Data quality and autism: Issues and potential impacts.

This study looked at how well hospitals record autism diagnoses in their computer systems. They found that nearly half the time (44%), when autistic patients returned to hospital, their autism wasn't recorded again. This was more common for older patients, women, and people from disadvantaged areas. These missing records could lead to misunderstandings about what healthcare services autistic people actually need and use.

This study examined data quality issues in the UK's Hospital Episodes Statistics database regarding autism diagnoses. Analyzing 172,324 patients with autism diagnoses between 2013-2021, researchers found that 43.7% of subsequent hospital admissions failed to record the autism diagnosis consistently. Data inconsistencies were most common among older patients, females, those from more deprived areas, patients with longer gaps between admissions, and when providers or specialties changed. A machine learning model successfully predicted inconsistencies with 86.4% accuracy.

For patients who died in hospital, inconsistencies were particularly associated with being female, elderly (80+), from deprived areas, and receiving palliative care. These findings highlight significant data quality concerns that could bias healthcare research and service planning for autistic individuals.

Data inconsistencies could bias research findings and healthcare planning for autistic individuals, particularly affecting understanding of service use among women, older adults, and socioeconomically disadvantaged groups. Healthcare systems need improved protocols for consistent autism diagnosis recording across admissions.

Study could not determine whether missing diagnoses or present diagnoses were the actual errors. Limited to UK hospital data only. Cannot assess impact on clinical care quality, only data recording patterns.

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.

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.

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. 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.