Healthcare organizations increasingly turn to population health to deliver higher-quality care and curb today’s unsustainable healthcare spending, projected to reach $6 trillion by 2027.
The industry has recognized population health as a remedy to these astronomical costs and path to improved care, allowing healthcare providers to identify their most impactable patients, apply the best intervention, and improve health and outcomes across the patients they serve.
Inconsistency and Lack of Data Erode Population Health Success
Despite its promise, population health is a work in progress for many organizations due to the complexity of identifying impactable populations across different contract types, defining and implementing interventions, and measuring those interventions’ success. These barriers to success and a lack of preparedness have led to more than a quarter of ACOs dropping out of ACO contract participation (Figure 1).
Figure 1: ACO contract participation decline.
Three Critical Mistakes Stand in the Way of Population Health Success
Whether health systems are in the earliest stages of population health management or deeply entrenched in population health delivery, they should take a data-driven approach to tackle three common population health mistakes:
Mistake #1: Lacking a Robust Platform to Support a Data-Driven Strategy
Problem: Legacy and disconnected data systems lack the aggregation and computing capabilities to guide care delivery for large populations. To compensate for these lagging systems, population health leaders often invest staffing resources into multiple disparate technology solutions instead of investing in a scalable data infrastructure that will support population health delivery and analytic needs.
Solution: A modern-day data platform has the capabilities to aggregate and compute massive amounts of data from broad and varied sources, then reveal insights for patient populations. This means health systems spend fewer limited resources measuring their populations and invest more in applying interventions and improving the health of their populations.
Mistake #2: Using Delayed Analytic Insight to Understand Performance and Opportunities
Problem: Many organizations lack a streamlined approach to look across their value-based care contracts and identify common trends. Historically, the process to understand and compare performance to benchmarks has been time consuming, but it is critical to identify improvement areas. Without access to timely information, leaders can’t pinpoint and prioritize the most impactable interventions and rely on guesswork or anecdotal hearsay to drive decision making.
Solution: With a tool that instantly identifies the most valuable benchmarked opportunities (e.g., Health Catalyst Value Optimizer™) for improvement across their populations’ care continuum, organizations have actionable guidance for success in value-based care. For example, with total-cost-of-care insights drillable to a patient root-cause level, leaders can regularly check against baselines to understand overall performance and implement the right interventions.
Mistake #3: Not Tracking Member-Level Data to Measure Intervention Effectiveness
Problem: Effective population health requires multiple interventions. Identifying and implementing interventions is a complex process. In a population health care model—in which success (i.e., improving patients’ health) relies on an intervention’s success—leaders must know if, and how, their interventions impact patient outcomes. Although using data to track an intervention’s impact on patients’ health is the only way leaders can measure an intervention’s success, many organizations don’t track patient-level outcomes at a member level because they lack the right tools. That is a mistake because a singular compilation of patient-level data (i.e., a patient’s longitudinal record) is the only way to accurately measure an interventions’ impact on both overall patient health and population health.
Solution: It is crucial for population health success to leverage effective interventions. However, to measure an intervention’s impact to ensure it’s working and evaluate individuals at a member level, organizations must leverage a robust data engine to access analytic insights on their member populations. An advanced platform also allows leaders to quickly identify the key drivers behind an intervention’s impact over a breadth of patient utilization activities. This allows organizations to truly understand which interventions drive better patient outcomes.
A Data-Driven Approach Proves Key to Successful, Sustainable Population Health Success
For the past decade, organizations have dabbled in population health, and some have delayed its full embrace for a myriad of reasons. The longer systems wait to invest in population health, the greater the consequence for organizations and their patients. Healthcare organizations can overcome three critical population health mistakes with data-driven solutions that support high-quality data and better data access for team members. These data-centric investments allow organizations to overcome inertia impeding population health and identify their most impactable populations, track intervention success, and ensure patients stay on the path toward optimal health.