Can AI Make Healthcare Less Expensive?

Summary

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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)

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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.

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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]

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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]

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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]

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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.

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Transcript

[A]

Terrence welcome to Thoughts on the Market. I'm Terence Flynn, Morgan Stanley's U.S. bioPharma analyst.

[B]

And I'm Erin Wright, U.S. healthcare Services analyst.

[A]

Thanks for joining us. We're actually in the midst of the second day of Morgan Stanley's annual Global Healthcare conference where we hosted over 400 companies. And there are a number of important themes that we discussed, including healthcare policy and capital allocation. Now, today on the show, we're going to discuss one of these themes, health care spending, which is one of the most pressing challenges facing the U.S. economy today. It's Tuesday, September 9th at 8:00am in New York. 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. Now, with healthcare costs in the US Higher than many other peer countries on a percentage of GDP basis, it's, it's no wonder that everyone, not just investors, is asking not just why is this happening, but how can we fix it? And that's why we're talking about AI today. Could it be the breakthrough needed to help rein in those costs and reshape how care is delivered? Now, I'm gonna go over to you, Erin. Why is US Healthcare spending growing so rapidly compared to peer countries?

[B]

Clearly, the aging population in the US and rising chronic disease burden here are clearly driving up demand for healthcare. We're seeing escalating demand across the senior population, for instance. It's coinciding with greater utilization of more sophisticated therapeutics and services. Overall, it's straining the health care system. We are seeing burnout and labor constraints at hospitals and broader health systems. Overall, NET net, the US spent 18% of GDP on health care in 2023, and that's compared to only 11% for peer countries. And it's projected to reach 25 to 30% of GDP by 2050. So the costs are clearly escalating here.

[A]

Thanks, Erin. That's a great way to frame the problem. Now, as we think about AI, where does that come in to help potentially bend the cost curve?

[B]

We think AI can drive meaningful efficiencies across healthcare delivery with estimated savings of about 300 to 900 billion by 2050. So the focus areas include here staffing, supply chain, scheduling, adherence. These are where AI tools can really address some of these inefficiencies in care and ultimately drive health outcomes. There are implementation costs and risks for hospitals, but we do think the savings here can be substantial.

[A]

Great. Well, let's unpack that a little bit more now. So if you think about the biggest cost buckets in hospitals, we where can AI help out?

[B]

The biggest cost bucket for a hospital today clearly is labor. It represents about half of spend for a hospital. AI can optimize staffing, reduce burnout with a new SCRIBE and some of these scribe technologies that are out there, and more efficient healthcare record keeping. I mean, this can really help to drive meaningful cost savings. Just to add another discouraging data point for you, there's estimated to be a shortage of about 100,000 critical healthcare workers in 2028. So AI can help to address that. AI tools can be used across administrative functions as well. That accounts for about 15 to 20% of spend for a hospital. So we see substantial savings as well across drugs, supplies, lab testing, where AI can reduce waste and improve adherence overall.

[A]

Great. Maybe we'll pivot over to the managed care and value based care side. Now, how is AI being used in.

[B]

These verticals, Aaron, for a healthcare insurer? And they're facing many challenges right now as well. AI can help personalize care plans and they can support better predictive analytics and ultimately help to optimize utilization trends. And it can also help to facilitate value based care arrangements which can ultimately drive better health outcomes and bend the cost curve. And ultimately that's the key theme that we're trying to focus on here. So I'll turn it over to you, Terrence. Now, while hospitals and payers could see notable benefits from AI, the biopharma side of the equation is just as critical here, especially when it comes to long term cost containment. You've been closely tracking how AI is transforming drug development. What exactly are you seeing?

[A]

Yeah, a number of key constituents are leaning in here on AI in a number of different ways. I'd say the most meaningful, meaningful way that could help bend the cost curve is on R and D productivity. As many people probably know, it can take a very long time for a drug to reach the market, anywhere from eight to 10 years. And if AI can be used to improve that cycle time or boost the probability of success, the probability of a drug reaching the market, that could have a meaningful benefit on costs. And so we think AI has the potential to increase drug approvals by 10 to 40%. And, and if that happens, you can ultimately drive cost savings of anywhere from 100 billion to 600 billion by 2050.

[B]

Yeah, that sounds meaningful. How do you think additional drug approvals lead to meaningful cost savings in the healthcare system?

[A]

Look, I mean, high level medicines at their best cure disease or prevent people from being admitted to a hospital or seeking care at a doctor's office. Equally important, medicines can get people out of the hospital quicker and back to contributing or participating in society. And there's data out there in the literature showing that new drugs can reduce hospital stays by anywhere from 11 to 16%. And so if you think about keeping people out of hospitals or physician offices or reducing hospital stays, that really can result in meaningful savings. And that would be the result of more or better drugs reaching the market over the next decades.

[B]

And how is the FDA now supporting or even helping to endorse AI driven drug development?

[A]

If companies are applying for more drug approvals here as a result of AI discovery capabilities, without modernization, the FDA could actually become the bottleneck and limit the number of drugs approved each year. And so in June, the agency rolled out an AI tool called ELSA that's looking to improve the drug review timelines. Now, ELSA has potential to accelerate these timelines for new therapies. It can take anywhere from six to 10 months for the FDA to actually approve a drug. And so these AI tools could potentially help decrease those timelines.

[B]

And are you actually seeing some of these biopharma companies actually investing in AI talent?

[A]

Yes, definitely. I mean, AI related job postings in our sector have doubled since 2021. Companies are increasingly hiring across the board for a number of different parts of their workflow, including discovery, which we just talked, but also clinical trials, marketing, regulatory, a whole host of different job descriptions.

[B]

So whether it's optimizing hospital operations or accelerating drug discovery, AI is emerging as a powerful lever here to bend the healthcare cost curve.

[A]

Exactly. The challenge is adoption, but the potential is transformative. Aaron, thanks so much for taking the time to talk with us.

[B]

Great speaking with you Terrence.

[A]

And thanks everyone for listening. If you enjoy thoughts on the market, thanks for watching. Please leave us a review wherever you listen and share the podcast with a friend or colleague today.

[C]

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