[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Bjorn Ang\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":50},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":19,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":24,"briefSummary":26,"conditions":27,"keywords":31,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":5},"100599226","person-centred-ai-support-in-interdisciplinary-rehabilitation-for-chronic-pain-100599226",false,"NCT07081737","Person-Centred AI Support in Interdisciplinary Rehabilitation for Chronic Pain","The Future of Pain Management: Can Person-centred and Precision AI-Based Decision Support Enhance Interdisciplinary Rehabilitation for Chronic Pain?","pAIn","Inclusion Criteria:\n\n* Aged 18 to 67 years\n* Diagnosed with chronic non-malignant pain persisting longer than 3 months\n* Pain condition includes, but is not limited to: fibromyalgia, widespread pain, back pain, neck pain, or shoulder pain\n* Eligible for and referred to interdisciplinary rehabilitation (IDT) at a participating clinic\n* Willing and able to participate in digital assessment and follow-up procedures\n* Able to communicate and complete study materials in Swedish\n* Provides written informed consent\n\nExclusion Criteria\n\n* Pain caused by malignancy or cancer-related treatment\n* Pain caused by systemic diseases such as rheumatoid arthritis, lupus, or other autoimmune or inflammatory conditions\n* Severe psychiatric conditions interfering with study participation (e.g., untreated psychosis or severe depression requiring immediate psychiatric care)\n* Documented cognitive impairment limiting ability to understand study participation or complete self-reported measures\n* Currently enrolled in another interventional clinical trial that may confound the outcomes of this study\n* Not expected to remain in the clinic's follow-up system for the duration of the study","ALL","18 Years","67 Years",{"count":21,"type":22},400,"ESTIMATED","INTERVENTIONAL",[25],"NA","This cluster randomized controlled trial evaluates whether a person-centred, AI-supported Clinical Decision Support System (CDSS) can improve outcomes and cost-effectiveness in interdisciplinary rehabilitation for people with complex chronic pain. The CDSS is designed to assist clinicians in making personalized treatment decisions within standard interdisciplinary treatment (IDT). It has been developed using machine learning models trained on real-world data from over 100,000 patients in the Swedish Quality Registry for Pain Rehabilitation (SQRP), linked to several national registers, including the National Patient Register, the Prescribed Drug Register, the Social Insurance Agency database (MiDAS), and the Cause of Death Register. This enables individualized predictions of treatment outcomes, work ability, and healthcare utilization.\n\nThe trial includes 400 adult patients with chronic pain, enrolled at 20 IDT clinics randomized to either CDSS-supported or standard IDT. The study has three phases: feasibility, effectiveness, and implementation. The primary outcome is a patient-prioritized composite single-index of health-related well-being, based on domains such as pain, sleep, physical and mental health, emotional distress, and work ability. Patients prioritize these domains together with their clinical team, enabling a person-centred assessment. Secondary outcomes include HRQoL (EQ-5D, SF-36), emotional distress (HADS), and work ability (WAI), measured at baseline, post-treatment, 6- and 12-month follow-up.\n\nA parallel mixed-methods process evaluation will examine implementation outcomes such as usability, clinician adherence, and workflow integration, using logs, surveys (e.g., S-NoMAD), and interviews. Normalization Process Theory guides the analysis. Cost-utility will be assessed using QALYs and ICERs from a societal perspective, with long-term projections using simulation models. Results will be reported in peer-reviewed publications.",[28,29,30],"Pain, Chronic","Chronic Pain, Widespread","Pain Management",[32,33,34,35,36,37],"chronic pain","prediction","Artificial Intelligence","Interdisciplinary pain rehabilitation","Machine learning","Clinical Decision Support System (CDSS)","NOT_YET_RECRUITING","2025-07-16",{"date":41,"type":42},"2025-07-23","ACTUAL",{"date":44,"type":22},"2026-05-01",{"date":46,"type":22},"2029-03-31",{"name":48,"class":49},"Bjorn Ang","OTHER",""]