The communication of a secondary care diagnosis of autoimmune hepatitis to primary care practitioners: a

Fumi Varyani1, Timothy Card, Philip Kaye

  • 1Department of Gastroenterology, Queens Medical Centre, Nottingham University Hospital NHS Trust, Derby Road, Nottingham, NG7 2UH, United Kingdom. fumi_varyani@hotmail.com

Insights

Accurate coding of Autoimmune Hepatitis (AIH) in UK primary care records is confirmed. This validates using electronic health data for population studies on AIH incidence and prevalence.

Area of Science:

  • Hepatology
  • Clinical Informatics
  • Epidemiology

Background:

  • Autoimmune Hepatitis (AIH) is a chronic liver disease with limited UK data on incidence, prevalence, and natural history.
  • Electronic primary care records offer a potential data source, but diagnostic coding accuracy is unknown.
  • Accurate coding is crucial for reliable population-based studies.

Purpose of the Study:

  • To assess the accuracy of Autoimmune Hepatitis diagnoses recorded in UK General Practice computerised records.
  • To determine the proportion of consultant hepatologist-diagnosed AIH cases accurately coded in primary care.

Main Methods:

  • Study population: Patients diagnosed with AIH by consultant hepatologists (2004-2009) at Nottingham University Hospitals.
  • Method: General Practitioners (GPs) of confirmed AIH patients were contacted to ascertain diagnostic coding.
  • Data collection: Response rate from GPs and the specific READ code used for AIH diagnosis were recorded.

Main Results:

  • Of 42 patients with confirmed AIH, 39 GPs (90.5%) responded.
  • 33 out of 37 responding GPs (89%) used the READ code J638.00 for Autoimmune Hepatitis.
  • This indicates high accuracy in primary care recording of specialist AIH diagnoses.

Conclusions:

  • Consultant Hepatologist diagnoses of Autoimmune Hepatitis are accurately communicated and recorded in UK primary care.
  • The reliability of these electronic records supports their use in population-based AIH studies.
  • Utilizing primary care data minimizes selection bias in epidemiological research on AIH.
Abstract

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