One hospital wanted to reduce readmissions among patients with congestive heart failure. Another hoped to improve upon its sepsis mortality rates. A third sought to determine whether its doctors were providing cost-effective care for pneumonia patients. All of them adopted the same type of technology to help identify a solution.
As the healthcare industry tilts toward accountable care, pay for performance and an increasingly
cost-conscious mindset, hospitalists and other providers are tapping into a fast-growing analytical tool collectively known as data mining to help make sense of the growing mounds of information. Although no single technology can be considered a cure-all, HM leaders are so optimistic about data mining’s potential to address cost, outcome, and performance issues that some have labeled it a “game changer” for hospitalists.
Karim Godamunne, MD, MBA, SFHM, chief medical officer at North Fulton Hospital in Roswell, Ga., and a member of SHM’s Practice Management Committee, says he can’t overstate the importance of hospitalists’ involvement in physician data mining. “From my perspective, we’re looking to hospitalists to help drive this quality-utilization bandwagon, to be the real leaders in it,” he says. With the tremendous value that can be generated through understanding and using the information, “it’s good for your group and can be good to your hospital as a whole.”
So what is data mining? The technology fully emerged in the mid-1990s as a way to help scientists analyze large and often disparate bodies of data, present relevant information in new ways, and illuminate previously unknown relationships.1 In the healthcare industry, early adopters realized that the insights gleaned from data mining could help inform their clinical decision-making; organizations used the new tools to help predict health insurance fraud and identify at-risk patients, for example.
Cynthia Burghard, research director of Accountable Care IT Strategies at IDC Health Insights in Framingham, Mass., says researchers in academic medical centers initially conducted most of the clinical analytical work. Within the past few years, however, the increasing availability of data has allowed more hospitals to begin analyzing chronic disease, readmissions, and other areas of concern. In addition, Burghard says, new tools based on natural language processing are giving hospitals better access to unstructured clinical data, such as notes written by doctors and nurses.
“What I’m seeing both in my surveys as well as in conversations with hospitals is that analytics is the top of the investment priority for both hospitals and health plans,” Burghard says. According to IDC estimates, total spending for clinical analytics in the U.S. reached $3.7 billion in 2012 and is expected to grow to $5.14 billion by 2016. Much of the growth, she notes, is being driven by healthcare reform. “If your mandate is to manage populations of patients, it behooves you to know who those patients are and what their illnesses are, and to monitor what you’re doing for them,” she says.
Practice Improvement
Accordingly, a major goal of all this data-mining technology is to change practice behavior in a way that achieves the triple aim of improving quality of care, controlling costs, and bettering patient outcomes.
A growing number of companies are releasing tools that can compile and analyze the separate bits of information captured from claims and billing systems, Medicare reporting requirements, internal benchmarks, and other sources. Unlike passive data sources, such as Medicare’s Hospital Compare website, more active analytics can help their users zoom down to the level of an individual doctor or patient, pan out to the level of a hospitalist group, or expand out even more for a broader comparison among peer institutions.