Using big data effectively
Only 12% of the survey respondents said their organization is effectively using big data to improve healthcare quality and reduce healthcare costs; 46% said they’ve made some progress toward using big data, but there’s still a lot of work to do; and 42% reported little or no progress in using big data.
Will Hinde, managing director of healthcare at West Monroe Partners, a management and technology consulting firm, believes that many organizations have made little progress in this area because health insurance and health system executives are weighed down by legacy technology that simply isn’t agile or scalable, which prevents them from leveraging big data to its potential. This is further complicated by unreliable and unclean data that result from most organizations lacking master data management discipline. “The result is disparate and lagging reporting, gaps in real-time data, and a lack of clinical analytics, making it difficult to manage populations and drive better quality and cost outcomes,” he says.
Along these lines, Paul Alexander Clark, director of healthcare research at Digital Reasoning, which builds artificial intelligence care management software for health systems, says healthcare leaders struggle to apply information technology innovations and information technology leaders struggle to understand healthcare. “Big data, data science, and artificial intelligence will only deliver value if innovations can be translated to patient care and clinical work flow,” he says. “Bridging the gap between these disciplines demands extraordinary vision, leadership, and committed partnership from healthcare executives and their technology partners.”
Providers and payers look to big data and analytics to do different things. Providers are more focused on the care they deliver, with less insight into cost, says Marcel Tetzlaff, vice president of provider experience and benefits management, SKYGEN USA. Providers have detailed clinical information to use when evaluating a patient’s overall health, whereas the information payers have is more oriented toward identifying the best practitioner who should provide it based on appropriateness of care (quality), efficiency of care (cost), and other measures such as patient satisfaction.
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Payers are concerned about the whole breadth of care members receive, because they pay for all of it. Payers also seek to understand member health challenges so they can help them stay healthier. “They have the added responsibility of managing their provider networks—making sure they do a good job for members and that they deliver cost-efficient care,” Tetzlaff says.
A health system or hospital may look at the efficiency of individual physicians within various specialties from time to time, especially as they work to standardize care and reduce costs, but they’re usually not trying to affect the patient’s decision of which physician to use like payers do.
How to deal
Clark advises healthcare organizations to build partnerships with best-in-class enterprises that will fill the gaps in core competencies to enable the organization to apply and derive value from leading-edge technologies.
“Don’t evaluate and select technology partners solely on their name recognition or success in other industries,” Clark says. “Deeply engage and ensure that they have the knowledge, understanding, and an effective plan to translate their technology into healthcare. To create value, big data, and artificial intelligence innovations must drive down to clinical work flow and point-of-care.”
Tetzlaff says the optimal situation is when payers and providers share data with one another to fill in gaps in their understanding. “Big data analytics work best when they have bigger data sets to work from and have meaningful and actionable results,” he says. “Full transparency allows patients to make the best possible choice for themselves.”
Karen Appold is a medical writer in Lehigh Valley, Pennsylvania.