Date: June 30, 2026
Series: The Business of Health with Chip Kahn
In the rapidly evolving landscape of medical technology, Artificial Intelligence (AI) has emerged as the most significant disruptor of the 21st century. However, as capital pours into the sector, a critical debate has surfaced regarding the true utility of these investments. On the latest episode of The Business of Health, host Chip Kahn sits down with Dr. Toyin Ajayi, CEO and co-founder of Cityblock Health, to confront a sobering reality: the vast majority of current AI innovation in health care is focused on administrative optimization rather than the actual delivery of patient care.
The Administrative Trap: Where the Money Goes
The central thesis of Dr. Ajayi’s argument is that the health care industry is misallocating its most promising technological assets. According to current data and industry analysis, roughly 60% of health care-related AI investment is directed toward billing, coding, and risk adjustment.
"We are building an ecosystem that is obsessed with making sure someone gets paid, rather than ensuring someone gets better," Dr. Ajayi noted during the interview.
This trend creates a dangerous paradox. By prioritizing the "business" side of health care—streamlining insurance claims, optimizing reimbursement cycles, and sharpening risk adjustment for revenue—the industry risks exacerbating existing systemic inequities. When AI is used primarily to navigate the complexities of billing for the most profitable patient populations, it leaves behind the most vulnerable, particularly those managing chronic conditions and those within the Medicaid system.
A Chronology of the AI Health Care Evolution
To understand the current state of the industry, it is essential to look at how we arrived at this point:
- 2018–2020: The Pilot Era. AI in healthcare was largely experimental. Early adopters focused on diagnostic imaging and administrative automation to reduce clerical burnout.
- 2021–2023: The Investment Boom. As Large Language Models (LLMs) gained prominence, venture capital flooded into health-tech. During this period, the focus shifted heavily toward "efficiency" tools, which were marketed as ways to save providers time by automating documentation and coding.
- 2024–2025: The Value-Based Shift. Policymakers and industry leaders began to question whether these efficiency gains were actually reaching the patient. The conversation moved from "How much money can we save the system?" to "How can we improve health outcomes?"
- 2026: The Reckoning. As seen in the latest discourse from figures like Dr. Ajayi, 2026 represents a turning point. The industry is being challenged to pivot from administrative automation to clinical intervention, or risk a future where AI increases the cost of care without improving its quality.
Supporting Data: The Case for Patient-Centric AI
Cityblock Health, which serves over 100,000 Medicaid and dual-eligible members across ten states, provides a unique case study in how AI can be redirected. Dr. Ajayi’s organization focuses on "value-based care," a model where providers are paid based on patient health outcomes rather than the volume of services provided.
When AI is applied to this model, the results differ significantly from the industry standard. Instead of focusing on billing codes, Cityblock utilizes AI to:
- Identify Social Determinants: Algorithms are used to flag which patients may be at risk of food insecurity, housing instability, or lack of transportation—factors that often contribute more to health outcomes than clinical care alone.
- Predictive Intervention: AI identifies patients who are at high risk of acute medical events before they occur, allowing for proactive, community-based care that prevents expensive hospitalizations.
- Care Coordination: AI tools help streamline communication between social workers, nurses, and primary care doctors, ensuring that the "whole person" is being treated.
If the industry continues to concentrate 60% of its AI resources on billing, Dr. Ajayi warns, the result will be a "hollowed-out" health system—one that is highly efficient at processing claims but fundamentally incapable of addressing the chronic, complex needs of the population.
Official Responses and Industry Perspectives
Chip Kahn, a veteran of health policy and senior visiting fellow at KFF, emphasizes the tension between the financial requirements of health systems and the moral imperative of patient care.
"The challenge," Kahn noted, "is that the existing financial incentives of the U.S. health care system favor billing accuracy. To change where the AI investment goes, we must fundamentally change the reimbursement models that govern how care is paid for."
Industry analysts suggest that the "AI for Billing" trend is a symptom of a larger problem: the complexity of the U.S. payer system. For many health systems, the AI tools that help them capture revenue are the only ones that demonstrate a clear Return on Investment (ROI) in the short term. Dr. Ajayi counters this by arguing that long-term sustainability lies in the "value-based" path. By investing in AI that improves the health of the most difficult-to-reach patients, providers can avoid the catastrophic costs associated with late-stage chronic disease management.
The Implications: A Call for a Strategic Pivot
The implications of this debate are profound for the future of public health policy and the trajectory of medical innovation.
1. Widening Health Inequities
If AI development continues to prioritize high-revenue patient demographics, the "digital divide" in health care will grow. Patients who require complex, long-term support—often people of color and those living in poverty—will find themselves outside the scope of AI-driven innovation.
2. The Cost Crisis
AI has been promised as the solution to the rising cost of health care. However, if that AI is only used to manage billing cycles and optimize insurance payouts, it adds a "tech tax" to the system. It creates more sophisticated billing rather than cheaper, better care.
3. The Need for Regulatory Guidance
As AI becomes more integrated into clinical decision-making, regulatory bodies like the FDA and the Department of Health and Human Services (HHS) will need to consider how to incentivize "care-first" AI. This could include tax credits for companies that deploy AI for social determinants of health or performance bonuses for health systems that prove their AI tools are reducing health disparities.
Conclusion: Redefining "Efficiency"
The conversation between Chip Kahn and Dr. Toyin Ajayi serves as a necessary intervention in the current AI hype cycle. It reminds us that technology is never neutral; it reflects the priorities of its creators.
If we want AI to be a tool that saves the health care system, we must move beyond the administrative layer. We need to build, invest in, and deploy technologies that understand the nuances of a patient’s life, that anticipate their needs before they become emergencies, and that treat the human being rather than the invoice.
As Dr. Ajayi eloquently put it, there is a better way. The technology to revolutionize care is already here; the only question is whether the industry has the courage to prioritize the patient over the process.
About the Guest: Dr. Toyin Ajayi
Dr. Toyin Ajayi is a Board-certified Family Medicine physician and a leader in the movement toward equitable, value-based care. Her career spans executive roles at Commonwealth Care Alliance and her current leadership at Cityblock. A member of the National Academy of Medicine and a TIME100 Next honoree, Dr. Ajayi has dedicated her practice to the most complex and underserved patient populations. Her work serves as a blueprint for how technology can be harnessed to bridge the gap between policy, business, and human health.
About the Host: Chip Kahn
Charles N. Kahn III brings decades of expertise in health policy to The Business of Health. As a senior visiting fellow at KFF, a visiting senior fellow at the American Enterprise Institute, and a nonresident senior scholar at the USC Schaeffer Center, Kahn provides the essential context needed to navigate the complexities of the modern health care economy. His podcast series continues to be a vital resource for understanding the intersection of technology, finance, and patient welfare.
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