AI-driven prior authorizations: an opportunity for advancing health equity

Brian Covino, M.D.

Attaining health equity remains a paramount objective for health care professionals aiming to transcend the impact of socioeconomic factors on individuals' health outcomes. The Centers for Medicare & Medicaid Services (CMS) has emphasized advancing health equity to address care disparities. Despite this effort, many communities grapple with health care access and outcomes disparities, often exacerbated by administrative hurdles like prior authorization processes.

Prior authorization can intensify the issue of health inequity further as it's a significant factor in using social determinants of health (SDOH) to make vital care decisions. Studies have shown that prior authorization disproportionately affects low-income patients, people of color, and those living in underserved areas. These populations often face barriers–such as limited access to technology and transportation, language barriers, and lack of health literacy–that worsen the challenges posed by traditional prior authorization. Consequently, delayed or denied authorizations can lead to adverse health outcomes and further perpetuate disparities in health care utilization. These biases stem from a lack of insight into SDOH data.

Social determinants of health: The missing piece

Health equity hinges on understanding SDOH data. This data, encompassing factors like access to transportation, safe housing, and healthy food, sheds light on an individual's overall well-being. However, inadequate data collection and documentation hinder its practical use. These data gaps make it difficult to identify and address the specific needs of vulnerable populations.

SDOH profoundly influences individual care journeys, impacting decisions such as inpatient admissions and discharge planning. Addressing these social factors is crucial for comprehensive health care delivery. While challenges persist, integrating SDOH data into health care processes holds promise for fostering more equitable care. With AI, health plans have a transformative opportunity to promote health equity through intelligent prior authorizations.

Intelligent prior authorization presents an opportunity to advance equity in care delivery through personalized care pathways, clinical nudges, and streamlined authorization. By responsibly leveraging AI algorithms, health care providers can tailor treatment plans to individual needs, empower physicians with real-time information on treatment alternatives, and streamline authorizations to enhance care coordination.

Let's look at these strategies in greater detail.

• Streamlining treatment decisions with clinical nudges: Physicians often lack real-time information on a patient's eligibility for specific treatments or the option for alternative, potentially more cost-effective options. This can lead to care disparities, as physicians may hesitate to request authorizations for treatments they perceive as unlikely to be approved. As a solution, AI-powered nudges can function as intelligent assistants for physicians during the prior authorization process, suggesting cost-effective alternative treatments, such as lower-cost services or in-network physicians, which are more likely to be accessible to the patient.

These nudges also can provide physicians with immediate feedback on the likelihood of approval based on the patient's medical history and the plan's guidelines. Additionally, AI can be used to explain the rationale behind specific approval criteria, fostering improved communication and collaboration between physicians and health plans.

• Uninterrupted care: How episodic authorizations simplify treatment: Fragmented care that requires multiple prior authorization approvals for a single treatment plan delays care and harms patients, especially in underserved areas. AI can create a single "episodic authorization" request by grouping related services together (i.e., pre-surgery consultations, surgery, and aftercare), which streamlines and speeds up approvals, improves care coordination, and reduces administrative burdens. This is especially beneficial for at-risk populations who might find navigating the health care system challenging and cumbersome.
• Tailoring treatment: Personalized care plans: Traditional prior authorization systems with rigid criteria often overlook individual circumstances, unfairly burdening underserved communities struggling with SDOH. AI offers a powerful opportunity to analyze vast datasets to create personalized care plans. These plans would recommend the most effective treatments for each patient, considering their unique needs, and address potential SDOH roadblocks. For example, an AI system might recommend a diabetes management program with transportation assistance for a homebound patient, ensuring they can access both the program and healthy food. This data-driven approach ensures patients receive the most suitable interventions and promotes health equity for all.

Taking these challenges and opportunities into account, this use of AI represents a step toward a more equitable health care system where social determinants are addressed comprehensively. Intelligent prior authorization represents a potentially powerful tool for promoting health equity in health care plans.

By leveraging AI and machine learning technology to streamline the authorization process, improve accuracy, and personalize care pathways, health plans and care providers can reduce barriers to access, mitigate disparities, and ensure that all patients receive equitable treatment.

However, it is essential for health plans to implement this technology responsibly and prioritize fairness, transparency, and continuous improvement in the design and implementation to maximize their potential for advancing health equity in care delivery. The responsible use of AI technology should never replace the work of physicians or deny care; rather, it should enhance the efficiency of the prior authorization process for health plans, patients, and physicians alike.

Brian Covino, M.D. is chief medical officer for Cohere Health.