Late last year, the Biden administration released “The U.S. Playbook to Address the Social Determinants of Health (SDOH),” – designed to serve as a starting point for reimagining new policies and organizational actions around SDOH.

The Playbook’s goal is to accelerate innovation across sectors and agencies to introduce practical solutions that enable Americans to live healthy lives, regardless of their socioeconomic circumstances. While it does not represent a comprehensive strategy for addressing SDOH, the document provides an initial framework for improving health outcomes by tackling Health Related Social Needs (HRSN).

The core pillars focus on expanding data collection, making funding more flexible to address social needs, and supporting backbone community organizations. Though these components are crucial to alleviating the persisting SDOH burden in our communities, it is imperative that we do more with data and analytics – especially sources that are not traditionally used – to address health equity at scale.

The Use Case for Screening Data

Historically, HRSN self-reported screenings have been the primary method to gather individual-level social risk data. However, there are significant challenges with this traditional, singular strategy. First and foremost, the data gathered is on less than 10% of the population and that 10% is unlikely to include those most vulnerable.

Consider – which individuals appear in screening data? It’s not those who have missed opportunities to get their health concerns resolved due to limited access to care. It’s not those that haven’t seen a care provider for several years because of transportation barriers or unstable housing. It’s certainly not those that don’t know how to navigate care because they lack the information and resources to do so. It’s clear that underserved and vulnerable populations are not fully captured in the data that most currently use to develop their health equity roadmap.

Additionally, screening surveys invoke countertransference bias, selection bias, and biased questioning methods. As the Playbook notes, these biases and data obstacles are numerous and pervasive.

All of this points to the better use for screening data that becomes “non-current” so quickly. Rather than serving as the singular source of truth for the challenges community members face, Screening is at best an important contributing data source that gives an idea of the challenges communities face at a given point in time. 

In addition to screening data, we should leverage data sets and methodological approaches that are as complete and as equitable as possible. This includes datasets not traditionally used in healthcare such as, consumer data, credit data, and alternative risk data. Otherwise, any attempt to use data will produce skewed results, and the actions taken and investments made based on those results will inhibit efforts around equity.

Leveraging Analytics with Net New Data

There is tremendous value in the Playbook’s emphasis on data gathering and sharing, and its acknowledgement that existing datasets revealing SDOH burden are costly to manage and resource-intensive. The good news is that analytics to assess SDOH in a consistent, disciplined, and comprehensive way exist in the market. SocialScape Explorer® is one such tool. Our proprietary data and analytics platform includes robust, up-to-date data rooted in the latest available community and individual data sources. 

Our data-driven, model first analytics provides deeper and more precise insights into SDOH risk (individuals and community). Allowing for better designed and matched and appropriately tailored programs. SocialScape offers the full-cycle social risk analytics solution that empowers risk-bearing entities to manage risk, improve outcomes, and advance equity – at scale.

To learn more about how Socially Determined empowers organizations to effectively address social risk for enterprise business impact at scale, please visit

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