The Right Prescription for Healthcare Analytics

Five essential changes healthcare organizations need to make to get the most from data analytics.

Data analytics is touted as a tool that will transform healthcare. 

Armed with analytics, providers should be able to keep patient populations healthier and improve the way care is bought and sold, all while delivering precise, patient-centric care. 

But for healthcare providers to see ROI on data analytics initiatives, they need to start making changes across their organization—and fast. 

Nearly all providers have moved to digital systems, but electronic medical records (EMRs) are only the beginning. Not all data fits neatly into those systems, and getting helpful insights from all that information is still a daunting task for most organizations. 

How can hospitals and clinics predict outcomes? How can they move to patient-centered medicine, where the right data about each person is in the right place at the right time?

It will require sophisticated analytics systems and tools—such as machine learning and artificial intelligence (AI)—that most healthcare organizations haven’t yet built into their IT infrastructure.

“We need to move beyond just having an effective yet limited relational database management infrastructure that was developed in the 1990s, and migrate to a 21st-century data model to more effectively support data-driven decision-making,” said Brett MacLaren, vice president of enterprise analytics at Sharp HealthCare in San Diego. 

MacLaren oversaw a proof-of-concept project using technologies from Cloudera and Intel to analyze EMR data to identify patients at risk of needing emergency intervention. 

The project was 80 percent accurate1, even with limited data, highlighting the potential of predictive analytics to help hospitals improve the quality and cost of patient care.

To use advanced analytics and make more effective use of healthcare staff and resources, here are five essential changes every healthcare organization needs to make.

It’s critical to put together a multidisciplinary team with representation from clinical teams to determine whether it will be useful, what to do with the data, and how to put it into clinical practice.

"There’s a lot of technology out there that’s been turned off by doctors because it’s wrong occasionally, and that can be very distracting."

— Bob Rogers
Chief Data Scientist for
Analytics and AI Solutions, Intel

"Be prepared to see every single assumption that you have on how this is going to play out in the world be completely wrong."

— Parsa Mirhaji
Clinical Research Informatics
Director at Montefiore
Medical Center


1 *80 percent accuracy indicates the level of accuracy observed when scoring a set of test data that was not used in the development of the model