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Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Machine Learning in Healthcare

Background:

  • Growing volumes of clinical data require robust methods for reuse.
  • Clinician trust is essential for adopting electronic health record (EHR) data in research.
  • Ensuring data quality is paramount for reliable machine learning predictions.

Purpose of the Study:

  • To assess the plausibility and fitness of structured EHR data for predicting work-related musculoskeletal disorder outcomes.
  • To evaluate data quality by comparing individual clinician data entry against group data.
  • To identify factors influencing clinician trust in EHR data for research.

Main Methods:

  • Utilized a proprietary, structured electronic health record system.
  • Assessed data plausibility by comparing individual clinician entries to group norms.
  • Employed machine learning techniques to evaluate data suitability for outcome prediction.

Main Results:

  • Individual clinician data entry generally correlated positively with group data, suggesting fitness for reuse.
  • Inconsistencies were identified from the clinician's perspective, potentially impacting data trust.
  • The study highlights the need for clinician engagement in data quality assessments.

Conclusions:

  • Clinician engagement is crucial for assessing and improving EHR data quality.
  • Recognizing clinicians as local knowledge experts can enhance data reliability for machine learning.
  • Data quality assessments must consider the clinician's viewpoint to build trust and ensure data utility.