Implementation of Telemedicine in a Tertiary Hospital–Based Ambulatory Practice in Detroit During the COVID-19 Pandemic: Observational Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic, caused by SARS-CoV-2, has forced the health care delivery structure to change rapidly. The pandemic has further widened the disparities in health care and exposed vulnerable populations. Health care services caring for such populations must not only continue to operate but create innovative methods of care delivery without compromising safety. We present our experience of incorporating telemedicine in our university hospital-based outpatient clinic in one of the worst-hit areas in the world. OBJECTIVE: Our goal is to assess the adoption of a telemedicine service in the first month of its implementation in outpatient practice during the COVID-19 pandemic. We also want to assess the need for transitioning to telemedicine, the benefits and challenges in doing so, and ongoing solutions during the initial phase of the implementation of telemedicine services for our patients. METHODS: We conducted a prospective review of clinic operations data from the first month of a telemedicine rollout in the outpatient adult ambulatory clinic from April 1, 2020, to April 30, 2020. A telemedicine visit was defined as synchronous audio-video communication between the provider and patient for clinical care longer than 5 minutes or if the video visit converted to a telephone visit after 5 minutes due to technical problems. We recorded the number of telemedicine visits scheduled, visits completed, and the time for each visit. We also noted the most frequent billing codes used based on the time spent in the patient care and the number of clinical tasks (eg, activity suggested through diagnosis or procedural code) that were addressed remotely by the physicians. RESULTS: During the study period, we had 110 telemedicine visits scheduled, of which 94 (85.4%) visits were completed. The average duration of the video visit was 35 minutes, with the most prolonged visit lasting 120 minutes. Of 94 patients, 24 (25.54%) patients were recently discharged from the hospital, and 70 (74.46%) patients were seen for urgent care needs. There was a 50% increase from the baseline in the number of clinical tasks that were addressed by the physicians during the pandemic. CONCLUSIONS: There was a high acceptance of telemedicine services by the patients, which was evident by a high show rate during the COVID-19 pandemic in Detroit. With limited staffing, restricted outpatient work hours, a shortage of providers, and increased outpatient needs, telemedicine was successfully implemented in our practice.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».