July 26, 2012 – Health care providers and payers do a lot of work with information technology, but don’t necessarily get better at decision making. That’s the view of Thomas Davenport, author of “Analytics at Work: Smarter Decisions, Better Results.”
But advances in information technology and emerging new processes in care delivery are setting the stage for “new decision frontiers,” Davenport said at Health Data Management’s Healthcare Analytics Symposium in Chicago. These include predictive modeling and scoring, checklists and care protocols, comparative effectiveness research, transparency around health care costs, and behavioral economics and nudges to change patient and provider behaviors.
With data analytics, decision making is becoming more automated, he asserted. But the analytics being done today are on a small scale and just scratching the surface, such as identifying patients’ clinical and financial risks, protocol adherence, embedding analytics into daily practice, and analyzing physician and patient networks.
Better capabilities are coming but are not yet ready for prime time, Davenport noted. These include provider-payer collaboration on business analytics, provider-payer-life sciences collaboration on clinical analytics, using analytics across the care continuum, and using analytics to personalize genetic medicine.
Davenport listed five levels of organizational analytics capability, with 1 being analytically impaired, 2 having localized analytics, 3 having analytical aspirations to go further, 4 representing analytical organizations that are really good but not the best, and 5 being analytical competitors.
Health care providers, he said, are almost always at levels 1 or 2. That’s because they haven’t gotten past a series of major obstacles: lack of good electronic health records data, absence of data standards, physician autonomy, little front-line knowledge of costs, payer and provider lack of communication and collaboration, and the inability to hire skilled analysts.
Too many organizations also focus on data aggregation when the priority needs to be on data agreement to make any headway, he advised. “Get the people who need to agree on definitions in a room and get to work.”
This story originally appeared at Health Data Management.
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