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Preventing hospital readmissions: the importance of considering ‘impactibility,’ not just predicted risk

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.

Unfortunately, many of the most effective interventions are costly, since they are multimodal and involve several components and multiple healthcare practitioners. 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, but an updated review that examined papers published up to 2015 found 68 more.

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