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Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers

Full Text: Gross2013_Article_DevelopmentOfAComputer-BasedCl.pdf PDF

PURPOSE: To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. METHODS: Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. RESULTS: The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. CONCLUSIONS: The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Citation

D. Gross, J. Zhang, I. Steenstra, S. Barnsley, C. Haws, T. Amell, G. McIntosh, J. Cooper, O. Zaiane. "Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers". Journal of Occupational Rehabilitation, 23(4), pp 597-609, December 2013.

Keywords:  
Category: In Journal
Web Links: Springer Link

BibTeX

@article{Gross+al:13,
  author = {Douglas P. Gross and Jing Zhang and Ivan Steenstra and Susan
    Barnsley and Calvin Haws and Tyler Amell and Greg McIntosh and Juliette
    Cooper and Osmar R. Zaiane},
  title = {Development of a computer-based clinical decision support tool for
    selecting appropriate rehabilitation interventions for injured workers},
  Volume = "23",
  Number = "4",
  Pages = {597-609},
  journal = {Journal of Occupational Rehabilitation},
  year = 2013,
}

Last Updated: November 01, 2019
Submitted by Sabina P

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