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 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 | 0.018 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".