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Can Data Solve Behavioral Health’s Outcome Disparity Dilemma?


Almost twenty years ago, the Institute of Medicine (now the National Academy of Medicine) published “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care.” In this landmark report, NAM stated that “a large body of published research reveals that racial and ethnic minorities experience a lower quality of health services and are less likely to receive even routine medical procedures than are White Americans.”

Nearly two decades later, Social Determinants of Health (SDoH)—also called Health-Related Social Needs (HRSN)—remain a significant root cause of disparity and account for up to 50% of health outcomes. SDoH are environmental conditions such as where people are born, live, learn, work, play, worship, and age. In mental health and substance use disorder (SUD), these disparities result in disproportionate overdose deaths within minority populations, limit access to care, and impact where and how patients are treated, including overrepresentation in the criminal justice system.

The sad reality is that zip code instead of genetic code may still matter more regarding our health care outcomes. For instance, in many studies, just ten miles could be the difference of 30 years of life expectancy. So, outcome-based care is here, but it’s just not openly available to all. We need a new approach.

Data is the answer, but what kind of data?

Many experts point to data as the most effective instrument in conquering these long-standing inequities. Data is essential for identifying disparities, directing efforts and resources to address these discrepancies, measuring progress, and achieving accountability. However, one of the most significant data barriers is the need for more standardized and holistic data models. With so many data types available to help track and guide healthcare decisions, what kind of data should behavioral health incorporate into its outcome models?

For example, the federal government has minimum standards for reporting race/ethnicity data. But, these standards were last revised in 1997 and may only partially reflect the diversity of today’s population. The Office of Minority Health (OMH) and the Affordable Care Act of 2010 (ACA) proposed more granular categories. However, the OMH, ACA, and many other types are not universally adopted. This categorical complexity amplifies when we talk about state-level data with states adopting different standards and reporting requirements.

Data insight unlocked and empowered through technology is the most effective way forward. To address gaps in standardization and quality, mental health providers need to leverage technology to explore how facilities can harness the power of connected data from purpose-built electronic medical records (EMR). With this approach, providers can identify high-cost and high-risk behavioral health patients through complete classification and analysis of SDoH.

Data: Beyond clinical and zip code

Many mental health EMR systems have used clinical data to understand patient outcomes. The problem is that clinical data tells us only part of the story. For instance, weekly teletherapy will only be impactful if the patient has reliable internet, computer, or smartphone access. The right prescription for an SSRI won’t help with a patient’s depression if they don’t understand the dosage, label, or delayed onset of action. And factors such as distance and available transportation to care can  impede a patient’s ability to make appointments.

SDoH must be the basis of a new data fabric for behavioral health in the future. First, providers must identify patients facing adverse SDoH and incorporate them into behavioral health management and population strategies. And predictive insights, along with artificial intelligence and automation, can help prioritize barriers to care for each  patient, helping providers surmount the mental health crisis that plagues Americans today.

We can lay the foundation for predictive insights by adding intelligent features to EMRs, proactive population-level assessment data, biometric and wearable integration, and existing social vulnerability indexes. Further, by connecting demographic data with treatment protocols and outcomes, we can trend and benchmark results and begin implementing and taking actions based on at-risk models through intelligent workflows for referrals and follow-up. And global data models can identify risk patterns, drive earlier and more proactive outreach to at-risk patients, and reveal new insights into the patient experience and risk factors.

These strategies can assist clinicians with targeted interventions that help patients more efficiently manage their health and maximize clinical resources, leading to better patient outcomes and long-term, sustained management of substance abuse. By connecting the clinical and SDoH data into care analysis, behavioral health practices can support adjustments to approved treatment protocols and lengths of stay. And by looking beyond just the physiological, we can create personalized treatment pathways based on the individual and their barriers to care. These holistic models will help close gaps where clinical data only tells part of the story.

Connected data, journeys, and outcomes

Connecting the journey touch points for those most vulnerable to mental challenges and risks can become a game-changer in the fight to curb substance abuse. For decades, SUD and behavioral health data have existed in siloes. And payment incentives aren’t aligned to outcomes—yet. It’s a disconnected system that contributes to disjointed and contrasting results.

Generally, behavioral health and SUD are behind other specialties in collecting health equity data to lower outcome disparities. Data standardization, reporting requirements, and interoperability are critical to quickly addressing gaps. We can start to lay the foundation for an open approach to outcomes sharing and data standardization through investments in data and technology, which can serve as a powerful springboard toward much-needed changes in addiction treatment and coverage.

Photo: tonefotografia, Getty Images



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