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

Abstract

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.

Reference Values of the Pain Disability Index in Patients With Painful Musculoskeletal and Spinal Disorders

“…Canadian data were obtained during a previous study aimed at developing a clinical decision support tool for selecting rehabilitation interventions (n = 1302). 6 All data were baseline data obtained from patients referred to treatment/rehabilitation of their pain. Patients fi lled out a comprehensive set of questionnaires including demographics and the PDI.…”

The association between rurality and return to work for workers’ compensation claimants with work-related musculoskeletal injuries: An analysis of workers who failed to return to work within typical healing time frames

“…The greater than expected pre-injury earnings in rural claimants is also likely attributable to the high earning opportunities due to the booming Alberta economy in 2010-2011. Although this study utilized a large, population-based database that has been previously used for research and includes a wide range of demographic, occupational, and health variables [31,34], some limitations were also present. As this study was a secondary analysis, the research team did not collect the data and as such had no control of what variables were collected or the quality of the data.…”

“…This was a secondary analysis of a cohort study conducted previously, which had been described in detail elsewhere [31]. The study used administrative data from WCBAlberta.…”

“…Data from across the province was systematically collected at the time of clinical assessment for determining readiness to return to work [32,33]. These claimant records had been used previously for investigating population health and rehabilitation from workplace MSK injuries [31,34]. This study was approved by the University of Alberta's Health Research Ethics Board.…”

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity

“…These claimants are assessed to (1) determine their readiness to RTW, (2) identify barriers to recovery and/or (3) be triaged to the most appropriate type of rehabilitation. This study was conducted on a population-based database created from clinical and administrative claims data from the Workers' Compensation Board of Alberta (WCB-Alberta) 20. This data set includes information on workers (workers' compensation claimants) experiencing work-related upper extremity MSK injuries in the province of Alberta, Canada, who underwent a comprehensive RTW assessment and/or received rehabilitation.…”