Leveraging Data To Increase Health Equity

Founder & CEO at Spatially Health, turning spatial data into a market-leading healthcare strategy.

Healthcare has a data optimization problem. Instead of continuing to catalog this under “acceptance,” let’s pivot our thinking: The industry also has an unprecedented opportunity to optimize that data. And doing so will advance health equity and lower the total cost of care.

Health data is abundant. One part is already aggregated in varying stages of integration or at least “bucketed” to some extent; the other part is available yet not usefully incorporated—or simply not incorporated at all.

To be clear, I’m not talking about the batch processing of billing records. This is about using modern tools to get more granular and to help accountable care organizations capitalize on pattern recognition and analysis with far greater nuance than before when it comes to patients and health applications—and where latency is the enemy when seeking to improve care coordination.

The aim is a comprehensive, end-to-end procedural system in which interlocking data sets foment complete, expedient solutions (and not information overload or chaotic, disorganized intel).

Once we harness the already collected data with what’s available, ACOs and physician networks can achieve that North Star: accessible, real-time analytics working hand-in-glove with real-world decision making, executed through prudent utilization models.

Here’s how we might accomplish that process.

Utilize The Data You Have; Get The Data You Need

It’s a phrase you’ve likely heard before: Get to know the patient before they become one. Once we start adjusting the strategy away from “last mile” treatment as the default, we’re taking that crucial first step toward developing data-driven relationships with people at the first mile of their health journey.

First, it’s important to remember that clinical care comprises about 20% of what shapes health outcomes. The other 80% is tied to behaviors and environments—the social, economic and physical factors that describe where and how people live their lives. The use of social determinants of health (SDoH) provides important building blocks in understanding these behaviors, but what’s necessary for optimization to occur is an additional layer that reduces ambiguity: on-the-ground, spatial-based intelligence that takes the form of useful health analytics.

Second, there is a noticeable gap to bridge: When compared to hospitals and health systems, many physician practices and provider organizations have trended toward markedly better outcomes with respect to Center for Medicare and Medicaid Innovation pilots, specifically around acute care and outpatient facility spending.

However, these same organizations are generally far less likely to have a population health strategy in place, and what strategy they do have (or are planning) may be ill-equipped to scale.

In other words, while an ACO may not have the resources to dedicate a team (or enable the tech) to develop a population health framework, they already possess an innovative spirit, one poised to achieve targeted benchmarks in improving health equity.

This is where trusted data partners play such an important role: By delivering the active analytics that help physician practices directly tie data to outcomes, these partners help ACOs embrace all the factors that constitute a truly consequential, 360-degree visualization of patients across the care continuum.

The Right Frame Of Mind: Applying Predictive Models To Propel Value-Based Care

Aligned mindsets help strengthen the tech solution built to optimize health data. The following factors may be viewed as part of an actionable path forward.

• Modern applications of population health initiated to configure programs that intelligently tie risk to outcomes.

• Being self-aware about how to improve performance and achieve benchmarks.

• Physician-led emphasis on value and quality, openness to innovation and identification of inefficiencies when they arise.

• ACOs entering into shared-risk agreements incentivizes the drive to improve outcomes.

That last point is crucial: How do ACOs successfully predict risk—one of the most significant drivers in hitting benchmarks in value-based care?

Straightforward use case examples help illustrate why progressive health tech analytics provide answers here and are a recurring benefit for care organizations.

1. Employing localized insight helps determine the factors behind risk and how enhanced analytics better effectuate linking risk to desired outcomes. By highlighting where local need is greatest, this directly translates to your team spending less time figuring out how to “strategize smarter,” and therefore, more time is available for successful implementation—immensely useful, for example, when conducting proactive campaigns of prevention.

2. Facilitate truly comprehensive market analysis to predict where specific services are in higher demand. Doing so means including and going beyond amassing claims data or the demographics connected to zip codes—important because of the dynamic nature of changing conditions across populations and markets and the inherent benefits of responding quickly.

The integration of spatial intelligence is key here. Predictive models can be customized so that the right data questions are answered before implementation: criteria such as, “Where is the most useful data concentrated?” and “How frictionless is sharing that data for preventative applications?”

Applying location-based insights opens a window into the unique characteristics of target populations and their subsequent interlocking connections. And it makes a quantifiable difference when developing strategic solutions, one measured both in saved time and improved outcomes.

In Sum

Physician-led ACOs tend to perform better than those led by hospitals or health systems. A trusted data partner delivering on-demand analytics translates to more personalized care models designed to be responsive by nature. This modern partnership—meeting members and patients where they are in their health journeys—is a success for everyone. Preventative strategies keep people healthier and costs down, and streamlined BI revolutionizes the day-to-day for medical teams drowning in redundant paperwork.

Tomorrow’s ACOs will shine through championing innovative thought, properly integrating useful data and utilizing customizable optimization models. This modern approach includes fully embracing the accessible, actionable and valuable health data delivered through intelligently measuring individual behaviors and community needs, thereby identifying how interlocking patterns at the local level impact human health.


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