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Enhancing Accuracy of Disease Classification and Prevalence Assessments through Integration of Primary and Secondary Care Electronic Health Records: A Case Study in Arthritis Research

  • By

  • Belay Birlie Yimer

  • Fangyuan Zhang

  • Jenny Humphreys

  • Mark Lunt

  • Meghna Jani

  • John McBeth

  • William G Dixon

  • September 17, 2025

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  • 1

    Linking primary care records with text-mined outpatient letters improves the accuracy of disease classification and prevalence estimates.

  • 2

    The study identified 245 cases of psoriatic arthritis (PsA) in primary care, revealing a prevalence of 0.13% among 188,286 adults.

  • 3

    Primary care codes missed 196 hospital-diagnosed PsA cases, leading to a significant underestimation of disease prevalence.

  • 4

    The adjusted prevalence of PsA, accounting for misclassification, was found to be 0.25%, highlighting the impact of false negatives.

  • 5

    Text-mining hospital letters is essential for correcting prevalence estimates due to the lack of coded secondary care diagnosis data.

Original Source(s)

Improving disease misclassification and prevalence estimates by linking primary and secondary care electronic health records: an illustration from arthritis research

  • by Belay Birlie Yimer, Fangyuan Zhang, Jenny Humphreys, Mark Lunt, Meghna Jani, John McBeth, William G Dixon

  • September 17, 2025

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