Postoperative nursing quality pass rate whole-process management and control system

Abstract

The application relates to the technical field of neurosurgery nursing management and control, in particular to a whole-process management and control system for the qualified rate of postoperative nursing quality in neurosurgery, which comprises an archive creation module, a procedure matching module, a state monitoring module and an event processing module. When a patient is transferred to a nursing unit after surgery, the system creates a unique digital nursing tracking archive, synchronously inputs basic pathological information, operation key operation record and preliminary nursing target, and matches and associates a corresponding standard nursing procedure template. Real-time physiological data flow is collected through a bedside sensor array, physiological state analysis engine and postoperative complication early warning rules are combined, physiological parameter abnormal fragments are identified, independent abnormal event records are generated and associated with the archive, and an alarm message containing abnormal fragment key features is pushed to a mobile nursing terminal. The system realizes integrated collection of nursing information, continuous analysis of physiological data, accurate identification of abnormal states and real-time pushing, and strengthens the whole-process standardized management and control of postoperative nursing.

Description

Technical Field

[0001] This invention relates to the field of neurosurgical nursing management technology, and in particular to a full-process management system for the pass rate of postoperative neurosurgical nursing quality. Background Technology

[0002] Current neurosurgical postoperative care uses a management approach that combines routine electronic medical records with manual monitoring. Nursing records are only established at routine postoperative process nodes, and a unique digital nursing tracking record is not generated at the moment the patient is transferred to the postoperative care unit. The patient's basic pathological information, key surgical operation records, and the preliminary nursing goals set by the surgeon cannot be collected and entered synchronously. Standard nursing procedure templates rely solely on manual retrieval and cannot be automatically linked and bound to the patient's individual digital record. Nursing procedures and patient records have long been in a state of separate management.

[0003] Current postoperative physiological data acquisition relies on a single monitoring device to obtain discrete parameters, without using a bedside sensor array to acquire continuous physiological data streams. Physiological state analysis depends solely on manual comparison of parameter values, without a dedicated physiological state analysis engine or pre-set rules for early warning of neurosurgical postoperative complications. It is impossible to analyze continuous data and identify abnormal segments of physiological parameters. Abnormalities are recorded only in a fragmented manner, and it is impossible to generate independent abnormal event records for each abnormal segment. Abnormal information cannot be linked to patient files in real time, nor can it push warning messages containing key features of abnormal segments to the responsible nurse's mobile nursing terminal.

[0004] This invention requires the immediate creation of a unique digital nursing tracking file when a patient is transferred to the postoperative care unit. Specific information is entered synchronously and associated with a matching standard nursing procedure template. Continuous physiological data streams are collected through a sensor array. Abnormal segments are identified based on a parsing engine and early warning rules. Independent abnormal event records are generated and associated with the file, and corresponding warning messages are pushed. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a comprehensive management system for the quality of postoperative neurosurgical care.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a full-process control system for the quality compliance rate of postoperative neurosurgical care, comprising: The file creation module creates a unique digital nursing tracking file for the patient when the patient completes neurosurgery and is transferred to the postoperative care unit. It also records the patient's basic pathological information, key surgical operation records, and preliminary nursing goals set by the attending physician through the nursing terminal device. The procedure matching module, based on the preliminary nursing goals, retrieves standard nursing procedure templates from the standard nursing knowledge base that match the patient's surgical type and disease severity, and associates the standard nursing procedure templates with the patient's digital nursing tracking file; The status monitoring module continuously collects real-time physiological data streams from patients through a vital sign sensor array placed next to the patient's bed; The event processing module inputs the real-time physiological data stream into the physiological state analysis engine. The physiological state analysis engine continuously analyzes the real-time physiological data stream according to a preset set of early warning rules for postoperative neurosurgical complications, identifies abnormal segments of physiological parameters that deviate from the normal range, generates an independent abnormal event record for each identified abnormal physiological parameter segment, and links the abnormal event record to the patient's digital nursing tracking file in real time. At the same time, it pushes an alert message containing the key features of the abnormal physiological parameter segment to the mobile nursing terminal held by the responsible nurse.

[0007] As a further aspect of the present invention, the physiological state analysis engine continuously analyzes the real-time physiological data stream based on a pre-set set of early warning rules for postoperative neurosurgical complications, identifying abnormal segments of physiological parameters that deviate from the normal range, including: The real-time physiological data stream includes intracranial pressure waveform signals, electrocardiogram signals, blood oxygen saturation values, and core body temperature readings. The physiological state analysis engine receives the intracranial pressure waveform signal from the vital signs sensor array, performs time-domain and frequency-domain feature extraction on the intracranial pressure waveform signal, and calculates the waveform mean, amplitude variation index and high-frequency oscillation energy. The calculated mean value of the waveform, amplitude variation index, and high-frequency oscillation energy are compared with the individualized intracranial pressure safety threshold range set for the patient's condition. If the mean value of the waveform continuously exceeds the upper limit of the individualized intracranial pressure safety threshold range, it is determined to be a high-risk segment of intracranial hypertension. If the amplitude variation index increases sharply in a short period of time, it is identified as a segment at risk of decreased brain compliance. The identified intracranial hypertension risk segments and the brain compliance decline risk segments are all marked as abnormal physiological parameter segments, and the start time, duration and maximum deviation value of each segment are recorded.

[0008] The construction steps of the physiological state analysis engine include: Complete monitoring records of multiple completed neurosurgical patients were obtained from the historical nursing database. The complete monitoring records included the real-time physiological data stream in a continuous sequence and the complication diagnosis labels that were eventually clinically confirmed. The time-series continuous real-time physiological data stream is segmented into multiple data segments with a preset duration, and each data segment is labeled with whether complications occurred within its corresponding time period and the type of complications. Physiological features of multiple dimensions are extracted from each of the data segments, including statistical features, trend features, and time-frequency domain transformation features of the waveform signal; Using data fragments labeled with complication types and their extracted physiological features from multiple dimensions, a machine learning classification model is trained so that the machine learning classification model learns the physiological feature patterns corresponding to different complication types. The trained machine learning classification model is combined with pre-set fixed rules based on clinical guidelines to form the physiological state analysis engine. In the physiological state analysis engine, the machine learning classification model is used to perform preliminary abnormal pattern identification on the input real-time physiological data stream, and the preset fixed rules are used to perform logical verification and urgency classification on the identification results of the machine learning classification model.

[0009] As a further aspect of the present invention, it also includes: The abnormal response recording module requires that when the mobile nursing terminal receives the warning message, the responsible nurse must arrive at the patient's bedside and conduct an on-site assessment within a preset response time limit. The responsible nurse retrieves a list of standardized treatment recommendations corresponding to the abnormal physiological parameter fragments from the standard nursing knowledge base using the mobile nursing terminal; The responsible nurse performs nursing procedures according to the standardized treatment suggestion list. After the procedure is completed, the nurse uses the interface of the mobile nursing terminal to check the performed procedures and enter the operation time, the patient's immediate response observation, and the name and dosage of any temporarily administered medications. The mobile nursing terminal packages the selected performed treatments, operation times, patient immediate response observations, and medication information into a nursing response record, uploads and integrates it into the corresponding abnormal event record in the patient's digital nursing tracking file.

[0010] As a further aspect of the present invention, it also includes: The quality inspection and control module predefines multiple key quality inspection nodes in the entire postoperative care process, and each key quality inspection node is associated with a list of nursing items that must be completed. When the system time arrives or the patient's status triggers a key quality check node, the system automatically pushes the node check task to the management terminal held by the head nurse. The head nurse can access all of the patient's nursing records up to the present through the management terminal, including the abnormal event records in the digital nursing tracking file and the associated nursing response records; The head nurse compares the actual completed nursing records with the list of mandatory nursing items associated with the key quality checkpoints item by item; For nursing items that are required in the list but are missing in actual records, or items whose actual execution time deviates significantly from the prescribed time, the head nurse marks them as nursing deviation items in the system through the management terminal. The nursing deviation items are associated with and saved with specific key quality check nodes and the responsible nurse's information.

[0011] As a further aspect of the present invention, it also includes: The multidimensional traceability analysis module automatically triggers a final analysis of the patient's digital nursing tracking file when the patient is discharged or transferred out of the postoperative care unit. The system extracts all marked nursing deviation items from the digital nursing tracking file and classifies and statistically analyzes them according to deviation type, associated key quality check nodes, and responsible personnel. The system simultaneously extracts the total number of all abnormal physiological parameter segments in the digital nursing tracking file, as well as the corresponding number of nursing response records that are completed and recorded within the response time limit; Based on the classification and statistical results of the nursing deviation items, the total number of abnormal physiological parameter segments, and the number of effective nursing response records, combined with the preset pass rate calculation model, an individual nursing quality pass rate report for the patient is generated. The individual care quality pass rate report includes not only the overall pass rate, but also the distribution statistics of various nursing deviation items, as well as analysis charts of the response time to abnormal physiological events.

[0012] As a further aspect of the present invention, the step of generating an individualized nursing quality pass rate report for patients by combining a pre-set pass rate calculation model includes: The qualified rate calculation model is set so that the quality of patient care is composed of three dimensions: completion of planned care, response effectiveness to abnormal events, and standardization of records. In the dimension of planned nursing care completion, the model calculates a weighted completion score based on the total number of key quality checkpoints and the ratio of the number of nursing care items actually completed to the total number of nursing care items that must be completed at each checkpoint. In terms of abnormal event response performance, the model calculates the response timeliness score based on the proportion of the number of abnormal event records that complete the response within the response time limit to the total number of abnormal event records. In terms of record standardization, the model calculates the record standardization rate score based on the proportion of all nursing record entries that are complete, logically consistent, and chronologically ordered. The weighted completion score, response time rate score, and record standardization rate score are combined according to preset weighting coefficients to obtain the overall pass rate value in the individual nursing quality pass rate report, which represents the overall nursing quality of the patient.

[0013] As a further aspect of the present invention, it also includes: The risk optimization module allows the system to periodically aggregate and analyze the individual nursing quality pass rate reports of all patients who have completed their nursing cycles. Identify the types of nursing deviations that recur among different patients from the aggregated data, as well as the abnormal segments of physiological parameters that are characterized by a generalized response delay. The identified high-frequency nursing deviation items and abnormal physiological parameter segments are correlated with the corresponding standard nursing procedure template, the neurosurgical postoperative complication early warning rule set, and the standardized treatment suggestion list. Based on the results of the correlation analysis, it was determined whether there were problems such as vague definitions, missing procedures, or inappropriate threshold settings in the existing nursing standard documents; For nursing standard documents that are identified as having problems, specific draft revision proposals are generated. These draft revision proposals include the content of the proposed amendments, a summary of the data analysis on which the amendments are based, and an assessment of the expected impact.

[0014] As a further aspect of the present invention, the determination of whether existing nursing standard documents have problems such as ambiguous definitions, missing procedures, or inappropriate threshold settings based on the results of correlation analysis includes: When a certain type of high-frequency nursing deviation items are concentrated in a certain key quality check node, and the list of nursing items that must be completed at the key quality check node is described as free text in the standard nursing procedure template, lacking clear operation steps or judgment criteria, it is determined that the process is missing or the definition is vague. When a physiological parameter abnormality with a general response delay is set to a uniform value across the hospital in the neurosurgical postoperative complication warning rule set without considering the differences between different surgical subcategories or patient age groups, it is determined that the threshold setting is inappropriate. When a certain type of abnormal physiological parameter occurs, the treatment measures performed by nurses are highly inconsistent in the records, and the recommendations given in the standardized treatment suggestion list are too general and lack operational guidance in specific scenarios, it is determined that the treatment suggestion list is not sufficiently instructive. The judgment results, along with a description of the specific issues, will be integrated into the draft revision proposal.

[0015] As a further aspect of the present invention, it also includes: The closed-loop management module establishes a personal competency development file for each nurse and continuously records relevant data from the individual nursing quality pass rate reports of all patients under their care. From the individual competency development file, extract the type distribution, average response time, and record standardization score of the nursing deviation items generated by the nursing staff. The extracted nursing deviation items, their type distribution, average response time, and record standardization scores are compared with the department's average level and excellent standards to identify the nursing staff's weaknesses in knowledge, skills, or process compliance. Based on the identified weaknesses, targeted training courses and learning materials are automatically matched and recommended from the online training course library; The system tracks the completion of recommended training by the nursing staff, compares and analyzes the data generated by their subsequent nursing work with the data before the training, and updates the competency assessment results in their personal competency development files.

[0016] As a further aspect of the present invention, the automatic matching and recommendation of targeted training courses and learning materials from the online training course library specifically includes: Each course and resource in the online training course library is pre-labeled with the type of nursing problem it addresses, the type of skill gap, or the knowledge blind spot it is targeting. The system will identify the weaknesses of nursing staff and convert them into one or more labels for the types of nursing problems, skill gaps, or knowledge gaps. In the online training course library, the matching degree between the preset tags of the retrieved courses and materials and the converted tags is determined; The search results are sorted according to their matching degree, and the top few courses and materials with the highest matching degree are selected to generate a personalized recommended training list. The recommended training list is pushed to the nurse's personal learning terminal, and a reminder is set for the completion deadline. At the same time, the recommended training list is recorded in the improvement plan in the nurse's personal competency development file.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0018] A unique digital nursing tracking file is created the instant a patient completes neurosurgical procedures and is transferred to the postoperative care unit. Basic pathological information, key surgical operation records, and preliminary nursing goals set by the surgeon are simultaneously entered into the nursing terminal device. Based on these preliminary nursing goals, a standard nursing procedure template matching the patient's surgical type and condition classification is retrieved from the standard nursing knowledge base and directly linked to the digital nursing tracking file. This allows initial postoperative nursing information to form a unified digital collection carrier, enabling the synchronous integration and entry of basic pathology, surgical procedures, and nursing goals. Standard nursing procedures establish a targeted correspondence with the individual patient's condition and surgical status. Nursing procedures and patient files are transformed from separate management to integrated binding. The collection of initial nursing information and procedure matching are completed synchronously at the same point. Various basic nursing data can be directionally collected into a dedicated file carrier, eliminating the information fragmentation caused by asynchronous information entry and procedure matching, and achieving integrated integration of information and procedures in the initial nursing stage.

[0019] The system continuously collects real-time physiological data streams from patients via a bedside vital signs sensor array. This data stream is then input into a physiological state analysis engine, which continuously analyzes the data stream based on a pre-set set of early warning rules for post-neurosurgical complications. Abnormal physiological parameters deviating from the normal range are identified, and an independent abnormal event record is generated for each abnormal physiological parameter segment. These abnormal event records are linked in real-time to the patient's digital nursing tracking file. Simultaneously, alert messages containing key features of the abnormal physiological parameter segments are pushed to the responsible nurse's mobile nursing terminal. The physiological data collection is continuous, and abnormality identification uses continuous segments as the analysis unit rather than discrete numerical values. Abnormal states can form independent and traceable event records, and abnormal information is directly archived into the patient's dedicated file. Alert messages carry the core features of the abnormal segments. The identification, recording, archiving, and push notifications of abnormal states form a coherent execution process, automating the connection between physiological data analysis and abnormality management. Attached Figure Description

[0020] Figure 1 This is a sequence diagram of the whole-process control system for the quality qualification rate of postoperative neurosurgical care as described in this invention. Figure 2 A flowchart illustrating the operation of the exception response recording module; Figure 3 A chart analyzing the completion rate of key quality checkpoints; Figure 4 A statistical chart of problem types in nursing standard documents; Figure 5 A comparative analysis chart of nursing deviation types for Nurse A. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] See Figure 1 The core of this invention, a comprehensive management system for the quality of postoperative neurosurgical care, lies in the structured tracking, real-time monitoring, and closed-loop management of the entire postoperative care process through digital means. The system is activated when a patient completes neurosurgical procedures and is transferred to the postoperative care unit. The file creation module creates a unique digital nursing tracking file for the patient. Nursing staff input the patient's basic pathological information, key surgical operation records, and preliminary nursing goals set by the surgeon into this file using nursing terminal devices. Based on the preliminary nursing goals, the procedure matching module retrieves a standard nursing procedure template matching the current patient's surgical type and condition grade from a standard nursing knowledge base integrating various neurosurgical postoperative care guidelines. This template is then linked to the patient's digital nursing tracking file to form a personalized nursing plan baseline. During the nursing execution phase, the status monitoring module continuously collects the patient's real-time physiological data stream through a vital sign sensor array deployed at the patient's bedside. The event processing module serves as the core of the system's intelligent perception. It inputs the acquired real-time physiological data stream into the physiological state analysis engine. This engine has a pre-set set of early warning rules for postoperative complications in neurosurgery. It can continuously and automatically analyze the data stream and identify any abnormal segments of physiological parameters that deviate from the normal range. Each identified abnormal segment generates an independent abnormal event record, which is linked to the corresponding digital nursing tracking file in real time. At the same time, the system immediately pushes an alert message containing key abnormal features to the mobile nursing terminal held by the responsible nurse, thereby triggering the subsequent manual intervention process.

[0024] In one embodiment of the present invention, the real-time physiological data stream collected by the status monitoring module specifically includes intracranial pressure waveform signals, electrocardiogram signals, blood oxygen saturation values, and core body temperature readings. After receiving the intracranial pressure waveform signals from the vital signs sensor array, the physiological state analysis engine performs deep feature extraction in the time and frequency domains of the signals, calculating key parameters such as waveform mean, amplitude variation index, and high-frequency oscillation energy. These calculated parameters are then compared and analyzed with an individualized intracranial pressure safety threshold range set for the patient's specific condition. If the waveform mean continuously exceeds the upper limit of the individualized intracranial pressure safety threshold range, the physiological state analysis engine determines that the time period is a high-risk segment for intracranial hypertension; if the amplitude variation index shows a sharp increase in a short period of time, it is determined to be a high-risk segment for decreased brain compliance. The aforementioned high-risk segments for intracranial hypertension and low-risk segments for decreased brain compliance are all marked as abnormal physiological parameter segments, and the system records the start time, duration, and maximum deviation value for each segment. The physiological state analysis engine is built upon historical data and machine learning techniques. Its construction steps include: retrieving a large number of complete monitoring records from a historical nursing database of neurosurgical patients after surgery. These records contain a continuous real-time physiological data stream and clinically confirmed complication diagnoses. This continuous real-time physiological data stream is then fragmented into multiple data segments of preset duration, with each segment labeled with whether complications occurred within its corresponding time period and the specific type of complication. Multiple physiological features are extracted from each data segment, including statistical features, trend features, and time-frequency domain transformation features of the waveform signal. Using these complication-labeled data segments and their extracted multi-dimensional physiological features, a machine learning classification model is trained, enabling the model to learn the physiological feature patterns corresponding to different complication types. The trained machine learning classification model is combined with a set of preset, fixed rules based on clinical guidelines to form the physiological state analysis engine. In this engine, the machine learning classification model is responsible for initial anomaly pattern recognition of the input real-time physiological data stream, while the preset fixed rules are used for logical verification and urgency classification of the machine learning classification model's recognition results.

[0025] In practical implementation, the status monitoring module of the neurosurgical postoperative nursing quality pass rate full-process control system collects real-time physiological data streams through integrated sensing devices deployed at the patient's bedside. These real-time physiological data streams include continuous intracranial pressure waveform signals, electrocardiogram (ECG) signals, blood oxygen saturation values, and core body temperature readings. For example, for a patient who has undergone brain tumor resection, the vital signs sensor array continuously captures postoperative intracranial pressure changes, ECG activity, blood oxygen levels, and body temperature data, forming a continuous time-series data stream that is transmitted to the event processing module. The physiological state analysis engine receives the intracranial pressure waveform signals from the vital signs sensor array and performs time-domain and frequency-domain feature extraction operations on the intracranial pressure waveform signals. In an exemplary calculation, the waveform mean is obtained by arithmetically averaging all sampling points within a time window, the amplitude variation index is quantified by calculating the standard deviation of the difference between consecutive peaks and troughs, and the high-frequency oscillation energy is obtained by spectral integration of the signal over a preset high-frequency band. The calculated mean waveform, amplitude variability index, and high-frequency oscillation energy values ​​are compared in real-time and continuously with the individualized intracranial pressure safety threshold range set by the doctor for the patient's condition. In some embodiments, the system sets the upper limit of the individualized intracranial pressure safety threshold range to 20 mmHg. When the physiological state analysis engine finds that the patient's mean intracranial pressure waveform remains at 22 mmHg for more than 5 minutes, this period is identified as a high-risk segment for intracranial hypertension. If the system detects that the patient's amplitude variability index rapidly increases from the usual 1.5 mmHg to 4.0 mmHg within 10 minutes, the physiological state analysis engine identifies this short-term, rapidly increasing period as a high-risk segment for decreased brain compliance. The physiological state analysis engine marks both the identified high-risk and low-risk segments for intracranial hypertension as abnormal physiological parameter segments and records the start time, duration, and maximum deviation value detected during each segment in its generated independent abnormal event record. The construction steps of the physiological state analysis engine include retrieving complete monitoring records from multiple completed neurosurgical patients from a historical nursing database. These records contain a continuous real-time physiological data stream and clinically confirmed complication diagnoses. The construction process involves segmenting the continuous real-time physiological data stream into multiple independent data segments, each segment labeled with a preset duration indicating whether complications occurred within that time period and the specific type of complication. Multiple physiological features are extracted from each data segment, including statistical features, trend features, and time-frequency domain transformation features of the waveform signal. Using a large number of data segments labeled with complication types and the extracted multiple physiological features, a supervised training method is used to train a machine learning classification model, enabling the model to learn the physiological feature patterns corresponding to different complication types.The trained machine learning classification model is combined with pre-defined, clinically-guided fixed rules to form the final physiological state analysis engine. In the actual operation of the physiological state analysis engine, the machine learning classification model is used to perform preliminary abnormal pattern recognition and classification on the input real-time physiological data stream, while the pre-defined fixed rules are used to logically verify and classify the recognition results of the machine learning classification model. For example, the model may identify a pattern resembling early edema, but the fixed rules will combine the patient's temperature and blood pressure data at that time to determine whether an immediate alarm should be triggered or only observation and recording are necessary. In the feature extraction calculation, the waveform mean (…). It can be calculated using the following formula: ; in: This represents the mean value of intracranial pressure waveform within a data segment. This indicates the total number of sampling points within the data segment. Indicates the first The intracranial pressure measurement values ​​at each sampling point are calculated within each data segment of a preset duration.

[0026] In one embodiment of the present invention, the system responds to abnormal events and performs process quality checks. See also... Figure 2When the mobile nursing terminal receives an alert message from the event handling module, the responsible nurse must arrive at the patient's bedside within a preset response time limit to conduct an on-site assessment. During the assessment, the responsible nurse uses the mobile nursing terminal to retrieve a standardized treatment suggestion list corresponding to the abnormal physiological parameter segment from the standard nursing knowledge base. Referring to this list, the responsible nurse performs the corresponding nursing procedures. After the procedures are completed, the responsible nurse uses the mobile nursing terminal's interactive interface to select the performed procedures and enter the specific time of the procedure, the patient's immediate reaction observation results, and the name and dosage information of any temporarily administered medications. The mobile nursing terminal packages these selected performed procedures, procedure times, patient immediate reaction observations, and medication information into a structured nursing response record and automatically uploads it, integrating this nursing response record into the corresponding abnormal event record in the patient's digital nursing tracking file. In terms of nursing process control, the system defines multiple key quality check nodes in the entire postoperative nursing process through a quality check control module. Each key quality check node is associated with a list of nursing items that must be completed. When the system time reaches a preset node or a change in patient status triggers a critical quality check node, the system automatically pushes the check task for that node to the management terminal held by the head nurse. The head nurse uses the management terminal to access all of the patient's nursing records up to the present, including abnormal event records in the digital nursing tracking file and their associated nursing response records. The head nurse compares the actually completed nursing records with the list of mandatory nursing items associated with that critical quality check node item by item. For nursing items required on the list but missing from the actual record, or items whose actual execution time deviates significantly from the specified time, the head nurse marks them as nursing deviation items in the system through the management terminal. These deviation items are then associated with and saved along with the critical quality check node and the corresponding responsible nurse's information.

[0027] In practice, after the event handling module identifies abnormal physiological parameters and generates an abnormal event record, the system pushes an alert message to the mobile nursing terminal held by the responsible nurse. The alert message received by the mobile nursing terminal contains key characteristics of the abnormal physiological parameters, such as "Patient ID-1037, intracranial hypertension risk segment detected at 14:25, duration 8 minutes, peak pressure 22.3 mmHg". The responsible nurse must arrive at the patient's bedside to conduct an on-site assessment within a preset response time limit. In practice, the response time limit can be set to 15 minutes, starting from the time the alert message is pushed to the mobile nursing terminal. After arriving at the patient's bedside, the responsible nurse logs into the system through the mobile nursing terminal and retrieves a standardized treatment suggestion list corresponding to the "intracranial hypertension risk" type from the standard nursing knowledge base. The standardized treatment suggestion list may include items such as "assess the patient's level of consciousness", "check the patency of the drainage tube", "adjust the head of the bed to a 30-degree elevation", and "notify the on-duty doctor". The responsible nurse performs nursing procedures one by one according to the standardized treatment suggestion list. After the procedure is completed, the nurse uses the interactive interface of the mobile nursing terminal to check the performed procedures and fills in the operation time, the patient's immediate response observation, and the name and dosage of any temporarily administered medications in the interface form. The mobile nursing terminal packages the checked performed procedures, operation time, patient's immediate response observation, and medication information into a structured nursing response record. This nursing response record is then automatically uploaded to the system server and integrated and linked to the corresponding abnormal event record marked "intracranial hypertension risk" in the patient's digital nursing tracking file, forming a closed loop from monitoring and early warning to manual response and recording.

[0028] In some embodiments, the quality inspection and control module predefines multiple key quality inspection nodes throughout the postoperative care process, such as "6 hours postoperatively," "24 hours postoperatively," and "2 hours before drainage tube removal." Each key quality inspection node is associated with a list of mandatory nursing items. For example, the mandatory nursing item list for the "6 hours postoperatively" node might include items such as "complete vital signs record," "pupil observation record," "wound dressing inspection," and "analgesia pump effectiveness assessment." When the system time reaches a preset node or the patient's condition meets specific conditions to trigger a key quality inspection node, the system automatically pushes the node inspection task to the management terminal held by the head nurse. The task notification includes patient information and the name of the node to be inspected. The head nurse can access all nursing records of the patient from the time of admission to the postoperative care unit up to the current time through the management terminal. These records are fully presented in the digital nursing tracking file, including all abnormal event records generated by the event handling module and nursing response records uploaded by the mobile nursing terminal and associated with each abnormal event record. The head nurse compares the list of completed nursing records with the list of mandatory nursing items associated with the current critical quality checkpoint on the management terminal interface. In some embodiments, the system interface displays the required items and completed records in a side-by-side view for intuitive comparison. For items required in the mandatory nursing item list but missing from the actual nursing records, or items whose actual execution time deviates significantly from the specified time, the head nurse marks the item as a nursing deviation in the system via the management terminal. The nursing deviation item is associated with and saved with the current critical quality checkpoint "6 hours post-operation" and the responsible nurse's information for that period, forming a traceable quality issue record. It can be understood that the total number of critical quality checkpoints... Together with the completion status of nursing items at each node, they form the basis for assessing the completion rate of planned nursing care, where the item completion rate of an individual node is the key factor. The calculation method is as follows: ; in: Indicates the first Project completion rate at key quality checkpoints Indicates the first The number of nursing care items actually completed and recorded at each node. Indicates the first The total number of items specified in the list of mandatory nursing care items associated with each node. This calculation is understandably part of a quality quantification assessment.

[0029] In one embodiment of the present invention, when a patient is discharged or transferred out of the postoperative nursing unit, the system automatically triggers a multidimensional traceability analysis module to perform a summative analysis of the patient's digital nursing tracking file. This module extracts all marked nursing deviations from the file and classifies them statistically according to deviation type, associated key quality check nodes, and responsible personnel. Simultaneously, the module extracts the total number of abnormal physiological parameter segments in the file, and the corresponding number of complete nursing response records completed and recorded within the response time limit. Based on the classification and statistical results of nursing deviations, the total number of abnormal physiological parameter segments, and the number of valid nursing response records, combined with the system's pre-set pass rate calculation model, an individual nursing quality pass rate report for the patient is generated. This individual nursing quality pass rate report not only includes the overall pass rate value but also the distribution statistics of various nursing deviations and analytical charts of the timeliness of responses to abnormal physiological events. The calculation logic of the pass rate model is based on the assumption that patient nursing quality is composed of three dimensions: planned nursing completion rate, abnormal event response effectiveness, and record standardization. In the dimension of planned nursing care completion, the model calculates a weighted completion score based on the total number of key quality checkpoints and the ratio of the number of nursing items actually completed to the total number of required nursing items at each checkpoint. In the dimension of adverse event response effectiveness, the model calculates a response timeliness score based on the proportion of adverse event records that were responded to within the response timeframe to the total number of adverse event records. In the dimension of record standardization, the model calculates a record standardization score based on the proportion of all nursing record entries that are complete, logically consistent, and chronologically ordered. The model integrates the weighted completion score, response timeliness score, and record standardization score according to preset weighting coefficients to derive the overall pass rate value in the individual nursing quality pass rate report, which characterizes the overall quality of patient care.

[0030] In practice, the multidimensional traceability analysis module is automatically triggered when a patient is discharged or transferred out of the postoperative care unit. It performs a summative analysis of the patient's digital nursing tracking record, extracting all nursing deviations marked by the quality control module. For example, taking a virtual patient A, during a five-day postoperative care period, their digital nursing tracking record recorded seven marked nursing deviations. The multidimensional traceability analysis module categorizes and statistically analyzes these deviations, as shown in Table 1.

[0031] Table 1: Classification and Statistical Table of Nursing Deviations for Patient A

[0032] Simultaneously, the multidimensional traceability analysis module extracts the total number of abnormal physiological parameter segments in Patient A's digital nursing tracking file, as well as the corresponding number of nursing response records completed and recorded within the preset response time limit. For example, the file records the total number of abnormal physiological parameter segments. The number of responses was 12, of which the number of responses completed and fully recorded within the 15-minute response timeframe was [number missing]. The number of instances was 9. Based on the statistical results of the classification of nursing deviation items, the total number of abnormal physiological parameter segments, and the number of effective nursing response records, combined with the system's pre-set pass rate calculation model, an individual nursing quality pass rate report for patient A was generated. The individual nursing quality pass rate report not only includes an overall pass rate value, but also includes statistical charts showing the distribution of various nursing deviation items, as well as analytical charts showing the response timeliness of abnormal physiological events, such as using a bar chart to display the number of abnormal events occurring on different dates and the average response time.

[0033] In some embodiments, the pre-defined pass rate calculation model sets patient care quality to consist of three dimensions: planned care completion, adverse event response effectiveness, and record standardization. In the planned care completion dimension, the model calculates based on the total number of key quality checkpoints. The weighted completion score is calculated by taking the ratio of the number of nursing care items actually completed to the total number of nursing care items that must be completed at each node. During the calculation, each key quality check node can be assigned a different weight. To reflect its importance, a weighted completion score is assigned. The calculation formula is: ; in: This represents the weighted completion score. It is the total number of key quality checkpoints. Is assigned to the first The weights of key quality checkpoints (satisfying) ), It is the first The model measures the completion rate of nursing care projects at each node. In terms of incident response effectiveness, the model calculates the on-time response rate score based on the proportion of incident records that were responded to within the response timeframe to the total number of incident records. ,For example In terms of record standardization, the model calculates the record standardization rate score based on the proportion of all nursing record entries that are complete, logically consistent, and chronologically ordered. It's understandable that the model will weight the completion score. Response timeliness score and record standardization score Linear synthesis is performed according to preset weighting coefficients to obtain the overall pass rate value in the individual nursing quality pass rate report, which characterizes the overall nursing quality of patients. ,Right now: ; in: Optionally, the individual care quality pass rate report can also include a weighted completion score. Response timeliness score and record standardization score The scores for each item are listed together to allow managers to conduct a more detailed quality analysis.

[0034] See Figure 3 This is a chart analyzing the completion rate of key quality checkpoints in neurosurgical postoperative care. It shows the completion rate of nursing items at four key quality checkpoints, serving as a core visual indicator of planned nursing completion. The completion rate shows a trend of first rising and then falling, peaking at the 24-hour postoperative wound assessment (90%) and bottoming out at the daily morning basic care checkpoint (65%). The 24-hour postoperative wound assessment (90%) showed the highest level of standardized implementation, indicating a high level of attention paid to early postoperative wound management by medical staff. The pre-removal check for drainage tubes (85%) had a good completion rate, reflecting the effective implementation of safety checks before invasive procedures. The daily morning basic care (65%) had the lowest completion rate, representing a major weakness in current nursing quality, possibly related to busy morning work, cumbersome procedures, or insufficient staffing. The 6-hour postoperative vital sign check (75%) had a low completion rate, suggesting room for improvement in adherence to early postoperative vital sign monitoring.

[0035] In one embodiment of the present invention, the system periodically performs aggregated analysis on the individual nursing quality pass rate reports of all patients who have completed their nursing cycles through a risk optimization module. The system identifies frequently occurring nursing deviation types and commonly present abnormal physiological parameters with delayed responses from the aggregated data. The identified frequently occurring nursing deviation types and abnormal physiological parameter fragments are then correlated with the corresponding standard nursing procedure templates, neurosurgical postoperative complication early warning rule sets, and standardized treatment suggestion lists within the system. Based on the results of the correlation analysis, the system determines whether existing nursing standard documents contain issues such as ambiguous definitions, missing procedures, or inappropriate threshold settings. For nursing standard documents identified as having problems, the system generates specific draft revision suggestions, which include the content of the proposed modifications, a summary of the data analysis on which the modifications are based, and an expected impact assessment. When determining problems, if a certain type of frequently occurring nursing deviation appears concentrated at a key quality checkpoint, and the list of required nursing items for that key quality checkpoint is described as free text in the standard nursing procedure template, lacking clear operational steps or judgment criteria, then it is determined that the process is missing or the definition is ambiguous. If a physiological parameter abnormality with a prevalent response delay is set to a uniform warning threshold across the entire hospital within the neurosurgical postoperative complication warning rules, without considering differences in different surgical subcategories or patient age groups, then the threshold setting is deemed inappropriate. If, after the occurrence of a certain type of physiological parameter abnormality, the nurses' interventions show significant inconsistency in the records, and the recommendations in the standardized intervention suggestion list are too general and lack specific scenario-based operational guidance, then the intervention suggestion list is deemed insufficiently instructive. The system integrates these judgments, along with specific problem descriptions, into the generated draft revision suggestions.

[0036] In practice, the risk optimization module runs automatically at fixed time intervals, aggregating and analyzing the individual nursing quality compliance reports of all patients who have completed their nursing cycles within that period. The aggregation analysis extracts and summarizes two core data categories from these reports: first, all tagged nursing deviations and their types; and second, all recorded abnormal physiological parameters and their response delays. For example, the system analyzes 300 discharged patient reports from a quarter, identifying frequently occurring nursing deviation types that recur among different patients. For instance, "missing vital signs records 6 hours post-surgery" appears in over 15% of patient reports, or "inadequate drainage tube care" occurs in over 20% of patients involving drainage tubes. Simultaneously, the aggregation analysis identifies prevalent abnormal physiological parameter segments with delayed responses, such as "nocturnal hypoxemia events" with an average response time of 22 minutes, significantly higher than the daily average response time of 15 minutes, or "risk segments for decreased brain compliance" with a nurse response delay rate as high as 30%.

[0037] The identified high-frequency nursing deviations and abnormal physiological parameters were correlated with the corresponding standard nursing procedure templates, neurosurgical postoperative complication early warning rule sets, and standardized treatment suggestion lists in the system. Correlation analysis was performed through data mapping and pattern matching. For example, the frequently occurring deviation of "non-standard drainage tube care" was correlated with the section on "drainage tube care" in the standard nursing procedure template, and the delayed response of "nocturnal hypoxemia events" was correlated with the clause on "oxygen saturation early warning threshold" in the neurosurgical postoperative complication early warning rule set. See Table 2 for a simplified correlation analysis.

[0038] Table 2: Correlation Analysis of High-Frequency Nursing Deviations and Standard Documents

[0039] Based on the results of correlation analysis, the system executes logical judgments to identify whether there are problems such as ambiguous definitions, missing procedures, or inappropriate threshold settings in existing nursing standard documents. When a certain type of high-frequency nursing deviation items are concentrated in a key quality check node, and the list of nursing items that must be completed at the key quality check node is described as free text in the standard nursing procedure template, lacking clear operational steps or judgment criteria, the system judges it as a missing procedure or ambiguous definition. For example, the standard nursing procedure template only states "assess the patient's state of consciousness" without specifying the assessment tools and recording format, leading to a high incidence of missing record deviations. When abnormal physiological parameters with common response delays are set with corresponding warning thresholds that are uniformly set across the hospital in the neurosurgical postoperative complication warning rule set, without considering the differences between different surgical subtypes or patient age groups, the system judges it as an inappropriate threshold setting. For example, the neurosurgical postoperative complication warning rule set uniformly sets the "low blood oxygen saturation" threshold to 90%, but elderly patients or patients undergoing skull base surgery may be more sensitive to hypoxia, and a uniform threshold may lead to nocturnal hypoxia events not being detected early or given insufficient attention. When nurses' interventions after a certain type of abnormal physiological parameter occurs show significant inconsistency in the records, and the standardized intervention suggestion list is too general and lacks specific operational guidance for particular scenarios, it is judged that the intervention suggestion list is insufficiently instructive. For example, for "transient increase in intracranial pressure," the standardized intervention suggestion list only suggests "assess and notify the doctor," without distinguishing between the different procedures for mild increases and rapid spikes, leading to high randomness in nurses' actions. It is understandable that the system calculates an aggregated risk score for frequently identified issues. Its calculation method can be expressed as: ; in: Indicates the first The overall risk value of the problem to be identified. This indicates the total number of times the problem occurs within one analysis period. This indicates the proportion of patients involved in this issue out of the total number of patients analyzed. This indicates a pre-defined weighting coefficient based on the question type. As you can understand, a higher weighting coefficient corresponds to a lower weighting. Values ​​will trigger higher-priority revision recommendations. For nursing standard documents identified as problematic, the system generates specific draft revision recommendations, which include the proposed changes to the clauses, a summary of the data analysis on which the changes are based, and an expected impact assessment. For example, regarding the issue of "delayed response to nocturnal hypoxemia events" and the lack of a unified threshold, a draft revision recommendation might suggest "adding a stratified hypoxemia warning threshold for patients older than 65 years or undergoing skull base surgery to the early warning rule set for postoperative neurosurgical complications." The data analysis summary would include data on the incidence and delay rate of nocturnal hypoxemia events in this patient group, and the expected impact assessment would be "potentially reducing the average response time to hypoxemia events in this specific population to less than 15 minutes." In some embodiments, draft revision recommendations are generated in a structured electronic document format and automatically pushed to the nursing standards committee's management terminal for review. Optionally, the system records each generated draft revision recommendation and its corresponding data analysis period, forming a traceability chain for continuous improvement of standards.

[0040] See Figure 4 This is a statistical chart of problem types in nursing standard documents, showing the distribution of the number of four types of problems identified in the neurosurgical postoperative nursing quality control system after aggregation analysis. It is the core output chart of the risk optimization module. Missing processes (15 cases) are the most serious problem in the current nursing standard documents, accounting for the highest proportion, indicating significant gaps in the operational procedures of some key quality check nodes. Vague definitions (10 cases) are the second most serious, reflecting unclear judgment criteria and operational boundaries for some nursing items in the standard documents, leading to execution deviations. Inappropriate threshold settings (8 cases) are mainly reflected in the failure to consider individual patient differences in physiological parameter warning thresholds, affecting the accuracy of abnormal event identification. Insufficient guidance (5 cases) is manifested in overly generalized standardized treatment suggestions, lacking operational guidance for specific scenarios.

[0041] In one embodiment of the invention, the system establishes a personal competency development profile for each nurse through a closed-loop management module. This profile continuously records relevant data from the individual nursing quality pass rate reports of all patients under the nurse's care. From the personal competency development profile, the system extracts the type distribution, average response time, and record standardization score of the nurse's nursing deviations. These data are then compared with the departmental average and excellent standards to identify the nurse's weaknesses in knowledge, skills, or process compliance. Based on these identified weaknesses, the system automatically matches and recommends targeted training courses and learning materials from an online training course library. Each course and resource in the online training course library is pre-labeled with the type of nursing problem it addresses, the type of skill deficiency, or the knowledge gap it addresses. The system converts the identified nurse weaknesses into one or more nursing problem types, skill deficiency types, or knowledge gap tags. Subsequently, the system retrieves the matching degree between the preset tags and the converted tags in the online training course library. The search results are sorted according to their matching degree, and the top-matching courses and materials are selected to generate a personalized recommended training list. This recommended training list is then pushed to the nurse's personal learning terminal, with a reminder set for completion. The list is also recorded in the nurse's personal skills development portfolio's improvement plan. The system tracks the nurse's completion of the recommended training and compares the data generated in subsequent nursing work with the data before the training, thereby updating the skills assessment results in the nurse's personal skills development portfolio.

[0042] In its implementation, the closed-loop management module automatically creates a personal competency development profile for each nurse in the neurosurgical postoperative care unit during system initialization. This profile serves as an electronic record of the nurse's competency growth within the system, continuously recording relevant data from the individual nursing quality pass rate reports generated for all patients under their care. For example, Nurse A was responsible for the care of 15 postoperative patients in the past quarter. The personal competency development profile extracts and integrates all quality data related to Nurse A from these 15 individual nursing quality pass rate reports. From the profile, the system extracts the type distribution of all nursing deviations generated by Nurse A during the recording period. For instance, statistics show that Nurse A's nursing deviations mainly fall into two categories: "missing records" and "non-standard operations." The system also extracts Nurse A's average response time to abnormal physiological parameters, for example, an average response time of 18 minutes. Furthermore, the system extracts the record-keeping standardization score of Nurse A's written nursing records, for example, an average record-keeping standardization score of 85 points. The extracted distribution of nursing deviation types, average response times, and record-keeping standardization scores are then compared with the departmental average and pre-set excellent standards. The department's average performance data is derived from the aggregated calculation of the individual competency development files of all nursing staff within the same time period. The standards for excellence are pre-set by the nursing management department based on historical best practices. Through comparison, the system identifies weaknesses in nursing staff's knowledge, skills, or process compliance. For example, Nurse A's "non-standard operation" deviation rate is 25% higher than the department average, and her response time to "intracranial hypertension risk" events is 5 minutes longer than the excellent standard. These comparative results are marked as Nurse A's weaknesses, specifically described as "unfamiliarity with intracranial hypertension event handling procedures" and "emergency response speed needs improvement." Based on the identified weaknesses, the system automatically matches and recommends targeted training courses and learning materials from the online training course library. Each course and material in the online training course library is pre-labeled with the type of nursing problem it addresses, the type of skill deficiency, or the knowledge gap tag. The system transforms the identified nursing staff weaknesses into one or more nursing problem types, skill deficiency types, or knowledge gap tags. For example, "unfamiliarity with the procedures for handling abnormal intracranial pressure events" is tagged as "intracranial pressure management" and "standardized operation," while "emergency response speed needs improvement" is tagged as "emergency response." In the online training course library, the system searches the preset tags of all courses and materials for their match with the generated tags. The match rate can be calculated based on the number and weight of overlapping tags. The search results are sorted according to the match rate, and the top-matching courses and materials are selected to generate a personalized recommended training list. This recommended training list is pushed to the nurses' personal learning terminals, with a deadline reminder set, and the list is also recorded in their personal skills development file's improvement plan.In some embodiments, the system tracks the completion of recommended training by nursing staff, including the progress and duration of course learning, and final assessment results. After a preset period of time has elapsed since the nursing staff completed the recommended training and returned to nursing work, the system compares and analyzes the data generated from their subsequent nursing work with historical data from before the training. The comparative analysis can focus on previously identified weaknesses, such as recalculating the incidence of "non-standard operation" deviations after training, the average response time to similar abnormal events, and the record-keeping compliance score. It can be understood that the system can calculate a quantitative competency development assessment value based on the comparison of data before and after training. To update the competency assessment results in your personal competency development profile, the competency development assessment value... The calculation formula is: ; in: Indicates that for the first The assessment value for the capacity development of each weak link is positive, indicating improvement, and negative, indicating regression. This represents the overall score for the relevant dimensions of the weak area after training; This represents the historical composite score on the same dimension prior to training. This represents the training completion coefficient, determined by both the course completion rate and assessment results. In some embodiments, the system will use a competency development assessment value. Automatically update the competency matrix or skills radar chart in your individual competency development profile. Optionally, if the competency development assessment value... If the improvement is not significant, the system may trigger a new round of vulnerability analysis and training recommendations, forming a closed loop of continuous improvement.

[0043] See Figure 5 This is a comparative analysis chart of nursing deviation types for Nurse A. It shows the frequency of different types of nursing deviations for Nurse A and provides a direct comparison with the department average and the excellent standard. It is the core visualization chart for individual ability analysis in the closed-loop management module. Nurse A had 5 instances of missing records, far exceeding the department average (3 times) and the excellent standard (1 time), indicating the most prominent problem and a significant weakness in the completeness of nursing records. Non-standard operation occurred 4 times, also significantly higher than the department average and the excellent standard, reflecting insufficient adherence to operating procedures. Improper positioning occurred 0 times, better than the department average. Medication management error occurred 1 time, slightly lower than the department average, a acceptable performance. Delayed assessment occurred 2 times, lower than the department average (3 times), but still higher than the excellent standard (1 time), indicating room for improvement.

[0044] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A comprehensive management system for the quality of postoperative neurosurgical care, characterized in that: The system includes: The file creation module creates a unique digital nursing tracking file for the patient when the patient completes neurosurgery and is transferred to the postoperative care unit. It also records the patient's basic pathological information, key surgical operation records, and preliminary nursing goals set by the attending physician through the nursing terminal device. The procedure matching module, based on the preliminary nursing goals, retrieves standard nursing procedure templates from the standard nursing knowledge base that match the patient's surgical type and disease severity, and associates the standard nursing procedure templates with the patient's digital nursing tracking file; The status monitoring module continuously collects real-time physiological data streams from patients through a vital sign sensor array placed next to the patient's bed; The event processing module inputs the real-time physiological data stream into the physiological state analysis engine. The physiological state analysis engine continuously analyzes the real-time physiological data stream according to a preset set of early warning rules for postoperative neurosurgical complications, identifies abnormal segments of physiological parameters that deviate from the normal range, generates an independent abnormal event record for each identified abnormal physiological parameter segment, and links the abnormal event record to the patient's digital nursing tracking file in real time. At the same time, it pushes an alert message containing the key features of the abnormal physiological parameter segment to the mobile nursing terminal held by the responsible nurse.

2. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 1, characterized in that, The physiological state analysis engine continuously analyzes the real-time physiological data stream based on a pre-set set of early warning rules for postoperative neurosurgical complications, identifying abnormal segments of physiological parameters that deviate from the normal range, including: The real-time physiological data stream includes intracranial pressure waveform signals, electrocardiogram signals, blood oxygen saturation values, and core body temperature readings. The physiological state analysis engine receives the intracranial pressure waveform signal from the vital signs sensor array, performs time-domain and frequency-domain feature extraction on the intracranial pressure waveform signal, and calculates the waveform mean, amplitude variation index and high-frequency oscillation energy. The calculated mean value of the waveform, amplitude variation index, and high-frequency oscillation energy are compared with the individualized intracranial pressure safety threshold range set for the patient's condition. If the mean value of the waveform continuously exceeds the upper limit of the individualized intracranial pressure safety threshold range, it is determined to be a high-risk segment of intracranial hypertension. If the amplitude variation index increases sharply in a short period of time, it is identified as a segment at risk of decreased brain compliance. The identified intracranial hypertension risk segments and the brain compliance decline risk segments are all marked as abnormal physiological parameter segments, and the start time, duration and maximum deviation value of each segment are recorded. The construction steps of the physiological state analysis engine include: Complete monitoring records of multiple completed neurosurgical patients were obtained from the historical nursing database. The complete monitoring records included the real-time physiological data stream in a continuous sequence and the complication diagnosis labels that were eventually clinically confirmed. The time-series continuous real-time physiological data stream is segmented into multiple data segments with a preset duration, and each data segment is labeled with whether complications occurred within its corresponding time period and the type of complications. Physiological features of multiple dimensions are extracted from each of the data segments, including statistical features, trend features, and time-frequency domain transformation features of the waveform signal; Using data fragments labeled with complication types and their extracted physiological features from multiple dimensions, a machine learning classification model is trained so that the machine learning classification model learns the physiological feature patterns corresponding to different complication types. The trained machine learning classification model is combined with pre-set fixed rules based on clinical guidelines to form the physiological state analysis engine. In the physiological state analysis engine, the machine learning classification model is used to perform preliminary abnormal pattern identification on the input real-time physiological data stream, and the preset fixed rules are used to perform logical verification and urgency classification on the identification results of the machine learning classification model.

3. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 2, characterized in that, Also includes: The abnormal response recording module requires that when the mobile nursing terminal receives the warning message, the responsible nurse must arrive at the patient's bedside and conduct an on-site assessment within a preset response time limit. The responsible nurse retrieves a list of standardized treatment recommendations corresponding to the abnormal physiological parameter fragments from the standard nursing knowledge base using the mobile nursing terminal; The responsible nurse performs nursing procedures according to the standardized treatment suggestion list. After the procedure is completed, the nurse uses the interface of the mobile nursing terminal to check the performed procedures and enter the operation time, the patient's immediate response observation, and the name and dosage of any temporarily administered medications. The mobile nursing terminal packages the selected performed treatments, operation times, patient immediate response observations, and medication information into a nursing response record, uploads and integrates it into the corresponding abnormal event record in the patient's digital nursing tracking file.

4. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 3, characterized in that, Also includes: The quality inspection and control module predefines multiple key quality inspection nodes in the entire postoperative care process, and each key quality inspection node is associated with a list of nursing items that must be completed. When the system time arrives or the patient's status triggers a key quality check node, the system automatically pushes the node check task to the management terminal held by the head nurse. The head nurse can access all of the patient's nursing records up to the present through the management terminal, including the abnormal event records in the digital nursing tracking file and the associated nursing response records; The head nurse compares the actual completed nursing records with the list of mandatory nursing items associated with the key quality checkpoints item by item; For nursing items that are required in the list but are missing in actual records, or items whose actual execution time deviates significantly from the prescribed time, the head nurse marks them as nursing deviation items in the system through the management terminal. The nursing deviation items are associated with and saved with specific key quality check nodes and the responsible nurse's information.

5. The whole-process control system for postoperative neurosurgical nursing quality qualification rate according to claim 4, characterized in that, Also includes: The multidimensional traceability analysis module automatically triggers a final analysis of the patient's digital nursing tracking file when the patient is discharged or transferred out of the postoperative care unit. The system extracts all marked nursing deviation items from the digital nursing tracking file and classifies and statistically analyzes them according to deviation type, associated key quality check nodes, and responsible personnel. The system simultaneously extracts the total number of all abnormal physiological parameter segments in the digital nursing tracking file, as well as the corresponding number of nursing response records that are completed and recorded within the response time limit; Based on the classification and statistical results of the nursing deviation items, the total number of abnormal physiological parameter segments, and the number of effective nursing response records, combined with the preset pass rate calculation model, an individual nursing quality pass rate report for the patient is generated. The individual care quality pass rate report includes not only the overall pass rate, but also the distribution statistics of various nursing deviation items, as well as analysis charts of the response time to abnormal physiological events.

6. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 5, characterized in that, The method, which combines a pre-set pass rate calculation model, generates an individualized nursing quality pass rate report for each patient, including: The qualified rate calculation model is set so that the quality of patient care is composed of three dimensions: completion of planned care, response effectiveness to abnormal events, and standardization of records. In the dimension of planned nursing care completion, the model calculates a weighted completion score based on the total number of the key quality checkpoints and the ratio of the number of nursing care items actually completed to the total number of nursing care items that must be completed at each checkpoint. In terms of abnormal event response performance, the model calculates the response timeliness score based on the proportion of the number of abnormal event records that complete the response within the response time limit to the total number of abnormal event records. In terms of record standardization, the model calculates the record standardization rate score based on the proportion of all nursing record entries that are complete, logically consistent, and chronologically ordered. The weighted completion score, response time rate score, and record standardization rate score are combined according to preset weighting coefficients to obtain the overall pass rate value in the individual nursing quality pass rate report, which represents the overall nursing quality of the patient.

7. The whole-process control system for postoperative neurosurgical nursing quality qualification rate according to claim 6, characterized in that, Also includes: The risk optimization module allows the system to periodically aggregate and analyze the individual nursing quality pass rate reports of all patients who have completed their nursing cycles. Identify the types of nursing deviations that recur among different patients from the aggregated data, as well as the abnormal segments of physiological parameters that are characterized by a generalized response delay. The identified high-frequency nursing deviation items and abnormal physiological parameter segments are correlated with the corresponding standard nursing procedure template, the neurosurgical postoperative complication early warning rule set, and the standardized treatment suggestion list. Based on the results of the correlation analysis, it was determined whether there were problems such as vague definitions, missing procedures, or inappropriate threshold settings in the existing nursing standard documents; For nursing standard documents that are identified as having problems, specific draft revision proposals are generated. These draft revision proposals include the content of the proposed amendments, a summary of the data analysis on which the amendments are based, and an assessment of the expected impact.

8. The whole-process control system for postoperative neurosurgical nursing quality qualification rate according to claim 7, characterized in that, Based on the results of the correlation analysis, the determination of whether existing nursing standard documents have problems such as vague definitions, missing procedures, or inappropriate threshold settings includes: When a certain type of high-frequency nursing deviation items are concentrated in a certain key quality check node, and the list of nursing items that must be completed at the key quality check node is described as free text in the standard nursing procedure template, lacking clear operation steps or judgment criteria, it is determined that the process is missing or the definition is vague. When a physiological parameter abnormality with a general response delay is set to a uniform value across the hospital in the neurosurgical postoperative complication warning rule set without considering the differences between different surgical subcategories or patient age groups, it is determined that the threshold setting is inappropriate. When a certain type of abnormal physiological parameter occurs, the treatment measures performed by nurses are highly inconsistent in the records, and the recommendations given in the standardized treatment suggestion list are too general and lack operational guidance in specific scenarios, it is determined that the treatment suggestion list is not sufficiently instructive. The judgment results, along with a description of the specific issues, will be integrated into the draft revision proposal.

9. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 8, characterized in that, Also includes: The closed-loop management module establishes a personal competency development file for each nurse and continuously records relevant data from the individual nursing quality pass rate reports of all patients under their care. From the individual competency development file, extract the type distribution, average response time, and record standardization score of the nursing deviation items generated by the nursing staff. The extracted nursing deviation items, their type distribution, average response time, and record standardization scores are compared with the department's average level and excellent standards to identify the nursing staff's weaknesses in knowledge, skills, or process compliance. Based on the identified weaknesses, targeted training courses and learning materials are automatically matched and recommended from the online training course library; The system tracks the completion of recommended training by the nursing staff, compares and analyzes the data generated by their subsequent nursing work with the data before the training, and updates the competency assessment results in their personal competency development files.

10. The whole-process control system for the quality of postoperative neurosurgical nursing care according to claim 9, characterized in that, The automatic matching and recommendation of targeted training courses and learning materials from the online training course library specifically includes: Each course and resource in the online training course library is pre-labeled with the type of nursing problem it addresses, the type of skill gap, or the knowledge blind spot it is targeting. The system will identify the weaknesses of nursing staff and convert them into one or more labels for the types of nursing problems, skill gaps, or knowledge gaps. In the online training course library, the matching degree between the preset tags of the retrieved courses and materials and the converted tags is determined; The search results are sorted according to their matching degree, and the top few courses and materials with the highest matching degree are selected to generate a personalized recommended training list. The recommended training list is pushed to the nurse's personal learning terminal, and a reminder is set for the completion deadline. At the same time, the recommended training list is recorded in the improvement plan in the nurse's personal competency development file.

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