Information management system for stress injury

Abstract

The invention relates to the technical field of data management, in particular to a pressure injury information management system which comprises a patient risk analysis module, a patient screening module, a nursing early warning module, an injury analysis module, an action guidance module and a statistical analysis module. According to the method and the system, the stress injury risk level of each patient is calculated by integrating the medical record data with the lying time, age and basic illness state of the patient, so that the accurate assessment of the risk state of the patient is remarkably improved, the demand of manual intervention is reduced, the information transmission speed is also accelerated, and the risk assessment efficiency is improved. According to the method and the system, nursing personnel can obtain key early warning information in real time, take corresponding prevention measures and collect and identify injury photos to further make injury degree assessment more accurate and timely, provided nursing action guidance is formulated based on actual injury conditions, the nursing working process is optimized, and the overall efficiency and safety of medical services are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an information management system for pressure injuries. Background Art

[0002] Data management is a technical field that involves the collection, verification, storage, protection and processing of data to ensure the accuracy, accessibility and reliability of data. It covers a variety of technologies from database management, data integration, data security to big data and cloud services. The main goal of data management is to improve the quality and efficiency of data use while ensuring the security and privacy of information. In today's rapidly developing information technology, effective data management is crucial for enterprises and institutions, which can help gain an advantage in data-driven decision-making and improve operational efficiency and business results.

[0003] Among them, the information management system for pressure injuries refers to an information system specifically used to process and manage data related to pressure injuries. Its main purpose is to collect detailed data on pressure injuries, including the type of injury, the environment in which it occurred, the affected individuals, and subsequent treatment measures. Through systematic management of data, relevant personnel can more effectively prevent and treat injuries, while providing data support for research on pressure injuries and promoting the development of the medical and health field.

[0004] Although existing technologies include a wide range of data management methods, in actual operations, they lack the ability to quickly and accurately identify high-risk patients, limiting the timeliness and effectiveness of preventive measures. For example, traditional data management in the prevention and treatment of pressure injuries cannot achieve real-time data updates and processing, resulting in caregivers being unable to immediately obtain the patient's latest risk status, delaying immediate attention and intervention for high-risk patients. The lack of an automated early warning system means that caregivers need to manually monitor the patient's status, which is not only inefficient, but also misses key warnings due to human negligence. Relying on manual operation modes, it is easy to affect the quality of care due to improper resource allocation, especially in medical environments with limited resources. The shortcomings show the limitations of existing technologies in providing real-time and effective medical intervention support when processing high-risk patient information, affecting the overall recovery effect and the patient's rehabilitation process. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an information management system for pressure injuries.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a pressure injury information management system comprising:

[0007] The patient risk analysis module extracts the patient's medical history, combines the patient's bed rest time, age and basic condition, calculates the risk level of pressure injury for each patient, and generates a risk rating result;

[0008] The patient screening module screens the patient information with high risk priority according to the risk rating result, sorts the patient's name, ward number and contact nurse information, and obtains a list of dangerous patients;

[0009] The nursing warning module uses the list of dangerous patients to set a scheduled task to send emails and text messages to nursing staff, including risk rating information of pressure injuries of patients and reminders of preventive measures, and obtains warning push records;

[0010] The injury analysis module uses the warning push record, combined with the patient's living habits and photos of the injured part, to take preventive measures for patients who have not suffered pressure injuries, evaluate the degree of injury and injury grade of patients with pressure injuries, and obtain injury assessment results;

[0011] The action guidance module provides nursing action guidance based on the injury level in the injury assessment result, analyzes the wound assessment information and the time for dressing change, and obtains a nursing guidance list;

[0012] The statistical analysis module performs statistical analysis on the patient's pressure injury management through the nursing guidance list, records real-time risk and nursing quality control data, and generates a management statistical log.

[0013] As a further solution of the present invention, the risk rating results include high and low risk scores and level classifications, the list of dangerous patients includes the patient's name, ward number and contact nurse information, the early warning push record includes the timestamp, sending status and content summary of the email and text message sent to the nursing staff, the injury assessment results include injury degree identification, injury level assessment and severity classification of the patient's injury site photos, the nursing instruction list includes wound assessment information, recommended wound treatment measures and optimal time for dressing change, and the management statistics log includes statistical analysis results of the patient's pressure injury management situation, recorded real-time risk assessment data and nursing quality control data.

[0014] As a further solution of the present invention, the patient risk analysis module includes:

[0015] The medical record extraction submodule extracts the patient's basic personal information, case history, and pressure injury response, screens eligible records, processes the data, unifies the formatting, and generates patient medical records;

[0016] The risk calculation submodule uses the patient's medical records to analyze the patient's age and bed rest time, calculates the patient's pressure injury risk through a risk assessment model, assesses the risk level of each patient, and obtains risk assessment data;

[0017] The data classification submodule uses the risk assessment data to classify the data, sort the data by risk level, track and process patients with high risk levels in real time, and generate risk rating results.

[0018] As a further solution of the present invention, the patient screening module comprises:

[0019] The risk ranking submodule uses the risk rating results and the comprehensive risk index model evaluation method to screen patients with high risk levels, sort the data according to the patient's pressure injury status and admission time, exclude patients with stable conditions, and generate priority patient data;

[0020] The information integration submodule extracts the patient's name and ward number based on the priority patient data, queries the contact information of the associated nurse, verifies the extracted data, and generates verified patient information;

[0021] The list output submodule uses the verified patient information in combination with a predefined risk template to automatically fill in the patient's information, including emergency contact information and nursing measures, to generate a list of dangerous patients.

[0022] As a further solution of the present invention, the formula of the comprehensive risk index model evaluation method is as follows:

[0023]

[0024] Among them, R z is the patient's risk score, P represents the patient's pressure injury degree, T represents the patient's admission time, S represents the patient's real-time stability index, D represents the number of days since the patient was admitted, and w1, w2 and w3 are weight coefficients.

[0025] As a further solution of the present invention, the nursing warning module includes:

[0026] The task scheduling submodule formulates a daily scheduled triggering task plan based on the list of dangerous patients, checks and sends updated patient information to the nursing team on a daily basis, and generates a scheduled task configuration;

[0027] The warning sending submodule adopts the timed task configuration to send emails and text messages to the target nursing staff list through the hospital's internal communication server, including the patient's pressure injury risk rating and preventive measures, and generates the warning message content;

[0028] The information update submodule uses the warning message content to record the time of each transmission, the recipient and the message content, synchronously updates the patient's pressure injury information, verifies the consistency of the information, and generates a warning push record.

[0029] As a further solution of the present invention, the injury analysis module includes:

[0030] The habit analysis submodule uses the warning push record to collect the patient's daily activity data, sorts and screens key behavior patterns through data, and obtains life pattern analysis results;

[0031] The injury photo analysis submodule uses the life pattern analysis results to scan and analyze photos of the injury site, annotate the injury features, and classify the features according to their severity to form an injury degree judgment result;

[0032] The preventive measures submodule determines the result of the injury degree, analyzes the patient's characteristics, identifies the successful preventive measures of the case, formulates a preventive plan, and predicts the effect of the plan to obtain the injury assessment result.

[0033] As a further solution of the present invention, the action guidance module includes:

[0034] The characteristic analysis submodule uses the injury grade information in the injury assessment results to analyze the characteristics of the differentiated grade injuries and the required nursing degree, provides treatment suggestions for the differentiated grade injuries, checks the matching degree between the nursing measures and the severity of the injury, and generates a preliminary draft of the action suggestion;

[0035] The action suggestion submodule calculates the optimal time interval for dressing change based on the draft action suggestion, the duration of the pressure injury and the dressing change requirement, optimizes the nursing action, and generates an action guidance plan;

[0036] The plan execution submodule adopts a decision tree algorithm to formulate key action steps and dressing change plans based on the needs of the action guidance plan and generates a nursing guidance list.

[0037] As a further solution of the present invention, the formula of the decision tree algorithm is as follows:

[0038]

[0039] Among them, C s Represents the classification result, P s Represents the patient's real-time health status index, Q s represents the patient's allergic reaction index to the target dressing, R s represents the risk assessment associated with the patient, S s Represents the urgency score of the preventive measures, a s and bs is the weight parameter.

[0040] As a further solution of the present invention, the statistical analysis module includes:

[0041] The associated information analysis submodule collects data associated with the patient's pressure injury information based on the nursing guidance list, including the implementation of nursing measures, the frequency of dressing changes, and the progress of injury recovery, and generates a pressure injury management data set;

[0042] The risk factor identification submodule analyzes the pressure injury management data set, evaluates the effectiveness of nursing measures and the timeliness of dressing changes, identifies potential risk factors, and generates risk and quality control analysis results;

[0043] The logging submodule utilizes the risk and quality control analysis results to record the associated statistical data, conducts real-time monitoring and review through formatted storage, verifies the speed of information transmission and archiving efficiency, and generates management statistical logs.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by integrating medical record data with the patient's bed rest time, age and basic condition, the pressure injury risk level of each patient is calculated, and a risk rating result is generated, which significantly improves the accurate assessment of the patient's risk status. The risk rating results can effectively screen out high-risk patients, and the screening mechanism ensures that medical resources are prioritized for individuals who need them most. The scheduled task of automatically sending emails and text messages not only reduces the need for manual intervention, but also speeds up the transmission of information, allowing nursing staff to obtain key warning information in real time and take corresponding preventive measures. The collection and identification of injury photos further makes the assessment of the degree of injury more accurate and timely, and the nursing action guidance provided is based on the actual injury situation, making each nursing more personalized and targeted. The implementation of statistical analysis more accurately reflects the real-time status of pressure injury management, provides data support for the continuous improvement of nursing quality, not only optimizes the nursing workflow, but also enhances the overall effectiveness and safety of medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 is a flow chart of the patient risk analysis module of the present invention;

[0049] Figure 4 This is a flow chart of the patient screening module of the present invention;

[0050] Figure 5 This is a flow chart of the nursing warning module of the present invention;

[0051] Figure 6 This is a flow chart of the injury analysis module of the present invention;

[0052] Figure 7 is a flow chart of the action guidance module of the present invention;

[0053] Figure 8 It is a flow chart of the statistical analysis module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] See also Figure 1 to Figure 2 , an information management system for pressure injuries comprises:

[0057] The patient risk analysis module extracts the patient's medical records, combines the patient's bed rest time, age and basic condition, uses a risk assessment model to perform data operations, calculates the risk level of pressure injury for each patient, and generates a risk rating result;

[0058] The patient screening module screens the patient information with high risk priority according to the risk level data in the risk rating results, sorts out the patient's name, ward number and contact nurse information, and obtains a list of dangerous patients;

[0059] The nursing warning module uses the list of dangerous patients to set up scheduled tasks to send emails and text messages to nursing staff, including risk rating information of pressure injuries of patients and reminders of preventive measures, and pushes information regularly to obtain warning push records;

[0060] The injury analysis module uses the warning push record, combined with the patient's living habits and photos of the injured part, to take preventive measures for patients who have not suffered pressure injuries, evaluate the degree of injury and injury grade of patients with pressure injuries, and obtain injury assessment results;

[0061] The action guidance module provides nursing action guidance based on the injury level in the injury assessment results, analyzes the wound assessment information and the time for dressing change, and obtains a nursing guidance list;

[0062] The statistical analysis module conducts statistical analysis on the patient's pressure injury management through the nursing guidance checklist, records real-time risk and nursing quality control data, and generates a management statistics log.

[0063] The risk rating results include high and low risk scores and level classifications; the list of dangerous patients includes the patient's name, ward number and contact nurse information; the early warning push records include the timestamp, sending status and content summary of the emails and text messages sent to nursing staff; the injury assessment results include the injury degree identification, injury level assessment and severity classification of the patient's injury site photos; the nursing guidance list includes wound assessment information, recommended wound treatment measures and the optimal time for dressing changes; the management statistics log includes the statistical analysis results of the patient's pressure injury management, recorded real-time risk assessment data and nursing quality control data.

[0064] See also Figure 2 , 3 , the patient risk analysis module includes:

[0065] The medical record extraction submodule extracts the patient's basic personal information, case history, and pressure injury response, screens records that meet the criteria, and processes the data, unifies the formatting, and generates the patient's medical record. The execution process is as follows;

[0066] Extract the patient's basic personal information, case history and pressure injury response, screen out records that meet specific conditions, such as age, gender, disease course and other key information, format the information through the database's data processing function, unify the data format, ensure that all data meet the needs of further analysis and storage, check and verify the integrity and accuracy of the data, and generate patient medical records with a unified format and easy to retrieve.

[0067] The risk calculation submodule uses the patient's medical records to analyze the patient's age and bed rest time, calculates the patient's pressure injury risk through the risk assessment model, and assesses the risk level of each patient. The execution process of obtaining risk assessment data is as follows;

[0068] Using patient medical records, the risk of pressure injury is calculated through a risk assessment model. Calculate the risk level of each patient. a represents the risk level of pressure injury, a represents the age coefficient, and t represents the bed rest time.

[0069] Formula explanation and formula calculation process: Set the age coefficient a, the value can be obtained based on the correlation between age and pressure injury in previous studies, and t is the patient's bed rest time in days. For example, for a patient with an age coefficient of 0.03 and a bed rest time of 10 days, substitute the value into the formula The calculated R≈0.406. The results show that the patient's pressure injury risk level is 0.406, indicating that the patient is at a medium risk level, which provides an important basis for assessing the level of care and preventive measures required by the patient.

[0070] The data classification submodule uses risk assessment data to classify and process the data, sorting them by risk level, and tracking and processing patients with high risk levels in real time. The execution process of generating risk rating results is as follows;

[0071] Using risk assessment data to classify and process the data requires the use of data classification algorithms and real-time data processing technology. The risk assessment data of all patients needs to be classified and sorted according to risk levels. The process involves complex data sorting algorithms, such as quick sort or heap sort. Automatic classification and sorting of data is achieved through programming to ensure that patients with high risk levels can be identified immediately and the data can be tracked in real time. By setting thresholds, high-risk patients who require emergency treatment or continuous monitoring can be identified, and high-risk patients can be given priority in the medical system to ensure timely intervention and recovery. The generated risk rating results will be used to guide the allocation of hospital resources and patient management strategies.

[0072] See also Figure 2 , 4 , the patient screening module includes:

[0073] The risk ranking submodule uses the comprehensive risk index model assessment method based on the risk rating results to screen patients with high risk levels, sort the data according to the patient's pressure injury status and admission time, exclude patients with stable conditions, and generate priority patient data. The execution process is as follows;

[0074] The formula of the comprehensive risk index model assessment method is as follows:

[0075]

[0076] Among them, R zis the patient's risk score, P represents the patient's pressure injury degree, T represents the patient's admission time, S represents the patient's real-time stability index, D represents the number of days since the patient was admitted, and w1, w2 and w3 are weight coefficients.

[0077] formula:

[0078]

[0079] Detailed explanation of the formula and the process of formula calculation and derivation:

[0080] The formula is used to assess the patient's risk level, where P represents the degree of pressure injury, which is scored by medical staff based on a clinical assessment form with a score range from 1 to 10, with high scores indicating severe injury.

[0081] The pressure injury score of a patient is set to 8, and T represents the time of admission, which is calculated in hours and obtained from hospital records. For example, 24 hours have passed since a patient was admitted to the hospital.

[0082] S is the stability index. The score is also given by medical staff, ranging from 1 to 5. A low score indicates an unstable condition. The stability index of this patient is set to 3.

[0083] D is the number of days since the patient was admitted to the hospital, which was obtained directly from the hospital's electronic health record and was set to 1 day.

[0084] The values ​​of weight coefficients w1, w2, and w3 need to be determined based on clinical experience analysis. According to statistical data, the degree of pressure injury has the greatest impact on the risk of illness, so w1 is set to 0.5. The square root of the hospitalization time is used to reduce the direct impact of the length of time, so w2 is set to 0.3. The absolute value change of the stability index also has a significant impact on the risk level, so w3 is set to 0.2.

[0085] Enter specific values ​​for calculation:

[0086]

[0087] The result is 2.835, which represents the risk level after considering the patient's pressure injury level, hospitalization time and stability. A higher score indicates that the patient's condition requires closer monitoring and timely intervention. The results show that the patient is at a higher risk level and the team should prioritize care to ensure the rational allocation of resources and improve recovery efficiency and patient safety.

[0088] The information integration submodule extracts the patient's name and ward number based on the priority patient data, and queries the contact information of the associated nurses, verifies the extracted data, and generates the following execution flow for verifying the patient information;

[0089] Based on the priority patient data, the priority rules for data extraction are set. According to the urgency and recovery needs of the patients, the data of patients who need immediate care or monitoring are extracted first. The process involves the use of database query languages, such as SQL statements for effective data extraction, such as SELECT name, ward number FROM patient database WHERE urgency level>7. At the same time, the information of nurses who need to be contacted is also obtained through similar query statements. Further data verification processing ensures the accuracy and timeliness of the extracted information. The consistency and accuracy of the newly extracted data are verified by comparing with the existing information in the database, ensuring the accurate transmission and effective use of information, which is of great significance for rapid response in emergency situations and generating verified patient information.

[0090] The list output submodule uses verified patient information and combines it with predefined risk templates to automatically fill in the patient's information, including emergency contact information and nursing measures. The execution process of generating a list of dangerous patients is as follows;

[0091] Use the verified patient information to automatically fill in the patient's information according to the formula N = f (P b ,E,M), generate a list of dangerous patients. In the formula, N represents the list of dangerous patients, P b represents the basic information of the patient, E represents the emergency contact information, and M represents the nursing measures. b Including name and ward number, emergency contact information E is obtained by querying the database, and nursing measures M are predefined according to the patient's specific condition and risk level. For example, patient John Doe, ward number 123, emergency contact number 8888-555-0000, and the required nursing measures are 24-hour ECG monitoring. Substituting the information into the formula N = f ("John Doe123", "8888-555-0000", "24-hour ECG monitoring"), the result is John Doe's dangerous patient list entry. The results show that generating a list of dangerous patients based on verified patient information and predefined risk templates is crucial to the rational allocation of medical resources and emergency response management.

[0092] See also Figure 2 , 5 , the nursing warning module includes:

[0093] The task scheduling submodule formulates a daily scheduled task plan based on the list of dangerous patients, checks the updated patient information sent to the nursing team on a daily basis, and generates the execution process of the scheduled task configuration as follows;

[0094] Based on the list of dangerous patients, scheduled tasks are set up through programming. The task plan is configured on the server using task scheduling software such as Cron. For example, a task is set to be triggered at 3 a.m. every day. SQL is used to query the updated patient information, such as SELECT name, condition update FROM patient list WHERE update time 〉= yesterday's date, and the information is packaged into tasks. The task scheduler is responsible for triggering tasks at the set time to ensure that the nursing team can obtain the latest patient information. The process also includes monitoring and logging of task execution in order to discover and resolve problems that arise during execution, including task execution time, execution frequency and target task details, to ensure the timeliness of information transmission and the accuracy of nursing responses, and generate scheduled task configurations.

[0095] The early warning sending submodule adopts a scheduled task configuration to send emails and text messages to the target nursing staff list through the hospital's internal communication server, including the patient's pressure injury risk rating and preventive measures. The execution process of generating the early warning message content is as follows;

[0096] The scheduled task configuration is adopted, and the configured scheduled task is used to trigger the message sending process. The content of the early warning message includes the patient's pressure injury risk rating and corresponding preventive measures. The message is sent through the email server and SMS gateway, which involves the format writing of emails and the simplification of SMS content to ensure the adaptability and reading efficiency of information on different platforms. At the same time, the system needs to optimize the sending time according to the working hours and job arrangements of nursing staff to improve the efficiency and timeliness of information transmission, and ensure that the nursing team can obtain important patient information and action guidelines at the first time. The generated early warning message content is designed to improve the response speed and efficiency of patient care.

[0097] The information update submodule uses the warning message content to record the time, recipient and message content of each transmission, synchronously updates the patient's pressure injury information, and verifies the consistency of the information. The execution process of generating the warning push record is as follows;

[0098] Using the warning message content, the patient's pressure injury information is updated synchronously. According to the formula U = g (T s ,R,C), generate warning push records. In the formula, U represents the warning push record, T s Represents the time of each sending, R represents the recipient, and C represents the message content.

[0099] Detailed explanation of the formula and the process of formula calculation: Time T for each transmission sIt is recorded as a specific date and time, such as 2023-09-23-14:00. The recipient R is matched from the employee database, such as the name of a nurse or doctor, and the message content C contains the patient's risk rating and preventive measures. Substituting the information into the formula U = g ("2023-09-23-14:00", "NurseA", "High risk rating, need to strengthen monitoring"), the result is a specific warning push record. The results show that by accurately recording the sending time, receiving personnel and message content, the system can effectively track and manage the push process of warning messages, providing detailed records and basis for subsequent patient monitoring.

[0100] See also Figure 2 , 6 , the damage analysis module includes:

[0101] The habit analysis submodule uses early warning push records to collect patients' daily activity data, sorts out key behavior patterns through data, and obtains the execution process of life pattern analysis results as follows;

[0102] Early warning push records are used to collect key behavioral patterns, systematically analyze patients' daily activity data, organize and screen out behaviors that affect quality of life, identify important behavioral patterns through data organization technology, and screen out behaviors that have the greatest impact on patients' lives by calculating parameters such as the frequency and duration of different activities. The process involves a large amount of data comparison and pattern recognition technology. The system can effectively extract valuable information from massive data, and then through algorithm optimization, ensure that the collected data can accurately reflect the patient's actual life pattern. The obtained life pattern analysis results can provide a scientific basis for subsequent injury risk assessment.

[0103] The injury photo analysis submodule uses the life pattern analysis results to scan and analyze the photos of the injury parts, annotate the injury features, and classify them according to the severity of the features. The execution process of forming the injury degree judgment result is as follows;

[0104] Scanning and analysis of photos of the damaged area, according to the formula Calculate the dispersion of damage characteristics. In the formula, S s represents the standard deviation of the damage characteristic, x s represents the measured value of each damage characteristic, represents the average value of the damage characteristic measurement, n s Represents the number of damage characteristics.

[0105] Detailed explanation of the formula and the process of formula calculation: The formula is used to calculate the standard deviation of the damage characteristic data, which indicates the variability between different damage characteristics. If the damage characteristic values ​​are 15, 20, 22, 20, and 15, then the average value is Substitute into the formula to calculate Ss ≈3.13, indicating that the dispersion of the damage characteristics on the data points is 3.13. The results help assess the severity of the damage and classify it.

[0106] The preventive measures submodule judges the results according to the degree of injury, analyzes the patient's characteristics, identifies the successful preventive measures of the case, formulates a preventive plan, and predicts the effectiveness of the plan. The execution process of obtaining the injury assessment results is as follows;

[0107] The results are judged based on the degree of injury, the patient's characteristics are analyzed, and a highly targeted prevention plan is identified and formulated. The data from historical successful cases is used, combined with the patient's specific situation, to predict the effect of the plan through a deep learning model. The prediction process includes the input and processing of many variables, such as the patient's past medical records, living habits, and reactions to changes. After the model is trained, the data can output the patient's injury risk and the effectiveness of preventive measures. This directly affects future treatment and care plans, ensuring that the proposed prevention plan can effectively reduce the patient's injury risk, improve the quality of life, and obtain injury assessment results.

[0108] See also Figure 2 , 7 , the action guidance module includes:

[0109] The characteristic analysis submodule uses the injury grade information in the injury assessment results to analyze the characteristics of differentiated grade injuries and the required nursing degree, provide treatment suggestions for differentiated grade injuries, check the matching degree between nursing measures and injury severity, and generate the first draft of action suggestions. The execution process is as follows;

[0110] Use the damage level information in the damage assessment results. According to formula D g =i g (L g ,N g ), generate a draft of action recommendations. g Representatives of the first draft of the action proposal, L g Represents the damage level, N g Represents the degree of care need.

[0111] Detailed explanation of the formula and the process of formula calculation: Damage Level L g It is obtained from the injury assessment results. Different levels represent injuries of different severity. The degree of care required is N g According to the injury level, for example, for high-level injuries, centralized monitoring and recovery are required. For example, if the patient's injury level is 3, a high-level nursing measure is required. Substitute the information into the formula D = i g(3, high-level care), the results generate specific nursing action recommendations. The results show that by systematically analyzing the matching of injury level and nursing needs, targeted treatment recommendations can be provided to the medical team, optimizing the patient's care plan, increasing the recovery effect and alleviating the patient's pain.

[0112] The action suggestion submodule calculates the optimal time interval for dressing change based on the draft action suggestion, the duration of pressure injury and the need for dressing change, optimizes nursing actions, and generates the execution process of the action guidance plan as follows;

[0113] Based on the first draft of the action recommendations, analyze the duration of the pressure injury and related nursing records, determine the service life and replacement frequency of the dressing, and use data analysis methods, such as statistical analysis or mathematical modeling, to calculate the optimal dressing change interval to ensure the patient's wound recovery speed and prevent infection. The calculation process takes into account the patient's condition changes, wound healing speed, and the work efficiency of the nursing team. The dressing change interval is detailed, such as changing it every 48 hours, and related nursing measures, such as regular cleaning and disinfection of the wound, to ensure that the patient receives consistent and scientific nursing services, optimize nursing actions, improve recovery effects, and generate an action guidance plan.

[0114] The plan execution submodule uses a decision tree algorithm to formulate key action steps and dressing change plans based on the needs of the action guidance plan, and generates the execution process of the nursing guidance list as follows;

[0115] The formula for the decision tree algorithm is as follows:

[0116]

[0117] Among them, C s Represents the classification result, P s Represents the patient's real-time health status index, Q s represents the patient's allergic reaction index to the target dressing, R s represents the risk assessment associated with the patient, S s Represents the urgency score of the preventive measures, a s and b s is the weight parameter.

[0118] formula:

[0119]

[0120] Detailed explanation of the formula and the process of formula calculation and derivation:

[0121] The formula used in the decision tree algorithm includes the patient's health status index P s , allergic reaction index Q s , risk assessment Rs , and the urgency score of the preventive measures S s . Weight parameter a s and b s Adjust according to actual conditions to balance the impact of various indicators on decision-making.

[0122] Parameter setting and quantization process:

[0123] P s (Health Status Index) is calculated by continuously monitoring the patient's physiological parameters (such as heart rate, blood pressure, etc.). For example, if the average heart rate is 80 bpm and the average blood pressure is 120 / 80 mmHg during a certain period of time, according to the preset standard, P s The calculation is (80 / 100)×1.2=0.96.

[0124] Q s (Allergic Reaction Index) is based on the patient's reaction data to specific drugs or materials, and is set up with a record of mild reactions in the past. The Q s =0.3.

[0125] R s (Risk assessment) is determined by a professional team based on the patient's medical history and recovery recommendations. For example, if a patient has mild heart disease, the risk assessment is 4 (scale 1-10), then R s =4.

[0126] S s (Preventive measures urgency score) is assigned a value based on the patient's current medical condition and the emergency situation that has occurred. For example, the urgency score is 3 (scale 1-5), that is, S s =3.

[0127] Weight a s and b s Through data analysis, it was concluded that the weight of health status was set to 0.7 and the weight of allergic reaction was set to 0.3.

[0128] Calculation example:

[0129] Set a s =0.7, b s =0.3, P s =0.96, Q s =0.3, R s =4, S s =3

[0130] The calculation process is as follows:

[0131] Calculate the numerator: a s ×P s +b s ×Q s=0.7×0.96+0.3×0.3=0.672+0.09=0.762

[0132] Calculate the denominator:

[0133] Final C s value:

[0134] The results showed that based on the current parameters, the patient's comprehensive score was 0.1524, and the value reflects the patient's recovery needs and urgency at a specific time point. Depending on the C value, the nursing team can decide to take appropriate nursing measures and preventive strategies. A high C value indicates the need for immediate and aggressive recovery intervention, while a lower value indicates that the current recovery plan is appropriate or needs to be slightly adjusted.

[0135] See also Figure 2 , 8 , the statistical analysis module includes:

[0136] The associated information analysis submodule collects data associated with the patient's pressure injury information based on the nursing guidance list, including the implementation of nursing measures, dressing change frequency, and injury recovery progress. The execution process of generating the pressure injury management data set is as follows;

[0137] Based on the nursing guidance checklist, by integrating various nursing records and patient monitoring data, such as detailed records of the execution of nursing measures, the specific time and frequency of dressing changes, and observation data of wound recovery, data acquisition technology and database management are used to systematically collect and store information, and the collected data are cleaned and formatted to ensure the accuracy and consistency of the data. Through data association analysis, the correlation between nursing measures and patient recovery is revealed, and the impact of different nursing methods on the progress of injury recovery is evaluated. Basic data is provided for subsequent risk assessment and nursing optimization, ensuring the comprehensiveness of information and the effectiveness of application, and generating a pressure injury management data set.

[0138] The risk factor identification submodule analyzes the pressure injury management data set, evaluates the effectiveness of nursing measures and the timeliness of dressing changes, identifies potential risk factors, and generates risk and quality control analysis results. The execution process is as follows;

[0139] Analyze the pressure injury management data set and follow the formula F s =v(E s ,T s ), generate risk and quality control analysis results. In the formula, F s Represents the results of risk and quality control analysis, E s Represents the effectiveness of nursing measures, T s Represents the timeliness of dressing changes.

[0140] Detailed explanation of the formula and the process of formula calculation and derivation: Effectiveness of nursing measures E s Timeliness of dressing changes as assessed by patient recovery rate and wound healing status s Measured based on the recorded frequency and time of dressing changes. For example, if the nursing measures are rated as efficient and the dressing changes are timed correctly, the information is substituted into the formula F = v (efficient, timed), and the results generate a detailed analysis of risk and quality control. The results show that by quantitatively analyzing the implementation quality of nursing measures and dressing changes, potential risk factors can be effectively identified, providing accurate data support for improving the quality of patient management and reducing future risks.

[0141] The logging submodule uses the risk and quality control analysis results to record the associated statistical data, conducts real-time monitoring and review through formatted storage, verifies the information transmission speed and archiving efficiency, and generates the management statistical log. The execution process is as follows;

[0142] Utilize the risk and quality control analysis results to ensure that all collected data and analysis results are formatted and stored in an automated manner, such as using stored procedures and triggers in the database system to ensure real-time updates and secure backups of data. Set up a data warehouse for long-term data storage and management, provide data query and review interfaces, and achieve rapid retrieval and analysis of data. At the same time, the system will monitor the speed of data transmission and storage processes, evaluate system performance and efficiency, and provide important real-time information for system maintenance by recording the timestamp, data size, and processing time of each data event, support continuous optimization of the system and efficiency improvement, and generate management statistical logs.

[0143] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A pressure injury information management system, characterized in that: The system comprises: The patient risk analysis module extracts the patient's medical history, combines the patient's bed rest time, age and basic condition, calculates the risk level of pressure injury for each patient, and generates a risk rating result; The patient screening module screens the patient information with high risk priority according to the risk rating result, sorts the patient's name, ward number and contact nurse information, and obtains a list of dangerous patients; The nursing warning module uses the list of dangerous patients to set a scheduled task to send emails and text messages to nursing staff, including risk rating information of pressure injuries of patients and reminders of preventive measures, and obtains warning push records; The injury analysis module uses the warning push record, combined with the patient's living habits and photos of the injured part, to take preventive measures for patients who have not suffered pressure injuries, evaluate the degree of injury and injury grade of patients with pressure injuries, and obtain injury assessment results; The action guidance module provides nursing action guidance based on the injury level in the injury assessment result, analyzes the wound assessment information and the time for dressing change, and obtains a nursing guidance list; The statistical analysis module performs statistical analysis on the patient's pressure injury management through the nursing guidance list, records real-time risk and nursing quality control data, and generates a management statistical log.

2. The pressure injury information management system according to claim 1, characterized in that: The risk rating results include high and low risk scores and level classifications, the list of dangerous patients includes the patient's name, ward number and contact nurse information, the early warning push records include the timestamp, sending status and content summary of the emails and text messages sent to nursing staff, the injury assessment results include injury degree identification, injury level assessment and severity classification of patient injury site photos, the nursing instruction list includes wound assessment information, recommended wound treatment measures and the optimal time for dressing changes, and the management statistics log includes statistical analysis results of patient pressure injury management, recorded real-time risk assessment data and nursing quality control data.

3. The pressure injury information management system according to claim 1, characterized in that: The patient risk analysis module includes: The medical record extraction submodule extracts the patient's basic personal information, case history, and pressure injury response, screens eligible records, processes the data, unifies the formatting, and generates patient medical records; The risk calculation submodule uses the patient's medical records to analyze the patient's age and bed rest time, calculates the patient's pressure injury risk through a risk assessment model, assesses the risk level of each patient, and obtains risk assessment data; The data classification submodule uses the risk assessment data to classify the data, sort the data by risk level, track and process patients with high risk levels in real time, and generate risk rating results.

4. The pressure injury information management system according to claim 1, characterized in that: The patient screening module includes: The risk ranking submodule uses the risk rating results and the comprehensive risk index model evaluation method to screen patients with high risk levels, sort the data according to the patient's pressure injury status and admission time, exclude patients with stable conditions, and generate priority patient data; The information integration submodule extracts the patient's name and ward number based on the priority patient data, queries the contact information of the associated nurse, verifies the extracted data, and generates verified patient information; The list output submodule uses the verified patient information in combination with a predefined risk template to automatically fill in the patient's information, including emergency contact information and nursing measures, to generate a list of dangerous patients.

5. The pressure injury information management system according to claim 4, characterized in that: The formula of the comprehensive risk index model evaluation method is as follows: Among them, R z is the patient's risk score, P represents the patient's pressure injury degree, T represents the patient's admission time, S represents the patient's real-time stability index, D represents the number of days since the patient was admitted, and w1, w2 and w3 are weight coefficients.

6. The pressure injury information management system according to claim 1, characterized in that: The nursing early warning module comprises: The task scheduling submodule formulates a daily scheduled triggering task plan based on the list of dangerous patients, checks and sends updated patient information to the nursing team on a daily basis, and generates a scheduled task configuration; The warning sending submodule adopts the timed task configuration to send emails and text messages to the target nursing staff list through the hospital's internal communication server, including the patient's pressure injury risk rating and preventive measures, and generates the warning message content; The information update submodule uses the warning message content to record the time of each transmission, the recipient and the message content, synchronously updates the patient's pressure injury information, verifies the consistency of the information, and generates a warning push record.

7. The pressure injury information management system according to claim 1, characterized in that: The damage analysis module includes: The habit analysis submodule uses the warning push record to collect the patient's daily activity data, sorts and screens key behavior patterns through data, and obtains life pattern analysis results; The injury photo analysis submodule uses the life pattern analysis results to scan and analyze photos of the injury site, annotate the injury features, and classify the features according to their severity to form an injury degree judgment result; The preventive measures submodule determines the result of the injury degree, analyzes the patient's characteristics, identifies the successful preventive measures of the case, formulates a preventive plan, and predicts the effect of the plan to obtain the injury assessment result.

8. The pressure injury information management system according to claim 1, characterized in that: The action guidance module includes: The characteristic analysis submodule uses the injury grade information in the injury assessment results to analyze the characteristics of the differentiated grade injuries and the required nursing degree, provides treatment suggestions for the differentiated grade injuries, checks the matching degree between the nursing measures and the severity of the injury, and generates a preliminary draft of the action suggestion; The action suggestion submodule calculates the optimal time interval for dressing change based on the draft action suggestion, the duration of the pressure injury and the dressing change requirement, optimizes the nursing action, and generates an action guidance plan; The plan execution submodule adopts a decision tree algorithm to formulate key action steps and dressing change plans based on the needs of the action guidance plan and generates a nursing guidance list.

9. The pressure injury information management system according to claim 8, characterized in that: The formula of the decision tree algorithm is as follows: Among them, C s Represents the classification result, P s Represents the patient's real-time health status index, Q s represents the patient's allergic reaction index to the target dressing, R s represents the risk assessment associated with the patient, S s represents the urgency score of the preventive measures, a s and b s is the weight parameter.

10. The pressure injury information management system according to claim 1, characterized in that: The statistical analysis module includes: The associated information analysis submodule collects data associated with the patient's pressure injury information based on the nursing guidance list, including the implementation of nursing measures, the frequency of dressing changes, and the progress of injury recovery, and generates a pressure injury management data set; The risk factor identification submodule analyzes the pressure injury management data set, evaluates the effectiveness of nursing measures and the timeliness of dressing changes, identifies potential risk factors, and generates risk and quality control analysis results; The logging submodule utilizes the risk and quality control analysis results to record the associated statistical data, conducts real-time monitoring and review through formatted storage, verifies the speed of information transmission and archiving efficiency, and generates management statistical logs.

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