A new survey from the American Health Information Management Association finds that 95 percent of the more than a thousand healthcare industry professionals queried believe that "high-value information" is essential for improving patient safety and care quality.
"I'm reassured," says Deborah Green, AHIMA's executive VP of operations and COO, and a contributor to the study. "That's what it is about."
This bit of news comes as analytics is slowly making its way from business intelligence – revenue-cycle management and financial performance – to the clinical side of healthcare. "Predictive analytics can help with readmissions prevention," notes Charles Christian, CIO at St. Francis Hospital in Columbus, Ga.
His and other community hospitals are just starting to make small strides in this direction, but larger organizations are seeing results. As the Wall Street Journal reported recently, the University of Pittsburgh Medical Center has put $105 million toward a massive data analytics program, which is employed to analyze the success of a patient-centered medical home pilot. UPMC found that those with medical homes had substantially better health outcomes after six months in the program, and the medical home reduced health expenditures by $15 million in the first year.
In a pilot at the Ohio State University's Wexner Medical Center, analytics have deployed cardiologists and oncologists to an ambulatory clinic in a part of Columbus, Ohio, with high rates of heart disease and cancer. With data algorithms, the university has been able to identify patients in need of intervention and personalize care for those individuals in order to reduce initial hospitalizations as well as readmissions, according to Burroughs Healthcare Consulting Network.
Jim Adams, executive director for research and insights at The Advisory Board Company, a Washington, D.C.-based consultancy and research firm, notes that analytics can help health systems add psychosocial factors to their decision-making. For example, if a hospital knows that a patient lives alone, the discharge plan can include arranging transportation for follow-up care.
Adams says that it's possible to do "simple analytics" just by getting people together in teams and brainstorming, which may be the best some can do at the moment, though he has seen many organizations applying business intelligence to stratify clinical risk and prioritize patient interventions. "Ideally they're doing it at admission time, not just at discharge," he says.
As the AHIMA survey suggests, healthcare organizations may get the importance of having good data, but many apparently still have a long way to go in building a foundation for high-functioning analytics programs.
The Advisory Board has developed a four-phase maturity model for business intelligence, starting at "fragmented," then progressing to analytics from an enterprise perspective, "advanced" analytics and, ultimately, big data.
Following this model, organizations cannot say they have achieved big data until they have developed "an engrained understanding of BI capabilities and limitations." They must be stewards of both internal and external data, employ "sophisticated delivery models" and be able to apply such things as natural language processing and genomics, according to Advisory.
Based on a survey of Advisory Board members, Adams sees three major challenges to business intelligence: data governance; culture; and organizational structure and resources. "It's really about usage and usability of the data," Adams says. Factors include quality of data, literacy of people using the data and, of course, privacy and security.
"Just because you can go out and buy a predictive modeling tool doesn't mean you can do predictive modeling," according to Adams. "It's kind of at the phase of hardwiring some of this now," both technically and culturally, he says.
