Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers.
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 f...
| Publicado en: | Journal of Occupational Rehabilitation Vol. 23; no. 4; pp. 597 - 610 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104146456&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104146456 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10530487 JOR jtl: Journal of Occupational Rehabilitation issn: 10530487 maglogo: N pubinfo: dt: Dec2013 vid: 23 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104146456 91257803 10.1007/s10926-013-9430-4 NLM23468410 104146456 ppf: 597 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Development of a Computer-Based Clinical Decision Support Tool for Selecting Appropriate Rehabilitation Interventions for Injured Workers. aug: au: Gross, Douglas Zhang, Jing Steenstra, Ivan Barnsley, Susan Haws, Calvin Amell, Tyler McIntosh, Greg Cooper, Juliette Zaiane, Osmar affil: Department of Computing Science, University of Alberta, Edmonton Canada sug: subj: Decision Support Techniques Occupational-Related Injuries Rehabilitation Musculoskeletal Diseases Rehabilitation Disability Evaluation Decision Support Systems, Clinical Evaluation Occupational-Related Injuries Classification Algorithms Alberta Job Re-Entry Patient Assessment Pain Measurement Visual Analog Scaling Systems Design Short Form-36 Health Survey (SF-36) Artificial Intelligence Decision Trees Clinical Assessment Tools Evaluation Research Nonconcurrent Prospective Studies ROC Curve Descriptive Statistics Chi Square Test T-Tests Data Analysis Software Sensitivity and Specificity Scales Questionnaires Adult Female Male Human Funding Source Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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