verify" link and the "handover prompt, subsequent observation" link, ultimately forming a nursing event sequence for subsequent modules to call. The stage identification module is used to input the nursing event sequence into a pre-established set of rules for determining the nursing stage in ENT head and neck surgery. Based on the surgical method, postoperative time, tube status, drainage changes, symptom combinations and vital sign changes, it identifies the current risk stage of the target patient and outputs the current risk stage identifier. In the intelligent management and control of nursing quality in otolaryngology-head and neck surgery, simply converting nursing records into general events is insufficient to support risk stage determination. It is still necessary to further extract key features that characterize the patient's current nursing risk status from the nursing event sequence, and complete risk stage identification and stage change judgment based on the stage determination rule set. Otherwise, the subsequent generation of nursing obligations will be difficult to maintain consistency with the patient's current risk status. Therefore, the stage identification module first collects features from the nursing event sequence, then forms stage determination feature groups, and finally completes rule matching, priority filtering, and stage change output. This implementation process includes the following steps: The stage identification module first extracts surgical procedure, surgical end time, tube-related events, drainage-related events, symptom-related events, vital sign-related events, nursing closure-loop markers, and handover continuation markers from the nursing event sequence. Surgical procedure and surgical end time are preferentially retrieved from the corresponding event entries in the surgical record. Tube-related events are filtered by whether the event attributes include fields such as tracheostomy tube, drainage tube, nasogastric tube, fixation status, and patency status. Drainage-related events are filtered by fields such as drainage volume, drainage color, drainage characteristics, and drainage patency. Symptom-related events are filtered by fields such as dyspnea, swallowing discomfort, pain, bleeding, and local swelling. Vital sign-related events are filtered by fields such as body temperature, pulse, respiration, blood pressure, and blood oxygen saturation. Then, starting from the target patient's surgical end time, the module calculates the time difference between each event record time and the surgical end time, and determines the corresponding postoperative time period according to preset postoperative time period division rules. These preset rules can be set as immediate postoperative time period, early postoperative time period, and continuous postoperative observation time period. Upon completion, the module outputs the stage determination input set. The stage identification module then reads tubing-related events from the stage judgment input set and merges the events according to the same patient and the same tubing object, prioritizing the reading of the most recent nursing observation events, nursing execution events, and nursing verification events. When there are consecutive events where fixation is completed and patency verification is completed, the tubing status is determined to be stable. When there are events where fixation is loose, displaced, dislodged, blocked, or patency verification is incomplete, the tubing status is determined to be abnormal. All other cases are determined to be in a state of concern. The tubing object value here is based on the event object field in the nursing event entry. When the names of the same event object are inconsistent, they are first unified according to the preset object correspondence table. For example, "tracheostomy tube" and "tracheal tube" are unified as the same tubing object. After the judgment is completed, the tubing object, status result, and corresponding record time are written into the stage feature set as the tubing status features for subsequent stage matching. The stage identification module continues to read drainage-related events, symptom-related events, and vital sign-related events from the stage determination input set. For drainage-related events, it compares the values ​​of adjacent events for the same drainage object in chronological order to obtain changes in drainage volume (increase, decrease, or stabilization), drainage color (deepening, lightening, or stabilization), and drainage flow (smooth, partially smooth, or obstructed), thereby extracting drainage change features. For symptom-related events, it summarizes symptom items appearing within the same postoperative period according to a preset continuous time window. When two or more symptom events appear within the same window, it generates symptom combinations, such as "dyspnea + decreased blood oxygen" or "local swelling + increased bleeding." For vital sign-related events, it reads the values ​​of two or more consecutive events, calculates the direction and magnitude of change between adjacent records, and compares them with a preset change threshold to determine whether the vital sign change features are stable, fluctuating, or abnormal. After the above feature extraction is completed, the corresponding feature name, feature value, and source event time are written into the stage feature set. The stage identification module then combines nursing loop markers and handover continuation markers to determine the validity of each feature in the stage feature set. Specifically, when the source event corresponding to a certain tube status, drainage change, symptom combination, or vital sign change has a nursing loop marker, it indicates that the feature originates from a complete nursing chain of "observation first, then execution, and then verification," and is directly identified as a valid feature. When a handover reminder has a handover continuation marker in a subsequent nursing event, it indicates that the event has been continuously monitored in the subsequent nursing process, and the corresponding feature is also identified as a valid feature. When there is neither a nursing loop marker nor a handover continuation marker, the corresponding feature is identified as a feature to be confirmed. Afterward, the stage identification module uniformly writes the surgical method, postoperative time period, tube status, drainage change, symptom combination, and vital sign change features into the stage feature set, and adds a validity identifier and the time of the source event to form a stage judgment feature group. When there are multiple values ​​for the same type of feature, the feature with the most recent time and a valid validity identifier is retained first. The stage identification module inputs the stage judgment feature group into the ENT head and neck surgery nursing stage judgment rule set and performs item-by-item matching according to different conditions in the rule set. The rule set pre-stores the correspondence between surgical methods and basic nursing stages. For example, airway-related surgery corresponds to the airway key observation basic nursing stage, and drainage-related surgery corresponds to the drainage key observation basic nursing stage. It also stores the stage entry conditions corresponding to the postoperative time period, the stage maintenance conditions corresponding to the tube status, the stage upgrade conditions corresponding to the drainage changes, and the stage adjustment conditions corresponding to the symptom combination and vital sign changes. During matching, the basic nursing stage is first determined by the surgical method, then the postoperative time period is used to screen whether the entry conditions are met, then the tube status is used to determine whether to maintain the current stage, and then the drainage changes, symptom combinations, and vital sign changes are used to determine whether to upgrade, downgrade, or adjust the nursing stage. All nursing stages that meet the corresponding conditions are written into the candidate risk stage set, and the corresponding hit feature source is recorded. When the candidate risk stage set contains multiple candidate risk stages, the stage identification module prioritizes and filters each candidate risk stage. Specifically, it first reads the number of nursing observation items, nursing execution items, and nursing verification items corresponding to each candidate risk stage in the stage nursing requirement mapping table, and then sorts them from most to least according to these three quantities. If the first indicator is the same, the second indicator is compared. If all three quantities are the same, the candidate risk stage with more abnormal state features or more valid features is selected first. After sorting, the candidate risk stage with the highest priority is determined as the current risk stage, and its identifier is output. This approach prioritizes the nursing stage with more complete nursing requirements and more comprehensive risk coverage when multiple stages simultaneously meet the conditions. Finally, the stage identification module compares the current risk stage identifier with the previous risk stage identifier. The current risk stage identifier can be obtained from the current rule matching result, and the previous risk stage identifier can be read from the previous round of stage identification results. When the two match, a stage maintenance result is generated. When the two do not match, a stage change marker is generated, and the current risk stage identifier and the stage change marker are output to the obligation generation module. The stage change marker includes at least the stage switching time, the stage before the switch, and the stage after the switch, which are used by the subsequent obligation generation module to extract the corresponding stage nursing requirements and trigger the stage switching re-authentication process. Through the above implementation process, the stage identification module can continuously extract surgical methods, postoperative time periods, tube status, drainage changes, symptom combinations, and vital sign changes from the nursing event sequence. It also combines nursing closed-loop markers and handover continuation markers to screen out effective features with a basis for judgment. Then, through the ENT head and neck surgery nursing stage judgment rule set, it completes candidate risk stage matching, priority screening, and stage change output, thereby providing input basis consistent with the patient's current risk status for the generation of subsequent nursing obligations, and relatively improving the continuity of nursing stage identification and the timeliness of nursing strategy switching. In practical application: For a target patient who has undergone neck surgery and has indwelling drainage, the stage identification module first reads events from the nursing event sequence, such as the end time of surgery, low drainage volume and light red color, completion of drainage fixation, drainage patency upon follow-up, handover reminder to pay attention to incision bleeding, subsequent observation of slight incision swelling, and decrease in blood oxygenation. It then calculates the time difference of each event relative to the end time of surgery to determine that they all fall within the early postoperative period. Subsequently, it extracts features such as patency of drainage, local swelling, and decrease in blood oxygenation, and determines effective features based on nursing closed-loop markers and handover continuation markers. The above features are then input into the stage determination rule set to match and obtain two candidate risk stages: "Drainage Key Observation Stage" and "Bleeding Key Observation Stage". Then, by comparing the number of nursing observation items, nursing execution items, and nursing verification items corresponding to each stage, the current risk stage is determined. If the current risk stage is different from the previous round of identification results, a stage change marker is generated and output to the obligation generation module so that the nursing strategy can be adjusted synchronously in the future.

[0029] The obligation generation module is used to match the current risk stage identifier with a pre-established stage nursing requirement mapping table, extract nursing observation items, nursing execution items, nursing verification items, execution time windows and stage switching re-authentication items corresponding to the current risk stage, and output the current stage nursing obligation set; In the intelligent management and control of nursing quality in otolaryngology-head and neck surgery, even if the current risk stage of the target patient has been identified, if this risk stage cannot be promptly converted into actionable nursing observation, nursing execution, and nursing verification requirements, it will be difficult to establish subsequent closed-loop verification and stage switching recertification on a unified and clear basis of obligations. This can easily lead to problems such as scattered nursing requirements, unclear sequence, and continued use of original conclusions after stage switching. To address this, the obligation generation module matches the current risk stage identifier with a pre-established stage nursing requirement mapping table, generating a stage nursing requirement group, a set of nursing obligation items, and a set of current stage nursing obligations corresponding to the current risk stage. This provides a unified input for subsequent closed-loop verification and recertification processing. The implementation process includes the following steps:

[0030] The obligation generation module first inputs the current risk stage identifier into the stage nursing requirement mapping table, and then reads the nursing observation items, nursing execution items, nursing verification items, execution time windows, and stage switching re-authentication items corresponding to the current risk stage identifier from the stage nursing requirement mapping table. The phased nursing care requirement mapping table is pre-established according to risk phases. Each risk phase corresponds to at least one set of nursing observation items, one set of nursing execution items, one set of nursing verification items, and one set of execution time windows. Among them, nursing observation items are used to characterize the nursing information that needs to be continuously or periodically obtained under this risk phase, such as observing drainage volume, observing incision bleeding, observing airway patency, and observing swallowing response. Nursing execution items are used to characterize the nursing actions that need to be performed under this risk phase, such as fixing the drainage tube, clearing airway secretions, adjusting the body position, and strengthening local pressure. Nursing verification items are used to characterize the results that need to be confirmed after nursing execution, such as rechecking drainage patency, confirming blood oxygenation recovery, and confirming reduction of local bleeding. The execution time window is used to limit the start and end times of each nursing obligation that can be performed, and can be recorded in the form of "within a certain number of minutes from the time of risk phase identification" or "within a certain number of minutes after the occurrence of the corresponding observation event". The phase switch recertification item is used to record the nursing items that need to be reconfirmed when the risk phase changes. After reading the above content, the obligation generation module first generates a set of nursing requirements for the current stage according to the rules of grouping items based on the same nursing object and associating items based on the same nursing purpose. Grouping items based on the same nursing object means merging nursing observation items, nursing execution items, and nursing verification items that act on the same anatomical site, the same device, or the same monitoring indicator into the same requirement group. For example, "observe drainage volume," "fix drainage tube," and "recheck drainage patency" are grouped into the requirement group corresponding to the drainage object. Associating items based on the same nursing purpose means associating multiple nursing items, although acting on different objects, with the same nursing purpose, into the same requirement group. For example, around the nursing purpose of "preventing airway obstruction," "observe respiratory status," "clear airway secretions," and "recheck blood oxygen" can be associated in the same requirement group. After completing the merging and association, the obligation generation module outputs the current stage's nursing requirement group.

[0031] After obtaining the current stage of nursing requirements, the obligation generation module reads nursing observation items, nursing execution items, and nursing verification items from each group. It then establishes obligation relationships in the order of nursing observation items first, followed by nursing execution items, and finally nursing verification items, generating a set of nursing obligation items. Specifically, the obligation generation module first assigns a unique obligation group identifier to each nursing requirement group, and then links the nursing observation items, nursing execution items, and nursing verification items under the same obligation group identifier into a single nursing obligation chain according to their execution order. Nursing observation items form the triggering basis, nursing execution items form the treatment actions, and nursing verification items form the result confirmation; together, these three constitute a complete nursing obligation. Subsequently, the obligation generation module configures the execution time window to the corresponding nursing observation items, nursing execution items, and nursing verification items. The configuration can be done using either relative or absolute time configuration. Relative time configuration uses the current risk stage identification time or the completion time of the previous obligation item as the starting point, writing a preset time length into the corresponding obligation item, such as "complete observation drainage within 15 minutes of risk stage identification" or "complete follow-up examination within 10 minutes of nursing execution." Absolute time configuration writes a specific time interval into the obligation item, such as "complete observation between 20:00 and 20:30" or "complete verification before 20:30 after observation." When multiple nursing execution items exist within the same nursing requirement group, the obligation generation module can sort them according to a preset order number, with higher-priority nursing execution items listed first. The priority can be preset in the stage nursing requirement mapping table or determined based on the risk level of the corresponding nursing subject. After completing the above processing, the obligation generation module writes the obligation group identifier, nursing subject, nursing purpose, execution order, and execution time window into each nursing obligation chain, forming a set of nursing obligation items.

[0032] After obtaining the set of nursing obligation items, the obligation generation module continues to read stage-switching recertification items from the set. It marks the nursing obligation items corresponding to these recertification items as recertification obligations, and marks the remaining nursing obligation items as regular obligations, outputting a set of nursing obligations for the current stage that includes both regular and recertification obligations. Specifically, the obligation generation module first reads the nursing object, nursing item, or nursing purpose corresponding to the stage-switching recertification item from the stage nursing requirement mapping table, and then matches it item by item with the corresponding fields in the nursing obligation item set. When the nursing object and nursing item are the same, the corresponding nursing obligation item is directly marked as a recertification obligation. When the nursing item names are different but can be mapped to the same nursing purpose through a preset item mapping table, they are also marked as recertification obligations. For example, if a nursing conclusion of "smooth drainage" has been reached in the previous risk stage, and "drainage observation" and "drainage verification" are stage-switching recertification items in the current risk stage, then the nursing obligation items related to the drainage object are marked as recertification obligations. Nursing obligation items that are not matched remain as regular obligations. After marking is completed, the obligation generation module adds an obligation type field to each nursing obligation entry. The value of the obligation type field includes at least regular obligations and re-certification obligations. All entries are sorted by obligation group identifier and execution order, and the current stage nursing obligation set is output. This current stage nursing obligation set retains all nursing requirements that should be performed under the current risk stage, and clearly distinguishes the obligations that must be reconfirmed after the stage switch, for use by the subsequent closed-loop verification module and re-certification module.

[0033] Through the above implementation process, the obligation generation module can convert the current risk stage identifier into a set of nursing obligations for the current stage that is structurally clear, sequentially clear, and time-configurable. By merging the same nursing object, associating the same nursing purpose, and marking the recertification item for stage switching, the nursing observation, nursing execution, and nursing verification requirements form a coherent chain of nursing obligations under the same risk stage. This provides a unified comparison basis for subsequent closed-loop verification and a clear trigger object for the recertification of nursing conclusions after stage switching, thereby relatively improving the pertinence of nursing requirement generation and the connection of nursing strategies after stage switching.

[0034] In practical application: For a target patient in the "drainage key observation stage," the obligation generation module first reads nursing items such as "observe drainage volume," "observe drainage color," "fix drainage tube," and "recheck drainage patency" from the stage nursing requirement mapping table based on the current risk stage identifier, along with the corresponding execution time window and stage switch re-certification items. Then, "observe drainage volume—fix drainage tube—recheck drainage patency" are grouped into the same nursing obligation chain according to the same drainage target, and time requirements such as "complete observation within 15 minutes of stage identification" and "complete re-check within 10 minutes of execution" are configured to the corresponding obligation items. If the patient switches from the previous risk stage to the current risk stage, and "drainage patency confirmation" is a stage switch re-certification item, then this nursing obligation chain is marked as a re-certification obligation, while other nursing obligation chains related to the current stage but not involving re-certification are marked as regular obligations. The final set of nursing obligations for the current stage can be directly provided to the closed-loop verification module for comparison and, when the risk stage changes, provided to the re-certification module to generate corresponding re-certification nursing tasks.

[0035] The closed-loop verification module is used to compare the current set of nursing obligations with the executed nursing behaviors in the nursing event sequence item by item, and determine whether the pre-existing evidence for each nursing obligation is complete, whether the execution time falls within the execution time window, and whether the corresponding nursing verification result is formed after execution. Nursing obligations that do not meet the conditions of complete pre-existing evidence, matching execution time, and completion of post-execution verification are identified as invalid closed-loop obligations, and the nursing obligation closed-loop judgment result is output. In the intelligent management and control of nursing quality in otolaryngology-head and neck surgery, simply generating the current stage's set of nursing obligations is insufficient to confirm whether the nursing process truly meets the requirements of the current risk stage. Further verification is needed to confirm whether nursing observation, nursing execution, and nursing verification form a complete chain around the same nursing subject, and to determine whether this chain meets the requirements for prior evidence, execution time limits, and result verification. Otherwise, situations may arise where nursing records exist but an effective closed loop is not formed. Therefore, the closed-loop verification module extracts corresponding data from the current stage's set of nursing obligations and the sequence of nursing events, establishes the correspondence between obligation events, completes the determination of closed-loop conditions and the classification of invalid closed loops, and outputs the nursing obligation closed-loop determination result. This implementation process includes the following steps: The closed-loop verification module first reads nursing observation items, nursing execution items, nursing verification items, and execution time windows item by item from the current stage's nursing obligation set. It then extracts the corresponding nursing observation events, nursing execution events, and nursing verification events from the nursing event sequence. Specifically, the extraction process uses the patient identifier, risk stage identifier, and nursing object from the current stage's nursing obligation set as search keys to filter nursing events in the nursing event sequence that have the same patient identifier, consistent risk stage, and event attributes matching the nursing object. When the nursing object names are not entirely consistent, they are first unified using a preset object mapping table; for example, "tracheostomy tube" and "tracheal cannula" are unified as the same nursing object. After filtering, the closed-loop verification module... Nursing observation items, nursing execution items, and nursing verification items are matched with nursing observation events, nursing execution events, and nursing verification events, respectively. Corresponding events for the same patient, the same nursing subject, and the same risk stage are written into the same obligation event unit, forming an obligation event correspondence. If a nursing obligation item has multiple matchable events in the nursing event sequence, the event with the closest recording time to the current nursing obligation execution time window and a valid event status is selected first. A valid event status means that the event has not been marked as pending confirmation, voided, or overwritten by subsequent corrections. After matching is completed, the closed-loop verification module outputs a set of obligations to be verified, where each obligation to be verified includes at least a nursing observation event, a nursing execution event, a nursing verification event, a corresponding execution time window, and an obligation group identifier. After obtaining the set of obligations to be verified, the closed-loop verification module reads nursing observation events, nursing execution events, and nursing verification events item by item from the set, and sequentially determines whether the pre-existing evidence, execution time, and post-execution verification meet the closed-loop conditions. For the determination of pre-existing evidence, the closed-loop verification module first reads the recording time, event attributes, and event status of the nursing observation event, and then reads the recording time and event attributes of the nursing execution event. When a nursing observation event exists, the recording time of the nursing observation event is earlier than or equal to the recording time of the nursing execution event, and the event attributes of the nursing observation event are consistent with the nursing object corresponding to the nursing execution event, the nursing observation event is determined to be pre-existing evidence for the nursing execution event. For the determination of execution time, the closed-loop verification module reads the recording time of the nursing execution event and the execution time window corresponding to the nursing obligation. If the execution time window adopts a relative time form, the current risk stage identification time or the completion time of the previous obligation item is used first. Starting from the beginning, the time difference between the recorded time of the nursing execution event and the starting time is calculated, and then it is determined whether the time difference falls within the preset time range. If the execution time window adopts the form of an absolute time interval, it is directly determined whether the recorded time of the nursing execution event is between the start time and the end time. For the post-execution verification judgment, the closed-loop verification module reads the recorded time, event attributes, and event status of the nursing verification event, and determines whether the nursing verification event occurred after the nursing execution event, and whether the verification object corresponding to the nursing verification event is consistent with the nursing object corresponding to the nursing execution event. At the same time, the event status of the nursing verification event should indicate that a valid verification result has been formed, such as "verification completed", "unobstructed", "relieved", "recovered and stable", etc. Only when the three conditions of the existence of prior evidence, execution time matching, and post-execution verification completion are met simultaneously, will the closed-loop verification module generate a valid closed-loop mark for the current nursing obligation and write the judgment result into the obligation verification result set. After obtaining the set of obligation verification results, the closed-loop verification module reads nursing obligations that have not generated valid closed-loop markers from the set and identifies them as invalid closed-loop obligations. Specifically, the closed-loop verification module first checks the results of the prior evidence assessment, the execution time assessment, and the post-execution verification assessment, and then classifies and marks them according to the reason for failure: when the nursing observation event does not exist, or the recording time of the nursing observation event is later than the recording time of the nursing execution event, or the nursing object of the nursing observation event is inconsistent with that of the nursing execution event, the current nursing obligation is marked as lacking prior evidence; when the recording time of the nursing execution event exceeds the execution time window, the current nursing obligation is marked as not having the required execution time. Matching; when a nursing verification event does not exist, or the recording time of the nursing verification event is earlier than the recording time of the nursing execution event, or the nursing verification event has not formed a verification result corresponding to the nursing execution event, the current nursing obligation is marked as post-execution verification incomplete; when the same nursing obligation has multiple failure reasons at the same time, the closed-loop verification module can write multiple classification tags in the order of prior evidence, execution time, and post-execution verification to completely retain invalid reasons; after completing the classification, the closed-loop verification module writes the obligation group identifier, nursing object, risk stage, invalid reason and corresponding event time into the nursing obligation closed-loop judgment result, which is used by the subsequent quality output module to identify nursing abnormalities and locate nursing breakpoints; Through the above implementation process, the closed-loop verification module can establish a verifiable chain of obligations involving nursing observation events, nursing execution events, and nursing verification events around the same patient, the same nursing object, and the same risk stage. It can also determine whether the nursing obligations form an effective closed loop from three aspects: pre-existing evidence, execution time, and post-execution verification. This allows nursing behaviors that are only recorded but do not meet the stage requirements to be identified as invalid closed-loop obligations. This provides a unified and clear basis for subsequent nursing quality anomaly judgment and nursing breakpoint location, and relatively improves the accuracy and timeliness of nursing process anomaly identification. In practical application: For a target patient in the key observation stage of drainage, the current nursing obligation set contains a nursing obligation chain of "observing drainage volume - fixing drainage tube - re-checking drainage patency". The closed-loop verification module first extracts the nursing observation event, nursing execution event and nursing verification event corresponding to the nursing object from the nursing event sequence. Then it checks whether the nursing observation event occurs before the nursing execution event of fixing the drainage tube, and determines whether the recording time of fixing the drainage tube falls within the preset execution time window. Finally, it checks whether the nursing verification event of re-checking drainage patency occurs after fixing the drainage tube and forms a valid verification result. If all three conditions are met, a valid closed-loop mark is generated. If the observation record is missing, the fixing drainage time exceeds the limit, or the re-checking patency record is missing, it is marked as missing pre-evidence, execution time mismatch, or post-execution verification incomplete, respectively, and output to the nursing obligation closed-loop judgment result for subsequent modules to continue to judge the nursing quality status.

[0036] The recertification module is used to set the nursing conclusions that have been completed in the previous risk stage and belong to the recertification items of the stage switch to the state of pending recertification when the current risk stage is determined to be different from the previous risk stage. It also regenerates the corresponding nursing observation tasks, nursing execution tasks and nursing verification tasks based on the current stage nursing obligation set, and outputs the recertification nursing task set after the stage switch. In the intelligent management and control of nursing quality in otolaryngology-head and neck surgery, once the risk stage of a target patient changes, the nursing conclusions formed under the previous risk stage are not necessarily applicable to the current risk stage. If such nursing conclusions are still directly used as the valid basis for the current risk stage, it is easy to cause delays in the switching of nursing strategies and inappropriate application of nursing outcomes. Therefore, after detecting a change in risk stage, the recertification module first screens out the nursing conclusions that need to be reconfirmed, then generates the corresponding recertification nursing tasks, and controls such nursing conclusions not to participate in the valid determination of the current risk stage before the recertification is completed. This allows the nursing strategy to be adjusted synchronously with the change in risk stage. The implementation process includes the following steps: Upon receiving the current risk stage identifier, the previous risk stage identifier, and the current stage's set of nursing obligations, the re-authentication module first compares the current risk stage identifier with the previous risk stage identifier. The current risk stage identifier is obtained from the current round of stage identification, while the previous risk stage identifier is obtained from the previous round of stage identification or the cached result from the previous moment. If the comparison results are consistent, it indicates that no stage switch has occurred, and the re-authentication module maintains the current nursing conclusion state. If the comparison results are inconsistent, it indicates that a stage switch has occurred, and the re-authentication module extracts the nursing conclusions corresponding to the stage switch re-authentication items from the completed nursing obligations corresponding to the previous risk stage, forming a set of nursing conclusions to be re-authenticated. Here, completed nursing obligations refer to those already completed under the previous risk stage. Nursing obligations that pass closed-loop verification and bear valid closed-loop markers are selected. Stage-switching recertification items are read from the obligation type field of the current stage's nursing obligation set. All nursing items marked as recertification obligations are used as the basis for this screening. During extraction, the recertification module matches completed nursing obligations from the previous risk stage with the current stage's stage-switching recertification items according to the nursing object, nursing item, and nursing purpose. When the nursing object and nursing item are the same, the corresponding nursing conclusion is directly extracted. When nursing item names are different but can be mapped to the same nursing purpose through a preset item correspondence table, the corresponding nursing conclusion is also extracted. The output set of nursing conclusions to be recertified includes at least the nursing object, nursing outcome, completion time, source risk stage, and source obligation group identifier. After forming a set of nursing conclusions to be recertified, the recertification module reads the nursing subject, nursing outcome, and completion time item by item from the set. It then matches the nursing subject with the nursing observation items, nursing execution items, and nursing verification items in the current stage's nursing obligation set. If a match is found, the corresponding nursing conclusion is set to a state of pending recertification, and a set of states to be recertified is output. Specifically, the recertification module first uses the nursing subject as the primary search key to search for nursing obligation entries identical to the nursing subject in the current stage's nursing obligation set. It then determines whether the nursing obligation entry simultaneously contains nursing observation items, nursing execution items, and nursing verification items. If all three types of obligations are included, it indicates that the nursing subject needs to complete the complete nursing obligation chain again under the current risk stage, and the corresponding nursing conclusion is set to a state of pending recertification. If only some obligations are matched, the module further reads the nursing conclusions... The nursing purpose field is used to set a nursing conclusion to a pending re-certification status when the nursing purpose is the same as that of the previous nursing conclusion. When setting the pending re-certification status, the re-certification module adds a status field, status generation time, and status source to the nursing conclusion. The status field value is pending re-certification, the status generation time is the moment when the current risk stage changes, and the status source is the current risk stage identifier. Subsequently, the re-certification module reads the nursing objects and nursing results corresponding to each nursing conclusion to be re-certified from the set of pending re-certification statuses, and removes nursing conclusions with pending re-certification statuses from the valid nursing conclusions of the current risk stage. The removal method can be to rewrite their valid mark as invalid, or to remove them from the cache table of valid nursing conclusions of the current risk stage. After processing, the module outputs the set of nursing conclusions to be re-certified after the stage switch for subsequent task reconstruction. After obtaining the set of nursing conclusions to be recertified after the phase switch, the recertification module reads the nursing subjects and their recertification status from it. It then extracts the nursing observation items, nursing execution items, nursing verification items, and execution time windows corresponding to the nursing subjects from the current stage's nursing obligation set. Following the order of nursing observation items first, nursing execution items second, and nursing verification items last, it regenerates the recertification nursing task items and outputs the recertification nursing task set. Specifically, during generation, the recertification module first assigns a recertification task group identifier to each nursing subject, then uses the corresponding nursing observation item as the starting point of the task chain, followed by the nursing execution item, and finally the nursing verification item as the ending point, forming a complete recertification nursing task chain. Simultaneously, it reads the execution time window corresponding to the nursing obligation and sets the execution time window accordingly. The time window is configured in the recertification nursing task items. If the execution time window adopts a relative time format, the deadline of each task item is recalculated starting from the stage switch time. If an absolute time interval format is adopted, the original interval is retained and extended according to the stage switch time if necessary. Then, the recertification module reads the nursing observation items, nursing execution items, nursing verification items, and execution time windows corresponding to each recertification nursing task item from the recertification nursing task set, and adds a stage switch mark and task generation time to each item. The stage switch mark includes at least the risk stage before the switch and the risk stage after the switch, and the task generation time is the time when the current recertification task is formed. After completion, the recertification nursing task set after the stage switch is output for subsequent task execution and quality status assessment. After the set of recertification nursing tasks is formed, the recertification module continuously monitors the completion status of the nursing observation tasks, nursing execution tasks, and nursing verification tasks corresponding to the set, and controls the validity of the nursing conclusions to be recertified accordingly. Specifically, the recertification module reads the newly generated nursing observation events, nursing execution events, and nursing verification events corresponding to the recertification nursing tasks in the nursing event sequence item by item, using the recertification task group identifier as the unit, and judges whether a new valid closure has been formed according to the same rules as the closed-loop verification module. Within the same recertification task group, only when all nursing observation tasks, nursing execution tasks, and nursing verification tasks are completed, and the closed-loop verification result is valid, will a new valid closure be formed. Only when the loop is closed will the corresponding nursing conclusion awaiting recertification be restored to a valid nursing conclusion for the current risk stage. During restoration, the recertification module rewrites the status of awaiting recertification to recertified, writes the current risk stage identifier into the nursing conclusion, and records the restoration time as the current valid time. If any task is not completed, exceeds the execution time window, or fails to generate a valid verification result, the nursing conclusion remains invalid and is not allowed to participate in the calculation of valid nursing conclusions for the current risk stage. Through this control method, the recertification module ensures that the nursing conclusion formed in the previous risk stage can only be used as a valid basis again after being re-observed, re-executed, and re-verified under the current risk stage. Through the above implementation process, the recertification module can promptly identify nursing conclusions that need to be reconfirmed when the current risk stage changes compared to the previous risk stage. It removes these conclusions from the valid nursing conclusions of the current risk stage and regenerates a set of recertification nursing tasks based on the current set of nursing obligations. Subsequently, by continuously monitoring the completion of the recertification nursing tasks, it controls the corresponding nursing conclusions to remain invalid before recertification is completed and to become valid after recertification is completed. This avoids the direct application of nursing conclusions formed under the previous risk stage to the current risk stage and relatively improves the timeliness of nursing strategy adjustments and the accuracy of nursing conclusion usage after risk stage switching. In practical application: For a target patient transitioning from the "Drainage Focus Observation Stage" to the "Bleeding Focus Observation Stage," if the nursing conclusion of "Unimpeded Drainage" was already established in the previous risk stage, the "Drainage Observation—Drainage Treatment—Drainage Verification" item in the current stage's nursing obligations set is marked as a stage transition recertification item. The recertification module first compares the current risk stage identifier with the previous risk stage identifier. After confirming the stage transition, it extracts the "Unimpeded Drainage" nursing conclusion from the completed nursing obligations of the previous risk stage and sets it to a pending recertification status, while simultaneously removing it from the valid nursing conclusions of the current risk stage. Subsequently, based on the current set of nursing obligations, a recertification nursing task chain is regenerated: "Observe drainage volume and color—adjust drainage treatment if necessary—recheck drainage patency," with a stage switch marker and task generation time added. Only after all new nursing observation events, nursing execution events, and nursing verification events are completed and form a new effective closed loop will the recertification module restore the "drainage patency" nursing conclusion to a valid nursing conclusion for the current risk stage. If the recheck is not completed or is not completed within the execution time window, the nursing conclusion remains invalid and is not considered a valid nursing basis for the current risk stage.

[0037] The quality output module is used to determine the nursing quality status of the target patient based on the nursing obligation closure judgment result and the set of recertification nursing tasks. Among them, the target patients with invalid closure obligations or uncompleted recertification nursing tasks are identified as nursing quality abnormal patients, and the corresponding abnormality type, abnormality occurrence stage and abnormality corresponding nursing breakpoint information are output. In the intelligent management and control of nursing quality in otolaryngology-head and neck surgery, simply completing the closed-loop verification of nursing obligations and generating recertification tasks is insufficient to directly support quality control procedures. This is because nursing staff and quality control personnel still need to clarify whether the abnormality is a closed-loop abnormality, a recertification abnormality, or a composite abnormality of both, and further pinpoint the risk stage, the patient being cared for, and the nursing process in which the abnormality occurred. Without unified abnormality merging, abnormality judgment, and breakpoint location processing, problems such as mixed abnormality types, unclear entry points for treatment, and difficulty in tracing responsible links can easily arise. Therefore, the quality output module summarizes, classifies, and locates the results of the closed-loop judgment of nursing obligations and the set of recertification nursing tasks, forming a nursing quality status record for the target patient and outputting it to the corresponding terminal. This implementation process includes the following steps: The quality output module first extracts the nursing obligation type, invalidity reason, risk stage, and occurrence time corresponding to invalid closed-loop obligations from the nursing obligation closure-loop judgment results. It then extracts the nursing subjects, task types, risk stages, and task status corresponding to incomplete recertification nursing tasks from the recertification nursing task set. During extraction, patients are first grouped by their identifiers, and then the risk stage and nursing subject fields are read within each patient group. The nursing obligation type is used to distinguish between nursing observation obligations, nursing execution obligations, and nursing verification obligations. The invalidity reason is derived from the closed-loop verification module's output of missing pre-existing evidence, mismatched execution time, or failure to verify after execution. Upon completion, the task status is taken from the output of the recertification module: not started, in progress, timed out, or verification failed. Subsequently, the quality output module merges tasks according to the same patient, the same risk stage, and the same nursing object: when invalid closed-loop obligations and incomplete recertification nursing tasks correspond to the same nursing object, both types of information are written into the same exception unit; when only one type of exception information exists, only the corresponding exception field is retained; after merging, an exception judgment data set is output, which includes at least the patient identifier, risk stage, nursing object, nursing obligation type, invalid reason, task type, task status, and occurrence time. After obtaining the anomaly assessment dataset, the quality output module examines each item in the dataset to determine whether the target patient has an invalid closed-loop obligation or has not completed a recertification nursing task, and determines the nursing quality status accordingly. Specifically, if a patient has at least one invalid closed-loop obligation and no uncompleted recertification nursing task in the current risk stage, the patient is identified as a closed-loop abnormal patient; if at least one uncompleted recertification nursing task and no invalid closed-loop obligation exists, the patient is identified as a recertification abnormal patient; if both types of abnormalities exist simultaneously, the patient is identified as a composite abnormal patient. After the assessment, the quality output module generates a nursing quality status assessment result, which includes at least the patient identifier, abnormality type, abnormality occurrence stage, nursing object, and status generation time. Subsequently, the quality output module generates abnormality type, abnormality occurrence stage, and nursing breakpoint information corresponding to closed-loop abnormal patients, recertification abnormal patients, or composite abnormal patients based on the nursing quality status assessment result. The abnormality type is either closed-loop abnormal, recertification abnormal, or composite abnormal; the abnormality occurrence stage is the corresponding risk stage; and the nursing breakpoint information is retained in the nursing quality status assessment result as an input field for the next breakpoint location step. After determining the nursing quality status, the quality output module further extracts the anomaly type, anomaly occurrence stage, nursing obligation type, nursing recipient, occurrence time, and task status from the nursing quality status determination results. It then identifies breakpoints for invalid closed-loop obligations and incomplete recertification nursing tasks. For invalid closed-loop obligations, the quality output module checks the fulfillment of closed-loop conditions item by item in the corresponding nursing observation items, nursing execution items, and nursing verification items according to the obligation chain order, identifying the nursing obligation that first fails to meet the closed-loop condition as the breakpoint obligation. If a nursing observation item fails to generate valid prior evidence, then the nursing... The observation item is the breakpoint obligation. If the nursing observation item is satisfied but the nursing execution item exceeds the execution time window, then the nursing execution item is the breakpoint obligation. If the first two items are satisfied but the nursing verification item is not completed, then the nursing verification item is the breakpoint obligation. For incomplete recertification nursing tasks, the quality output module checks the completion status of the corresponding nursing object's nursing observation tasks, nursing execution tasks, and nursing verification tasks in the order of task generation, and determines the first incomplete recertification nursing task as the breakpoint task. After completing the above processing, the breakpoint location result is output. The breakpoint location result includes at least the breakpoint type, breakpoint object, breakpoint occurrence stage, and breakpoint occurrence time. After obtaining the breakpoint location results, the quality output module generates a set of nursing breakpoint information based on the breakpoint type. Specifically, when the preceding evidence corresponding to the breakpoint obligation is missing, preceding evidence breakpoint information is generated, including at least the nursing subject, the corresponding nursing observation item, the missing time, and the corresponding risk stage; when the execution time corresponding to the breakpoint obligation is mismatched, time window breakpoint information is generated, including at least the nursing execution item, the preset execution time window, the actual execution time, and the timeout status; when the post-execution verification corresponding to the breakpoint obligation is not completed, verification result breakpoint information is generated, including at least the nursing verification item, the expected completion time, and the current verification status; when the task status corresponding to the breakpoint task is not completed, a set of nursing breakpoint information is generated. The re-authentication task breakpoint information includes at least the re-authentication nursing object, the type of incomplete task, the task generation time, and the current task status. After generating the nursing breakpoint information set, the quality output module writes the exception type, the stage of the exception, and the nursing breakpoint information set into the nursing quality status record corresponding to the target patient. The nursing quality status record can be stored in a structured form, with fields including at least patient identifier, risk stage, exception type, nursing object, breakpoint information, recording time, and output terminal identifier. After writing, the quality output module outputs the nursing quality status record to the nursing terminal, nurse station terminal, or quality control management terminal for nursing staff to handle, hand over, and review. Through the above implementation process, the quality output module can unify ineffective closed-loop obligations and incomplete recertification nursing tasks into identifiable abnormal data. Then, it classifies target patients into closed-loop abnormalities, recertification abnormalities, and complex abnormalities, and further locates the nursing obligation or recertification nursing task that first caused the abnormality. This results in a nursing quality status record with clear abnormality types, clear abnormality stages, and traceable breakpoint locations. As a result, nursing quality abnormalities can be transformed from simple prompts into structured results that can be classified, located, and output, thereby relatively improving the pertinence of ENT head and neck surgery nursing quality control and the clarity of subsequent treatment. In practical application: For a target patient in the critical observation stage of bleeding, the nursing obligation closed-loop judgment result shows that the nursing obligation chain of "observation of incision bleeding - local pressure treatment - re-examination of bleeding reduction" lacks a re-examination record, and the recertification nursing task set also shows that the task of "re-examination of drainage patency" is in an overdue and incomplete state. The quality output module first merges the invalid closed-loop obligation of "execution verification not completed" and the recertification nursing task of "overdue and incomplete" according to the same patient, the same risk stage, and the same nursing object, and then judges the patient as a compound abnormal patient. Subsequently, the nursing verification item corresponding to "re-examination of bleeding reduction" is identified as the breakpoint obligation in the closed-loop obligation chain, and "re-examination of drainage patency" is identified as the breakpoint task in the recertification nursing task chain, and verification result breakpoint information and recertification task breakpoint information are generated respectively. Finally, the quality output module writes the abnormality type, abnormality occurrence stage, and nursing breakpoint information into the nursing quality status record and outputs it to the nurse station terminal and quality control management terminal, so that nursing staff can promptly perform re-examinations and quality control personnel can track the abnormality handling results.

[0038] Working Principle: This solution can be understood as an intelligent management process that "first transforms nursing records into calculable events, and then dynamically manages the nursing process according to the patient's current risk stage." First, the system unifies and organizes information such as surgical procedure, medical orders, vital signs, tubing, drainage, symptoms, nursing records, and handover records, breaking them down into nursing events such as nursing observation, nursing execution, nursing verification, and handover prompts, and establishing chronological relationships between these events. Then, the system combines surgical procedure, postoperative time period, tubing status, drainage changes, symptom combinations, and vital sign changes to identify the patient's current risk stage from these nursing events. Next… The system automatically generates corresponding nursing observation items, nursing execution items, nursing verification items, and execution time windows based on the current risk stage, and distinguishes which items need to be reconfirmed after the stage switch. Then, the system compares the actual nursing events with these nursing obligations one by one to determine whether an effective closed loop of "observation first, execution then verification" has been formed. If the risk stage has changed, the relevant nursing conclusions formed in the previous stage are invalidated first, and then the corresponding recertification nursing tasks are regenerated. Finally, the system merges the invalid closed loops and incomplete recertification tasks, and outputs the anomaly type, anomaly occurrence stage, and nursing breakpoint information, thereby realizing continuous tracking and dynamic control of nursing quality. For example, a patient in ENT head and neck surgery with an indwelling drainage tube might have their nursing record recorded as "low drainage volume, pale red color," "fixed drainage tube," "follow-up check for unobstructed drainage," and "pay attention to incision bleeding during shift handover." The system first breaks these sentences down into nursing observation events, nursing execution events, nursing verification events, and handover reminder events. Then, it combines this information with postoperative time, subsequent slight incision swelling, and changes in blood oxygenation to determine whether the patient is currently in the key observation stage for drainage or the key observation stage for bleeding. If the system identifies a change in the risk stage, it will automatically update the tasks to be completed under that stage. The system will assign nursing duties and mark the nursing conclusions such as "drainage is unobstructed" from the previous stage as requiring recertification, requiring the observation, treatment, and verification to be completed again. If the nurse fails to complete the follow-up examination within the specified time or lacks verification records, the system will determine the patient as a patient with abnormal nursing quality and clearly tell the nursing staff "which stage, which nursing subject, and which step was interrupted", such as "failure to complete the follow-up examination of drainage patency" or "missing follow-up examination of incision bleeding". In this way, nursing staff and quality control personnel can identify the problem more quickly and take targeted measures.

[0039] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent management and control system for nursing quality in otolaryngology-head and neck surgery, characterized in that, include: The data processing module collects information on the target patient's surgical procedure, medical orders, vital signs, tubing, drainage, symptoms, nursing records, and handover records. It performs time alignment, field standardization, and patient association processing on the collected information and outputs a sequence of nursing events for the target patient. The stage identification module inputs the nursing event sequence into a pre-established set of rules for determining the nursing stage in ENT head and neck surgery. Based on the surgical method, postoperative time, tube status, drainage changes, symptom combinations, and vital sign changes, it identifies the current risk stage of the target patient and outputs the current risk stage identifier. The obligation generation module matches the current risk stage identifier with a pre-established stage nursing requirement mapping table, extracts the nursing observation items, nursing execution items, nursing verification items, execution time windows and stage switching re-authentication items corresponding to the current risk stage, and outputs the current stage nursing obligation set; The closed-loop verification module compares the current set of nursing obligations with the executed nursing behaviors in the nursing event sequence item by item, and determines whether the pre-existing evidence for each nursing obligation is complete, whether the execution time falls within the execution time window, and whether a corresponding nursing verification result is formed after execution. Nursing obligations that do not meet the conditions of complete pre-existing evidence, matching execution time, and completion of post-execution verification are identified as invalid closed-loop obligations, and the nursing obligation closed-loop judgment result is output. The recertification module, when it determines that the current risk stage has changed relative to the previous risk stage, sets the nursing conclusions that have been completed under the previous risk stage and belong to the recertification items of the stage switch to the state of pending recertification, and regenerates the corresponding nursing observation tasks, nursing execution tasks and nursing verification tasks based on the current stage nursing obligation set, and outputs the recertification nursing task set after the stage switch. The quality output module determines the nursing quality status of the target patient based on the nursing obligation closure judgment result and the set of recertification nursing tasks. Among them, the target patients with invalid closure obligations or uncompleted recertification nursing tasks are identified as nursing quality abnormal patients, and the corresponding abnormality type, abnormality occurrence stage and abnormality corresponding nursing breakpoint information are output.

2. The system according to claim 1, characterized in that, The data processing module is used to extract the record text, record time, recorder identifier and patient identifier from nursing record information and handover record information, perform semantic segmentation of the record text according to preset sentence segmentation rules and nursing action vocabulary, and split the segmentation results into nursing observation fragments, nursing execution fragments, nursing verification fragments and handover prompt fragments according to the action target object and the result target object, and output a fragmented record set. The system reads the nursing action words, status description words, result description words, and risk warning words corresponding to each segment from the fragmented record set. According to the rules of matching observation actions with observation objects to nursing observation segments, execution actions with execution objects to nursing execution segments, verification actions with verification results to nursing verification segments, and handover warning words with matters to be concerned to handover warning segments, the system determines the type of each segment and extracts the corresponding event attributes, event values, and event status, and outputs a standard nursing event item set. The system reads patient identification, recording time, event type, event attributes, and event status from the standard nursing event entry set. It then associates nursing observation events, nursing execution events, nursing verification events, and handover prompt events with the same patient identification and whose recording time is within a preset continuous time window. When there is a chain of observation followed by execution and then verification, or a chain of handover prompt and subsequent observation, it generates an association marker and outputs a nursing event sequence with the association marker.

3. The system according to claim 2, characterized in that: The stage identification module is used to extract surgical method, surgical end time, tube-related events, drainage-related events, symptom-related events, vital sign-related events, nursing closed-loop markers and handover continuation markers from the nursing event sequence, and determine the postoperative period according to the time difference between the target patient's surgical end time and the time of each event recording, and output the stage determination input set; The stage identification module is also used to read tubing-related events, drainage-related events, symptom-related events and vital sign-related events from the stage determination input set, extract tubing status, drainage changes, symptom combinations and vital sign changes features respectively, and combine nursing closed-loop markers and handover continuation markers to determine the validity of each feature, and output the stage feature set; The stage identification module is also used to write the surgical method, postoperative time period, tube status, drainage changes, symptom combinations and vital sign changes into the stage feature set, forming a stage judgment feature group for matching the ENT head and neck surgery nursing stage judgment rule set.

4. The system according to claim 3, characterized in that: The stage identification module is used to input the stage judgment feature group into the ENT head and neck surgery nursing stage judgment rule set, and match them one by one according to the basic nursing stage corresponding to the surgical method, the stage entry condition corresponding to the postoperative period, the stage maintenance condition corresponding to the tube status, the stage upgrade condition corresponding to the drainage change, and the stage adjustment condition corresponding to the symptom combination and vital sign change characteristics, and output a set of candidate risk stages. The stage identification module is also used to determine the candidate risk stage with the highest priority as the current risk stage when the candidate risk stage set contains multiple candidate risk stages, according to the number of nursing observation items, nursing execution items and nursing verification items from most to least, and output the current risk stage identifier; The phase identification module is also used to generate a phase change marker when the current risk phase identifier is inconsistent with the previous risk phase identifier, and output the current risk phase identifier and the phase change marker to the obligation generation module.

5. The system according to claim 4, characterized in that: The obligation generation module is used to input the current risk stage identifier into the stage nursing requirement mapping table, read the nursing observation items, nursing execution items, nursing verification items, execution time windows and stage switching re-authentication items corresponding to the current risk stage identifier, and generate stage nursing requirement groups according to the same nursing object and the same nursing purpose, and output the current stage nursing requirement group. The obligation generation module is also used to read each nursing observation item, nursing execution item, and nursing verification item from the current stage nursing requirement group, establish obligation association relationships in the order of nursing observation items first, nursing execution items last, and nursing verification items last, configure the execution time window to the corresponding nursing observation item, nursing execution item, and nursing verification item, and output a set of nursing obligation items; The obligation generation module is also used to read phase switching re-authentication items from the set of nursing obligation items, mark the nursing obligation items corresponding to the phase switching re-authentication items as re-authentication obligations, mark the remaining nursing obligation items as regular obligations, and output the current phase nursing obligation set containing regular obligations and re-authentication obligations.

6. The system according to claim 5, characterized in that: The closed-loop verification module is used to read nursing observation items, nursing execution items, nursing verification items and execution time windows from the current stage nursing obligation set one by one, and extract nursing observation events, nursing execution events and nursing verification events corresponding to each nursing observation item, nursing execution item and nursing verification item from the nursing event sequence, establish the obligation event correspondence relationship according to the same patient, the same nursing object and the same risk stage, and output the obligation set to be verified. The closed-loop verification module is also used to read nursing observation events, nursing execution events and nursing verification events one by one from the set of obligations to be verified, determine whether the nursing observation event serves as preliminary evidence for the nursing execution event, whether the occurrence time of the nursing execution event falls within the execution time window, and whether the nursing verification event forms a corresponding nursing verification result after the nursing execution event. When the preliminary evidence exists, the execution time matches and the post-execution verification is completed simultaneously, a valid closed-loop marker is generated and the set of obligation verification results is output. The closed-loop verification module is also used to read nursing obligations that have not generated valid closed-loop markers from the obligation verification result set, identify them as invalid closed-loop obligations, and classify and mark them according to the lack of prior evidence, mismatch of execution time and failure to complete post-execution verification, and output the nursing obligation closed-loop judgment result.

7. The system according to claim 6, characterized in that: The re-authentication module is used to, after receiving the current risk stage identifier, the previous risk stage identifier and the current stage nursing obligation set, first determine whether the current risk stage identifier is consistent with the previous risk stage identifier. If they are inconsistent, extract the nursing conclusions corresponding to the stage switching re-authentication items from the completed nursing obligations corresponding to the previous risk stage, and output the set of nursing conclusions to be re-authenticated. The re-certification module is also used to read the nursing object, nursing result and completion time corresponding to each nursing conclusion from the set of nursing conclusions to be recertified, and match the nursing object with the nursing observation items, nursing execution items and nursing verification items in the current stage nursing obligation set. When a corresponding relationship exists, the corresponding nursing conclusion is set to the state to be recertified and the set of states to be recertified is output. The recertification module is also used to read the nursing subjects and nursing results corresponding to each nursing conclusion to be recertified from the set of states to be recertified, remove the nursing conclusions with the state to be recertified from the valid nursing conclusions of the current risk stage, and output the set of nursing conclusions to be recertified after the stage switch.

8. The system according to claim 7, characterized in that: The re-certification module is used to read the nursing object and the status to be recertified from the set of nursing conclusions to be recertified after the stage switch, and extract the nursing observation items, nursing execution items, nursing verification items and execution time windows corresponding to the nursing object from the current stage nursing obligation set. The module regenerates the recertification nursing task items in the order of nursing observation items first, nursing execution items second, and nursing verification items last, and outputs the recertification nursing task set. The re-certification module is also used to read the nursing observation items, nursing execution items, nursing verification items and execution time windows corresponding to each re-certification nursing task item from the re-certification nursing task set, and to add a stage switching mark and task generation time to each re-certification nursing task item, and output the re-certification nursing task set after the stage switching. The recertification module is also used to keep the corresponding nursing conclusions to be recertified in an invalid state before all nursing observation tasks, nursing execution tasks and nursing verification tasks corresponding to the recertification nursing task set are completed, and to restore the corresponding nursing conclusions to be recertified to valid nursing conclusions of the current risk stage after all tasks are completed.

9. The system according to claim 8, characterized in that: The quality output module is used to extract the nursing obligation type, invalid reason, risk stage and occurrence time corresponding to invalid closed-loop obligations from the nursing obligation closed-loop judgment result, and to extract the nursing object, task type, risk stage and task status corresponding to the uncompleted recertification nursing task from the recertification nursing task set, and to merge them according to the same patient, the same risk stage and the same nursing object, and output the abnormal judgment data set. The quality output module is also used to determine whether the target patient has an invalid closed-loop obligation or has not completed the recertification nursing task from the abnormal judgment data set. When there is an invalid closed-loop obligation, the corresponding target patient is identified as a closed-loop abnormal patient. When there is a recertification nursing task that has not been completed, the corresponding target patient is identified as a recertification abnormal patient. When there are both invalid closed-loop obligations and recertification nursing tasks that have not been completed, the corresponding target patient is identified as a compound abnormal patient, and the nursing quality status judgment result is output. The quality output module is also used to generate information on the abnormality type, abnormality occurrence stage, and nursing breakpoint corresponding to closed-loop abnormal patients, recertified abnormal patients, or combined abnormal patients based on the nursing quality status judgment results, and output the nursing quality status of the target patient.

10. The system according to claim 9, characterized in that: The quality output module is used to extract the abnormality type, abnormality occurrence stage, nursing obligation type, nursing object, occurrence time and task status from the nursing quality status judgment result. Among the nursing observation items, nursing execution items and nursing verification items corresponding to invalid closed-loop obligations, the nursing obligation that first fails to meet the closed-loop condition is identified as the breakpoint obligation. Among the nursing observation tasks, nursing execution tasks and nursing verification tasks corresponding to incomplete recertification nursing tasks, the recertification nursing task that first fails to be completed is identified as the breakpoint task, and the breakpoint location result is output. The quality output module is also used to generate pre-evidence breakpoint information when the pre-evidence corresponding to the breakpoint obligation is missing, to generate time window breakpoint information when the execution time corresponding to the breakpoint obligation is mismatched, to generate verification result breakpoint information when the verification after execution corresponding to the breakpoint obligation is not completed, and to generate re-authentication task breakpoint information when the task status corresponding to the breakpoint task is not completed, based on the breakpoint location results, and output a set of nursing breakpoint information. The quality output module is also used to write the abnormality type, abnormality occurrence stage and nursing breakpoint information set into the nursing quality status record corresponding to the target patient, and output the nursing quality status record to the nursing terminal, nurse station terminal or quality control management terminal.

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