Health equity is a focus of providers, regulatory agencies, and payers as they seek ways to eliminate care disparities across race and ethnicity, gender, sexual orientation, and socioeconomic status lines. Its significance is further impacted by new quality-based care models beyond those established by the Patient Protection and Affordable Care Act of 2010.
The challenge for many healthcare organizations participating in these new reimbursement models is how to view health equity and social determinants of health (SDoH) to understand the actual value of this information. Often overlooked is that healthcare organizations’ coding and revenue cycle management (RCM) departments already aggregate information that can help better understand inequities in care delivery and health equity across their patient populations.
A Primer on SDOH Impacts
SDoH impact many health risks and outcomes, which is why this data is vital for clinical care and reimbursements. Defining factors can include anything from geography, race, gender, and age to disability, health plan, or any other shared characteristic. Of increased importance, SDoH issues are most often experienced by the most vulnerable members of society: the poor, less educated, and other disadvantaged groups.
SDoH is linked negatively with outcomes, including higher hospital readmissions, length of stay (LOS), and increased need for post-acute care. Value-based payment programs, therefore, may penalize organizations that disproportionately serve disadvantaged populations if they do not collect and respond to SDoH data.
For example, addressing food insecurity — a key SDoH data point — by connecting patients to programs like Meals on Wheels, Supplemental Nutrition Assistance Programs (SNAP), or food pantries is proven to reduce malnutrition rates and improve short and long-term health outcomes.
In the case of SNAP, which is the primary source of nutrition assistance for more than 42 million low-income Americans, participants are more likely to report excellent or very good health than low-income non-participants. Low-income adults participating in SNAP incur about 25% less medical care costs (~$1,400) per year than low-income non-participants.
Artificial intelligence (AI) is transforming healthcare, especially on its clinical side, where 62% of providers have already adopted an AI strategy. The American Hospital Association recently reported that when successfully implemented, clinical AI can improve patient outcomes and lower costs at each stage of the care cycle, from prevention and detection to diagnosis and treatment. Expect to see continued growth in AI adoption in 2020.
Here are a few of the most exciting AI trends to watch for:
Social Determinants of Health (SDoH) will become a core focus for healthcare AI solutions: AI has significant potential for helping reduce socioeconomic barriers to care. Across 2019, we have seen investment by CMS in the Accountable Health Communities Model, which is the first model to include social determinants of health. This model codifies what we already know — that socioeconomic factors influence an individual’s health and risk. Emerging AI technologies are actively creating value for patients by helping to make sense of large socioeconomic and environmental datasets, driving meaningful investments and action that will help to prevent avoidable utilization and guide effective distribution of community resources.
We will start to move into the “Slope of Enlightenment” within the Hype Cycle: Healthcare AI will move out of the “Trough of Disillusionment” as more evidence of AI’s ability to improve health outcomes emerge. AI-related topics will continue to gain prominence in research and the media. And the results of funded projects and pilots will become available to the broader industry.
The AI discussion will broaden beyond imaging and natural language processing: With the exponential increase in patient data, it’s only logical that it’s time for AI – the best way to synthesize that data – to have its moment. While imaging and natural language processing have dominated the healthcare AI conversation over the past few years, 2020 will mark an expanded understanding of AI solutions to include those focused on clinical decision support. These solutions integrate into existing clinical workflows to help direct resources to modifiable patients at risk of a target adverse event. Expect to see more investment and discussion around these solutions across 2020.
