Competition or Complementarity Among Telemedicine Tools in Ambulatory Care Practice: Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: Telemedicine use surged due to its capacity to deliver safe, remote care. As the public health crisis subsides, evaluating the interplay among various tools, such as video, audio, and text, becomes critical to sustained use. With health care shifting back to in-person models, understanding whether telemedicine tools complement or compete provides valuable insights for future technology design and usage strategies. OBJECTIVE: This study investigates whether different types of telemedicine technology tools complement or compete while physicians deliver health care services through them. A clear understanding of the relationships between telemedicine technology tools, physicians' satisfaction, evaluation of care quality, and patient visit percentages is crucial for the design of new telemedicine technology platforms and ensuring quality of care services through technology platforms. METHODS: To fulfill our objective, we analyzed data from the 2021 National Electronic Health Records Survey. We used ordered logit and probit regression models to evaluate the effects of telemedicine technology tools on physicians' overall satisfaction, quality of health care evaluation, and the percentage of patient visits via telemedicine. RESULTS: A total of 1875 office-based physicians in the United States completed the survey. Three main outcomes were assessed, including physician satisfaction (n=1614), evaluation of health care quality (n=1617), and the percentage of patient visits conducted via telemedicine (n=1558). Ordered logit and probit regression analyses revealed that the aggravated use of telemedicine tools had a significant impact on improvements in all 3 outcomes. A unit increase in telemedicine tools was associated with a 4.2 percentage point increase in the predicted probability of physicians being "very satisfied" (P<.001) and a 5.2 percentage point increase in evaluating telemedicine quality as "to a great extent" (P<.001). For patient visits, a unit increase in telemedicine tools was associated with a 1.8 percentage point increase in the likelihood of reporting "≥75% of visits via telemedicine" (P<.001). Disaggregated analysis indicated that all individual tools were positively associated with physician satisfaction and quality evaluation (P<.05). Bundle models revealed patterns consistent with complementarity (several bundles exceeded their constituent tools) and competition (some significant bundles were smaller than at least one constituent tool), aligning with the presence of both reinforcing and overlapping functionalities. CONCLUSIONS: Our study demonstrates that telemedicine tools interact in ways that can be either complementary or competitive, depending on how their functionalities align within physicians' workflows. Videoconferencing tools, especially when integrated with electronic health record platforms, act as a central complementary component that enhances physicians' satisfaction and evaluation of care quality. In contrast, combinations lacking video capability or involving multiple nonintegrated platforms fragment workflows and increase cognitive burden. These findings emphasize the importance of designing telemedicine tool bundles that align media capabilities with clinical communication needs, thereby improving satisfaction and supporting sustainable, high-quality telemedicine practice.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".