Observational studyBMJ open2024
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.
Observational study in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed.
- Article
- Access to Medications for Opioid Use Disorder Among Veterans With Homeless Experience in Permanent Supportive Housing.JAMA network open · 2026Article
- Two distinct healthcare utilisation trajectories prior to a COPD diagnosis in a multi-ethnic Asian population: a comparison of transient and chronic users.BMJ open respiratory research · 2026Article
- Dementia data for people living in care homes: cross-sectoral national data linkage study of care home, healthcare and administrative data sources.International journal of population data science · 2026Article
- Building ADMISSION - A research collaborative to transform understanding of multiple long-term conditions for people admitted to hospital.Journal of multimorbidity and comorbidityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
objectivesAnalysis 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.
designRetrospective observational study of routinely collected hospital electronic health record data.
settingQueen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub.
participantsPatients 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.
resultsSeven 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.
conclusionA 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.
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