Effect of change in coding rules on recording diabetes in hospital administrative datasets |
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| Institution: | 1. The Simpson Centre for Health Services Research, South Western Sydney Clinical School, The University of New South Wales, Sydney;2. Biostatistics, Dean''s Office Dunedin School of Medicine, University of Otago, Dunedin, New Zealand and formerly Senior Lecturer, Epidemiology, School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia;3. Liverpool Hospital, Liverpool, Australia;4. Liverpool Hospital, Liverpool, Australia;5. Emergency Care Institute, Agency for Clinical Innovation, Chatswood, Australia;6. The George Institute for Global Health, The University of New South Wales, Newtown, Australia;7. ICU, Liverpool Hospital, Liverpool, Australia;8. Associate Professor, Intensive Care, The University of Western Sydney, Australia;9. The Simpson Centre for Health Services Research, South Western Sydney Clinical School, The University of New South Wales, Sydney, Australia;10. ICU, Liverpool Hospital, South Western Sydney, and Foundation Director, The Simpson Centre for Health Services Research University of New South Wales, Liverpool, Australia;1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia;2. Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Tarragona, Spain |
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| Abstract: | AimDuring 2008–2011 Australian Coding Standards mandated a causal relationship between diabetes and inpatient care as a criterion for recording diabetes as a comorbidity in hospital administrative datasets. We aim to measure the effect of the causality mandate on recorded diabetes and associated inter-hospital variations.MethodFor patients with diabetes, all admissions between 2004 and 2013 to all New South Wales acute public hospitals were investigated. Poisson mixed models were employed to derive adjusted rates and variations.ResultsThe non-recorded diabetes incidence rate was 20.7%. The causality mandate increased the incidence rate four fold during the change period, 2008–2011, compared to the pre- or post-change periods (32.5% vs 8.4% and 6.9%). The inter-hospital variation was also higher, with twice the difference in the non-recorded rate between hospitals with the highest and lowest rates (50% vs 24% and 27% risk gap). The variation decreased during the change period (29%), while the rate continued to rise (53%). Admission characteristics accounted for over 44% of the variation compared with at most two per cent attributable to patient or hospital characteristics. Contributing characteristics explained less of the variation within the change period compared to pre- or post-change (46% vs 58% and 53%). Hospital relative performance was not constant over time.ConclusionThe causality mandate substantially increased the non-recorded diabetes rate and associated inter-hospital variation. Longitudinal accumulation of clinical information at the patient level, and the development of appropriate adoption protocols to achieve comprehensive and timely implementation of coding changes are essential to supporting the integrity of hospital administrative datasets. |
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| Keywords: | Administrative dataset Clinical coding Coding rules Diabetes Inconsistency Risk adjustment |
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