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How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data.
Lewis, Jadene; Evison, Felicity; Doal, Rominique; Field, Joanne; Gallier, Suzy; Harris, Steve; le Roux, Peta; Osman, Mohammed; Plummer, Chris; Sapey, Elizabeth; Singer, Mervyn; Sayer, Avan A; Witham, Miles D.
Afiliação
  • Lewis J; PIONEER Hub, University of Birmingham, Birmingham, UK.
  • Evison F; Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Doal R; PIONEER Hub, University of Birmingham, Birmingham, UK.
  • Field J; Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Gallier S; PIONEER Hub, University of Birmingham, Birmingham, UK.
  • Harris S; Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • le Roux P; Digital Services, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • Osman M; PIONEER Hub, University of Birmingham, Birmingham, UK.
  • Plummer C; Health Informatics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
  • Sapey E; Critical Care Department, University College London Hospitals NHS Foundation Trust, London, UK.
  • Singer M; Institute of Health Informatics, University College London, London, UK.
  • Sayer AA; Digital Services, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
  • Witham MD; AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
BMJ Open ; 14(2): e080678, 2024 Feb 13.
Article em En | MEDLINE | ID: mdl-38355192
ABSTRACT

OBJECTIVES:

Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses.

DESIGN:

Retrospective observational study of routinely collected hospital electronic health record data.

SETTING:

Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.

PARTICIPANTS:

Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date. OUTCOME

MEASURES:

We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission.

RESULTS:

Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2-3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback.

CONCLUSION:

A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Acidente Vascular Cerebral / Doença Pulmonar Obstrutiva Crônica Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Acidente Vascular Cerebral / Doença Pulmonar Obstrutiva Crônica Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: BMJ Open Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido