The Implementation of Measurement-Based Care in the Context of Telemedicine: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Measurement Based Care in Mental Health Initiative launched by the Department of Veterans Affairs in 2016 is an example of an evidence-based practice that uses patient-reported outcome measures (PROMs) to improve patient outcomes. The acceptance of measurement-based care (MBC) among Veterans Affairs providers is relatively high. However, there are barriers to MBC for telehealth providers. Health information technologies might afford opportunities to address some of the barriers related to the uptake of MBC. OBJECTIVE: This paper reports on an implementation effort to integrate MBC into mental health care telehealth practice using eHealth solutions. METHODS: Qualitative data were generated from 22 semistructured interviews with psychiatrists (n=4), psychologists (n=3), social workers (n=3), nurses (n=6), a pharmacist (n=1), and administrative staff (n=5) who provide telemental health care through a community-based outpatient clinic in the rural Midwestern United States. The interviews were conducted during the pilot phase of an implementation initiative to increase the adoption of MBC by revising clinic workflows to integrate the use of eHealth technologies. Data were analyzed using thematic analysis. RESULTS: Time burden and workflow issues were the most common barrier to provider adoption of MBC; sharing and reviewing pencil-and-paper measures and results in the same room was no longer possible in novel telehealth workflows necessitated by the COVID-19 pandemic. Providers voiced concerns about how long it would take to collect, adequately score, interpret, share, and document the PROMs during the telehealth visit. Concerns about time might also correspond to a gap in providers' familiarity with these assessments, greater comfort in assessing symptoms through clinical interviews, and being accustomed to using the assessments as screening tools more so than longitudinal outcome measures. Capacities associated with eHealth technologies may address workflow concerns and promote providers' understanding and use of the measures as tracking tools. CONCLUSIONS: The need to use limited appointment time well was a top priority for telemental health providers. eHealth technologies provided operative supports that protect time in appointments by shifting when and how PROMs are collected. Bolstering providers' familiarity with how to use PROMs in the course of treatment may impact providers' buy-in by encouraging them to reconsider how sharing and acting on PROMs could be time well spent.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| 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".