Abstract
Purposes and Background
Musculoskeletal disorders including as back and neck pain are leading causes of work disability. Effective interventions exist (i.e. functional restoration, multidisciplinary biopsychosocial rehabilitation, workplace-based interventions, etc.), but it is difficult to select the optimal intervention for specific patients. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions. The WATT algorithm categorizes patients based on individual, occupational, and clinical characteristics according to likelihood of successful return-to-work following rehabilitation. Internal validation showed acceptable classification accuracy, but WATT has not been tested beyond the original development sample. Our purpose was to externally validate the WATT.
Methods and Results
A population-based cohort design was used, with administrative and clinical data extracted from a Canadian provincial compensation database. Data were available on workers being considered for rehabilitation between January 2013 and December 2016. Data was obtained on patient characteristics (ie. age, sex, education level), clinical factors (ie. diagnosis, part of body affected, pain and disability ratings), occupational factors (ie. occupation, employment status, modified work availability), type of rehabilitation program undertaken, and return-to-work outcomes (receipt of wage replacement benefits 30 days after assessment). Analysis included classification accuracy statistics of WATT recommendations for selecting interventions that lead to successful RTW outcomes. The sample included 5296 workers of which 33% had spinal conditions. Sensitivity of the WATT was 0.35 while specificity was 0.83. Overall accuracy was 73%.
Conclusion
Accuracy of the WATT for selecting successful rehabilitation programs was modest. Algorithm revision and further validation is needed.
Musculoskeletal disorders including as back and neck pain are leading causes of work disability. Effective interventions exist (i.e. functional restoration, multidisciplinary biopsychosocial rehabilitation, workplace-based interventions, etc.), but it is difficult to select the optimal intervention for specific patients. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions. The WATT algorithm categorizes patients based on individual, occupational, and clinical characteristics according to likelihood of successful return-to-work following rehabilitation. Internal validation showed acceptable classification accuracy, but WATT has not been tested beyond the original development sample. Our purpose was to externally validate the WATT.
Methods and Results
A population-based cohort design was used, with administrative and clinical data extracted from a Canadian provincial compensation database. Data were available on workers being considered for rehabilitation between January 2013 and December 2016. Data was obtained on patient characteristics (ie. age, sex, education level), clinical factors (ie. diagnosis, part of body affected, pain and disability ratings), occupational factors (ie. occupation, employment status, modified work availability), type of rehabilitation program undertaken, and return-to-work outcomes (receipt of wage replacement benefits 30 days after assessment). Analysis included classification accuracy statistics of WATT recommendations for selecting interventions that lead to successful RTW outcomes. The sample included 5296 workers of which 33% had spinal conditions. Sensitivity of the WATT was 0.35 while specificity was 0.83. Overall accuracy was 73%.
Conclusion
Accuracy of the WATT for selecting successful rehabilitation programs was modest. Algorithm revision and further validation is needed.
| Original language | English |
|---|---|
| Title of host publication | Orthopaedic Proceedings |
| Pages | 4-4 |
| Number of pages | 1 |
| Volume | 101 |
| Publication status | Published - 1 Sept 2019 |
| Externally published | Yes |
Keywords
- rehabilitation
- interventions
- injured workers