Primary Care Clinician Perspectives on Older Adult Chronic Pain Management and Clinical Decision Support: Qualitative Study
Bibliographic record
Abstract
Background: Chronic pain management in older adults can be challenging for primary care clinicians due to comorbidities, side effects, and complicated guideline recommendations. Clinical decision support systems (CDSSs) may improve care by integrating guideline-based recommendations, synthesizing relevant patient data, and facilitating shared decision-making. I-COPE (Improving Chicago Older Adult Opioid and Pain Management through Patient-centered Clinical Decision Support and Project ECHO) is an electronic health record-based CDSS designed to gather patient-reported data and support primary care clinicians in managing chronic pain, opioid use, and opioid use disorder in older adults. Objective: This study examined clinicians' views on challenges in managing chronic pain and their opinions on I-COPE. Methods: We conducted semi-structured interviews with 18 clinicians (16 physicians and 2 advanced practice nurses) from 2 University of Chicago Medicine primary care clinics (internal medicine and geriatrics) piloting the I-COPE CDSSs in 2021. The interview guide was informed by the Consolidated Framework for Implementation Research and explored current practices in chronic pain management, challenges, and feedback on I-COPE tools. Results: Of the 18 participants, 12 (67%) identified as female, 13 (72%) as White, and 9 (50%) had practiced for 10 years or less. Participants stressed the importance of a comprehensive, patient-centered approach to chronic pain management and prioritized multimodal and nonpharmacological treatments. Major barriers to effective chronic pain management were comorbidities, limited visit time, insurance coverage restrictions, and opioid misuse concerns. Most clinicians found the CDSSs beneficial for standardizing multimodal care discussions, enhancing visit efficiency, eliciting patient goals, and facilitating shared decision-making conversations. Clinicians raised concerns about the complexity of the intervention, anticipated issues with clinic workflow, and desired more adaptability. The primary care clinicians in this study demonstrated strong alignment with current pain management guidelines, prioritizing patient-centered pain management using multimodal treatments. They identified I-COPE as a promising tool to reinforce evidence-based practices, increase efficiency, and strengthen patient-clinician communication. However, implementation challenges-particularly around accessibility for older adults, workflow integration, and tool complexity-highlight the need for further refinement and support. Conclusions: I-COPE offers a promising approach to support primary care clinicians in providing patient-centered guideline-based chronic pain and opioid management for older adults. Further efforts to improve usability and adaptability for real-world workflows and equitable access for older adults should be prioritized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".