Coaching Patients to Understand and Use Patient-Reported Outcome Data: Intervention Design and Evaluation
Bibliographic record
Abstract
Background: Providing patients with information about their health and treatment options is important to ensure care that best reflects patient needs, values, and preferences. Patient-reported outcomes (PROs), measures of health status, are regularly collected in clinical contexts and scores can be returned to patients in personalized decision aids. One example of a PRO-based decision aid is the Arthritis care through Shared Knowledge (ASK) report, which shares individual PRO data on knee and hip arthritis-related pain and functional limitations with patients. However, given that the use of such data in clinical consultations is unfamiliar to many patients, support may be required to ensure this information is understood and used as intended. Objective: This paper describes ASK coaching, an online 1-hour group session designed to ensure patients understood the ASK report, including their PRO scores, and how to use the information in conversations with their clinicians. We present (1) quantitative evaluation results associated with attendance and self-assessment of learning and (2) qualitative evaluation results on motivation to attend, acceptability of the session format, and achievement of session goals. Methods: The session was designed and refined collaboratively with clinical experts and patient advisers. Patients in one arm of a pragmatic cluster-randomized trial evaluating the ASK report were invited to attend this session. To understand the profile of attendees (N=438) sociodemographic and clinical data were compared with all participants invited to coaching (N=1545) and a patient-reported assessment of self-efficacy was collected on a subset (N=692). In addition, a postsession survey was used to self-assess learning. Qualitative data were synthesized from semistructured postcoaching interviews, paired pre- and postcoaching interviews, and free-text responses to a postsession survey. A qualitative descriptive approach was used for analysis. Results: Compared with nonattendees, patients reporting higher education, greater health literacy, Medicare insurance, and lower self-efficacy for managing treatments were more likely to attend ASK coaching when invited. Participants' self-assessment of learning showed an improved understanding of current and projected osteoarthritis symptoms and where to find additional information. Qualitatively, patients reported attending coaching to gain information that could benefit their treatment or aid in research. The online group format was generally described as acceptable, and the session goals related to understanding the report and preparing for future conversations with clinicians were met. Suggestions for improvement, such as providing more opportunities for within-group interaction, were also provided. Conclusions: Our results highlight the value of coaching as an intervention to help patients understand and use novel health information, including PRO data, in conversations with clinicians. Given that it was well-liked by patients, promoting a greater understanding of the PRO-based ASK report, and increased feelings of preparedness for clinical consultation, coaching appears to be a promising intervention to support patients in understanding and using their personal health data.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".