Extending care beyond the clinic: integrating patient-reported outcomes in chronic pain management through human factors engineering
Bibliographic record
Abstract
Objectives: Tapering prescription opioid pain medication through evidence-based guidelines can help in combating the opioid epidemic. Integrating clinical decision support (CDS) into the clinical workflow of tapering can help in translating guidelines to formulate and implement a tapering plan that manages pain symptoms while minimizing withdrawal, and optimally engages with the patient. The purpose of our project was to develop patient- and clinician-facing CDS in the area of chronic pain management in one integrated application (app) called Tapering And Patient Reporting outcomes for Chronic Pain Management (TAPR-CPM) App. Methods: We leveraged human factors methodologies and a user-centered design (UCD) approach through guideline review, stakeholder interviews, ethnographic workflow analysis, process mapping, design workshops, and usability testing. Participants included patients with chronic noncancer pain, their family members, pain management physicians, primary care physicians, and health IT developers who focus on patient- and provider-facing technologies. Results: Based on interview findings and workflow analysis, the provider-facing app had five sections: Patient Context, Taper Settings, Create Taper Plan, Withdrawal and Non-opioid Pain Plan, and Summary Dashboard. The patient-facing app had three sections: Maintaining a Pain Journal, Sharing Pain Scores with Provider, and Connecting to Resources about Opioid Tapering. Conclusions: This project leveraged a multi-method approach based in human factors and UCD to develop the TAPR-CPM app. Engaging with a diverse set of stakeholders including patients, caregivers, primary care providers, pain specialists, and health information technology developers was critical to develop a user-friendly experience with accessible technology to support patient engagement and provider decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".