Wanted: an effective data science strategy for every healthcare organization
How’s your data science strategy been working for you, lately?
Chances are, more than a few healthcare providers would be somewhat mystified by such a question, but writing recently at the NEJM Catalyst blog, three informatics experts make a compelling case that for healthcare organizations to succeed moving forward, an organization-wide data science strategy is indispensable.
Not surprisingly, Kathrin M. Cresswell, PhD, David W. Bates, MD Aziz Sheikh, MD, of the University of Edinburgh, Harvard T. H. Chan School of Public Health and Harvard Medical School, respectively, point out that, as often happens when it comes to digital data, healthcare is behind many other industrial sectors. Data science “strategies are now commonplace in most industries such as banking and retail,” they point out. “Banks can offer their customers targeted needs-based services and improved fraud protection because they collect and analyze transactional data. Retailers such as Amazon routinely collect data on shopping habits and preferences to profile their customers and use sophisticated predictive algorithms to tailor marketing strategies to customer demand.”
But despite the fact that individual pieces of data can have “life-or-death importance” to healthcare organizations, many flat out fail to aggregate data effectively to gain insights into their overall care processes. Consequently, “without a data science strategy, healthcare organizations can’t draw on increasing volumes of data and medical knowledge in an organized, strategic way, and individual clinicians can’t use that knowledge to improve the safety, quality, and efficiency of the care they provide.”
Done the right way, they argue, an effective strategy can help healthcare organizations develop the precision medicine techniques increasingly being looked to to help tailor treatments to specific patients), while also helping them build learning health systems that will ultimately help them better predict outcomes and identify specific areas for improvement.
“Ideally, every decision a provider makes about a patient should be informed by the data of both that specific patient and other similar patients,” they argue. “In a learning health system, prior experiences improve future choices.”
Among the key components of an effective data science strategy are a secure organization-wide data repository that allows organizations to keep a complete inventory of their data assets, an integrated approach to data that enables organizations to break down silos, and support teams with a wide range of skills in, among other things, data processing and cleaning, operational research and change management and artificial intelligence.
Finally, they say, “strategic approaches to analysis should create a virtuous cycle in which data are repeatedly scrutinized as they are reused for different purposes, driving improvements in data quality.”
And make no mistake, they argue, the stakes are high. “Organizations without an effective data science strategy may never realize returns on their investment in electronic health records (EHRs), may have disillusioned physicians, and may face potentially catastrophic security risks resulting from inadequate data protection. . . . Implementation of a data science strategy represents one of the cornerstones of better care, as well as greater operational efficiency and, eventually, more effective approaches to population health.”