How to use data-based collaboration to jumpstart ACO health equity

Unless patients at high risk can be efficiently identified, ACOs may face cost overruns and fail to adequately improve health equity in patient populations.

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ACOs must develop their own templates for health equity by cultivating both available data and data-gathering methods.

Ever since pandemic data revealed the enormous cost of health inequities, the pressing question of how to rectify unfair access to quality healthcare has become a major policy discussion. Now, the debate is turning into action, as the first value-based payment model to address health equity comes online.

ACO REACH is Medicare’s response to blending several key values in its most advanced payment model to date. In addition to traditional ACO quality and cost values, ACO REACH also includes population-based payments (including global payments), rather than fee-for-service, and promotes health equity as a central goal.

If your ACO is an ACO REACH participant, one of your first requirements is to establish a health equity plan. The financial stakes are higher in ACO REACH; not only are payments risk-based, but also the costs of identifying and addressing health equities are uncertain.

As you prepare to implement the most challenging alternative payment model (APM) in the ACO spectrum, you must figure out how to get the data that will help develop and implement that plan. It’s a herculean task. The first challenge is how to quantify health equity and identify patients who are vulnerable because of racial bias, poverty, food or housing insecurity, and job or transportation issues.

There are options for how to tackle this. But unless an ACO can efficiently identify patients at higher risk because of unmet needs, the second challenge is that it could face cost overruns and still fail to adequately improve health equity in its patient population.

Even if you operate a traditional ACO Medicare Shared Savings Program (MSSP), understand that resolving health equity – even if not currently defined by the payment model – is also in your future. That’s because as you transition into risk, vulnerable patients are where opportunities for improving outcomes and savings can be found.

Here’s the dilemma for all ACOs: The need to improve access and quality of care is more urgent than ever, but there is insufficient data on patients to identify the individual circumstances that influence their health and ability to access care. And there is lack of consensus on how to get that data. The bottom line: an ACO must innovate to develop its own template for health equity, by cultivating both available data and data-gathering methods while planning long term solutions.

Let’s address the fastest and most effective way to get started now.

Challenges in collecting SDOH data

Obtaining social determinants of health (SDOH) data is the first step in helping patients in need to get better healthcare by identifying circumstances that prevent them from accessing services or quality care.

In any effort to measure health equity, including CMS initiatives, using SDOH is a specific point of CMS guidance, based on the assumption that all patients are screened on SDOH. However, the question remains: Is it feasible to conquer the SDOH data hurdle in the short term?

SDOH defines individuals’ circumstances, such as poverty or homelessness, that affect their ability to access care or treatments. The data is currently classified by ICD-10 “Z codes,” a set of 10 categories of 70 separate psychological, family, living environment and economic circumstances that can affect health status or access to quality care. Theoretically, SDOH provides the measurement construct for identifying the precise triggers for inequity and points to solutions.

SDOH seeks to identify patients who are marginalized in one of two ways – those without access to any care and those facing obstacles in the care process itself. Capturing SDOH information is an enormous undertaking and not a short-term project.

It’s idealistic to believe that every patient will consent to being questioning about personal vulnerabilities by their medical providers. Lack of trust, feelings of embarrassment or shame, and the level of staff interview expertise can affect the process. Whether most, if not all, patients are able to participate in the process is also a hurdle; while some practices deploy patient-reported tools, individuals with literacy issues are likely to be underreported.

In short, building SDOH data to help identify health equity issues is often unrealistic in the near term. In fact, there is slow progress on SDOH data collection, with paltry results, at best. To date, SDOH data for only about two percent of the patient population in the U.S. has been collected, even among state Medicaid populations where most patients, by eligibility, should have at least one SDOH factor, and often less in aggregate patient populations.

An additional problem is that practices on the front line of patient care do not have the capacity to interview and collect SDOH data on patients. Even for those who try to do so, recent information indicates that key variables for patients are often missed.

It’s clear that provider organizations’ ability to capture SDOH data is still highly variable and, overall, insufficient to support usage of a full SDOH-data collection model as a basis for health equity.

Jumpstarting health equity with existing data

Even without adequate SDOH data, ACOs still have some tools to address health equity. ACOs can already access a wealth of data to build an engine that will target the most vulnerable patients and jumpstart their health equity processes. This approach significantly conserves resources while providing ACOs with the patients who are likely to need attention. Here’s an approach that can be used.

Use coverage data to reveal patients with income constraints. An ACO has coverage data that is the basis for billing. If an ACO is aggregating data from all practices for analytics, quality, and other purposes — and it already needs to do that — it can identify patients by payer source or ZIP Code as an initial proxy for patients with SDOH circumstances. Medicaid is the first place to begin segmenting the list to identify those who are dual-eligible for Medicare and Medicaid, as income is the first eligibility requirement. For both criteria, the goal should simply be to produce a smaller patient list for targeting data collection to make its job more manageable.

Work with community organizations to identify patients’ insecurities in vulnerable geographies by ZIP Code. Start with the assumption that physician practices lack the capacity and capability to elicit patient responses about circumstances, even if this is not always the case. Trust is a huge issue if patients have experienced poor access or bias. Alternatively, community organizations are well situated and already working in areas of financial and social vulnerability. Collaborating with them to interview patients in an ACO and the patients’ communities can be a powerful strategy. This will reinforce referral relationships in the community and begin to furnish some SDOH data for an organization. By working effectively with existing organizations that also have connections to resolve the identified issues, an ACO can have a real positive impact on patient circumstances and their health status. Deploy resources that would be used internally to support sustainable outposts in the community and maximize efforts.

Curate EHR data to identify patients by criteria indicating gaps, poor outcomes, treatment issues, and utilization. Aggregated, patient-centric data is a second source to identify patients with issues such as persistently poor outcomes over time, poor visit profiles, missing health data, and problematic admissions or emergency department use. The data must be developed into patient episodes, by conditions and other criteria, to examine their trended values (such as hemoglobin A1C) and to find patients who are not improving, who have frequent instances of utilization or exacerbation, or who have poor visit profiles for their conditions.

From this point, an organization can further analyze the data to dive into such areas as lack of treatment or medication change (indicative of financial hardship for pharmaceuticals), lack of patient self-management programs or referrals to nutritional resources (possible patient transportation or employment-related obstacles) for further patient identification by the practice or community contacts. Episodes of care are the best way of comparing patient outcomes and costs to reveal equity, outcomes and cost differences for each condition or set of circumstances.

Clinical data is a bedrock of patient histories that can, if creatively examined, reveal patients who have unaddressed issues. If those patients are not getting what they need to improve, the reasons are often outside the healthcare system.

Waiting for all-patient SDOH data is not ever going to be the easy way to establish the foundation for tackling health equity. Creativity and collaboration are key tools. Data plus community are an ACO’s resources to make the best use of those tools. Look beyond the healthcare system in a sincere quest to improve patient health, and an ACO can jumpstart the process to achieve equitable healthcare for all.

Theresa Hush is co-founder and CEO of Roji Health Intelligence, a company that guides healthcare systems, providers and patients on the path to better health through solutions that help providers improve their value and succeed in risk.

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