Think Small Data Before Big Data, Healthcare Gurus Argue
Two health IT experts say that providers already have plenty of "small" data to improve patient outcomes, if they use it correctly.
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Big data has become increasingly attractive to healthcare providers seeking to prepare for accountable care. But, in a recent post in The HealthCare Blog, consultants David C. Kibbe, MD, and Vince Kuraitis--both respected observers of health IT--argue that instead of succumbing to the siren song of big data analytics, providers should focus on using "small" data better. In other words, concentrate on the clinical data already available in digitized form and use only those health IT tools that are directly applicable to care management.
Big data deploys sophisticated analytics to parse mounds of data from many disparate sources to discover patterns that could be useful in problem solving. In healthcare, for example, researchers are employing the big data approach to study genetic and environmental factors in multiple sclerosis to search for personalized treatments; to drill down into large genomic databases to find the best ways of treating cancer; and to combine geographical information system software with clinical databases to find correlations between the places where people live and their health conditions.
Some of this research may lead to exciting payoffs down the road, but IT companies are not waiting. As Kibbe and Kuraitis point out, technology firms are touting big data analytics as a must-have for healthcare systems and physician groups that aim to become accountable care organizations or make ACO-like arrangements with payers. As these ACOs and healthcare organizations try to profit under shared-savings or financial risk contracts, these proponents claim, big data can help them crunch the data for quality improvement and cost reductions.
Last spring, the Ewing Marion Kauffman Foundation, based in Kansas City, issued a report calling for more use of big data in both patient care and medical research. John Wilbanks, one of the report's authors, said in an announcement that healthcare data held by doctors, hospitals, drug companies, and insurers should all be made available for this purpose.
Combining information on responses to drugs with genomic data, he said, could help people receive the right therapies, for example. "This could substantially reduce the need for trial-and-error medicine, with all its discomforts, high costs, and sometimes tragically wrong guesses," he opined.
Some providers are already using big data in patient care. According to BusinessWeek, "many [providers] are turning to companies such as Microsoft, SAS, Dell, IBM, and Oracle for their data-mining expertise." And healthcare analytics is a growth business. Frost & Sullivan projects that half of hospitals will be using advanced analytics software by 2016, compared to 10% today.
Kibbe and Kuraitis assert that it's premature and unnecessary to use big data in patient care. Instead, they maintain, providers should use "relatively low tech, high touch, data-driven interventions" to improve care management.
They suggest establishing electronic registries to identify and manage high-risk, high-cost patients. Electronic health records certified for Meaningful Use stage 1, they note, can generate clinical summaries known as continuity of care documents (CCDs) that providers can use to export data to those registries, they point out. They also express the hope that embedding Direct messaging capability into EHRs in stage 2 will make it easier for providers to exchange clinical data during transitions of care.
"Simple data mining" of registries, they point out, has been used successfully to:
-- Identify patients who have multiple diagnoses and need nurse case management;
-- Report physician-specific patient benchmarks for people with diabetes, such as patients meeting targets for blood pressure and LDL cholesterol;
-- Find patients with specific ER visits and assign them care managers; and
-- Recognize preventive care gaps for diabetic patients during visits and assign a care manager to make sure those gaps are filled.
Will this common-sense advice slow the mad rush toward big data? Unlikely. But when healthcare organizations actually start to measure the return on investment in analytics that don't improve outcomes, they may decide that "small data" provides a bigger bang for the buck.
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