Data quality and autism: Issues and potential impacts.
Introduction: Large healthcare datasets can provide insight that has the potential to improve outcomes for patients. However, it is important to understand the strengths and limitations of such datasets so that the insights they provide are accurate and useful. The aim of this study was to identify...
| Published in: | International Journal of Medical Informatics Vol. 170 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Elsevier B.V.
Feb2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161307728&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161307728 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13865056 JR4 jtl: International Journal of Medical Informatics issn: 13865056 maglogo: N pubinfo: dt: Feb2023 vid: 170 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 161307728 161307728 NLM36455477 10.1016/j.ijmedinf.2022.104938 NLM36455477 161307728 ppct: 1 formats: tig: atl: Data quality and autism: Issues and potential impacts. aug: au: Heyl, Johannes Hardy, Flavien Tucker, Katie Hopper, Adrian Marchã, Maria J. Liew, Ashley Reep, Judith Harwood, Kerry-Anne Roberts, Luke Yates, Jeremy Day, Jamie Wheeler, Andrew Eve-Jones, Sue Briggs, Tim W.R. Gray, William K. affil: Getting It Right First Time, NHS England and NHS Improvement, London, UK sug: ab: Introduction: Large healthcare datasets can provide insight that has the potential to improve outcomes for patients. However, it is important to understand the strengths and limitations of such datasets so that the insights they provide are accurate and useful. The aim of this study was to identify data inconsistencies within the Hospital Episodes Statistics (HES) dataset for autistic patients and assess potential biases introduced through these inconsistencies and their impact on patient outcomes. The study can only identify inconsistencies in recording of autism diagnosis and not whether the inclusion or exclusion of the autism diagnosis is the error.Methods: Data were extracted from the HES database for the period 1st April 2013 to 31st March 2021 for patients with a diagnosis of autism. First spells in hospital during the study period were identified for each patient and these were linked to any subsequent spell in hospital for the same patient. Data inconsistencies were recorded where autism was not recorded as a diagnosis in a subsequent spell. Features associated with data inconsistencies were identified using a random forest classifiers and regression modelling.Results: Data were available for 172,324 unique patients who had been recorded as having an autism diagnosis on first admission. In total, 43.7 % of subsequent spells were found to have inconsistencies. The features most strongly associated with inconsistencies included greater age, greater deprivation, longer time since the first spell, change in provider, shorter length of stay, being female and a change in the main specialty description. The random forest algorithm had an area under the receiver operating characteristic curve of 0.864 (95 % CI [0.862 - 0.866]) in predicting a data inconsistency. For patients who died in hospital, inconsistencies in their final spell were significantly associated with being 80 years and over, being female, greater deprivation and use of a palliative care code in the death spell.Conclusions: Data inconsistencies in the HES database were relatively common in autistic patients and were associated a number of patient and hospital admission characteristics. Such inconsistencies have the potential to distort our understanding of service use in key demographic groups. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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