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Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers

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Abstract

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.

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Acknowledgments

The Workers’ Compensation Board of Alberta facilitated data collection and provided funding in support of this research.

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Authors and Affiliations

  1. Department of Physical Therapy, University of Alberta, 2-50 Corbett Hall, Edmonton, AB, T6G 2G4, Canada

    Douglas P. Gross

  2. Workers’ Compensation Board-Alberta Millard Health, Edmonton, AB, Canada

    Douglas P. Gross & Calvin Haws

  3. Department of Computing Science, University of Alberta, Edmonton, AB, Canada

    Jing Zhang & Osmar Zaiane

  4. Institute for Work and Health, Toronto, ON, Canada

    Ivan Steenstra

  5. Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

    Ivan Steenstra

  6. Workers’ Compensation Board-Alberta Health Care Services, Edmonton, AB, Canada

    Susan Barnsley

  7. Centric Health, Calgary, AB, Canada

    Tyler Amell

  8. CBI Health, Toronto, ON, Canada

    Greg McIntosh

  9. University of Manitoba, Winnipeg, MB, Canada

    Juliette Cooper

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  1. Douglas P. Gross
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Correspondence to Douglas P. Gross.

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Gross, D.P., Zhang, J., Steenstra, I. et al. Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers. J Occup Rehabil 23, 597–609 (2013). https://doi.org/10.1007/s10926-013-9430-4

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