Tag Archives: Predictive

Re-inventing Healthcare: Cutting Re-admission rates with predictive analytics

Managing unplanned re-admissions is a persistent and enduring problem for healthcare providers.  Analysis of Medicare claims from over a decade ago showed that over 19% of beneficiaries were re-admitted within 30 days.  Attention on this measure increased when the Affordable Care Act introduced penalties for excessive re-admits.  However, many hospitals – including those in South Florida and Texas – are losing millions in revenue because of their inability to meet performance targets.

Carolinas HealthCare System has applied predictive analytics to the problem, using Predixion Software and Premier Inc.  Essentially, by using patient and population data, Carolinas is able to calculate a more timely, more accurate assessment of the re-admit risk.  The hospital can then put in place a post-acute care plan to try and minimize the risk of re-admission.  You can find a brief ten minute webinar presented by the hospital here.  But, from an analytics, information management  and decision making perspective, here are the key points:

  • The risk assessment for readmission is now done before the patient examination, not after it. Making that assessment early means there is more time to plan for the most appropriate care after discharge.
  • The risk assessment is now more precise, accurate, and consistent.  In the past, the hospital just categorized patients into two buckets – high risk and low risk.  There are now four bands of risk so the care team can make a more nuanced assessment of risk and plan accordingly.  Further, the use of Predixion’s predictive analytics software means that far more variables can be considered to make the determination of risk.  Us puny human’s can only realistically work with a few variables well to make a decision.  Predictive analytics allowed more than 40 data points from the EMR, ED etc. to be used to make a more accurate assessment of risk.  Finally, calculating the risk using software meant that Carolinas could avoid any variability introduced by case managers with different experience and skills.
  • The risk assessment is constantly updated.  In practice, the re-admission risk for any individual patient is going to change throughout the care process in the hospital.  So, a patients re-admission risk is now recalculated and updated hourly – not just once at the time of admission which was situation in the past.
  • The overall accuracy of risk assessment gets better over time.  A software-centered approach means that suggested intervention plans can be built in – so again reducing variability in the quality of care.  But, the data-centric approach means that the efficacy of treatment plans can also be easily measured and adjusted over the long-term.

Overall, this data-driven approach to care is a win-win.  It results in higher care quality and better outcomes for the patient.  And Carolinas HealthCare System improves its financial performance too.  This is all possible because more of the risk assessment is now based on hard data, not intuition.