Despite decades of panels, pledges, and policy proposals, healthcare inequity remains a systemic issue in day-to-day experience of the American healthcare consumer. The data is overwhelming – patients in lower-income ZIP codes have worse healthcare outcomes, people of lower socio-economic tiers are more likely to suffer from preventable conditions. Access to timely, quality care is still a privilege. Equity in healthcare is different from the equity discussion that’s playing out right now in education or the federal government.
This is not a blanket argument for more DEI. When maternal health outcomes are worse for African American & Hispanic populations, all of us are impacted. The American taxpayer carries the economic burden of healthcare inequity – we spend more on Medicare, Medicaid and other federal and state healthcare programs. Or in the context of employer-sponsored insurance, we split the economic downside with our corporate co-workers and employers.
In the last five years, we’ve achieved widespread awareness of these gaps, but is awareness alone enough to move the needle?
Health systems and health plans have hired Chief Health Equity officers and launched initiatives – but have these worked? Organizations are stuck without the data, the tools and the interventions to truly address their challenges.
Research from Accenture and HIMSS Market Insights reveals that although 93% of U.S. healthcare executives believe health equity initiatives are important and 89% consider them part of their core business strategy, only 36% have a specific budget dedicated to advancing these agendas.
What’s needed now is action. Not another promise, but a reframe in a shift from theory to implementation. We need to look at not just blanket solutions but tailored and personalized approaches, which we could call the next evolution of healthcare or – health equity 2.0.
The promise of advanced technologies has been discussed for decades, but now with the power of AI-powered agents, real change is possible. Health equity 2.0 weaves intelligence into the daily realities of patients’ lives, and AI agents create a critical bridge between data and impact, especially for underserved communities.
Where do health disparities come from? They are not just a function of broken policy or cultural bias, but they have been encoded into the very systems we use to deliver care. Scientific studies have documented how clinical algorithms often reflect historical inequalities.
One study titled “Dissecting racial bias in an algorithm used to manage the health of populations,” published in the journal Science in October 2019, revealed how an algorithm underestimated the care needs of Black patients by using prior healthcare spending as a proxy for health status, an input that inherently ignored decades of unequal access. This wasn’t intentional discrimination, but a blind spot in the design. AI gives us a chance to correct those blind spots. And, addressing racial disparities is essential, but it’s just one part of a larger, more complex web of health inequities that also includes factors like income, geography, disability, and more.
My thesis is that for AI to improve equity, it must go beyond traditional decision-support tools. It must be embedded into the fabric of patient engagement, communication, and follow-through. That’s where AI agents come in.
AI agents aren’t just chatbots. They’re dynamic, context-aware, and deeply integrated tools that can process millions of data points, including clinical, behavioral, and social determinants, and use those insights to connect with patients in meaningful ways. These agents don’t replace clinicians. They extend their reach. And they do it in ways that traditional systems never could.
Take the example of a patient who receives a text message reminder about a mammogram. In a standard system, this might be a generic blast sent to thousands. But an AI agent can tailor that message, adjusting the language, timing, tone, and content based on what it knows about the patient. If the patient speaks Spanish, the message is delivered in Spanish. If they’re working during the day, it arrives in the evening when they’re more likely to respond. If prior data shows transportation is a barrier, the message includes information on nearby clinics offering weekend hours or ride-share support.
This level of personalization matters because most care decisions happen outside the four walls of the exam room. Yet the healthcare system has historically treated patient engagement as an afterthought. AI agents flip that equation. They ensure that the critical touch points between appointments are filled with supportive, culturally aligned nudges that can drive better health outcomes.
And culture, language, and communication style aren’t peripheral to care, but they are central. Patients disengage not because they don’t care, but because they don’t feel seen. A non-English-speaking mother may avoid seeking care at a large hospital where no one looks or sounds like her. But if she receives a warm, conversational reminder in her language, one that anticipates her needs and meets her with respect, she’s more likely to act.
This isn’t speculation. Research has consistently shown that culturally aligned communication improves treatment adherence, boosts trust, and increases the likelihood of preventive care. And from an economic perspective, it reduces no-shows, lowers emergency visits, and drives down avoidable costs.
This is supported by findings from a report from the National Institutes of Health, which underscored the vital role of cultural and linguistic competency in improving health outcomes for underserved populations. The report found that communication gaps rooted in cultural and language differences often undermine trust between patients and providers, leading to reduced treatment adherence and poor management of chronic conditions like diabetes and heart disease.
It specifically noted that minority and immigrant patients may feel intimidated or misunderstood when providers are not culturally aligned, which directly affects whether they follow through on medical advice or preventive care.
The promise of AI agents is their ability to scale patient care. We don’t have enough clinicians to serve every population in a personalized way, and we never will. But AI agents can help fill the gap, not by replacing humans, but by supplementing their efforts with intelligence and consistency. A clinician may not be able to check in with every Medicaid patient after discharge. But an AI agent can. It can ensure that prescriptions were filled, that follow-up appointments are scheduled, that social needs are being met—or escalated to a human if they’re not.
We now have a powerful new opportunity to solve for health equity – health equity 2.0. This is a pragmatic, data-driven approach that uses AI to continuously engage patients, contextualize their experiences, and support them through the care continuum. It’s not about throwing more technology at the problem but aligning the right technology with the right people at the right time. That makes every healthcare consumer feel heard and connected.
AI agents must be trained on diverse datasets, designed with equity as a core objective, and tested to ensure they’re closing gaps. That means integrating social determinants into the input, designing interfaces that are intuitive for all literacy levels, and constantly measuring real-world impact.
AI agents are part of a powerful broader system that includes clinicians, community health workers, policymakers, and most importantly, patients themselves. They offer something the healthcare system has long lacked — the ability to deliver empathy and relevance at scale to all patients.
If we say we believe in health equity, we must build for it and not just talk about it. AI agents offer us a chance to do that.
Equity isn’t a feature, but it is a foundation of a functional health system. The tools are here. Now we must use them, not just to automate the status quo, but to fundamentally shift it.
About Anmol Madan
Anmol Madan, PhD, is an entrepreneur, computer scientist and executive who has been leading the digital health and AI revolution over the last two decades. He is currently the CEO and Founder of RadiantGraph, a machine learning and AI company bringing Intelligent Personalization to health plans and health services organizations. Anmol served as Chief Data Scientist at Teladoc Health & Livongo and previously co-founded Ginger.io serving as its CEO for 7 years. During his tenure as CEO, Ginger.io built an AI-driven member and clinical product, established a high-growth distribution model with employers, became broadly available as an in-network health-plan benefit, and raised $35M in venture funding from top-tier Silicon Valley VCs. Anmol received a PhD in machine learning applied to human behavior from the MIT Media Lab, has authored 20+ scientific publications and holds over a dozen patents related to machine learning in healthcare. He has been recognized as one of Fast Company’s 100 Most Creative people and as a World Economic Forum Technology Pioneer.
