Validity of the work assessment triage tool for selecting rehabilitation interventions for workers’ compensation claimants with musculoskeletal conditions

Douglas P Gross, Ivan A Steenstra, William Shaw, Parnian Yousefi, Colin Bellinger, Osmar Zaiane

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Pages (from-to)318-330
Number of pages13
JournalJournal of Occupational Rehabilitation
Volume30
DOIs
Publication statusPublished - 2020

Keywords

  • classification
  • compensation
  • redress
  • machine learning
  • musculoskeletal diseases
  • prediction
  • rehabilitation

Fingerprint

Dive into the research topics of 'Validity of the work assessment triage tool for selecting rehabilitation interventions for workers’ compensation claimants with musculoskeletal conditions'. Together they form a unique fingerprint.

Cite this