The United States spends more money on healthcare than any other country, but its outcomes are poor compared to nations with significantly lower expenditures. Population health management is gaining traction as a way to close the spending-quality gap by improving health outcomes on a population level while also reducing healthcare expenses, in line with the ongoing shift from volume to value and quality-driven CMS reimbursements. To realize these population health goals, organizations must rely on actionable data to manage patients with chronic disease, focus on prevention, and apply the right interventions to help patients reach and maintain optimum health.
Many health systems lack access to accurate, real-time data, making it challenging to manage the health of significant populations effectively. Care teams often must base decisions on outdated data with no way to measure the effectiveness of their improvements. A comprehensive analytics platform, such as the Health Catalyst Data Operating System (DOS™), can improve data access and quality for health systems, enabling organizations to focus on improving population health efforts with confidence that the data behind their decisions is accurate, reliable, and up to date.
Without a reliable analytics platform, accessing the “latest” data isn’t possible. For example, it can take some health systems up to six weeks on average to review data reports. The delay means the data is out of date by the time the decision maker sees it, forcing leaders to make decisions based on outdated information.
Delayed data is also a roadblock to effective value-based care because it makes it difficult for health systems to respond to new, upcoming legislation. For example, before the University of Texas Medical Branch (UTMB Health) implemented DOS, decision makers had to wait over one month on average to access data. The lack of timely data made it difficult for UTMB Health to respond to the new Medicaid 1115 Waiver in Texas—Delivery System Reform Incentive Payment (DSRIP) performance—an alternative reimbursement model based on outcomes for Medicaid and low-income, uninsured patients.
With a comprehensive analytics platform to produce up-to-date analytics (e.g., DOS), population health teams can measure success in value-based performance. In this case, UTMB used timely data to determine which strategies achieved the desired results to improve DSRIP performance metrics and saved $2.1 million in additional pay-for-performance dollars and improved performance measures by 72 percent.
Another common challenge population health teams face is leveraging data to improve poor care coordination, which can lead to worse outcomes. Many health systems lack the data infrastructure and tools to identify gaps in the care continuum, making it difficult to pinpoint the problem and create a solution.
One of the opportunities for care coordination care teams often overlook is increasing clinician engagement and input. Providers play a crucial role in delivering quality care to populations, but health systems need to have the tools to measure clinician engagement to identify it as a problem. When population health teams leverage data to drive not only patient stratification, workflow, and interventions but measure clinician engagement, they can implement the right processes to increase engagement and therefore improve care coordination and delivery.
For instance, MultiCare Health System’s Pulse Heart Institute (Pulse Heart) improved cardiovascular outcomes by enhancing care team coordination. After implementing a robust analytics platform that offered widespread access to meaningful data around care team performance, providers were engaged more and aligned better with overall strategies. Improved clinician engagement and organizational alignment—which access to analytic insights drove—helped MultiCare generate $48,000 in revenue and increase overall market share in every submarket.
After population health teams have stratified patient groups and studied each group, they have an idea which interventions or treatments the patients need. However, without accurate data, care teams can’t know for sure that they are targeting appropriate groups according to a specific illness. With healthcare delivery so fluid, even processes that have been effective for years might not work in the future, making it critical for health systems to evaluate interventions and measure progress continually.
For example, after Allina Health identified patients with Type 2 diabetes, it needed to optimize its diabetes self-management program to more effectively meet the needs of patients across 42 different clinics. To better align limited resources and meet patient demand while also maintaining high-quality clinical outcomes, the care team needed accurate analytics. With an analytics engine that acted as one source of analytics truth for Allina Health’s data, the health system better understood what changes to make in the new process and how to measure effectiveness. Its data-informed diabetes self-management program sustained an average 13.4 percent reduction in HbA1c, completed 1,567 more visits, and generated $142,000 in new net revenue during its first year.
As health systems closely examine the challenges of the populations they manage, they must base care decisions on near real-time, accurate data. For providers to make data-informed decisions that have far-reaching effects on the health and well-being of significant patient populations, health systems can’t afford to rely on outdated, siloed data that doesn’t offer the big picture. As reimbursements focus more on quality over quantity and resources grow scarce, reliable, actionable data is a critical piece in the population health success puzzle.
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