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.
In New York state, where state spending per Medicaid enrollee is among the highest anywhere, state officials have bet that the algorithm developed by Billings and others five years ago may answer which variables can predict which patients will be hospitalized soon after leaving.
Roughly 15% of hospitalized New Yorkers returned to the hospital within a month in 2008 at a cost of $3.7 billion, according to an analysis by Mathematica Policy Research. Repeat hospitals visits from complications and infections cost $1.3 billion that year. Some 2% of Medicaid patients landed back in the hospital 30 days after leaving because of complications or infections (See chart).
Researchers who developed the algorithm teased out 21 common variables from more than 60 possibilities to find the most informative measures. Diabetes, chronic obstructive pulmonary disease and alcohol-related diagnoses were included, as was older age (at least 65) and recent emergency admissions, among others.
Billings and other researchers tried to refine the algorithm by testing additional variables from electronic health records—such as lipid concentrations, blood pressure, glycated hemoglobin levels, accident and emergency data—but found hospitals' differing record systems made access to uniform data difficult.
As healthcare predictive modeling evolves, some research suggests not all high-risk patients will benefit.
One 2008 survey published in the Milbank Quarterly of 30 organizations that build, use or evaluate predictive healthcare models—including the Veterans Affairs Department, Kaiser Permanente, Humana and UnitedHealth Group—found some care-management companies exclude the highest-risk, and often highly vulnerable, patients as unlikely to respond to support.
But others sought to tailor preventive care to the specific circumstance of patients, developing different approaches based on marketing research for patients with similar diagnoses, the survey found.
For hospital admissions, a review of recent hospital stays won't identify who will return repeatedly the following year, Billings says. “A lot of people who were high-cost last year will be high-cost next year, but not everybody,” he says. And some high-cost patients require high-cost care; not all repeat visits are unnecessary, he says.
Hospitals that use the algorithm can expect to identify two out of three patients who will land back in the hospital but didn't have to, Billings says. He describes the algorithm's predictive power as “strong” and one that does not falsely identify too many patients as chronic users.
The ability to weed out patients who will stop returning to the hospital without intervention from caregivers is important to prevent hospitals from mistakenly directing limited resources where they're not needed, he says.
“An effective case-finding tool is one that identifies as many patients as possible who will have future high costs or hospital resource use without intervention but is not so broad that it includes large numbers of patients who will not incur such costs,” wrote Billings and colleagues in the British Medical Journal. The formula, first developed for the National Health Service in England, relied on statistical analysis embraced by Beane and Sandy Alderson, his predecessor in the Oakland A's front office.