A pain care follow-up system

Abstract

The application discloses a pain nursing follow-up system, comprising: an information management module for establishing a pain specialty electronic archive to provide complete baseline data for personalized follow-up; a pain assessment module for accurately quantifying pain and multidimensional influence; a nursing intervention management module for recording and tracking the implementation and effect feedback of drug and non-drug analgesic measures; a follow-up execution module for developing follow-up strategies according to disease stratification to ensure continuous post-hospital care; a decision module for converting assessment data into visual insights and early warnings to drive evidence-based clinical decision-making; and a safety management module for monitoring nursing quality indicators and risk events, the application relates to the technical field of pain nursing; the application can realize continuous intelligent management of patient pain nursing follow-up with the aid of whole-cycle digital closed loop, greatly reduce the pain nursing follow-up intensity of medical staff, and greatly improve the accuracy, efficiency and safety of pain management.

Description

Technical Field

[0001] This invention relates to the field of pain management technology, specifically a pain management follow-up system. Background Technology

[0002] Pain management follow-up is a core link in modern comfortable medical systems that connects in-hospital pain intervention with out-of-hospital rehabilitation. It is led by professionally trained pain specialist nurses and is aimed at all populations, including postoperative analgesia patients and cancer pain patients. It simultaneously provides individualized medication guidance, non-pharmacological analgesia intervention, psychological counseling and health education. It can promptly correct patients' misconceptions such as "enduring pain without taking medication" and "being afraid to take medication for fear of addiction", and effectively reduce the incidence of moderate to severe pain. However, traditional pain management relies on a fragmented model that relies on paper quality forms, manual follow-up, experience-based judgment, and post-event quality control. Because patient information is scattered across multiple isolated systems such as HIS, pharmacy, and nursing records, it is difficult to form a continuous pain trajectory. The overall management model exhibits prominent characteristics of fragmentation, lag, and inefficiency.

[0003] Therefore, the present invention provides a pain care follow-up system to solve the above problems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a pain care follow-up system that solves the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a pain care follow-up system, comprising: The information management module is used to establish electronic records for pain specialists, providing complete baseline data for personalized follow-up. The pain assessment module is used to accurately quantify pain and its multidimensional impact. The nursing intervention management module is used to record and track the implementation and effectiveness feedback of pharmacological and non-pharmacological analgesic measures; The follow-up execution module is used to develop follow-up strategies based on disease type to ensure uninterrupted post-discharge continuous care. The decision-making module is used to transform assessment data into visual insights and early warnings to drive evidence-based clinical decisions. The safety management module is used to monitor nursing quality indicators and risk events to ensure the standardization of the follow-up process and data security.

[0006] Preferably, the information management module includes: The basic layer is used to establish a unique patient identification and basic contact information to ensure that the follow-up subjects are accurate and accessible. The clinical layer is used to aggregate information on disease and pain diagnosis and treatment, providing a medical basis for the selection of assessment tools and the development of analgesia regimens. The sociopsychological layer is used to capture the sociopsychological factors that influence pain perception and rehabilitation, and to identify follow-up compliance and prognostic risks. At the system layer, tags and operation logs are intelligently generated through a rules engine to achieve automated data flow, hierarchical management, and security auditing.

[0007] Preferably, the system layer also includes a weekly automatic tag quality audit script, with the following steps: The first step is to extract the new or changed tags this week; The second step is to cross-validate the label conditions against the actual data. The third step is to output a list of abnormal tags: Type A: tags that should have been labeled but were not, or labels that were incorrectly labeled; Type B: tags that were labeled even though the conditions were not met; Type C: tags for changes in the condition that were not updated; Type D: mutually exclusive tags that exist simultaneously. The fourth step is to forward the request to quality control personnel for processing within a specified timeframe.

[0008] Preferably, when the label rule is positive, the condition is met and a label is generated; when the label rule is negative, the condition is not met and the label is removed or downgraded; when label rules conflict, priority arbitration is performed for multiple rule collisions.

[0009] Preferably, the logic of the pain assessment module is as follows: S1, the system automatically pushes evaluation tasks; S2, matching the optimal combination of scales based on patient characteristics; S3, Information Entry; S4, real-time logic verification, abnormal data blocks submission; S5, abnormal data triggers a response; S6, cross-validated with drug administration records and physiological indicators; S7, automatic upgrade processing for poor continuous control or frequent burst pain; S8. The assessment results are written into the file, driving adjustments to the follow-up plan.

[0010] Preferably, the patient characteristics are denoted as NRS, and the pain level of NRS is divided into 0-3 mild, 4-6 moderate and 7-10 severe, and a response is triggered when the pain level is reached.

[0011] Preferably, the decision module is based on parallel computation using a multi-layer model, which includes: Description layer: a snapshot of the current state; Diagnostic layer: Used to perform root cause analysis based on dosage, compliance, and disease progression, and to identify abnormal root causes; The prediction layer is used to predict the probability of risk over the next 7, 30, and 90 days. Prescription layer, used for intervention recommendations based on evidence-based guidelines.

[0012] Preferably, the abnormal root cause is determined by comparing NRS and physiological indicators at the same time period. If the correlation coefficient r < 0.5, it indicates assessment distortion or establishes a logical verification rule. If NRS = 0 after drug administration, an abnormality is triggered, and the original record is traced back.

[0013] Preferably, the security module includes: At the monitoring and execution level, real-time rule-based interception, regular audit reviews, and in-depth special inspections are conducted to ensure that the pain management process meets standards and is corrected in a timely manner. The access control layer is used to manage data access and operation scope according to roles, with full auditing and disaster recovery assurance.

[0014] Preferably, the ultimate security layer also includes a blockchain verification module for storing key quality control data on the blockchain.

[0015] Beneficial effects This invention provides a pain management follow-up system. Compared with existing technologies, it has the following advantages: (1) This pain care follow-up system can realize continuous intelligent management of patient pain care follow-up by means of a full-cycle digital closed loop, which greatly reduces the pain care follow-up intensity of medical staff and significantly improves the accuracy, efficiency and safety of pain management. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention; Detailed Implementation 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.

[0017] Example 1: Please see Figure 1 A pain care follow-up system, comprising: The information management module is used to establish electronic records for pain specialists, providing complete baseline data for personalized follow-up. The pain assessment module is used to accurately quantify pain and its multidimensional impact. The nursing intervention management module is used to record and track the implementation and effectiveness feedback of pharmacological and non-pharmacological analgesic measures; The follow-up execution module is used to develop follow-up strategies based on disease type to ensure uninterrupted post-discharge continuous care. The decision-making module is used to transform assessment data into visual insights and early warnings to drive evidence-based clinical decisions. The safety management module is used to monitor nursing quality indicators and risk events to ensure the standardization of the follow-up process and data security. In this embodiment, the information management module includes: The basic layer is used to establish a unique patient identification and basic contact information to ensure that the follow-up subjects are accurate and accessible. The clinical layer is used to aggregate information on disease and pain diagnosis and treatment, providing a medical basis for the selection of assessment tools and the development of analgesia regimens. The sociopsychological layer is used to capture the sociopsychological factors that influence pain perception and rehabilitation, and to identify follow-up compliance and prognostic risks. At the system layer, tags and operation logs are intelligently generated through a rules engine to achieve automated data flow, hierarchical management, and security auditing. The more detailed contents of the basic, clinical, psychosocial, and systemic layers are shown in the table below: More detailed information about the rule engine's tag types is shown in the table below: In this embodiment, the system layer also includes an automatically running label quality audit script every week, with the following steps: The first step is to extract the new or changed tags this week; The second step is to cross-validate the label conditions against the actual data. The third step is to output a list of abnormal tags: Type A: tags that should have been labeled but were not, or labels that were incorrectly labeled; Type B: tags that were labeled even though the conditions were not met; Type C: tags for changes in the condition that were not updated; Type D: mutually exclusive tags that exist simultaneously. The fourth step is to forward the request to quality control personnel for processing within a specified timeframe. The complete process of postoperative pain management for a certain patient is shown in the table below: The logic behind the audit sampling and the matching of Zhang's case is as follows: The system's automatic full-scale screening rules are as follows: 1. Postoperative patients are 100% included in the daily audit list; 2. Cases with an NRS score ≥ 7 are 100% included in the weekly in-depth audit; 3. Cases with pain control achievement time < 2 hours are 100% included in the monthly benchmarking study; 4. Any adverse events are 100% included in the quarterly root cause analysis; 5. 5% of rare success / failure cases are randomly sampled and included in the annual research audit.

[0018] The patient's hit path is as follows: 1. Daily audit: Post-operative patients → hit rate; 2. Weekly Audit: Initial NRS = 6 ≥ 7? No, but the closed loop is complete → manually marked as important; 3. Monthly Audit: Achieve target within 1.5 hours but less than 2 hours → Hit the benchmark learning pool; 4. Quarterly Audit: As part of the evidence chain for the effectiveness of improvements in Q1 → Hit; 5. Annual Audit: Random sampling from the benchmark pool → Hitting the research cohort; In this embodiment, when the label rule is positive, the condition is met and a label is generated; when the label rule is negative, the condition is not met and the label is removed or downgraded; when label rules conflict, multiple rule collisions are arbitrated based on priority. In this embodiment, the logic of the pain assessment module is as follows: S1, the system automatically pushes evaluation tasks; S2, matching the optimal combination of scales based on patient characteristics; S3, Information Entry; S4, real-time logic verification, abnormal data blocks submission; S5, abnormal data triggers a response; S6, cross-validated with drug administration records and physiological indicators; S7, automatic upgrade processing for poor continuous control or frequent burst pain; S8. The assessment results are written into the file, driving adjustments to the follow-up plan; More specifically, patient characteristics are denoted as NRS, and the pain level of NRS is divided into mild (0-3), moderate (4-6), and severe (7-10). A response is triggered when the pain level is reached. In this embodiment, the decision module is based on parallel computation using a multi-layer model, which includes: Description layer: a snapshot of the current state; Diagnostic layer: Used to perform root cause analysis based on dosage, compliance, and disease progression, and to identify abnormal root causes; The prediction layer is used to predict the probability of risk over the next 7, 30, and 90 days. Prescription layer, used for intervention recommendations based on evidence-based guidelines; In this embodiment, the abnormal root cause is determined by comparing NRS and physiological indicators at the same time period. If the correlation coefficient r < 0.5, it indicates assessment distortion or the establishment of a logical verification rule. If NRS = 0 after drug administration, an abnormality is triggered, and the original record is reviewed. The authenticity determination of NRS = 6 immediately after a certain patient is shown in the table below: Based on the table above, a comprehensive judgment was made, the five sources of evidence converged, and the authenticity of NRS=6 was confirmed. Therefore, it was included in the subsequent root cause analysis. If the patient's NRS is 6, but the cross-validation is as shown in the table below: Then a subsequent distortion determination process is required, as follows: The first step is to mark an anomaly (the system triggers an assessment authenticity warning: the difference between self-assessment and other-assessment scores > 3 points); The second step is to investigate technical factors, which include: 1. Was the scale misused? 2. Check for data entry errors: If the patient verbally states "6" and the nurse enters "6" into the system, then they are consistent. 3. Was the timing inappropriate? Verification: The assessment was conducted during a resting period, with no operational interference. The third step is to attribute the causes to psychological and social factors, which include: 1. Anxiety and catastrophizing: PCS score = 38 (high catastrophizing), HADS-A score = 15; 2. Whether attention was sought, and verification: whether the person lives alone, and whether family members were present after the surgery; 3. Whether medication was sought; verification: no history of substance abuse. More specifically, the security modules include: At the monitoring and execution level, real-time rule-based interception, regular audit reviews, and in-depth special inspections are conducted to ensure that the pain management process meets standards and is corrected in a timely manner. The core principle of the monitoring execution layer is to first convert quality control standards into executable code for the system (such as the requirement for re-evaluation within 30 minutes after postoperative drug administration), then capture system events that trigger monitoring nodes in real time, and then compare the actual behavior with the expected rules in real time. If they meet the requirements, the system will pass silently; if they deviate, a graded warning will be issued. Then, intervention will be carried out accordingly, and finally, abnormal events will be archived for regular auditing and trend analysis. Furthermore, a sample case for monitoring the execution layer is as follows: Timeline: March 15, 2026: Postoperative pain management; 1. At 14:32:05, the nurse entered "NRS=6 immediately after surgery," and then real-time verification was triggered. Rule R1: The first postoperative assessment must be conducted within 2 hours of returning to the ward; Actual: Surgery ended at 14:00, assessment completed at 14:32; Rule R2: Analgesia intervention must be initiated if NRS score is ≥4. Actual: NRS = 6, the system automatically generates a request to perform analgesia task to trigger the intervention process. The system action is as follows: push the task to the responsible nurse's workstation and the pharmacy system; 2. At 14:35:18, the pharmacy system recorded the intravenous administration of 2mg of morphine, triggering a timing check. Rule R3: A reassessment must be conducted within 30 minutes after intervention for moderate to severe pain; Expected review event window: 14:35~15:05; System action: countdown timer starts; if review is not completed by 15:05, a yellow warning will be issued. 3. At 15:05:33, the nurse entered the post-administration reassessment NRS value as 4; immediately following, the closed-loop verification was triggered: 1. Rule R3 verification: The re-evaluation time was 15:05, which is 30 minutes and 15 seconds after the administration time of 14:35. It passed. 2. Rule R4: If the NRS score is still ≥4 after reassessment, it is necessary to evaluate whether to adjust the plan; 3. Actual: NRS=4, the system automatically pushes suggestions to doctors to adjust the treatment plan to the doctor's workstation; 4. At 15:10:45, the doctor's workstation recorded a PCA background dose of +0.5 mg / h, followed by authorization and compliance checks. 1. Rule R5: PCA dosage adjustment requires the authority of an attending physician or higher. In practice: Doctor D005 is the attending physician, and the result is: approved. 2. Rule R6: The single adjustment range shall not exceed 50% of the original dose. Actual: The original background dose was 1 mg / h, and after adjustment it was 1.5 mg / h, an increase of 50%. The result was: Pass (critical warning). The system action was to record the reason for the adjustment and trigger the next re-evaluation reminder. 5. At 16:02:11, the nurse entered NRS=3 for the reassessment, and immediately afterwards, the target assessment was triggered; Rule R7: The target pain control NRS is ≤3 within 24 hours postoperatively. Actual NRS = 3, which is considered achieved. System action: downgrade to routine monitoring frequency (assessment every 4 hours). 6. At 20:15:47, the nurse entered NRS=2 during night rounds, and immediately afterward, the stability check was triggered; Rule R8: If three consecutive assessments score ≤3 and there is no upward trend, maintain the current plan. Actual: 16:02 (NRS=3) → 20:15 (NRS=2), the result is stable, the system action is: no warning, and the record is archived. The access control layer is used to manage data access and operation scope according to roles, with full auditing and disaster recovery assurance. In this implementation, the ultimate security layer also includes a blockchain verification module, which is used to store key quality control data on the blockchain for verification. In more detail, the on-chain hash calculation of the data in the blockchain verification module is as follows: in, For the event to occur, The identifier is the patient's real ID after HMAC-SHA256 desensitization. Encode the event type. JSON serialization of the core fields of the event. The hash value of the previous record forms a chain structure; Then, by relying on the Merkle tree to verify a single record, only the hash path of the record and its sibling nodes needs to be provided, without exposing the full amount of data, thus protecting privacy while completing the integrity verification. To prove that a 68-year-old male patient underwent total knee replacement surgery on March 15, 2026, and that the patient claims severe postoperative pain that was not treated promptly, leading to delayed recovery; and that the hospital claims it conducted a standard assessment and adjusted the treatment plan accordingly, and that the patient has a low pain tolerance threshold, the following evidence is available on the blockchain: The subsequent cross-examination process will proceed as follows: 1. The hospital submits on-chain evidence; The following materials should be submitted: 1. Patient ID anonymization identifier: HMAC(PatientID, Hospital Key) = 0x4a2b... 2. Disputed time period block range: #1523401~#1523407 3. Merkel Path Proof: Proves that the target block is contained within the chain. 4. Timestamp Certificate: Issued by a Trusted Timestamp Service (TSA) II. Verification by the arbitration institution; 1. Hash recalculation, such as locally calculating the hash of #1523403 and substituting it into the formula. As can be seen from the above, the result is consistent with the record on the chain; 2. Chain verification The chain did not break. 3. Merkel path verification, the specific process is as follows: 1. Provide the hash of sibling node #1523404 2. Recalculate the parent node 3. Tracing back layer by layer to RootHash = 0x7e3c... 4. The result is consistent with the RootHash of the latest block in the chain; 4. Timestamp verification, the process is as follows: TSA certificate: RFC 3161 format, issuance event: 2026-03-15 15:05:33.247, the difference between the certificate and the block timestamp is less than 500ms, indicating that the synchronization is valid; III. With the patient's authorization, the data was decrypted and restored online, as follows: Timeline: 14:32 Postoperative NRS=6 (severe) → 14:35 Morphine 2mg IV → 15:05 Reassessment NRS=4 (moderate), 15:10 Doctor increases PCA background dose → 16:02 NRS=3 (reached target) → Nighttime maintenance NRS=2; The key facts are: 1. The assessment-intervention-reassessment closed loop is complete, and the intervals meet the standards (reassessment within 30 minutes). 2. Severe pain subsided to mild pain within 1.5 hours, indicating timely and effective adjustment of the treatment plan; 3. No assessment omissions, no drug administration delays, and no rigid protocols; The ruling concluded that the on-chain evidence was complete, the timeline was clear, and the operation was compliant, thus the hospital's claim was valid. However, the patient's accusation of "failure to process in a timely manner" contradicted the evidence data and was therefore not supported.

[0019] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0021] 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 pain management follow-up system, characterized in that: include: The information management module is used to establish electronic records for pain specialists, providing complete baseline data for personalized follow-up. The pain assessment module is used to accurately quantify pain and its multidimensional impact. The nursing intervention management module is used to record and track the implementation and effectiveness feedback of pharmacological and non-pharmacological analgesic measures; The follow-up execution module is used to develop follow-up strategies based on disease type to ensure uninterrupted post-discharge continuous care. The decision-making module is used to transform assessment data into visual insights and early warnings to drive evidence-based clinical decisions. The safety management module is used to monitor nursing quality indicators and risk events to ensure the standardization of the follow-up process and data security.

2. The pain care follow-up system according to claim 1, characterized in that: The information management module includes: The basic layer is used to establish a unique patient identification and basic contact information to ensure that the follow-up subjects are accurate and accessible. The clinical layer is used to aggregate information on disease and pain diagnosis and treatment, providing a medical basis for the selection of assessment tools and the development of analgesia regimens. The sociopsychological layer is used to capture the sociopsychological factors that influence pain perception and rehabilitation, and to identify follow-up compliance and prognostic risks. At the system layer, tags and operation logs are intelligently generated through a rules engine to achieve automated data flow, hierarchical management, and security auditing.

3. The pain care follow-up system according to claim 2, characterized in that: The system layer also includes a weekly automated label quality audit script, with the following steps: The first step is to extract the new or changed tags this week; The second step is to cross-validate the label conditions against the actual data. The third step is to output a list of abnormal tags: Type A: tags that should have been labeled but were not, or labels that were incorrectly labeled; Type B: tags that were labeled even though the conditions were not met; Type C: tags for changes in the condition that were not updated; Type D: mutually exclusive tags that exist simultaneously. The fourth step is to forward the request to quality control personnel for processing within a specified timeframe.

4. The pain care follow-up system according to claim 3, characterized in that: When the label rule is positive, the condition is met and a label is generated. When the label rule is negative, the condition is not met, and the label is removed or downgraded. When label rules conflict, multiple rule collisions are arbitrated based on priority.

5. A pain management follow-up system according to claim 1, characterized in that: The logic of the pain assessment module is as follows: S1, the system automatically pushes evaluation tasks; S2, matching the optimal combination of scales based on patient characteristics; S3, Information Entry; S4, real-time logic verification, abnormal data blocks submission; S5, abnormal data triggers a response; S6, cross-validated with drug administration records and physiological indicators; S7, automatic upgrade processing for poor continuous control or frequent burst pain; S8. The assessment results are written into the file, driving adjustments to the follow-up plan.

6. A pain management follow-up system according to claim 5, characterized in that: The patient characteristics are denoted as NRS. The pain level of NRS is divided into mild (0-3), moderate (4-6), and severe (7-10). A response is triggered when the pain level is reached.

7. The pain care follow-up system according to claim 1, characterized in that: The decision-making module is based on parallel computation using a multi-layer model, which includes: Description layer: a snapshot of the current state; Diagnostic layer: Used to perform root cause analysis based on dosage, compliance, and disease progression, and to identify abnormal root causes; The prediction layer is used to predict the probability of risk over the next 7, 30, and 90 days. Prescription layer, used for intervention recommendations based on evidence-based guidelines.

8. A pain management follow-up system according to claim 7, characterized in that: The abnormal root cause is determined by comparing NRS and physiological indicators at the same time period. If the correlation coefficient r < 0.5, it indicates assessment distortion or the establishment of logical verification rules. If NRS = 0 after drug administration, an abnormality is triggered, and the original records are traced back.

9. A pain management follow-up system according to claim 1, characterized in that: The security module includes: At the monitoring and execution level, real-time rule-based interception, regular audit reviews, and in-depth special inspections are conducted to ensure that the pain management process meets standards and is corrected in a timely manner. The access control layer is used to manage data access and operation scope according to roles, with full auditing and disaster recovery assurance.

10. A pain management follow-up system according to claim 9, characterized in that: The ultimate security layer also includes a blockchain verification module, which is used to store key quality control data on the blockchain for verification.

Images