Mental Health Providers’ Challenges and Solutions in Prescribing Over Telemedicine: Content Analysis of Semistructured Interviews
Bibliographic record
Abstract
BACKGROUND: In response to the COVID-19 pandemic, the United States extended regulatory flexibilities to make telemedicine more accessible to providers and patients. Some of these flexibilities allowed providers to intake patients over telemedicine and prescribe certain scheduled medications without an in-person visit. OBJECTIVE: We aim to understand providers' parameters for their comfort in prescribing over telemedicine and report on solutions providers have adopted in response to potential barriers and challenges in prescribing via telemedicine. METHODS: As part of a larger mixed methods study between February and April 2024, we conducted 16 semistructured interviews with mental health providers who prescribe via telemedicine within the United States. We used the results of a web-based, cross-sectional survey to develop a codebook and support recruitment. We analyzed a subsection of the 16 interviews using content analysis to capture comfort, barriers, and workarounds in telemedicine prescribing. We reported codes by frequency and by provider. RESULTS: Participants were typically male (11/16, 69%), provided care mostly or completely over telemedicine (11/16, 69%), and were psychiatrists (8/16, 50%) or other physician (3/16, 19%). Providers' primary states (10/16, 62%) of practice included Oregon, Texas, New York, and California. The content analysis yielded a total of 234 codes, with three main codes-comfort (98/234, 41.9%), barriers or challenges (85/234, 36.3%), and workarounds or solutions (27/234, 11.5%)-and two subcodes-uncomfortable prescribing (30/98, 31%) and comfortable prescribing (68/98, 69%) over telemedicine. Participants reported being comfortable prescribing over telemedicine as long as they could meet their main parameters of working within their expertise, having access to needed patient health information, and being compliant with rules and regulations. Participants reported frustrations with e-prescription workflows and miscommunications with pharmacies. Solutions to ease frustrations and alleviate discomforts in prescribing over telemedicine included developing workflows to help patients complete laboratory tests and physical examinations and directly communicating with pharmacies. CONCLUSIONS: By applying content analysis to the semistructured provider interviews, we found that physicians are comfortable prescribing via telemedicine when they feel they are practicing within their personal parameters for safety. While many providers experience frustrations such as miscommunication with pharmacies, these barriers appear to not prevent them from telemedicine prescribing. With expected changes in 2024 and 2025 to the US laws and regulations for telemedicine prescribing, we may see changes in provider comfort in prescribing.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".