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

Gross, Douglas P. ; Zhang, Jing ; Steenstra, Ivan ; Barnsley, Susan ; Haws, Calvin ; Amell, Tyler ; McIntosh, Greg ; Cooper, Juliette ; Zaiane, Osmar

Journal of occupational rehabilitation, 2013-12, Vol.23 (4), p.597-609 [Periódico revisado por pares]

Boston: Springer US

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Título:
Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers
Autor: Gross, Douglas P. ; Zhang, Jing ; Steenstra, Ivan ; Barnsley, Susan ; Haws, Calvin ; Amell, Tyler ; McIntosh, Greg ; Cooper, Juliette ; Zaiane, Osmar
Assuntos: Adult ; Alberta ; Algorithms ; Artificial Intelligence ; Back pain ; Chronic pain ; Classification - methods ; Clinical decision making ; Clinical Psychology ; Computers ; Decision making ; Decision support systems ; Decision Support Systems, Clinical ; Environmental Health ; Female ; Health care ; Health Psychology ; Humans ; Injuries ; Interdisciplinary aspects ; Intervention ; Machine learning ; Male ; Medicine ; Medicine & Public Health ; Middle Aged ; Musculoskeletal diseases ; Musculoskeletal Diseases - classification ; Musculoskeletal Diseases - rehabilitation ; Occupational accidents ; Occupational Injuries - classification ; Occupational Injuries - rehabilitation ; Occupational Medicine/Industrial Medicine ; Orthopedics ; Pain management ; Patients ; Physical therapy ; Population ; Rehabilitation ; Return to Work ; Software Design ; Studies ; Variables ; Vocational rehabilitation ; Work-related injuries ; Workers compensation
É parte de: Journal of occupational rehabilitation, 2013-12, Vol.23 (4), p.597-609
Descrição: 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.
Editor: Boston: Springer US
Idioma: Inglês

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

Gross, Douglas P. ; Zhang, Jing ; Steenstra, Ivan ; Barnsley, Susan ; Haws, Calvin ; Amell, Tyler ; McIntosh, Greg ; Cooper, Juliette ; Zaiane, Osmar

Boston: Springer US

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