Unlocking Equitable Healthcare: Harnessing AI-Powered Prior Authorization

By Dr. Brian Covino, Chief Medical Officer, Cohere Health
LinkedIn: Brian Covino
LinkedIn: Cohere Health

Adequate access to healthcare remains out of reach for many Americans. Despite progress, underserved communities continue to face significant barriers to quality care, often leading to poorer health outcomes. America has a significant uninsured population–roughly 10% of the population lacks health insurance–often leading to difficulty in finding a primary care physician and affording necessary medical care.

This disparity, rooted in social and economic factors as much as medical access, demands a critical examination of America’s current state of health equity. To spotlight these barriers and make a call for change, The Centers for Medicare & Medicaid Services (CMS) has taken steps to address health disparities by prioritizing health equity.

A stark contrast in outcomes is observed between communities where equity thrives and those in which it’s sorely lacking. This observation has fueled a commitment to advancing health equity and ensuring patients receive the care they need and deserve regardless of socioeconomic status. To truly grasp the importance of health equity, the industry must confront the challenges inherent in achieving it.

Drivers of social and economic health inequities

The circumstances of where we live, learn, work, and play have a profound impact on our health. These factors are known as social determinants of health (SDOH). In America, these determinants are unfairly distributed, like access to education, healthy food, quality housing, and exposure to environmental hazards. These disparities lead to health inequities, wherein specific populations experience poorer health outcomes despite having the same biological makeup. Addressing these social determinants is crucial to creating a healthcare system where everyone has an equal opportunity to live a long and healthy life.

SDOH data is a critical part of bridging this gap. Unfortunately, a lack of SDOH data documentation limits the ability to effectively address some of our underserved communities’ specific needs. However, AI-powered or “intelligent” prior authorization offers an encouraging and exciting opportunity: promoting health equity.

Here are three primary ways AI-driven prior authorization can advance health equity:

1.  Tailored care pathways: Traditional prior authorization methods often overlook individual patient circumstances, potentially disadvantaging those facing significant SDOH challenges. AI algorithms, however, can sift through extensive datasets encompassing clinical records, socioeconomic factors, and claims data to devise personalized care plans with the assistance of clinicians. For instance, AI might recommend a medication adherence program and in-home monitoring for an elderly patient who lives alone and has difficulty managing their medications.

2.  Physician support through clinical prompts: Physicians frequently lack real-time insights into patients’ eligibility for specific treatments or more cost-effective alternatives. This knowledge gap can contribute to care disparities, as physicians may hesitate to seek authorization for treatments they perceive as likely to be approved. AI-driven clinical prompts, or “nudges,” can offer physicians suggestions for alternative treatment avenues—such as lower-cost services or in-network providers—that are likely more accessible to the patient. Physicians also receive instant feedback on approvals based on the patient’s medical history and health plan guidelines. Furthermore, AI technology can clarify the reasoning behind specific authorization criteria, developing better communication and collaboration between physicians and health plans.

3.  Enhanced efficiency with episodic authorizations: Fragmented care delivery–necessitating multiple authorizations for different facets of a single treatment plan–often leads to substantial approval delays that result in care gaps and impact health outcomes. AI can address this issue by consolidating related services into a unified authorization request, particularly benefiting patients in underserved communities who may have limited transportation options to a healthcare facility.

Building responsible AI: A tool for good, not a silver bullet

AI and machine learning technologies are revolutionizing how organizations approach healthcare access and delivery. Still, their implementation requires careful clinical oversight by physicians to ensure equitable access and benefit for all, and it’s essential to recognize that this technology is not a universal remedy for all healthcare disparities.

Responsible use of AI entails a robust collaboration between clinical experts and software engineers, ensuring that deep, evidence-based clinical practices guide the creation, assessment, and refinement of AI models. However, the effectiveness of AI depends on the quality of input data, necessitating the recognition of its inherent biases and limitations to ensure responsible usage.

Biases within training data can lead to biased AI systems, potentially denying critical care to specific demographics. An overreliance on technology could also disadvantage those individuals with limited access to technology. To mitigate risks, healthcare providers should utilize diverse datasets and prioritize ensuring everyone receives necessary care, regardless of technical proficiency.

AI-powered prior authorization used responsibly represents a significant step toward building a more equitable healthcare system. By leveraging responsible AI to both address the social determinants of health and empower physicians with intelligent tools, we can ensure that patients receive the care they need to thrive. As we continue this journey toward health equity, we must remain steadfast in our commitment to leveraging innovation for the benefit of patients.