Evidence map›Paper›PMID 38739344›Full record

ArticleJournal of occupational rehabilitation2025

Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.

M Wassell, A Vitiello, K Butler-Henderson, K Verspoor, H Pollard

Abstract read
In one paragraph

Article in Journal of occupational rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

M WassellSchool of Computing Technologies, RMIT University, Melbourne, Australia. drmelinda.wassell@gmail.com.ORCID http://orcid.org/0000-0002-5171-5271
A VitielloSchool of Health, Medical and Applied Sciences, Central Queensland University, Queensland, Australia.
K Butler-HendersonSTEM|Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
K VerspoorSchool of Computing Technologies, RMIT University, Melbourne, Australia.
H PollardFaculty of Health Sciences, Durban University of Technology, Durban, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeElectronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims.

methodsVariables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals.

resultsThere were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar.

conclusionThe study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.

Indexed as

Electronic Health RecordsMusculoskeletal DiseasesOccupational DiseasesOccupational InjuriesAdultAustraliaFemaleHumansMaleMiddle AgedWorkers' CompensationChiropracticElectronic health recordsMusculoskeletal diseasesOccupational health physiciansOccupational injuriesRehabilitation

Identifiers

PMID38739344
PMCPMC11839684

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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.