Evidence map›Paper›PMID 38536622›Full record

ArticleJournal of occupational rehabilitation2024

Electronic Health Records for Predicting Outcomes to Work-Related Musculoskeletal Disorders: A Scoping Review.

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

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of occupational rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
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

6 authors.

M WassellSchool of Computing Technologies, RMIT University, Melbourne, Australia. drmelinda.wassell@gmail.com.
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.
P McCannSTEM Health and Biomedical Sciences, 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

purposeThrough electronic health records (EHRs), musculoskeletal (MSK) therapists such as chiropractors and physical therapists, as well as occupational medicine physicians could collect data on many variables that can be traditionally challenging to collect in managing work-related musculoskeletal disorders (WMSDs). The review's objectives were to explore the extent of research using EHRs in predicting outcomes of WMSDs by MSK therapists.

methodA systematic search was conducted in Medline, PubMed, CINAHL, and Embase. Grey literature was searched. 2156 unique papers were retrieved, of which 38 were included. Three themes were explored, the use of EHRs to predict outcomes to WMSDs, data sources for predicting outcomes to WMSDs, and adoption of standardised information for managing WMSDs.

resultsPredicting outcomes of all MSK disorders using EHRs has been researched in 6 studies, with only 3 focusing on MSK therapists and 4 addressing WMSDs. Similar to all secondary data source research, the challenges include data quality, missing data and unstructured data. There is not yet a standardised or minimum set of data that has been defined for MSK therapists to collect when managing WMSD. Further work based on existing frameworks is required to reduce the documentation burden and increase usability.

conclusionThe review outlines the limited research on using EHRs to predict outcomes of WMSDs. It highlights the need for EHR design to address data quality issues and develop a standardised data set in occupational healthcare that includes known factors that potentially predict outcomes to help regulators, research efforts, and practitioners make better informed clinical decisions.

Indexed as

Electronic Health RecordsMusculoskeletal DiseasesOccupational DiseasesHumansElectronic health recordsMedical informaticsOccupational health physiciansOccupational injuriesRehabilitation

Identifiers

PMID38536622
PMCPMC11550283

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.