Competition or Complementarity Among Telemedicine Tools in Ambulatory Care Practice: Cross-Sectional Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».