A postoperative pain rehabilitation nursing data management system for a rehabilitation medicine department
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- THE FIRST PEOPLES HOSPITAL OF XIAN YANG
- Filing Date
- 2026-02-05
- Publication Date
- 2026-05-29
AI Technical Summary
Traditional nursing data management systems suffer from limitations in pain assessment, including incompleteness and lag, lack of integration of psychological state assessment, information silos between medical information systems, non-pharmacological interventions not being digitized, and a lack of real-time dynamic monitoring and emergency disaster recovery mechanisms. These issues result in inaccurate pain management and insufficient safety.
It employs a multi-dimensional data dynamic acquisition module, a cross-platform data interaction module, a data encryption module, a pain risk real-time early warning module, an intelligent intervention plan generation module, a pain rehabilitation assessment module, and a system emergency disaster recovery module to achieve multi-dimensional data fusion, real-time dynamic early warning, intelligent and precise intervention, and highly reliable emergency support.
It enables a three-dimensional, panoramic perception of patients' pain status, seamless data flow across platforms, provides personalized pain intervention plans, ensures system security and continuity, and improves the scientific nature and timeliness of pain management.
Smart Images
Figure CN122117199A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical information technology, specifically to a data management system for postoperative pain rehabilitation nursing in the Department of Rehabilitation Medicine. Background Technology
[0002] In the field of postoperative pain management in rehabilitation medicine, traditional nursing data management systems suffer from significant technical bottlenecks and functional shortcomings. Currently, most systems still rely on a single subjective pain score as their core indicator, failing to effectively integrate and correlate psychological state assessments (such as anxiety scales). This results in one-sided and delayed pain assessments, making it difficult to accurately reflect the patient's actual pain experience and potential pathological changes. Simultaneously, a severe "information silo" phenomenon exists between medical information systems—nursing systems lack standardized interfaces with electronic medical records, laboratory tests, and drug management platforms, requiring repetitive manual data entry. This not only increases the workload of medical staff but also increases the risk of medical complications due to human error. Furthermore, existing systems generally employ static data storage models, failing to achieve real-time dynamic monitoring of patients' vital signs and pain status. When acute postoperative pain suddenly occurs, the system struggles to trigger an early warning mechanism, delaying intervention. More significantly, non-pharmacological interventions (such as parametric control of physical therapy and the delivery of psychological counseling plans) have not yet been digitally integrated, hindering the formation of synergistic multimodal analgesia programs with drug therapy and restricting the implementation of individualized rehabilitation strategies. In emergency scenarios, the system lacks rapid disaster recovery and fault switching mechanisms. A technical failure would directly disrupt the continuity of pain management in the emergency response process, threatening the clinical safety of critically ill patients. These technical deficiencies highlight the urgent need to build an intelligent, integrated, and real-time postoperative pain rehabilitation and nursing data management system. It is imperative to overcome existing technical barriers and improve the scientific rigor and timeliness of pain management through technological innovations such as cross-system data fusion, multi-source heterogeneous data processing, and intelligent decision support. Summary of the Invention
[0003] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a postoperative pain rehabilitation nursing data management system for the Department of Rehabilitation Medicine. It has the advantages of multi-dimensional data fusion analysis, real-time dynamic early warning, intelligent and precise intervention, cross-platform data interoperability, and highly reliable emergency support. It solves the problems of traditional pain assessment model being singular, pain monitoring being lagging, treatment strategies being fragmented, medical data being isolated, and uncontrollable system operation risks.
[0004] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a postoperative pain rehabilitation nursing data management system for the Department of Rehabilitation Medicine, comprising nine processing modules: a multi-dimensional data dynamic acquisition module, a cross-platform data interaction module, a data encryption module, a data analysis module, a real-time pain risk early warning module, an intelligent intervention plan generation module, a pain rehabilitation assessment module, a nursing feedback module, and a system emergency disaster recovery module; The multidimensional data dynamic acquisition module is responsible for collecting postoperative pain-related data from multiple sources in real time, including physiological parameters, pain scores, patient behavior data, and postoperative medication records, and supports dynamic updates. The cross-platform data interaction module enables data exchange and synchronization between the system and multiple platforms, including hospital information systems, electronic medical record systems, mobile nursing terminals, and wearable devices. The data encryption module encrypts all sensitive data during the collection, transmission, and storage process. It uses the RSA asymmetric encryption algorithm to achieve key exchange and the TLS 1.3 protocol to ensure encrypted data transmission. The key is managed by a designated person from the hospital's information department and is automatically rotated every 7 to 10 days. The data analysis module calculates a comprehensive pain score based on the collected pain-related data. With the recovery progress index To identify trends in pain, assess the effectiveness of interventions, and identify potential risk factors, thereby providing a scientific basis for clinical decision-making; The real-time pain risk early warning module is pre-set with a risk model and combines data analysis results to monitor the patient's pain status in real time. When the overall pain score is detected... Abnormal or recovery progress index If the progress is slow, an early warning will be automatically triggered to notify medical staff to handle the situation promptly. The intelligent intervention plan generation module automatically generates personalized pain intervention plans based on the patient's individual differences, pain assessment results, and clinical guidelines. The pain rehabilitation assessment module systematically assesses the patient's pain rehabilitation process, outputs rehabilitation assessment results, and assists doctors in adjusting treatment plans. The nursing feedback module collects patient feedback on pain care services and incorporates the feedback results into the nursing quality improvement process. The system's emergency disaster recovery module provides a system-level disaster recovery backup and emergency response mechanism, including data backup, automatic fault switching, and disaster recovery plans.
[0005] Preferably, the multidimensional data dynamic acquisition module includes a subjective pain assessment unit, a psychological state assessment unit, a physiological and behavioral data monitoring unit, and a treatment execution record unit.
[0006] Preferably, the subjective pain assessment unit collects the patient's postoperative pain score and frequency of attacks in real time through a bedside smart terminal.
[0007] Preferably, the psychological state assessment unit automatically pushes the anxiety self-rating scale and the depression self-rating scale to the patient's end between 8 and 9 a.m. every day and collects scores.
[0008] Preferably, the physiological and behavioral data monitoring unit transmits physiological parameters such as heart rate, blood pressure, heart rate variability and blood oxygen saturation in real time through a wireless wearable device, while also recording the patient's sleep quality and other outdoor behavior data.
[0009] Preferably, the treatment execution recording unit is used by medical staff to input drug intervention and non-drug intervention data through a system terminal.
[0010] Preferably, the data encryption module uses the RSA asymmetric encryption algorithm to achieve key exchange, and its calculation process is as follows: S1.1 Key generation process: Select two distinct large prime numbers x and y, and calculate their product. L is the key length; then calculate the Euler totient function. Select an integer ,1< < ,and and Coprime, ( , ) forms the public key; calculate b for Modular inverse element That is, satisfying ≡1( ),final This is composed of a private key; S1.2 The encryption calculation formula is as follows: ; In the formula, This represents the original data to be encrypted, i.e., the patient's plaintext medical data, during the calculation. It needs to be converted to a value less than integers, and Represents the public key. This represents the encrypted data.
[0011] Preferably, the data analysis module calculates a comprehensive pain score. The calculation formula is as follows: ; In the formula, This indicates the overall pain score. Indicates real-time heart rate. This indicates the patient's baseline heart rate at rest. This indicates the equivalent daily dose of analgesic medication. , , These represent the weighting coefficients for VAS score, heart rate variability, and drug dosage, respectively.
[0012] Preferably, the data analysis module calculates the rehabilitation progress index. The calculation formula is as follows: ; In the formula, This indicates the current feature rating. This indicates the baseline functional score before the start of rehabilitation therapy. This indicates the time elapsed from the baseline assessment to the current assessment. Indicates during the evaluation period Inside, all pain scores The average value, Indicates during the evaluation period Internal pain comprehensive score assessment for patients. Total number of times Indicates the side effect index. This represents the weighting coefficient of the side effect index. It is the natural logarithm function, and 𝑒 is the base of the natural logarithm.
[0013] Preferably, the real-time pain risk warning module is based on the comprehensive pain score output by the data analysis module. Three levels of early warning thresholds were defined; The intelligent intervention plan generation module takes into account the patient's individual differences and comprehensive pain score. Rehabilitation Progress Index Based on clinical guidelines, it automatically generates personalized pain intervention plans and can dynamically adjust and optimize them: the plan generation logic combines the output of the data analysis module. score, The index, along with patient allergy history and surgical type obtained from the cross-platform data interaction module, generates a synergistic drug and non-drug treatment plan.
[0014] Compared with the prior art, the present invention provides a postoperative pain rehabilitation nursing data management system for the Department of Rehabilitation Medicine, which has the following beneficial effects: 1. This invention integrates patients' subjective feelings, objective physiological indicators, treatment execution, and psychological state data through a multi-dimensional data dynamic acquisition module, achieving a three-dimensional and panoramic perception of the patient's recovery status and breaking down the barriers of one-sided and lagging information in traditional assessments. Through a cross-platform data interaction module, it realizes data interoperability and synchronization between the system and multiple platforms such as hospital information systems (HIS), electronic medical record systems (EMR), mobile nursing terminals, and wearable devices, avoiding manual re-entry and medication risks, and ensuring seamless data flow and sharing between different systems and devices.
[0015] 2. This invention uses the RSA algorithm to encrypt a temporary symmetric encryption key (such as an AES key), and then uses this symmetric key to encrypt large amounts of actual medical data at high speed. This hybrid encryption mechanism can balance the security and efficiency of system data. Using such a high-strength encryption algorithm helps the system comply with the mandatory requirements of laws and regulations such as the Personal Information Protection Law and the Measures for the Administration of Network Security of Medical and Health Institutions for the processing of sensitive personal information.
[0016] 3. This invention calculates a comprehensive pain score. This method integrates patients' subjective feelings, objective physiological indicators, and clinical medication information to generate a single, comprehensive pain assessment index. This effectively avoids the potential biases that may arise from using VAS scores alone, providing healthcare professionals with a more comprehensive and reliable pain assessment tool. Attached Figure Description
[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Please see Figure 1 A postoperative pain rehabilitation nursing data management system for the Department of Rehabilitation Medicine includes nine processing modules: a multi-dimensional dynamic data acquisition module, a cross-platform data interaction module, a data encryption module, a data analysis module, a real-time pain risk early warning module, an intelligent intervention plan generation module, a pain rehabilitation assessment module, a nursing feedback module, and a system emergency disaster recovery module. The multidimensional data dynamic acquisition module is responsible for collecting postoperative pain-related data from multiple sources in real time, including physiological parameters (such as heart rate, blood pressure, and blood oxygen saturation), pain scores (such as VAS and NRS scales), patient behavior data (such as sleep quality and activity level), and postoperative medication records. It also supports dynamic updates to ensure the timeliness and completeness of the data. The cross-platform data interaction module enables data interoperability and synchronization between the system and multiple platforms such as the Hospital Information System (HIS), Electronic Medical Record System (EMR), mobile nursing terminals, and wearable devices. It adopts the HL7 FHIR international medical data standard interface. Specific interaction functions include: automatically obtaining the patient's preoperative diagnosis and surgical type from the EMR, synchronizing blood routine and inflammatory indicators (such as C-reactive protein) from the LIS, verifying drug allergy history and drug inventory from the HIS, synchronizing patient bedside data in real time through the mobile nursing terminal, and automatically receiving physiological and behavioral monitoring data from wearable devices. This avoids manual re-entry and medication risks, and ensures seamless data flow and sharing between different systems and devices. The data encryption module connects the multi-dimensional data dynamic acquisition module and the cross-platform data interaction module to ensure patient privacy and data security, prevent data leakage and tampering, and encrypt all sensitive data during the collection, transmission and storage process. The specific operation is as follows: First, distinguish the types of privacy information, classifying patient ID numbers, medical history details and genetic information as highly sensitive privacy data, and vital signs and pain scores as low-sensitivity business data; then, adopt a layered encryption scheme that complies with national medical data security standards (such as the "Information Security Technology Health and Medical Data Security Guide" GB / T 39725-2020), using the AES-256 symmetric encryption algorithm to encrypt the storage of highly sensitive data, using the RSA asymmetric encryption algorithm to realize key exchange, and using the TLS1.3 protocol to ensure data transmission encryption. The key is managed by a designated person in the hospital's information department and is automatically rotated every 7 to 10 days. The data analysis module calculates a comprehensive pain score based on the collected pain-related data. With the recovery progress index To identify trends in pain, assess the effectiveness of interventions, and identify potential risk factors, thereby providing a scientific basis for clinical decision-making; The real-time pain risk early warning module connects the data analysis module and the intelligent intervention plan generation module, solving the problem of traditional monitoring lag. It uses a pre-set risk model (a machine learning model trained on historical patient data) and combines it with data analysis results to monitor the patient's pain status in real time. When a comprehensive pain score is detected... Abnormalities (such as pain score) When the level rises sharply and vital signs become abnormal, or when the recovery progress index rises sharply. When progress is low (e.g., recovery progress index) If the vital signs are below the threshold and abnormal, an automatic warning will be triggered to notify medical staff for timely handling. The intelligent intervention plan generation module automatically generates personalized pain intervention plans based on individual patient differences, pain assessment results, and clinical guidelines. These plans include medication adjustments, physical therapy recommendations, and psychological intervention measures, and can be dynamically adjusted and optimized. The pain rehabilitation assessment module connects the intelligent intervention plan generation module and the nursing feedback module to systematically assess the patient's pain rehabilitation process, output rehabilitation assessment results, and assist doctors in adjusting treatment plans. The nursing feedback module collects patient feedback on pain care services (such as satisfaction, pain relief experience, and nursing suggestions), and incorporates the feedback results into the nursing quality improvement process to enhance the pertinence and humanization of nursing services. The system emergency disaster recovery module provides a system-level disaster recovery backup and emergency response mechanism, including data backup, automatic fault switching and disaster recovery plans, to ensure that the system can still operate stably in the event of an emergency, and to ensure that medical data is not lost and services are not interrupted.
[0020] The multidimensional data dynamic acquisition module includes a subjective pain assessment unit, a psychological state assessment unit, a physiological and behavioral data monitoring unit, and a treatment execution record unit.
[0021] The subjective pain assessment unit collects patients' postoperative pain scores (including digital score NRS 0-10 points and visual analog scale VAS 0-10cm), pain nature (such as stabbing / throbbing pain), and frequency of attacks (times / day) in real time through a bedside smart terminal.
[0022] The psychological state assessment unit automatically sends the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) to the patient's device between 8 and 9 a.m. every day and collects scores.
[0023] The physiological and behavioral data monitoring unit transmits physiological parameters such as heart rate, blood pressure, heart rate variability (HRV), and blood oxygen saturation (SpO2) in real time through wireless wearable devices (such as smart bracelets and sleep monitors). It also records patients' sleep quality (such as sleep duration and number of awakenings), activity levels (such as daily steps) and other outdoor behavior data.
[0024] The treatment execution record unit allows medical staff to input data on drug intervention (dosage, administration time, and type of medication) and non-drug intervention (duration of physical therapy and number of psychological counseling sessions) through the system terminal, achieving full coverage of multi-dimensional assessment data.
[0025] The advantages are: by integrating the patient's subjective feelings, objective physiological indicators, treatment implementation and psychological state data through the above units, a three-dimensional and panoramic perception of the patient's recovery status can be achieved, breaking the barriers of one-sided and delayed information in traditional assessments.
[0026] The data encryption module uses the RSA asymmetric encryption algorithm to exchange keys, ensuring patient privacy and data security. The calculation process is as follows: S1.1 Key generation process: Select two distinct large prime numbers x and y, and calculate their product. L is the key length; then calculate the Euler totient function. Select an integer ,1< < ,and and Coprime, ( , ) forms the public key; calculate b for Modular inverse element That is, satisfying ≡1( ),final This is composed of a private key; S1.2 The encryption calculation formula is as follows: ; In the formula, This represents the raw data to be encrypted, i.e., the patient's plaintext medical data (such as pain scores, physiological parameters, etc.), which is used in the calculation. It needs to be converted to a value less than integers, and Represents the public key. This represents the ciphertext data obtained after encryption; The advantages are: by encrypting a temporary symmetric encryption key (such as an AES key) using the RSA algorithm, and then using this symmetric key to encrypt large amounts of actual medical data at high speed, this hybrid encryption mechanism can balance the security and efficiency of system data. Using such a high-strength encryption algorithm helps the system comply with the mandatory requirements of laws and regulations such as the Personal Information Protection Law and the Measures for the Administration of Network Security of Medical and Health Institutions for the processing of sensitive personal information.
[0027] The data analysis module calculates a comprehensive pain score. The calculation formula is as follows: ; In the formula, The pain score, also known as the subjective pain score, is represented by the internationally recognized visual analog scale (VAS). Patients self-mark their pain on a scale of 0-10, with 0 indicating no pain and 10 indicating unbearable, severe pain. This reflects the patient's subjective experience of pain. Indicates real-time heart rate. This indicates the patient's baseline heart rate at rest. Heart rate is a key objective indicator reflecting physiological stress and sympathetic nerve activity. Pain usually triggers physiological stress, leading to an increase in heart rate. This quantifies the degree of physiological changes caused by pain in the patient. The equivalent daily dose of analgesics is calculated by converting all different types and routes of administration of opioid analgesics used by the patient within 24 hours into the equivalent milligrams of oral analgesics using a standardized conversion factor. This indicator objectively reflects the severity of pain and the intensity of drug intervention. , , These represent the weighting coefficients for VAS score, heart rate variability, and drug dosage, respectively. System administrators or clinical experts will dynamically adjust these weights based on the department's treatment philosophy and patient characteristics. For example, for patients who cannot express themselves clearly, the weight of objective indicators will be increased. The advantage is that it calculates a comprehensive pain score. This method integrates patients' subjective feelings, objective physiological indicators, and clinical medication information to generate a single, comprehensive pain assessment index. This effectively avoids the potential biases that may arise from using VAS scores alone, providing healthcare professionals with a more comprehensive and reliable pain assessment tool.
[0028] The data analysis module calculates the rehabilitation progress index. The calculation formula is as follows: ; In the formula, This indicates the current functional score, measured using standardized scales (such as joint range of motion, muscle strength rating, Barthel index, etc.). This indicates the baseline functional score before the start of rehabilitation therapy. This indicates the time elapsed from the baseline assessment to the current assessment (in days). Indicates during the evaluation period Inside, all pain scores The average value represents the average level of pain experienced by the patient during this period. Indicates during the evaluation period Internal pain comprehensive score assessment for patients. Total number of times The Side Effect Index is a comprehensive quantitative value calculated by weighting the severity and frequency of common drug side effects such as nausea, vomiting, sedation, and constipation. The weighting coefficients of the side effect index are used to adjust for the impact of side effects in the overall assessment. It is the natural logarithm function, and its internal... The structure ensures that even if there is a slight regression in function in the short term (resulting in a negative value), the function is still defined and computable. 𝑒 is the base of the natural logarithm. This function is used to perform a non-linear transformation on the magnitude of functional improvement, so that small progress in the early stages of rehabilitation can produce meaningful exponential changes, while avoiding exponential explosive growth caused by huge progress in the later stages. The advantage is that it allows for the calculation of the rehabilitation progress index. In its calculation formula, the numerator represents the rehabilitation benefits, i.e., the extent and speed of functional recovery, while the denominator represents the rehabilitation costs, i.e., the pain (average pain level) and side effects suffered by the patient during the process. Therefore, when A higher calculated value indicates that the patient experiences less pain and side effects in exchange for faster and better functional recovery. This calculation formula can provide strong quantitative evidence for assessing rehabilitation efficiency, comparing the effectiveness of different intervention programs, and predicting rehabilitation outcomes.
[0029] The real-time pain risk warning module is based on the comprehensive pain score output by the data analysis module. Three levels of early warning thresholds are defined: ① Level 1 Warning (Emergency): ≥7 points, accompanied by a heart rate >100 beats / min or blood pressure >160 / 95 mmHg; ② Level II Warning (Attention): 5≤ <7 points, accompanied by SpO2 <95%; ③ Level 3 Early Warning (Observation): 3≤ <5 points, with no downward trend for 2-3 consecutive hours; Real-time pain risk warning module monitors pain in real time. Numerical range, when it appears In case of an anomaly, the early warning trigger mechanism is activated immediately, and the system automatically calculates every 4 to 5 minutes. When the threshold is reached, a notification will be sent to the responsible nurse and attending physician via a pop-up window on the hospital intranet and a mobile APP. At the same time, a prompt for medical assessment will be displayed on the smart terminal at the patient's bedside to ensure that medical staff can handle the situation in a timely manner. The intelligent intervention plan generation module connects the real-time pain risk early warning module and the pain rehabilitation assessment module, solving the problem of fragmented treatment strategies. It considers individual patient differences (such as age and underlying diseases) and pain assessment results (comprehensive pain score). Rehabilitation Progress Index Based on data analysis and clinical guidelines (such as the "Expert Consensus on Postoperative Pain Management in China"), the system automatically generates personalized pain intervention plans, which can be dynamically adjusted and optimized. The plan generation logic combines the output of the data analysis module... score, Based on the index and patient allergy history and surgical type obtained from the cross-platform data interaction module, a combined drug and non-drug treatment plan is generated, specifically as follows: ① Drug intervention: When For patients with a score of ≥7 and no history of peptic ulcer, intravenous parecoxib sodium 30-40 mg is recommended, repeated at 12-13 hour intervals. ② Non-pharmacological intervention: When When the score exceeds 50 points, a 10-12 minute breathing relaxation training video is automatically pushed, and an intervention by a psychologist is scheduled within 12-13 hours; simultaneously, the drug inventory is checked in real time from the HIS, and when the target drug is out of stock, a similar alternative drug is automatically recommended (e.g., parecoxib sodium is replaced with flurbiprofen ester); when the patient... When the index remains below 0.4, the system automatically triggers a plan optimization, prompting medical staff to adjust intervention methods (such as increasing the frequency of physical therapy).
[0030] The advantages are: by utilizing a pre-set model of comprehensive scoring and progress index, raw data is transformed into clinical insights, personalized plans are generated, and real-time warnings are issued, realizing a transformation of the nursing model from "post-event recording" to "pre-event warning and in-event intervention," thereby significantly improving the accuracy and timeliness of the systemic treatment of this invention. Furthermore, by forming a "closed-loop, continuous improvement" nursing feedback and system assurance mechanism, patient feedback is incorporated into the quality improvement process, and the emergency disaster recovery module ensures that the service is never interrupted, so as to achieve a spiral increase in the quality of systemic nursing services and system reliability, and ensure the synchronous optimization of medical safety and patient experience.
[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A data management system for postoperative pain rehabilitation nursing in the Department of Rehabilitation Medicine, characterized in that, It includes nine processing modules: multi-dimensional dynamic data acquisition module, cross-platform data interaction module, data encryption module, data analysis module, real-time pain risk early warning module, intelligent intervention plan generation module, pain rehabilitation assessment module, nursing feedback module, and system emergency disaster recovery module. The multidimensional data dynamic acquisition module is responsible for collecting postoperative pain-related data from multiple sources in real time, including physiological parameters, pain scores, patient behavior data, and postoperative medication records, and supports dynamic updates. The cross-platform data interaction module enables data exchange and synchronization between the system and multiple platforms, including hospital information systems, electronic medical record systems, mobile nursing terminals, and wearable devices. The data encryption module encrypts all sensitive data during the collection, transmission, and storage process. It uses the RSA asymmetric encryption algorithm to achieve key exchange and the TLS 1.3 protocol to ensure encrypted data transmission. The key is managed by a designated person from the hospital's information department and is automatically rotated every 7 to 10 days. The data analysis module calculates a comprehensive pain score based on the collected pain-related data. With the recovery progress index To identify trends in pain, assess the effectiveness of interventions, and identify potential risk factors, thereby providing a scientific basis for clinical decision-making; The real-time pain risk early warning module is pre-set with a risk model and combines data analysis results to monitor the patient's pain status in real time. When the overall pain score is detected... Abnormal or recovery progress index If the progress is slow, an early warning will be automatically triggered to notify medical staff to handle the situation promptly. The intelligent intervention plan generation module automatically generates personalized pain intervention plans based on the patient's individual differences, pain assessment results, and clinical guidelines. The pain rehabilitation assessment module systematically assesses the patient's pain rehabilitation process, outputs rehabilitation assessment results, and assists doctors in adjusting treatment plans. The nursing feedback module collects patient feedback on pain care services and incorporates the feedback results into the nursing quality improvement process. The system's emergency disaster recovery module provides a system-level disaster recovery backup and emergency response mechanism, including data backup, automatic fault switching, and disaster recovery plans.
2. The postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 1, characterized in that: The multidimensional data dynamic acquisition module includes a subjective pain assessment unit, a psychological state assessment unit, a physiological and behavioral data monitoring unit, and a treatment execution record unit.
3. The postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 2, characterized in that: The subjective pain assessment unit collects the patient's postoperative pain score and frequency of attacks in real time through a bedside smart terminal.
4. The postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 2, characterized in that: The psychological state assessment unit automatically sends the anxiety self-rating scale and the depression self-rating scale to the patient's end between 8 and 9 a.m. every day and collects scores.
5. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 2, characterized in that: The physiological and behavioral data monitoring unit transmits physiological parameters such as heart rate, blood pressure, heart rate variability and blood oxygen saturation in real time through a wireless wearable device, while also recording the patient's sleep quality and other outdoor behavior data.
6. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 2, characterized in that: The treatment execution record unit is where medical staff input data on drug intervention and non-drug intervention through a system terminal.
7. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 1, characterized in that: The data encryption module uses the RSA asymmetric encryption algorithm to achieve key exchange, and its calculation process is as follows: S1.1 Key generation process: Select two distinct large prime numbers x and y, and calculate their product. L is the key length; then calculate the Euler totient function. Select an integer ,1< < ,and and Coprime, ( , The public key is composed of ( ) elements. Calculate b for Modular inverse element That is, satisfying ≡1( ),final This is composed of a private key; S1.2 The encryption calculation formula is as follows: ； In the formula, This represents the original data to be encrypted, i.e., the patient's plaintext medical data, during the calculation. It needs to be converted to a value less than integers, and Represents the public key. This represents the encrypted data.
8. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 1, characterized in that: The data analysis module calculates a comprehensive pain score. The calculation formula is as follows: ； In the formula, This indicates the overall pain score. Indicates real-time heart rate. This indicates the patient's baseline heart rate at rest. This indicates the equivalent daily dose of analgesic medication. , , These represent the weighting coefficients for VAS score, heart rate variability, and drug dosage, respectively.
9. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 1, characterized in that: The data analysis module calculates the rehabilitation progress index. The calculation formula is as follows: ； In the formula, This indicates the current feature rating. This indicates the baseline functional score before the start of rehabilitation therapy. This indicates the time elapsed from the baseline assessment to the current assessment. Indicates during the evaluation period Inside, all pain scores The average value, Indicates during the evaluation period Internal pain comprehensive score assessment for patients. Total number of times Indicates the side effect index. This represents the weighting coefficient of the side effect index. It is the natural logarithm function, and 𝑒 is the base of the natural logarithm.
10. A postoperative pain rehabilitation nursing data management system for rehabilitation medicine department according to claim 1, characterized in that: The real-time pain risk early warning module is based on the comprehensive pain score output by the data analysis module. Three levels of early warning thresholds were defined; The intelligent intervention plan generation module takes into account the patient's individual differences and comprehensive pain score. Rehabilitation Progress Index Based on clinical guidelines, it automatically generates personalized pain intervention plans and can dynamically adjust and optimize them: the plan generation logic combines the output of the data analysis module. score, The index, along with patient allergy history and surgical type obtained from the cross-platform data interaction module, generates a synergistic drug and non-drug treatment plan.