ArticleJournal of occupational rehabilitation2013
Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.
Article in Journal of occupational rehabilitation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 48 citations in OpenAlex.
- Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes.BMC medical informatics and decision making · 2022Trial
- Exploring Barriers to Patients' Progression in the Cardiac Rehabilitation Journey From Health Care Providers' Perspectives: Qualitative Study.Interactive journal of medical research · 2025Article
- Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review.Journal of occupational rehabilitation · 2024Article
- Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review.JMIR AI · 2024Article
- Rehabilitation needs screening to identify potential beneficiaries: a scoping review.BMJ public health · 2024Article
- A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.Journal of occupational rehabilitation · 2024Article
- Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments.Frontiers in robotics and AI · 2024Review
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- Characteristics and Prognostic Factors for Return to Work in Public Safety Personnel with Work-Related Posttraumatic Stress Injury Undergoing Rehabilitation.Journal of occupational rehabilitation · 2021Article
- Intelligent Robotics Incorporating Machine Learning Algorithms for Improving Functional Capacity Evaluation and Occupational Rehabilitation.Journal of occupational rehabilitation · 2020Review
- Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers' Health Assessments.Journal of occupational rehabilitation · 2020Review
- Machine Learning for Work Disability Prevention: Introduction to the Special Series.Journal of occupational rehabilitation · 2020Article
- Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions.Journal of occupational rehabilitation · 2020Article
- Development of a quantitative evaluation system for visuo-motor control in three-dimensional virtual reality space.Scientific reports · 2018Article
- Physiotherapy for injured workers in Canada: are insurers' and clinics' policies threatening good quality and equity of care? Results of a qualitative study.BMC health services research · 2018Article
- Procedures to develop a computerized adaptive test to assess patient-reported physical functioning.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2018Article
- TrhOnt: building an ontology to assist rehabilitation processes.Journal of biomedical semantics · 2016Article
- Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review.Journal of occupational rehabilitation · 2016Article
- Reference values for the SF-36 in Canadian injured workers undergoing rehabilitation.Journal of occupational rehabilitation · 2015Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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.
methodsPopulation-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.
resultsThe 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.