Can AI Be the Catalyst to Bend the US Healthcare Cost Curve?

As Morgan Stanley’s U.S. biopharma analyst Terrence Flynn and healthcare services analyst Erin Wright discuss, the United States is facing a mounting crisis in healthcare spending. With the U.S. currently spending 18% of its GDP on healthcare—compared to only 11% in peer countries—and projections suggesting this could reach 25% to 30% by 2050, the need for systemic change is undeniable. The analysts explore whether Artificial Intelligence (AI) could serve as the transformative tool needed to "bend the cost curve" and reshape care delivery.

The Drivers of Escalating Costs

The rapid growth in U.S. healthcare spending is driven by a convergence of factors. Erin Wright highlights that an "aging population" and a "rising chronic disease burden" are fueling an "escalating demand" for services. This demand is further complicated by the use of increasingly "sophisticated therapeutics," which, while effective, strain a system already suffering from "burnout and labor constraints" at hospitals and health systems.

AI in Hospital Operations: Addressing Inefficiencies

AI offers significant potential to drive "meaningful efficiencies" in hospital administration and operations, with estimated savings ranging from $300 billion to $900 billion by 2050.

• Labor Optimization: Labor represents the "biggest cost bucket" for hospitals, accounting for about half of their spending. AI can optimize staffing and mitigate the projected shortage of 100,000 critical healthcare workers by 2028. Tools such as "scribe technologies" are already helping to reduce clinician burnout and improve "healthcare record keeping."
• Administrative and Supply Chain Efficiency: Administrative tasks consume 15% to 20% of hospital budgets. AI can streamline these functions, alongside optimizing "drugs, supplies, and lab testing" to reduce waste and improve patient adherence.
• Managed Care: In the insurance vertical, AI supports "predictive analytics" and the personalization of "care plans," which are essential for facilitating "value-based care arrangements" that prioritize patient outcomes over volume.

Revolutionizing Biopharma and R&D Productivity

Terrence Flynn emphasizes that the biopharma sector is a critical pillar in long-term cost containment. The most significant impact of AI here is on "R&D productivity."

• Accelerating Drug Development: Traditional drug development is a slow process, often taking "8 to 10 years" to reach the market. By improving cycle times and increasing the "probability of success," AI could boost drug approvals by 10% to 40%, potentially saving between $100 billion and $600 billion by 2050.
• Systemic Savings through Better Medicine: Medicines that effectively "cure disease or prevent people from being admitted to a hospital" create massive downstream savings. Data indicates that new therapies can "reduce hospital stays by anywhere from 11% to 16%," keeping patients out of high-cost clinical settings.
• Regulatory Modernization: To prevent the FDA from becoming a "bottleneck" due to an influx of AI-generated drug applications, the agency has introduced an AI tool called "ELSA." This tool is designed to improve "drug review timelines," potentially shortening the current 6-to-10-month approval process.

The Path Forward: Investment and Adoption

The commitment to this technological shift is evident in the labor market, where "AI related job postings" in the biopharma sector have doubled since 2021. Companies are aggressively hiring for roles across "discovery, clinical trials, marketing, and regulatory" functions.

While "implementation costs and risks" remain, the experts conclude that AI is emerging as a "powerful lever" to address systemic inefficiencies. The ultimate challenge lies in adoption, but the potential for AI to transform the healthcare landscape and stabilize the U.S. economy remains a profound opportunity for the coming decades.