Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC)

By A. Herbert, L. Wijlaars, A. Zylbersztejn, D. Cromwell, P. Hardelid. International Journal of Epidemiology, 2017

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s published since July 2011. We identified 264 relevant publications where the primary analysis involved the use of HES APC data, and a further 130 papers where HES data had been linked to cohorts created in other datasets. The annual number of publications using HES APC data has increased from 2 in 1993 to 88 in 2015. Published studies using HES APC data have covered a diverse range of topics. They have explored the incidence of conditions across regions and over time. They have also examined cross-sectional and longitudinal patterns of treatment by organization, including comparing NHS and privately contracted providers or regions, both from descriptive and analytical perspectives. Regional comparisons have included evaluating the impact of clinical evidence or guidelines as well as health care policies. They have examined the outcome of medical as well as surgical therapies (such as survival, short-term postoperative mortality, complications, reoperation and hospital readmissions), with some seeking to identify factors that are associated with these outcomes, in terms of both patient characteristics and organizational factors such as surgical volume or day of week. Methodological studies include creating coding frameworks, applying comorbidity scores, developing risk prediction models and using look-back methods to impute missing data items. Many high profile routinely produced reports on the quality of secondary care are based on HES APC data. These include hospital mortality monitoring reports produced by NHS Digital and commercial organizations, and research reports by independent think-tanks and Royal Medical Colleges. Strengths and weaknesses

Subfields: Epidemiology, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/73b0eb18cad7ebce21ce33b99d934430195e22d4

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