AI-Powered Prior Authorization: A Catalyst For Building A More Equitable Healthcare System

I look forward to a time when a person’s zip code and income level do not dictate their life expectancy. That’s the core principle of health equity: ensuring everyone has a fair and just opportunity to live a healthy life. Unfortunately, in many communities, some patients have significantly worse outcomes. The Centers for Medicare & Medicaid Services (CMS) has prioritized advancing health equity to eliminate disparities in healthcare access and outcomes.

I have focused on health equity throughout my career. As a physician at Federally Qualified Health Centers (FQHCs), I’ve seen both sides of the coin: communities in which health equity flourishes, and others in which it’s sorely lacking. This experience equipped me with a better understanding of the factors that contribute to, or hinder, achieving health equity. It has also strengthened my passion for ensuring all patients get the quality medical care they need and deserve. To understand why health equity is so vital, we should examine the challenges of pursuing equitable healthcare for all.

The Power Of Social Determinants Of Health Data

A patient’s zip code is often an indicator of their overall well-being. Social determinants of health (SDOH), like access to transportation, safe housing, and healthy food, play a huge role in a person’s outcomes. Effectively capturing and utilizing this data is crucial for attaining health equity.

The first hurdle? SDOH data collection itself. Currently, a significant gap exists: less than 2% of Medicaid and commercially insured patients have documented zip codes, which are used to analyze SDOH data. This lack of information creates a blind spot, making it challenging to identify and address the specific needs of vulnerable populations.

Beyond a patient’s medical condition, SDOH significantly impacts their care journey. Inpatient prior authorization requests, for example, are often driven by a patient’s SDOH risk factors. Lack of caregivers, social support, or a safe home environment can influence whether a patient is admitted as an inpatient or observation-level care and determine their discharge plan. Studies have shown that SDOH can account for 70-80% of a patient’s overall care needs, highlighting the profound influence these social factors have on a person’s health.

But there’s hope. The dream of equitable healthcare starts by capturing SDOH data at a granular level. Once we have this information, we must ensure it is interoperable and standardized so patients, physicians, and health plans can easily access and share this critical data. In my prior practice, we screened patients for food insecurity and used their zip codes to connect them with local food banks; this was especially helpful for a patient with uncontrolled diabetes who had trouble accessing and affording nutritious food. The patient frequently relied on eating macaroni and cheese, which increased their a1c (a measure of diabetes control) to dangerously high levels. After we helped the patient access nutritious food resources, their diabetes significantly improved. These kinds of interventions can make a real difference in a patient’s health and recovery.

Of course, challenges remain. While capturing and sharing SDOH data is a vital first step, it is not enough. Many SDOH factors, like transportation assistance, are not reimbursable for physicians. Physicians often struggle to find enough time to care for their patients’ medical issues; adding transportation to a physician’s already full plate can take time away from physicians providing necessary care to their patients. This disconnect between data and resources must be addressed.

The good news: innovation and equity are now at the forefront of the healthcare industry. By harnessing the power of SDOH data and building a system that incentivizes addressing these social factors, we can move closer to a future where healthcare is equitable. Health plans can play a critical role in reaching this goal by using AI technologies to analyze SDOH data like access to transportation and social services. By using AI, health plans can leverage this information to create personalized patient care pathways and reduce administrative burdens for physicians.