Implementation of Telehealth Services at the US Department of Veterans Affairs During the COVID-19 Pandemic: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: At the onset of the COVID-19 pandemic, there was a rapid increase in the use of telehealth services at the US Department of Veterans Affairs (VA), which was accelerated by state and local policies mandating stay-at-home orders and restricting nonurgent in-person appointments. Even though the VA was an early adopter of telehealth in the late 1990s, the vast majority of VA outpatient care continued to be face-to-face visits through February 2020. OBJECTIVE: We compared telehealth service use at a VA Medical Center, Greater Los Angeles across 3 clinics (primary care [PC], cardiology, and home-based primary care [HBPC]) 12 months before and 12 months after the onset of COVID-19 (March 2020). METHODS: We used a parallel mixed methods approach including simultaneous quantitative and qualitative approaches. The distribution of monthly outpatient and telehealth visits, as well as telephone and VA Video Connect encounters were examined for each clinic. Semistructured telephone interviews were conducted with 34 staff involved in telehealth services within PC, cardiology, and HBPC during COVID-19. All audiotaped interviews were transcribed and analyzed by identifying key themes. RESULTS: Prior to COVID-19, telehealth use was minimal at all 3 clinics, but at the onset of COVID-19, telehealth use increased substantially at all 3 clinics. Telephone was the main modality of patient choice. Compared with PC and cardiology, video-based care had the greatest increase in HBPC. Several important barriers (multiple steps for videoconferencing, creation of new scheduling grids, and limited access to the internet and internet-connected devices) and facilitators (flexibility in using different video-capable platforms, technical support for patients, identification of staff telehealth champions, and development of workflows to help incorporate telehealth into treatment plans) were noted. CONCLUSIONS: Technological issues must be addressed at the forefront of telehealth evolution to achieve access for all patient populations with different socioeconomic backgrounds, living situations and locations, and health conditions. The unprecedented expansion of telehealth during COVID-19 provides opportunities to create lasting telehealth solutions to improve access to care beyond the pandemic.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".