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Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk
  1. Adam Steventon1,
  2. John Billings2
  1. 1 Data Analytics, The Health Foundation, London, UK
  2. 2 NYU Wagner, New York University, New York, New York, USA
  1. Correspondence to Dr Adam Steventon, The Health Foundation, 90 Long Acre, London, WC2E 9RA, U; adam.steventon{at}health.org.uk

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Reducing 28-day or 30-day readmissions has become an important aim for healthcare services, spurred in part by the introduction of financial incentives for hospitals with high readmission rates in the USA, England, Denmark, Germany and elsewhere.1 Unfortunately, many of the most effective interventions are costly, since they are multimodal and involve several components and multiple healthcare practitioners.2 Therefore, some healthcare teams are turning to predictive models in order to identify patients at high risk for readmission and focus resource intensive readmission prevention strategies on such ‘at risk’ patients. Recent years have seen an explosion in these predictive models, which use patterns observed within large data sets to generate readmission risks for individual patients. In 2011, a systematic review found 26 models for readmissions,3 but an updated review that examined papers published up to 2015 found 68 more.4

While doubts remain about the practical value of predictive risk models (for example because it is not clear whether interventions are more effective when targeted at high-risk than low-risk patients5), it is undeniable that many models accurately predict readmission risk. Among the 14 published models that target all unplanned readmissions (rather than readmissions for specific patient groups), the ‘C statistic’ ranges from 0.55 to 0.80, meaning that, when presented with two patients, these models correctly identify the higher risk individual between 55% and 80% of the time. As a benchmark, consider one study6 that asked practitioners to estimate the 30-day readmission risk for patients discharged from their tertiary medical centre in 2008. Staff physicians, residents and interns correctly predicted patients who would return to hospital within 30 days with a C statistic of around 0.58 (considerably below the typical target for acceptable discrimination of at least 0.7). Nurses and case managers performed little better than chance (with C statistics of …

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