“If you challenge conventional wisdom, you will find ways to do things much better than they are currently done.” — Baseball statistician Bill James, as quoted in Michael Lewis’ Moneyball: The Art of Winning an Unfair Game

The health care field is on the cusp of entering the era of “Moneyball” medicine. Just ask Eric Topol, M.D., director of San Diego-based Scripps Translational Science Institute. He recently hired Paul DePodesta, a data analytics guru who transformed the Oakland Athletics baseball team and now has his algorithms set on doing the same for STSI and health care. And he is not alone. More than 200 data analytics companies are vying for the attention of health care organizations, which are sitting on an untapped trove of data.

Story Overview

  • The health care industry is decades behind other consumer-oriented businesses in using analytics to anticipate future needs and costs.
  • The shift to value-based reimbursement is fueling rapid deployment of predictive analytics systems.
  • Effective use of analytics requires data warehouses and integration of data from all available sources.
  • Understanding a health care system’s current state is a prerequisite to being able to forecast a desired future state.

With the near universal adoption of electronic health records, large hospitals and health systems have begun to recognize something that consumer retailers have relied on for more than a decade: With the right analytics, data can predict the future and help organizations get out in front of consumer trends. In the context of health care, predictive analytics systems are being used, for instance, to understand which patients are at higher risk for hospital readmission, to reduce hospital stays after joint replacement and to anticipate staffing needs while reducing overtime.

"In disciplines as disparate as baseball, financial services, trucking and retail, people are realizing the power of data to help make better decisions," DePodesta said when he took the job as an assistant professor of bioinformatics at STSI (he is also chief strategy officer with the Cleveland Browns football team). "Medicine is just beginning to explore this opportunity, but it faces many of the same barriers that existed in those other sectors — deeply held traditions, monolithic organizational and operational structures and a psychological resistance to change." Working on the STSI faculty, he said, "allows me to apply the things I learned in baseball to a critical sector of our lives and our economy that is ripe for this kind of revolution."

[Supporting article: IBM Health Swinging Big in Predictive Analytics]

Some health care organizations have opted to build their own data analysis systems to suit their needs, while others have found industry partners offering predictive analytics systems tailored to the health care industry. Analytics options range from huge general use vendors such as SAS, IBM and Oracle to niche players developing specialized tools for the health care market. For organizations looking to implement analytics, those that have already taken the plunge suggest starting by taking stock of your organization’s current state.

“The first thing you need to know is what is happening in your population,” says Rishi Sikka, M.D., senior vice president of clinical transformation for Advocate Health Care in Illinois. “Do you know who is an attributed patient in your population? Do you know who is being readmitted today in your population? Do you know who is visiting the ER? Everyone wants to do all the sexy models and advanced analytics, but just understanding that current state, what is happening, is the first and the most important challenge.”

Operating as a value-based care organization for several years, Advocate has been moving from fee-for-service revenue models to value-based reimbursement, which is driving health care organizations toward sophisticated analytics systems that can quantify quality measures and track process improvements.

Sikka says that five years ago Advocate had a big problem with siloed data spread across many EHR systems that did not play well together. With motivation to improve patient care and control costs, Advocate’s leadership chose to invest in a partnership with Cerner, a cloud-based analytics platform that could integrate data from all of the EHRs within Advocate’s existing information technology infrastructure.

Cerner’s EHR-agnostic approach was attractive. “Being able to have a data platform that can take in all that data regardless of source and then push it out into a variety of EMRs was important,” says Sikka.

Capturing clinicians’ hearts and minds

Cerner worked directly with physician-led teams to pick a case study in which an intervention could truly make a difference in patient care. To get physicians on board, the Advocate clinical innovation team held focus groups and explained how analytics models that integrate data from many sources can have more predictive power than models that rely solely on retrospective administrative data.

[Supporting article: 7 Factors to Consider Before Investing In An Analytics System]

“As an industry, when we talk about population health, we tend to talk a lot about cost and utilization, and those are extremely important … but that’s not something that gets clinicians excited,” Sikka says. “If you start with the clinical scenario and why it is important to patients … that’s where you really start to capture the hearts and minds of physicians.” (Continued.)