Improving Patient Health Literacy During Telehealth Visits Through Remote Teach-Back Methods Training for Family Medicine Residents: Pilot 2-Arm Cluster, Nonrandomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: As telemedicine plays an increasing role in health care delivery, providers are expected to receive adequate training to effectively communicate with patients during telemedicine encounters. Teach-back is an approach that verifies patients' understanding of the health care information provided by health care professionals. Including patients in the design and development of teach-back training content for providers can result in more relevant training content. However, only a limited number of studies embrace patient engagement in this capacity, and none for remote care settings. OBJECTIVE: We aimed to design and evaluate the feasibility of patient-centered, telehealth-focused teach-back training for family medicine residents to promote the use of teach-back during remote visits. METHODS: We codeveloped the POTENTIAL (Platform to Enhance Teach-Back Methods in Virtual Care Visits) curriculum for medical residents to promote teach-back during remote visits. A patient participated in the development of the workshop's videos and in a patient-provider panel about teach-back. We conducted a pilot, 2-arm cluster, nonrandomized controlled trial. Family medicine residents at the intervention site (n=12) received didactic and simulation-based training in addition to weekly cues-to-action. Assessment included pre- and postsurveys, observations of residents, and interviews with patients and providers. To assess differences between pre- and postintervention scores among the intervention group, chi-square and 1-tailed t tests were used. A total of 4 difference-in-difference models were constructed to evaluate prepost differences between intervention and control groups for each of the following outcomes: familiarity with teach-back, importance of teach-back, confidence in teach-back ability, and ease of use of teach-back. RESULTS: Medical residents highly rated their experience of the teach-back training sessions (mean 8.6/10). Most residents (9/12, 75%) used plain language during training simulations, and over half asked the role-playing patient to use their own words to explain what they were told during the encounter. Postintervention, there was an increase in residents' confidence in their ability to use teach-back (mean 7.33 vs 7.83; P=.04), but there was no statistically significant difference in familiarity with, perception of importance, or ease of use of teach-back. None of the difference-in-difference models were statistically significant. The main barrier to practicing teach-back was time constraints. CONCLUSIONS: This study highlights ways to effectively integrate best-practice training in telehealth teach-back skills into a medical residency program. At the same time, this pilot study points to important opportunities for improvement for similar interventions in future larger-scale implementation efforts, as well as ways to mitigate providers' concerns or barriers to incorporating teach-back in their practice. Teach-back can impact remote practice by increasing providers' ability to actively engage and empower patients by using the features (whiteboards, chat rooms, and mini-views) of their remote platform.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".