Advocate Health Care's first foray into predicting patients' future medical needs focused on those at greatest risk for repeat hospitalizations.
It made sense. Medicare two years ago began penalizing hospitals with excessive patient readmissions within 30 days of discharge. The policy set hospitals scrambling to identify and head off potential repeat visitors. Penalties to date have cost hospitals more than $500 million, including as much as $5 million for some Advocate hospitals.
Advocate's initial investment in predictive analytics paid off. The big-data initiative, which combined information gleaned from patients' medical history, claims, demographics, laboratory results, pharmacy use and patients' self-description of their health status, was 20% more accurate than alternative algorithms in the marketplace in predicting who might be readmitted after discharge, system officials said.
But now, with the new system in place at 8 of its 11 hospitals, Advocate is looking to take the strategy to the next level. The Downers Grove, Ill.-based health system and its medical records partner later this year will launch a predictive-analytics initiative that analyzes all patients receiving care from affiliated physicians. The goal is to identify patients who are likely candidates for interventions to prevent disease, better manage their health conditions outside the hospital and prevent future hospitalizations, all of which could save insurers and the system money.
The model sorts patients by the complexity of their conditions, and then identifies those factors that signal those who are ripe targets for intervention such as unfilled prescriptions or poor communication between patients' multiple providers.
For providers, the preventive interventions enabled by predictive analytics could deliver profits under new payment models, which are moving toward various forms of capitation. And for policymakers, the savings could ease the fiscal stress that U.S. health spending puts on taxpayers and employers.
“It's enabling strategic resource allocation among the total population,” said Dr. Rishi Sikka, Advocate's senior vice president of clinical transformation. “If you really want to move the entire population … you need to work on the entire population, not just the most expensive.”
Advocate's push to employ predictive modeling across its broad population base is an early test of the latest front in healthcare's march to using big data to improve healthcare outcomes and reduce costs. It is occurring against a backdrop where much of the industry is still struggling to boost the weak accuracy of existing models, which focus on preventing hospital readmissions and have so far yielded only modest results (some no better than flipping a coin), according to a 2011 review of more than two decades of studies and more recent published research.