3 Important Considerations before Implementing a Health Data-Powered Strategy
Director Robert Altman once compared making a movie to chipping away at a boulder. “You know that there's something in there, but you're not sure exactly what it is until you find it,” he said.
It’s an apt analogy for hospitals and other healthcare providers looking to uncover insights in massive slabs of data in the healthcare landscape—in their electronic health records (EHRs), clinical and financial systems.
In order to sculpt out a strategy that supports better health data decisions, hospitals should take some lessons from the data masters across other industries.
The Keys to Better Health Data Insights
Several years ago, global research and consulting company McKinsey noted that data-driven strategies represent the competitive differentiators between leaders and laggards across six data-rich industries. What factors do organizations that excel at leveraging data have in common?
1. They tap multiple sources of data.
Hospitals have a bounty of internal data. As interoperability improves, so too will data sharing across the industry. But in order to focus in on the right data, healthcare systems need to look at individual business goals—from reducing costs and boosting quality to advancing population health and improving patient engagement—and identify the specific types of data required to move the needle on those goals. And they need to look beyond traditional data sources.
Rather than an all-or-nothing approach, healthcare systems need to focus on more manageable goals.
The c2b Consumer Diagnostic market research can provide valuable insights into the best messages and channels to use based on unique attitudes and preferences of patients. This is a national study of consumers’ attitudes, preferences and behaviors across channels of healthcare delivery. Such data empower hospitals to meet individual healthcare consumer expectations, much in the way that Amazon uses data to create personalized browsing experiences that delight customers.
2. They strive towards incremental progress.
According to Gartner, big data analytics projects have an abysmal success rate, failing more than 50 percent of the time. Rather than an all-or-nothing approach, healthcare systems need to focus on more manageable goals. Using psychographic segmentation, for example, hospitals can target motivated, health-conscious consumers—the low-hanging fruit—with wellness messaging to make a dent in preventable chronic illness. Wearable devices can be used to track behaviors and trigger messages based on an individual’s progress—or lack of it.
Alternatively, healthcare providers might target consumers who are least engaged with their health and wellness and use the right, segment-specific messaging to motivate preventative behaviors. Psychographics can be the lens that provides context for interpreting the ocean of data and provide structure and direction for patient engagement tactics.
Based on small successes, the hospital can expand additional population health initiatives with unique approaches designed to engage audiences more effectively. Likewise, hospitals can focus in on core services, rather than a hospital-wide strategy. The Cleveland Clinic has taken this approach, identifying specific services—such as heart surgery and knee replacement surgery—and leveraging evidence-based best practices and patient, clinical and financial data to develop bundled-payment programs. After proving out the strategy at a single hospital, the Clinic then rolls it out across the entire system. As a result, the Clinic excels at delivering positive patient outcomes that win approval from patients as well as cost-conscious employers and insurers— while also ensuring the health system maintains high patient volumes.
3. They create a culture that embraces data-driven decision making.
This isn’t as easy as it sounds. In addition to eliminating the barriers presented by siloed data, hospitals and other healthcare organizations must overcome resistance to data-driven decision making. As McKinsey points out, “Such problems often arise because of a mismatch between an organization’s existing culture and capabilities and emerging tactics to exploit analytics successfully. The new approaches either don’t align with how companies actually arrive at decisions or fail to provide a clear blueprint for realizing business goals.”
"By looking at clinical, cost and outcomes data, hospitals can identify and eliminate unwarranted variations in care."
Take variations in the cost or quality of care. As healthcare providers face increasing pressure to shift to value- and quality-based reimbursement, variations in care must be addressed. These variations often result because individual physicians follow different protocols. Within the same hospital, for instance, surgeons performing knee replacements may source implants from different manufacturers based on personal preference and experience. By looking at clinical, cost and outcomes data, hospitals can identify and eliminate unwarranted variations in care, but it requires buy-in from the C-suite and clinicians to make that happen.
Not every attempt at health data decision making will result in a masterpiece. But if hospitals focus on the right data, implementation approach and culture, transformation is inevitable. Just keep chipping away.