Podcast Summary: Thoughts on the Market

Episode: Can AI Make Healthcare Less Expensive?
Date: September 9, 2025
Hosts: Terence Flynn (U.S. BioPharma Analyst) & Erin Wright (U.S. Healthcare Services Analyst)

Brief Overview

This episode, broadcasting from Morgan Stanley’s annual Global Healthcare Conference, takes a focused look at the surging costs of healthcare in the United States and explores whether artificial intelligence can be the breakthrough needed to make healthcare more affordable. The discussion examines why healthcare spending in the US is markedly higher than in peer countries, pinpoints the most significant cost drivers, and unpacks the potential for AI to optimize operations, staffing, and drug development, thereby bending the healthcare cost curve.

Key Discussion Points & Insights

1. The Escalating Cost of US Healthcare (00:08–01:58)

• Framing the Problem:

  • US healthcare costs are among the highest in the world, constituting 18% of GDP in 2023 (compared to 11% for peer countries) and are projected to hit up to 30% by 2050.
  • Main drivers: aging population and rise in chronic diseases, leading to higher utilization of sophisticated treatments and immense pressure on the system.

  Quote:
  "Overall, NET net, the US spent 18% of GDP on health care in 2023, and ... it's projected to reach 25 to 30% of GDP by 2050."
  — Erin Wright [01:38]

2. Where AI Can Make a Difference (01:58–03:32)

• Potential Savings:

  • AI could generate savings of $300–900 billion by 2050 through efficiencies in staffing, supply chain, scheduling, and medication adherence.
  • Key focus: Alleviating administrative burdens and optimizing labor—the largest expense for hospitals (about 50% of costs).
  • AI-powered scribe technologies and improved record-keeping offer productivity boosts.
  • Administrative functions are another area ripe for AI-driven efficiency, representing 15–20% of hospital spend.

  Quote:
  "AI can optimize staffing, reduce burnout ... and more efficient healthcare record keeping. ... There’s estimated to be a shortage of about 100,000 critical healthcare workers in 2028. So AI can help to address that."
  — Erin Wright [02:44, 03:00]

3. AI in Managed Care & Value-Based Care (03:32–04:25)

• Improving Outcomes for Insurers:

  • AI enables personalization of care plans and more robust predictive analytics.
  • Potential to facilitate value-based care arrangements, which reward outcomes rather than volume, aiming for better results at lower cost.

  Quote:
  "AI can help personalize care plans ... and facilitate value-based care arrangements which can ultimately drive better health outcomes and bend the cost curve."
  — Erin Wright [03:38]

4. Transforming Drug Development with AI (04:25–05:56)

• Accelerating R&D:

  • AI could increase R&D productivity, shortening the drug development timeline (currently 8–10 years) and increasing the probability of approval.
  • Projected to boost drug approvals by 10-40% and generate $100–600 billion in healthcare savings by 2050.
  • Additional, effective medicines can keep patients out of hospitals, reducing costs by 11–16% through shorter hospital stays.

  Quote:
  "We think AI has the potential to increase drug approvals by 10 to 40%. ... you can ultimately drive cost savings of anywhere from 100 billion to 600 billion by 2050."
  — Terence Flynn [04:47]

  Quote:
  "New drugs can reduce hospital stays by anywhere from 11 to 16%."
  — Terence Flynn [05:42]

5. Regulatory Support: FDA and AI (05:56–06:37)

• FDA Modernization:

  • The FDA has launched an AI tool called ELSA to streamline drug review processes (aiming to shorten 6–10 month review periods).
  • Growing number of AI-driven applications requires modernization to avoid regulatory slowdowns.

  Quote:
  "In June, the agency rolled out an AI tool called ELSA that's looking to improve the drug review timelines."
  — Terence Flynn [06:15]

6. Industry Adoption & AI Talent (06:37–07:03)

• Shifting Workforce:

  • Job postings related to AI in biopharma have doubled since 2021, reflecting surging investment across the drug development value chain (discovery, trials, marketing, regulatory, etc).

  Quote:
  "AI related job postings in our sector have doubled since 2021. Companies are increasingly hiring ... for a number of different parts of their workflow."
  — Terence Flynn [06:42]

7. The Bottom Line: Transformative Potential & the Adoption Challenge (07:03–07:20)

• AI holds promise to optimize both operational and clinical aspects of healthcare.

• Challenges around adoption remain, but the impact could be transformative for cost containment and quality of care.

  Quote:
  "Whether it's optimizing hospital operations or accelerating drug discovery, AI is emerging as a powerful lever ... to bend the healthcare cost curve."
  — Erin Wright [07:03]

Memorable Moments & Notable Quotes

• Sticker Shock:
  "Imagine getting a bill for a routine doctor's visit and seeing a number that makes you do a double take. Maybe it's $300 for a quick checkup or thousands of dollars for a simple procedure. For many Americans, those moments of sticker shock aren't rare. They're the reality."
  — Terence Flynn [00:28]

• On Future Potential:
  "The challenge is adoption, but the potential is transformative."
  — Terence Flynn [07:13]

Important Timestamps

• Escalating US Costs & Global Comparison: [01:15–01:58]
• AI-Driven Operational Efficiencies: [02:06–03:32]
• AI in Managed and Value-Based Care: [03:32–04:25]
• AI in Drug Development & Potential Savings: [04:25–05:56]
• FDA's New AI Tool 'ELSA': [06:03–06:37]
• Adoption and Workforce Impact: [06:37–07:03]
• Summary and Final Thoughts: [07:03–07:20]

Conclusion

This episode presents a comprehensive, practical overview of how AI is positioned to disrupt healthcare economics in the US, from hospital operations to drug discovery, while acknowledging current labor and policy limitations. The conversation is candidly grounded in data, blending optimism about AI’s financial and clinical potential with realism about implementation hurdles.