Barriers to Telemedicine Use: Qualitative Analysis of Provider Perspectives During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Though telemedicine is a promising approach for removing barriers to care and improving access for patients, telemedicine use for many medical specialties has decreased from its peak during the acute COVID-19 public health crisis. Understanding the barriers and facilitators to the maintenance of web-based visits-one key component of telemedicine-is critical for ensuring the continuous availability of this service for patients. OBJECTIVE: The purpose of this study is to describe medical providers' perceived barriers and facilitators to the continued use of web-based visits to inform quality improvement efforts and promote sustainability. METHODS: We performed a qualitative content analysis of free-text responses from a survey of medical providers administered from February 5-14, 2021, at a large, midwestern academic institution, including all providers from medical professions that offered telemedicine (eg, physicians, residents or fellows, nurse practitioners, physicians assistants, or nurses) who completed at least 1 web-based visit from March 20, 2020, to February 14, 2021. The primary outcome was the experience of providing web-based visits, including barriers and facilitators to continued usage of web-based visits. Survey questions included 3 major domains: quality of care, technology, and satisfaction. Responses were coded using qualitative content analysis and further analyzed through a matrix analysis to understand the providers' perspectives and elucidate key barriers and facilitators of web-based visit usage. RESULTS: Of 2692 eligible providers, 1040 (38.6%) completed the survey, of whom 702 were providers from medical professions that offered telemedicine. These providers spanned 7 health care professions and 47 clinical departments. The most common professions represented were physicians (486/702, 46.7%), residents or fellows (85/702, 8.2%), and nurse practitioners (81/702, 7.8%), while the most common clinical departments were internal medicine (69/702, 6.6%), psychiatry (69/702, 6.6%), and physical medicine and rehabilitation (67/702, 6.4%). The following 4 overarching categories of provider experience with web-based visits emerged: quality of care, patient rapport, visit flow, and equity. Though many providers saw web-based visits as a tool for improving care access, quality, and equity, others shared how appropriate selection of web-based visits, support (eg, patient training, home devices, and broadband access), and institutional and nationwide optimization (eg, relaxation of licensing requirements across state borders and reimbursement for phone-only modalities) were needed to sustain web-based visits. CONCLUSIONS: Our findings demonstrate key barriers to the maintenance of telemedicine services following the acute public health crisis. These findings can help prioritize the most impactful methods of sustaining and expanding telemedicine availability for patients who prefer this method of care delivery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".