ArticleJournal of occupational rehabilitation2020
Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions.
Article in Journal of occupational rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- Attitudes, Needs, and Expectations Regarding the Application of AI in Occupational Healthcare: A Multiple Stakeholder Perspective.Journal of occupational and environmental medicine · 2026Article
- Work Complexity and Musculoskeletal Symptoms in Healthcare Workers.Healthcare (Basel, Switzerland) · 2026Article
- User-Centred Design to Inform Requirements for a Remotely Administered Hybrid Functional Test (RAHFT) Protocol.Journal of occupational rehabilitation · 2025Article
- Evaluation of the Accuracy of the Smart Work Injury Management (SWIM) System to Assist Case Managers in Predicting the Work Disability of Injured Workers.Journal of occupational rehabilitation · 2025Article
- Mapping and Summarizing the Research on AI Systems for Automating Medical History Taking and Triage: Scoping Review.Journal of medical Internet research · 2025Article
- Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review.JMIR AI · 2024Article
- Rehabilitation needs screening to identify potential beneficiaries: a scoping review.BMJ public health · 2024Article
- A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.Journal of occupational rehabilitation · 2024Article
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- A Descriptive Study of the Implementation of Remote Occupational Rehabilitation Services Due to the COVID-19 Pandemic Within a Workers' Compensation Context.Journal of occupational rehabilitation · 2021Article
- Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management.Journal of occupational rehabilitation · 2020Article
- Machine Learning for Work Disability Prevention: Introduction to the Special Series.Journal of occupational rehabilitation · 2020Article
- Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers' Health Assessments.Journal of occupational rehabilitation · 2020Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions for patients with musculoskeletal disorders. The WATT categorizes patients based on individual characteristics according to likelihood of successful return to work following rehabilitation. A previous validation showed acceptable classification accuracy, but we re-examined accuracy using a new dataset drawn from the same system 2 years later. Methods A population-based cohort design was used, with data extracted from a Canadian compensation database on workers considered for rehabilitation between January 2013 and December 2016. Data were obtained on demographic, clinical, and occupational characteristics, type of rehabilitation undertaken, and return to work outcomes. Analysis included classification accuracy statistics of WATT recommendations. Results The sample included 28,919 workers (mean age 43.9 years, median duration 56 days), of whom 23,124 experienced a positive outcome within 30 days following return to work assessment. Sensitivity of the WATT for selecting successful programs was 0.13 while specificity was 0.87. Overall accuracy was 0.60 while human recommendations were higher at 0.72. Conclusions Overall accuracy of the WATT for selecting successful rehabilitation programs declined in a more recent cohort and proved less accurate than human clinical recommendations. Algorithm revision and further validation is needed.
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