Preventing readmissions and reducing LOS with predictive analytics

27 May 2014 | Sarah Muckler, Director of Health Marketing, Worldwide

readmissionsPreventing readmissions and reducing length of stay (LOS). These are two top priorities for hospitals as they look to cut costs, improve care, and provide a better patient experience. Not only that, as part of healthcare reform in many countries, hospitals will be financially penalized when patients are readmitted within a certain time frame or when LOS extends beyond target guidelines. The good news is that today’s analytics technologies can help hospitals reduce their readmission rate and average LOS.

Analytics can help bring together data on patients’ individual risk factors when they’re admitted to a hospital and throughout their stay and then apply predictive models to assess the patient’s probability for readmission or extended LOS. Microsoft partner Predixion is doing some very cool stuff in these areas.

For example, Predixion’s Cloud-based Predictable Readmissions Solutions  identify which patients are at risk of readmission before they leave the hospital and can do so with up to 86 percent accuracy, according to Predixion. Staff can then target patients who are at risk with appropriate care intervention before they’re discharged to help prevent their readmission.

In addition, Predixion is working with Carolinas HealthCare System, one of the largest nonprofit healthcare systems in the US, on the development of a solution that will use analytics to help reduce patients’ length of stay.

Traditionally, hospitals have used historical data and standardized benchmarks assigned by an intake nurse at the time of admittance to manage patients’ LOS. But often that data wouldn’t be updated or reassessed until the patient had already exceeded the estimated LOS.

Predixion LOS Insight applies predictive modeling to up-to-date data throughout the hospital stay to anticipate the patient’s potential risk factors and health outcomes. That way as the risk factors evolve, health professionals can adjust the patient’s care plan accordingly to help prevent an extended LOS.

In addition, the solution will help hospitals identify common factors driving LOS to continually improve their clinical protocols and achieve better outcomes.

“The ability to identify high-risk patients before excess LOS days have occurred is an exceptionally valuable capability for hospitals and their care managers," said Allen Naidoo, PhD, vice president for analytics at Carolinas HealthCare System, in a press release from Predixion. "Across their different teams and facilities, this solution will give healthcare organizations like Carolinas HealthCare System unprecedented insight into length of stay so they can more effectively manage resources and increase the efficiency of patient care.”

These are just a couple examples of how today’s predictive, real-time analytics technologies can help health organizations make a real impact on patient care and their bottom line. We look forward to continuing to share examples right here in the Microsoft in Health blog, so keep checking back. And please share with us your stories of how analytics are helping your health organization tackle your toughest challenges via email, Twitter, or on Facebook.

Sarah Muckler
Director of Health Marketing, Worldwide

Microsoft in Health Blog

About the Author

Sarah Muckler | Director of Health Marketing, Worldwide

Sarah Muckler leads Worldwide Health Marketing for Microsoft where she focuses on global marketing strategy and executing programs for continued growth in the health industry. Read more