Frustration with electronic health record (EHR) systems notwithstanding, the data aggregation processes that have grown out of healthcare’s adoption of the electronic health record are now spawning analytical capabilities that were unthinkable just 15 years ago. By leveraging big data to track everything from patient recovery rates to hospital finances, healthcare organizations are capturing and storing data sets that are changing the way doctors, caregivers and payers tackle larger scale health issues.
It’s not just happening on the clinical side, either, where EHRs are extending real-time patient information to doctors and predictive analytics are helping physicians to better track and understand their patients' medical conditions.
In Kentucky, for example, tech investments by the state’s largest provider systems are estimated at over $600 million, a number that doesn’t even reflect investments from two of the biggest local organizations, Baptist Health and University of Kentucky HealthCare. The data collected by these hospitals includes—and far exceeds—the EMR basics mandated under ARRA, according to an article in The Lane Report.
While the goal of improving quality of care is, of course, a key driver of such investments, so is the government mandate tying Medicare and Medicaid reimbursement to outcomes. According to a recent report from McKinsey & Company, more than 50 percent of doctors’ offices and almost 75 percent of hospitals nationwide are managing patient information electronically. So, it’s not surprising that big data is catching the attention of healthcare’s management teams.
By quantifying and analyzing an endless variety of metrics—including things like R&D, claims, costs, and insights gleaned from patients—the industry is refining its approach to both preventative care and treatment, and saving money in the process. A good example can be found in the analysis of data surrounding regression rates, which some hospitals are now using to stave off premature releases and, by extension, exorbitant penalties.
Others, such as Brigham and Women’s Hospital, already are applying algorithms to generate savings beyond readmissions, in areas that include: high-cost patients, triage, decompensation, adverse events, and treatment optimization.
While there’s room to debate the extent to which big data is improving patient outcomes—or the scope of savings attributable to big data initiatives given the associated system costs—the trend toward leveraging data for better outcomes and savings will only continue to grow as CIOs advance meaningful implementations of solutions, and major technology companies continue to expand the industry’s basket of options.
How is your healthcare organization applying big data to overcome challenges? Have the results proven worthwhile?
As a B2B journalist, John Farrell has covered healthcare IT since 1997 and is a sponsored correspondent for Intel Health & Life Sciences.
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