The Real Takeaway:
AI adoption in healthcare is accelerating, but without intentional design, it can reinforce the same access gaps healthcare organizations are trying to close. By grounding AI in community context, testing for bias, and applying it to high-friction workflows — such as Medicare Star Ratings engagement (including care gaps, medication adherence, and member experience) and prior authorization (faster turnaround times, reduced peer-to-peer burden, and more timely access to care) — payers and health systems can improve access, outcomes, and operational performance for the people who need support most.
AI Adoption Is High. Equitable Access Isn’t.
AI adoption in healthcare is becoming nearly universal. Equitable access to care is not. That gap — between investment and impact, deployment and reach — is one of the most urgent challenges facing payers and health systems. The American Medical Association reports that more than four in five physicians use AI for at least one use case in 2026. Yet the patients who could benefit most from faster decisions, better-targeted care, and more personalized outreach are often the least likely to be reached by these tools.
The issue is not AI itself. It is the data, assumptions, and workflows behind how AI is designed and deployed.
Many healthcare AI models are trained on decades of clinical and operational data that already reflect uneven access, treatment, and outcomes. Without intentional design and continuous testing, those models can replicate existing disparities at digital scale — directing resources toward patients who are easiest to engage while overlooking those facing the greatest barriers.
Bias in AI Is Inherited, Not Accidental
AI bias shows up in measurable ways:
- Risk models can understate need when they rely on historical utilization data, because lower use often reflects lower access, not better health.
- Diagnostic tools can perform unevenly when dermatology, imaging, or clinical datasets underrepresent darker skin tones or specific populations.
- Patient-facing tools can exclude people when they assume English fluency, digital literacy, broadband access, or smartphone availability.
- Care navigation algorithms can reinforce disengagement when they interpret low response rates as low willingness to engage, rather than evidence that previous outreach failed.
These are signs that AI is learning from systems where access has never been evenly distributed.
Community Context Turns AI Into an Access Strategy
The path forward is building AI around the people, places, and barriers that shape access.
Priyal Patel, Associate Vice President of Healthcare Strategy and Solutions at Perficient, emphasizes the need for customized tools informed by geography, population needs, and individual circumstances. Personalized communication only works when it reflects the realities people are navigating — not when it sends the same message through another channel.
That starts with better inputs. Geography, economic context, language, digital access, transportation, trust, and social determinants should inform model training, outreach, and care navigation. Health systems and payers that use local community intelligence can coordinate care in ways generic models cannot.
Equity is not a messaging principle. It is a design requirement with clinical, operational, and financial consequences.
Putting AI to Work Where Access Breaks Down
Two workflows show how AI can help close access gaps in practical, measurable ways: Stars ratings and member engagement, where traditional outreach often misses people who need support most; and prior authorization, where payer-provider friction can delay decisions and prevent timely care.
1. Star Ratings and Member Engagement
As CAHPS and HOS remain central to Medicare Advantage Stars performance, plans cannot rely on engagement strategies that only work for easy-to-reach members. Lower-trust members and those facing greater access barriers are more likely to experience unresolved issues, delayed care, and negative survey experiences — outcomes that directly affect quality scores and bonus revenue.
An AI-enabled Stars performance strategy can help plans:
- Identify members at risk for poor experiences before CAHPS survey windows
- Pinpoint care gaps, access barriers, and unresolved service issues
- Prioritize outreach based on member risk, need, and engagement history
- Personalize communications by language, channel, timing, and context
- Improve care gap closure, member experience, and Stars performance
Perficient’s AI-driven Stars platform supports this approach by combining data, analytics, and predictive modeling to surface risk earlier and guide more targeted member engagement.
2. Prior Authorization
Prior authorization is one of the clearest examples of administrative friction becoming an access barrier. When the process is manual, slow, or unpredictable, members wait longer for decisions, providers spend more time on follow-up requests, and payers carry avoidable administrative burden.
AI can help reduce friction by:
- Extracting relevant clinical notes from core systems
- Scoring documentation for completeness and readiness
- Strengthening submissions before they reach a reviewer
- Helping payer teams review, prioritize, and manage requests from a centralized dashboard
- Accelerating decisions while reducing administrative cost and avoidable delays
For patients with fewer resources, faster and more accurate authorization decisions can mean the difference between delayed care and timely access.
Equitable AI Requires Intentional Design
Equitable AI does not happen by default. It requires intentional design, disciplined governance, and continuous testing across populations.
Perficient’s PACE Framework gives healthcare organizations a structured path for responsible AI innovation. Built around Policies, Advocacy, Controls, and Enablement, PACE helps governance keep pace with adoption through model audits, human oversight, ethical boundaries, and controls that identify disparate outcomes before they scale.
For health insurers and providers, the mandate is clear: build AI-enabled care and engagement strategies that work across the full patient population. When solutions are designed for people who have historically been hardest to reach, they become better solutions for everyone.