Predictive modeling being tested in data-driven effort to strike out hospital readmissions
Hospitals waiting on Congress to squeeze spending may sympathize with the Oakland Athletics’ reversal of fortune more than a decade ago, when the ball club’s owners refused to continue profligate spending. Soon thereafter, as readers of the bestseller
Moneyball know, Oakland A’s General Manager Billy Beane relied on fancy math and economic theory to put together a competitive baseball team on a shoestring major league budget.
Beane’s conviction—that statistical analysis could trump baseball tradition that blinded the sport to valuable players and left October to teams able to afford errors—became the subject of Michael Lewis’ 2003 book and a newly released movie.
“We take 50 guys and we celebrate if two of them make it,” Beane fumes about the major league draft in Lewis’ book. “In what other business is 2-for-50 a success?”
Now hospital executives, flooded with data from information technology investments and under pressure to curb waste and spending, face the same question that Beane and others before him, including Bill James, an influential author and baseball statistician, sought to answer: What measures matter the most?
In healthcare, the question is critical when it comes to who ends up in the hospital.
Hospitals, which house squadrons of nurses, pricey technology, pharmacies and laboratories, are costly places to care for patients, with hospital care accounting for healthcare’s single largest expense. Policymakers see significant potential to curb spending by keeping more patients out of the hospital. In 2005, Medicare paid hospitals $7,200, on average, and $12 billion in total for repeat hospital visits that could have been avoided, according to one estimate by the Medicare Payment Advisory Commission.
Hospitals also can be unsafe. Patients risk infections or other avoidable complications during a hospital stay that, paradoxically, can land them back in the hospital shortly after leaving.
And hospitals now have added incentives to better identify such patients. Medicare, the single largest customer for many hospitals, will penalize those with too many repeat patients starting in 2013. The penalty starts at up to 1% of hospital Medicare revenue, then increases over the next several years to as much as 3%.
Indeed, within hospitals and health plans, some have started to use the same fancy math as the Oakland A’s and other predictive models to identify patients at risk for unnecessary hospital stays.
“Nothing good happens when you go to the hospital,” says John Billings, director of the Center for Health and Public Service Research at New York University’s Robert F. Wagner Graduate School of Public Service. Billings and colleagues developed an algorithm being tested by Medicaid in New York. “Going to the hospital means something went wrong outside of the hospital,” Billings says.
Similarities between baseball’s adherence to sometimes ineffective and costly convention and the inefficiency of U.S. healthcare have not escaped notice.
“America’s healthcare system behaves like a hidebound, tradition-based ball club that chases after aging sluggers and plays by the old rules: We pay too much and get too little in return,” wrote Beane and health policymakers Newt Gingrich and Sen. John Kerry (D-Mass.) in a 2008 New York Times editorial.