Validity of the Work Assessment Triage Tool for Selecting Rehabilitation Interventions for Workers' Compensation Claimants with Musculoskeletal Conditions

被引:12
作者
Gross, Douglas P. [1 ]
Steenstra, Ivan A. [2 ]
Shaw, William [3 ]
Yousefi, Parnian [4 ]
Bellinger, Colin [5 ]
Zaiane, Osmar [4 ]
机构
[1] Univ Alberta, Dept Phys Therapy, 2-50 Corbett Hall, Edmonton, AB T6G 2G4, Canada
[2] Morneau Shepell, Toronto, ON, Canada
[3] Univ Connecticut, Sch Med, Dept Med, Farmington, CT USA
[4] Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada
[5] Natl Res Council Canada, Ottawa, ON, Canada
关键词
Rehabilitation; Musculoskeletal diseases; Compensation and redress; Machine learning; Classification; Prediction; DECISION-SUPPORT TOOL; LOW-BACK-PAIN; CONCEPT DRIFT; MANAGEMENT; VALIDATION; DISORDERS;
D O I
10.1007/s10926-019-09843-4
中图分类号
R49 [康复医学];
学科分类号
100215 ;
摘要
PurposeThe Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions for patients with musculoskeletal disorders. The WATT categorizes patients based on individual characteristics according to likelihood of successful return to work following rehabilitation. A previous validation showed acceptable classification accuracy, but we re-examined accuracy using a new dataset drawn from the same system 2 years later.MethodsA population-based cohort design was used, with data extracted from a Canadian compensation database on workers considered for rehabilitation between January 2013 and December 2016. Data were obtained on demographic, clinical, and occupational characteristics, type of rehabilitation undertaken, and return to work outcomes. Analysis included classification accuracy statistics of WATT recommendations.ResultsThe sample included 28,919 workers (mean age 43.9 years, median duration 56 days), of whom 23,124 experienced a positive outcome within 30 days following return to work assessment. Sensitivity of the WATT for selecting successful programs was 0.13 while specificity was 0.87. Overall accuracy was 0.60 while human recommendations were higher at 0.72.ConclusionsOverall accuracy of the WATT for selecting successful rehabilitation programs declined in a more recent cohort and proved less accurate than human clinical recommendations. Algorithm revision and further validation is needed.
引用
收藏
页码:318 / 330
页数:13
相关论文
共 36 条
  • [1] Primary care management of non-specific low back pain: key messages from recent clinical guidelines
    Almeida, Matheus
    Saragiotto, Bruno
    Richards, Bethan
    Maher, Chris G.
    [J]. MEDICAL JOURNAL OF AUSTRALIA, 2018, 208 (06) : 272 - 275
  • [2] Ambrosino R, 1999, J AM MED INFORM ASSN, P192
  • [3] Work Disability in Australia: An Overview of Prevalence, Expenditure, Support Systems and Services
    Collie, Alex
    Di Donato, Michael
    Iles, Ross
    [J]. JOURNAL OF OCCUPATIONAL REHABILITATION, 2019, 29 (03) : 526 - 539
  • [4] External validation of multivariable prediction models: a systematic review of methodological conduct and reporting
    Collins, Gary S.
    de Groot, Joris A.
    Dutton, Susan
    Omar, Omar
    Shanyinde, Milensu
    Tajar, Abdelouahid
    Voysey, Merryn
    Wharton, Rose
    Yu, Ly-Mee
    Moons, Karel G.
    Altman, Douglas G.
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2014, 14
  • [5] Management of neck pain and associated disorders: A clinical practice guideline from the Ontario Protocol for Traffic Injury Management (OPTIMa) Collaboration
    Cote, Pierre
    Wong, Jessica J.
    Sutton, Deborah
    Shearer, Heather M.
    Mior, Silvano
    Randhawa, Kristi
    Ameis, Arthur
    Carroll, Linda J.
    Nordin, Margareta
    Yu, Hainan
    Lindsay, Gail M.
    Southerst, Danielle
    Varatharajan, Sharanya
    Jacobs, Craig
    Stupar, Maja
    Taylor-Vaisey, Anne
    van der Velde, Gabrielle
    Gross, Douglas P.
    Brison, Robert J.
    Paulden, Mike
    Ammendolia, Carlo
    Cassidy, J. David
    Loisel, Patrick
    Marshall, Shawn
    Bohay, Richard N.
    Stapleton, John
    Lacerte, Michel
    Krahn, Murray
    Salhany, Roger
    [J]. EUROPEAN SPINE JOURNAL, 2016, 25 (07) : 2000 - 2022
  • [6] Effectiveness of Workplace Interventions in Return-to-Work for Musculoskeletal, Pain-Related and Mental Health Conditions: An Update of the Evidence and Messages for Practitioners
    Cullen, K. L.
    Irvin, E.
    Collie, A.
    Clay, F.
    Gensby, U.
    Jennings, P. A.
    Hogg-Johnson, S.
    Kristman, V.
    Laberge, M.
    McKenzie, D.
    Newnam, S.
    Palagyi, A.
    Ruseckaite, R.
    Sheppard, D. M.
    Shourie, S.
    Steenstra, I.
    Van Eerd, D.
    Amick, B. C., III
    [J]. JOURNAL OF OCCUPATIONAL REHABILITATION, 2018, 28 (01) : 1 - 15
  • [7] Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data
    Dinov, Ivo D.
    [J]. GIGASCIENCE, 2016, 5
  • [8] Finch E., 2002, Physical Rehabilitation Outcome Measures: A Guide to Enhance Clinical Decision Making
  • [9] Prevention and treatment of low back pain: evidence, challenges, and promising directions
    Foster, Nadine E.
    Anema, Johannes R.
    Cherkin, Dan
    Chou, Roger
    Cohen, Steven P.
    Gross, Douglas P.
    Ferreira, Paulo H.
    Fritz, Julie M.
    Koes, Bart W.
    Peul, Wilco
    Turner, Judith A.
    Maher, Chris G.
    [J]. LANCET, 2018, 391 (10137) : 2368 - 2383
  • [10] Gama J, 2006, LECT NOTES COMPUTER, V4093