Literature DB >> 31267266

Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions.

Douglas P Gross1, Ivan A Steenstra2, William Shaw3, Parnian Yousefi4, Colin Bellinger5, Osmar Zaïane4.   

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.

Entities:  

Keywords:  Classification; Compensation and redress; Machine learning; Musculoskeletal diseases; Prediction; Rehabilitation

Year:  2020        PMID: 31267266     DOI: 10.1007/s10926-019-09843-4

Source DB:  PubMed          Journal:  J Occup Rehabil        ISSN: 1053-0487


  2 in total

Review 1.  Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers' Health Assessments.

Authors:  Marianne W M C Six Dijkstra; Egbert Siebrand; Steven Dorrestijn; Etto L Salomons; Michiel F Reneman; Frits G J Oosterveld; Remko Soer; Douglas P Gross; Hendrik J Bieleman
Journal:  J Occup Rehabil       Date:  2020-09

2.  A Descriptive Study of the Implementation of Remote Occupational Rehabilitation Services Due to the COVID-19 Pandemic Within a Workers' Compensation Context.

Authors:  Douglas P Gross; Alexander Asante; Joanne Pawluk; Riikka Niemeläinen
Journal:  J Occup Rehabil       Date:  2020-10-28
  2 in total

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