Predicting Future Hospital Admissions: Can We Focus Intensive Readmission Avoidance Efforts More Effectively?

Published in Outcomes and Insights in Health Management

Author(s): Adam Hobgood, MS; Huiwen Zeng, PhD; Jay Chyung, MD, PhD

Outcomes and Insights in Health Management Readmission to the hospital after discharge represents a cause of distress for patients and significant unnecessary cost to the health care system. Although better hospital care transitions with follow-up support for the discharge plan and general health management are proven approaches to decrease readmission rates, identifying the admitted patients in greatest need of additional post-discharge support is a challenge. Healthways developed and evaluated two predictive models to identify patients at risk of readmission within 30 days of discharge among a population diverse with respect to age, gender and diagnosis.

Both predictive models generate a readmissions risk index (RRI), a score that represents an individual’s relative risk of 30-day readmission based on a variety of variables associated with this outcome. One model incorporates both a patient’s historical (prior year’s claims) and current health care data (hcRRI). The other uses a patient’s current health care data alone (cRRI), built from claims data that replicate data also available to health care providers during an admission, for use in settings in which historical claims are unavailable. A split-sample design was implemented to derive and validate the risk index in a commercially insured population. The hcRRI model yielded slightly higher discriminatory power than the cRRI model. Among patients admitted between June 2008 and May 2009 and who were assigned by each model to a 25% cohort with the highest risk among all admissions, the hcRRI captured 45% of all actual 30-day readmissions and the cRRI captured 44%, both nearly doubling the rate as identified by chance in the general population.

Both models represent useful tools to direct programs aimed at reducing readmissions through personalized interventions and each may be uniquely applicable to different settings based on the data availability. A primary advantage of using these models in the clinic or for managing the health of a larger population is to allow readmission-avoidance programs to be delivered at scale in a cost-effective manner. Identifying patients at highest risk for readmission allows care teams to direct limited resources most efficiently for the purpose of reducing 30-day readmissions. Effective programs guided by these models represent a significant step toward improving quality of care and containing healthcare costs.

Key Takeaways:

  • Sharecare developed and validated two models that predict patients most likely to have a 30-day readmission.
  • The models provide the opportunity to increase efficiency of intensive discharge support. For example, the hcRRI provides the opportunity to impact up to 52% of readmissions by deliver- ing a program to only 30% of admitted patients.
  • The models can be used during hospitalization for early intervention and provide flexibility for application in settings with varying data availability and with broad patient populations.
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