Population Health Success: Three Ways to Leverage Data

The United States spendsmore moneyon 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.

Three Ways Comprehensive Data Access Drives Favorable Population Health Results

Many health systems lack access to accurate, real-timedata, 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 Care teams often must base decisions on outdated data with no way to measure the effectiveness of their improvements.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.

Access to Timely Data Lays the Groundwork for Success in Value-Based Care

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 The delay means the data is out of date by the time the decision maker sees it, forcing leaders to make decisions based on outdated information.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 theUniversity of Texas Medical Branch(UTMB Health)实施DOS后，决策者平均需要等待一个多月才能访问数据。The lack of timely data made it difficult for UTMB Health to respond to the newMedicaid 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 invalue-based performance. In this case, UTMB used timely data to determine which strategies achieved the desired results toimprove DSRIP performance节省了210万美元的额外绩效工资，并提高了72%的绩效指标。

Improved Care Coordination Improves Patient Outcomes

人口卫生团队面临的另一个常见挑战是利用数据来改善可能导致更糟糕结果的不良护理协调。许多卫生系统缺乏数据基础设施和工具来确定护理连续体中的差距，这使得难以查明问题并制定解决办法。

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’sPulse Heart Institute(Pulse Heart)improved cardiovascular outcomesby 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.

Accurate Data Reveals Need for Patient-Centric Programs

在人口健康小组对患者群体进行分层和研究后，他们就知道患者需要哪些干预或治疗。然而，没有准确的数据，护理团队无法确定他们是针对特定疾病的适当群体。由于医疗服务的流动性如此之大，即使多年来一直有效的流程在未来也可能不起作用，因此卫生系统持续评估干预措施和衡量进展至关重要。

For example, afterAllina Healthidentified patients with Type 2 diabetes, it needed to optimize itsdiabetes self-management programto 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.

Data Proves Essential in Population Health Success

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.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. Four Population Health Management Strategies that Help Organizations Improve Outcomes
2. Achieving Stakeholder Engagement: A Population Health Management Imperative
3. Identifying Vulnerable Patients and Why They Matter
4. Three Must-Haves for a Successful Healthcare Data Strategy

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