Evaluating Efficiency of a Provincial Telerehabilitation Service in Improving Access to Care During the COVID-19 Pandemic.
Artificial intelligence
Call utilization
Machine learning
Qualitative description
Journal
International journal of telerehabilitation
ISSN: 1945-2020
Titre abrégé: Int J Telerehabil
Pays: United States
ID NLM: 101604321
Informations de publication
Date de publication:
2023
2023
Historique:
medline:
4
12
2023
pubmed:
4
12
2023
entrez:
4
12
2023
Statut:
epublish
Résumé
Early in the COVID-19 pandemic, community rehabilitation stakeholders from a provincial health system designed a novel telerehabilitation service. The service provided wayfinding and self-management advice to individuals with musculoskeletal concerns, neurological conditions, or post-COVID-19 recovery needs. This study evaluated the efficiency of the service in improving access to care. We used multiple methods including secondary data analyses of call metrics, narrative analyses of clinical notes using artificial intelligence (AI) and machine learning (ML), and qualitative interviews. Interviews revealed that the telerehabilitation service had the potential to positively impact access to rehabilitation during the COVID-19 pandemic, for individuals living rurally, and for individuals on wait lists. Call metric analyses revealed that efficiency may be enhanced if call handling time was reduced. AI/ML analyses found that pain was the most frequently-mentioned keyword in clinical notes, suggesting an area for additional telerehabilitation resources to ensure efficiency.
Identifiants
pubmed: 38046552
doi: 10.5195/ijt.2023.6523
pii: ijt.2023.6523
pmc: PMC10687995
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e6523Informations de copyright
Copyright © 2023 Katelyn Brehon, Jay Carriere, Katie Churchill, Adalberto Loyola-Sanchez, Elizabeth Papathanassoglou, Rob MacIsaac, Mahdi Tavakoli, Chester Ho, Kiran Pohar Manhas.
Déclaration de conflit d'intérêts
The Authors declare that there is no conflict of interest.
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