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Can Social Determinants of Health Meet Its Moment in a Pandemic?

By Matt Schlossberg

For years, it was difficult to find a conference, technology solution, or plucky that didn’t extol social determinants of health (SDOH)as a panacea for rising costs and widening care disparities.

Today, with the COVID-19 spreading uncontrolled in nearly every state and disproportionately impacting minority populations, SDOH is being positioned as an essential tool in pandemic response and a pathway toward a more equitable public health system.

Joe Nicholson, DO, a board-certified physician and chief medical officer of CareAllies, a subsidiary of Cigna that partners with providers in the transition to value-based care, says that COVID-19 offers an opportunity for deep and widespread adoption of SDOH initiatives—not just as a response to the pandemic, but a viable pathway for closing long-standing disparities and inequities in our public health system.

Health information management (HIM) professionals are positioned as key stakeholders in formulating an interdisciplinary data and documentation strategy; measuring the effectiveness of care interventions and outcomes; and aligning problem-solving methodologies with organizational initiatives to reduce cost, improve quality of care, and foster a patient-centered experience that is broadly accessible and equitable to the communities a health system serves.

In this interview, Nicholson explores how the strategic incorporation of social determinants data—often from multiple, disparate, and siloed sources—can drive innovations in whole-person healthcare and drive the response to the COVID-19 pandemic.

Nicholson is a contributor to the Journal of AHIMA and will be a presenter at AHIMA20.

This conversation was edited for clarity.

JAHIMA: Before the pandemic, what was driving the practical applications of SDOH as a piece of holistic healthcare?

Nicholson: Part of it is the general turn toward population health management. Another piece is our shift to value-based models of care designed to lower overall costs while improving quality. At the micro-level, providers realize that we need to address community-based factors in order to better manage overall health.

JAHIMA: There’s an argument to be made that social determinants initiatives can be useful in the COVID-19 response. How can hospitals with existing SDOH initiatives reorient to a COVID-19 response? For hospitals without an SDOH initiative, what should leadership be thinking about as part of their general mitigation and suppression strategy?

Nicholson: Any disaster that inflicts pain and misery on a vulnerable population also exaggerates pre-existing social disparities. So, you’ve already got a gap and any subsequent disaster, like COVID-19, just widens that gap.

When I think about SDOH in a “typical” environment, I think in terms of our vulnerable population. These are people who lived on that bubble of life, where they don’t have a cushion. And then the pandemic hits and all of a sudden, they are stuck in a public health crisis in addition to a personal crisis.

Now is the time for all health systems, both payer and providers, to double down on SDOH investments. Let’s talk less, let’s do more. It’s a matter of focus and attention, and a real investment, which frankly has been lacking both on the regional and national level and I hope that changes.

JAHIMA: Whatever the influence SDOH has on health outcomes, the immediate challenge is acquiring data from sources outside the hospital, which may be unstructured, fragmented, or inconsistent. How do HIM professionals address this at the systemic level?

Nicholson: I think you start with whatever data you have access to—whether it’s pharmaceutical data, EHR data or patient survey data. Any of these things alone is going to be flawed, but I think the more data points that you can get better informs an algorithm.

It’s the stickiest part of this conversation because it’s a sea of data. Ultimately for people in the HIM space, this is a conversation around big data management. Where I would like to see this go is data collection that has standardization and rigor, which will allow us to step into something that feels more like predictive modeling.

JAHIMA: Let’s take a circuitous route to the “how” by asking you a couple of questions about the state of data, particularly when it comes to racial disparities. More states are collecting racial demographic data for the pandemic, but it’s still not every state. As an expert on social determinants, how should data collection proceed to make information useful for our response strategies? What sort of elements do you think would really begin to paint a picture of who is more vulnerable to this disease?

Nicholson: I’ve been advocating for the collection of demographic data for a long time. Without collecting that demographic data, it’s like we’re creating a hole in the fabric of our own analysis. This is an opportunity. Z-codes allow for an opportunity to do that. Z-codes are not specific to disease or injury, but they identify issues related to psychosocial circumstances. I think that’s a great place to start in terms of what to populate.

At CareAllies, we’ve moved from more of a traditional, reactive SDOH workflow to a proactive SDOH workflow. We are actively leveraging not only ZIP code level demographic data, but also consumer data that drills down even further—if you are a smoker, if you’ve got a car payment, if you’ve had three different housing situations in the last year. Overlaying nontraditional data elements to help inform your more traditional data is absolutely the way to go.

JAHIMA: I’m glad you brought up contextualization. You could have all these different tools and resources to collect, aggregate, and analyze data, but the same data can tell very different stories. For example, there is evidence that entrenched racial and socio-economic factors lead to poorer health outcomes in general for Black Americans. However, certain data presented in a certain way could actually lay blame for those disparities on the population that’s suffering from them rather than looking at inequities in public health systems and structural racism in institutions. Could you talk a little bit about how you think about data contextualization?

Nicholson: It’s a provocative area to ponder, because context is key, and we’ve seen unintended consequence pop up in the news in the past year on how data can shape narratives.

Context is critical to defining a clean set of data, especially when you’re dealing with unstructured data. I think you just have to think in terms of ways to create more structured and less unstructured data as one particular approach to that.

And I think workflow is probably not discussed as often as it should be in terms of contextualization and managing structured versus unstructured fields. Human nature is to follow the least path of resistance and I think it’d be miles ahead if the workflow organically enhances the endpoint in terms of ordering the data in a structured way on the front end so you’re not having to come up with solutions on the back end to get around it.

JAHIMA: Part of surfacing actionable intelligence from that ‘sea of data’ is knowing what to what to look for. How can HIM professionals surface information germane to SDOH?

Nicholson: Sometimes it’s the lack of data, right? When HIM teams are looking to tackle SDOH data, it’s going to be a matter of pulling together a disparate team, with pharmacists and social workers and doctors to help define the kind of data that you’re going to leverage to create the sort of data-driven opportunities to identify at-risk patients.

That’s going to be an iterative process. Every team must aggregate their best guess at metrics that are deemed to be impactful. Once you define the data list that you think might be impactful, the next step is to look at which [data sets] are actionable.

JAHIMA: Can you talk about your perspective on overcoming the challenge of working with information that is so different from what hospitals have been accustomed to in the course of a patient journey?

Nicholson: It’s a matter of collecting unconventional data points that become very impactful in terms of approach to the solutions set for that unique patient. For some of these data points, there is no code. It’s not something clean, but some of these atypical data points become inflection points for activity-driven solutions.

JAHIMA: Where do you think that the electronic health record (EHR) is in the social determinants ecosystem?

Nicholson: That’s a fascinating question. When you think about it, the set of data in the EHR is the cohort of patients that are showing up for healthcare. Our cohort in SDOH doesn’t always include those people. There might be lots of reasons for SDOH opportunities to be completely absent from EHR data. Sometimes [patients] are not showing up in our data at all, because either they don’t have a car to get to a health system or they live in a ZIP code where there are zero healthcare providers.

Clearly, a critical partner in this whole area are the community-based organizations and faith-based organizations, the nonprofits. The data that they have access to is more likely to be highly impactful in SDOH than the data in an EHR.

JAHIMA: When this pandemic is in the rearview mirror, where social determinants will be?

Nicholson: What I really hope happens from this crisis is that it really puts a lens on the reality of healthcare inequities, health inequities for vulnerable populations. And it’s my real hope that you will see organizations across the country, big and small, who are having the difficult conversations and then investing in SDOH resources.

Read More

Catch up on our continuing coverage of social determinants of health, at the Journal of AHIMA.


Matt Schlossberg ( is the Editor of the Journal of AHIMA.

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