Deliver Better Population Health by Avoiding 3 Mistakes

Healthcare organizations increasingly turn to population health to deliver higher-quality care and curb today’s unsustainable healthcare spending, projected to reach$6 trillionby 2027. The industry has recognizedpopulation healthas a remedy to these astronomical costs and path to improved care, allowing healthcare providers to identify their most impactable patients within theirpopulations, quickly apply the best intervention, and improve health and outcomes across the patients the serve.

Inconsistency and Lack of Data Erode Population Health Success

Despite its promise, population health is a work in progress for many organizations, gaining prominence within the last decade as payers and healthcare organizations have developed more value-based incentives. The rise in population health care models and a commitment to higher-quality care has introduced more than1,200 ACOssince 2010.

However, many organizations consistently struggle to succeed at scale in population health care and payment models due to the complexity of identifying impactable populations across different contract types (e.g., commercial versusMedicare Advantage), 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 theACOs dropping outof all ACO contracts participation (Figure 1).

Three Critical Mistakes Stand in the Way of Population Health Success

Whether health systems are in the earliest stages ofpopulation health managementor deeply entrenched in population health delivery, they should take a data-driven approach to tackle three all-too-common population health mistakes:

Mistake #1: Lacking a Robust Platform to Support a Data-Driven Strategy

Problem:Legacy and disconnecteddatasystems lack the aggregation and computing capabilities to guide care delivery for large populations. These fragmented systems mean organizations do not adequately structure data to allow for immediate and potential use cases. To compensate for these lagging systems, population health leaders often invest staff development time and resources into multiple disparate technology solutions instead of investing in an effective, scalable data infrastructure that will effectively support population health delivery andanalytic needs.

Solution:When organizations invest in a scalable data infrastructure, they see massive returns in analytic capabilities and staff efficiencies due to the infrastructure’s ability to aggregate different data sets (e.g., claims data, EHR clinical data, and social determinants of health data). 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 health of their populations.

Mistake #2: Using Delayed Analytic Insight to Understand Performance and Opportunities

Problem:Many organizations don’t have a streamlined way to look across all their value-based care contracts to identify common trends throughout their populations. Historically, the process to receive and coalesce the data, then turn it into meaningful information has been time and resource intensive. However, it is critical for organizations to understand performance and compare against contracts and benchmarks to identify improvement areas so that care teams can intervene. Care teams need access to benchmarked contract performance and benchmarked patient population root cause analysis regardless of contract type.

For example, leaders need to understand their patients’ total medical spend against a variety of dimensions to understand baseline improvement opportunities. But without access to timely, actionable information, leaders can’t pinpoint and prioritize the most impactable interventions and, in some cases, rely on guesswork or anecdotal hearsay to drive decision making.

Solution: With an advanced tool, such as theHealth Catalyst Value Optimizer™solution, that instantly identifies the most valuable benchmarked opportunities for improvement across their populations’ care continuum, organizations have actionable guidance for success inrisk-based contracts. For example, with timely total-cost-of-care insights drillable to a patient root-cause level (e.g., demographics, site of service, health equity status, etc.), leaders can regularly check against baselines to understand overall performance. With this insight, leaders can drill down to surface opportunities and implement the right interventions for population health success.

Mistake #3: Not Tracking Member-Level Data to Measure Intervention Effectiveness

Problem:Effective population health requires multiple interventions. Identifying and implementing effective interventions is acomplex process这可能包括将患者护理间隙信息直接嵌入到临床工作流程中，使患者能够利用适当的服务站点，或通过不同的访问点在护理过程的早期与患者接触。

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 the patients’ outcomes. And using data to track an intervention’s impact on patients’ health is the only way leaders can measure if an intervention is working and targeting its intended area. However, many organizations don’t track patient-level outcomes at a member level, usually due to lack of data capability. That is a mistake. A singular compilation of patient-level data—also known as a patient’s longitudinal record—is the only way to effectively measure an interventions’ impact on overall patient health and the overall impact on population health.

Solution:It is crucial for population health success to leverage effective interventions. For example, an integration engine that delivers analytic insight directly into the EHR workflow empowers clinicians to closepatient care gapsin real time and deliver better, more cost-effective care. Embedding unfilled gaps in care directly into an EHR enables providers to easily drive outcomes, while enhancing physician satisfaction. Another intervention—providing effectivecare managementfor the right patients—requires leveraging patient-level data sets to quickly assess, intervene, and support a variety of patient populations’ diverseclinical and social needs. However, to measure an intervention’s impact and evaluate individuals at a member level, organizations must leverage a scaled, robust data engine to view organized analytic insights on their member populations. An advanced platform will also allow for quick review of underlying cost and quality outliers and the various reasons for an intervention’s impact. Organizations leveraging their comprehensive patient-level data (or longitudinal record) can more easily 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.

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, from existing fee-for-service contracts to a lack of executive support. Nevertheless, the longer systems wait to invest in population health, the greater the consequence for organizations and their patients.

Healthcare organizations can overcome the three common population health mistakes listed above with data-driven solutions that support high-quality data and better access for team members. These data-centric investments allow organizations to bypass much of the typical inertia impeding successful population health, allowing leaders to identify their most impactable populations, track intervention success, and ensure patients stay on the path toward optimal health.

Additional Reading

你想了解更多关于这个话题吗?Here are some articles we suggest:

• Own Your Value-Based Care Future with the Health Catalyst Value Optimizer™ Solution
• Understanding Population Health Management: A Diabetes Example
• The Right Way to Build Predictive Models for the Most Vulnerable Patient Populations
• Four Population Health Management Strategies that Help Organizations Improve Outcomes
• Care Management Analytics: Six Ways Data Drives Program Success