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A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses

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Abstract

Purpose

Decisions to increase work participation must be informed and timely to improve return to work (RTW). The implementation of research into clinical practice relies on sophisticated yet practical approaches such as machine learning (ML). The objective of this study is to explore the evidence of machine learning in vocational rehabilitation and discuss the strengths and areas for improvement in the field.

Methods

We used the PRISMA guidelines and the Arksey and O’Malley framework. We searched Ovid Medline, CINAHL, and PsycINFO; with hand-searching and use of the Web of Science for the final articles. We included studies that are peer-reviewed, published within the last 10 years to consider contemporary material, implemented a form of “machine learning” or “learning health system”, undertaken in a vocational rehabilitation setting, and has employment as a specific outcome.

Results

12 studies were analyzed. The most commonly studied population was musculoskeletal injuries or health conditions. Most of the studies came from Europe and most were retrospective studies. The interventions were not always reported or specified. ML was used to identify different work-related variables that were predictive of return to work. However, ML approaches were varied and no standard or predominant ML approach was evident.

Conclusions

ML offers a potentially beneficial approach to identifying predictors of RTW. While ML uses a complex calculation and estimation, ML complements other elements of evidence-based practice such as the clinician’s expertise, the worker’s preference and values, and contextual factors around RTW in an efficient and timely manner.

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Acknowledgements

GT was supported for this work by the Fulbright Foundation in Greece and the Fulbright program.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Rehabilitation and Movement Science, College of Nursing and Health Sciences, University of Vermont, 106 Carrigan Dr, Burlington, VT, 05405, USA

    Reuben Escorpizo & Georgios Theotokatos

  2. Swiss Paraplegic Research, Nottwil, Switzerland

    Reuben Escorpizo

  3. School of Physical Education and Sport Science, National and Kapodistrian University of Athens, Athens, Greece

    Georgios Theotokatos

  4. School of Health Professions, University of Texas- Medical Branch, Galveston, TX, USA

    Carole A. Tucker

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  1. Reuben Escorpizo
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  3. Carole A. Tucker
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Contributions

Reuben Escorpizo (RE) led the study’s conception and design. Material preparation, data collection, and analysis were performed by RE, Georgios Theotokatos (GT), and Carole A Tucker (CT). All authors participated in the writing of the manuscript. All authors read and approved the revised manuscript and its submission.

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Correspondence to Reuben Escorpizo.

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Appendices

Appendix A: PubMed

((“rehabilitation, vocational”[MeSH Terms] OR ((“work“[MeSH Terms] OR “work”[All Fields]) AND “rehabilitation”[MeSH Terms]) OR ((“occupant”[All Fields] OR “occupant s”[All Fields] OR “occupants”[All Fields] OR “occupational”[All Fields] OR “occupations”[MeSH Terms] OR “occupations”[All Fields] OR “occupation”[All Fields]) AND “rehabilitation”[MeSH Terms]) OR “return to work”[MeSH Terms] OR “employment”[MeSH Terms] OR “sick leave”[MeSH Terms] OR “work”[MeSH Terms] OR “absenteeism”[MeSH Terms]) AND “2012/07/19 00:00”:“3000/01/01 05:00”[Date - Publication] AND ((“learning health system”[MeSH Terms] OR “machine learning”[MeSH Terms]) AND “2012/07/19 00:00”:“3000/01/01 05:00”[Date - Publication])) AND (y_10[Filter])

Appendix B: CINAHL

(MH “Learning Health System”) OR (MH “Machine Learning+”) AND ((MH “Rehabilitation, Vocational+”) OR (MH “Job Re-entry”) OR (MH “Employment+”) OR (MH “Sick Leave”))

Appendix C: PsycInfo

((su(learning health system) OR su(machine learning)) AND PEER(yes)) AND ((su(vocational rehabilitation) OR su(occupational rehabilitation) OR su(work rehabilitation) OR su(return to work) OR su(sick leave) OR su(work) OR su(employment)) AND PEER(yes))

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Escorpizo, R., Theotokatos, G. & Tucker, C.A. A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses. J Occup Rehabil 34, 71–86 (2024). https://doi.org/10.1007/s10926-023-10127-1

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