Reducing emergency admissions through community based interventions
BMJ 2016; 352 doi: https://doi.org/10.1136/bmj.h6817 (Published 28 January 2016) Cite this as: BMJ 2016;352:h6817
- Emma Wallace, general practice lecturer1,
- Susan M Smith, professor of general practice1,
- Tom Fahey, professor of general practice1,
- Martin Roland, professor of health services research2
- 1HRB Centre for Primary Care Research, Department of General Practice, Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Ireland
- 2Cambridge Centre for Health Services Research, University of Cambridge, UK
- Correspondence to: E Wallace emmawallace{at}rcsi.ie
- Accepted 30 November 2015
Reducing emergency admissions to hospital, both as a measure of care quality and to contain spiralling healthcare expenditure, is gathering interest internationally. Emergency admissions in the United Kingdom rose by 47% from 1998 to 2013, from 3.6 million to 5.3 million, with only a 10% increase in population over this period.1 These admissions are expensive; in 2012 they cost the NHS £12.5bn (€16.8bn; $18.3bn).1
Emergency admission is used as a performance measure for healthcare systems. One of the quality measures for accountable care organisations under the US Affordable Care Act2 is to reduce emergency admissions for three chronic medical conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure, and asthma.3 UK policy makers took a step further and introduced a financial incentive for general practitioners to identify the 2% of their practice population at highest risk of emergency admission and to manage them proactively (case management).
We discuss the uncertainties around identification, prevention, and management of patients at high risk of emergency admission and suggest alternative approaches.
Limited potential for reducing admissions
Risk prediction models use clinical, demographic, and healthcare use data to identify people at risk of emergency admission.4 5 6 A systematic review identified 27 models to predict future emergency admission in community dwelling adults.7 The six best performing models showed good discrimination for the outcome of future emergency admission (c statistics 0.79-0.83).7
We used two models—the Scottish patients at risk of readmissions and admission (SPARRA) model8 and the UK Nuffield trust model9—to estimate their likely effects on emergency admissions. SPARRA (version 3) was developed in a cohort of over 3.5 million Scottish people,8 and the Nuffield trust model was developed in a cohort of over 1.8 million English …
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