Evidence map›Paper›PMID 36307148›Full record

ArticleBMJ health & care informatics2022

Data consistency in the English Hospital Episodes Statistics database.

Flavien Hardy, Johannes Heyl, Katie Tucker, Adrian Hopper, Maria J Marchã, Tim W R Briggs, Jeremy Yates, Jamie Day, Andrew Wheeler, Sue Eve-Jones and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Five-Year Hospital Readmission After Isolated Coronary Artery Bypass Grafting in the United Kingdom.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Flavien HardyGetting It Right First Time, NHS England and NHS Improvement London, London, UK flavien.hardy.17@ucl.ac.uk.ORCID http://orcid.org/0000-0003-1939-8744
Johannes HeylGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
Katie TuckerInnovation and Intelligent Automation Unit, Royal Free London NHS Foundation Trust, London, UK.
Adrian HopperGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
Maria J MarchãScience and Technology Facilities Council Distributed Research Utilising Advanced Computing High Performance Computing Facility, London, UK.
Tim W R BriggsGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
Jeremy YatesScience and Technology Facilities Council Distributed Research Utilising Advanced Computing High Performance Computing Facility, London, UK.
Jamie DayGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
Andrew WheelerGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
Sue Eve-JonesGetting It Right First Time, NHS England and NHS Improvement London, London, UK.
William K GrayGetting It Right First Time, NHS England and NHS Improvement London, London, UK.ORCID http://orcid.org/0000-0002-9597-5446

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo gain maximum insight from large administrative healthcare datasets it is important to understand their data quality. Although a gold standard against which to assess criterion validity rarely exists for such datasets, internal consistency can be evaluated. We aimed to identify inconsistencies in the recording of mandatory International Statistical Classification of Diseases and Related Health Problems, tenth revision (ICD-10) codes within the Hospital Episodes Statistics dataset in England.

methodsThree exemplar medical conditions where recording is mandatory once diagnosed were chosen: autism, type II diabetes mellitus and Parkinson's disease dementia. We identified the first occurrence of the condition ICD-10 code for a patient during the period April 2013 to March 2021 and in subsequent hospital spells. We designed and trained random forest classifiers to identify variables strongly associated with recording inconsistencies.

resultsFor autism, diabetes and Parkinson's disease dementia respectively, 43.7%, 8.6% and 31.2% of subsequent spells had inconsistencies. Coding inconsistencies were highly correlated with non-coding of an underlying condition, a change in hospital trust and greater time between the spell with the first coded diagnosis and the subsequent spell. For patients with diabetes or Parkinson's disease dementia, the code recording for spells without an overnight stay were found to have a higher rate of inconsistencies.

conclusionsData inconsistencies are relatively common for the three conditions considered. Where these mandatory diagnoses are not recorded in administrative datasets, and where clinical decisions are made based on such data, there is potential for this to impact patient care.

Indexed as

DementiaDiabetes Mellitus, Type 2Parkinson DiseaseHospitalsHumansInternational Classification of Diseasesinformation technology

Identifiers

PMID36307148
PMCPMC9621173

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.