Evidence map›Paper›PMID 38046552›Full record

ArticleInternational journal of telerehabilitation2023

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

Katelyn Brehon, Jay Carriere, Katie Churchill, Adalberto Loyola-Sanchez, Elizabeth Papathanassoglou, Rob MacIsaac, Mahdi Tavakoli, Chester Ho, Kiran Pohar Manhas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of telerehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Katelyn BrehonDepartment of Physical Therapy, University of Alberta, Edmonton, Alberta, Canada.
Jay CarriereDepartment of Electrical and Software Engineering, University of Calgary, Calgary, Alberta, Canada.
Katie ChurchillAllied Health Professional Practice and Education, Alberta Health Services, Alberta, Canada.
Adalberto Loyola-SanchezDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Elizabeth PapathanassoglouNeurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Alberta, Canada.
Rob MacIsaacSpinal Cord Injury Alberta, Edmonton, Alberta, Canada.
Mahdi TavakoliDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada.
Chester HoDepartment of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Kiran Pohar ManhasNeurosciences, Rehabilitation, and Vision Strategic Clinical Network, Alberta Health Services, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial intelligenceCall utilizationMachine learningQualitative description

Identifiers

PMID38046552
PMCPMC10687995

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.