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. 2023 May 11;15(1):e6523.
doi: 10.5195/ijt.2023.6523. eCollection 2023.

Evaluating Efficiency of a Provincial Telerehabilitation Service in Improving Access to Care During the COVID-19 Pandemic

Katelyn Brehon  1 , Jay Carriere  2 , Katie Churchill  3   4 , Adalberto Loyola-Sanchez  5 , Elizabeth Papathanassoglou  6   7 , Rob MacIsaac  8 , Mahdi Tavakoli  9 , Chester Ho  5   6 , Kiran Pohar Manhas  6   7   10
Affiliations

Evaluating Efficiency of a Provincial Telerehabilitation Service in Improving Access to Care During the COVID-19 Pandemic

Katelyn Brehon et al. Int J Telerehabil. .

Abstract

Scope: 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.

Methodology: 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.

Conclusions: 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.

Keywords: Artificial intelligence; Call utilization; Machine learning; Qualitative description.

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Conflict of interest statement

The Authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
Number of Calls, Call Backs, Total Calls, and Abandoned Calls by Week
Figure B1
Figure B1
Outline of Analysis Performed on Caller Data by the Combined AI/ML System
Figure D1
Figure D1
Call Volume by Healthcare Zone for AI/ML Analyzed Calls During Period (May 12, 2020 – October 31, 2020)
Figure D2
Figure D2
Caller Age for AI/ML Analyzed Calls During Period (May 12, 2020 – October 31, 2020)
Figure D3
Figure D3
Call Duration for AI/ML Analyzed Calls During Period (May 12, 2020 – October 31, 2020)
Figure D4
Figure D4
Call Type Found Using AI/ML Analysis of Calls During Period (May 12, 2020 – October 31, 2020)
See this image and copyright information in PMC

References

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