DGIN - Will AI Transform The Healthcare Industry For Investors?

2023-06-09 07:36:00 ET

Summary

• Healthcare companies are beginning to explore how artificial intelligence might unlock efficiencies for patients and medical systems.
• Companies that put cash to work by investing in innovation are signaling efforts to secure consistent long-term profitability.
• Investors in healthcare companies should always focus on durable businesses that reinvest above their cost of capital.

By Vinay Thapar, CFA

Healthcare companies are beginning to explore how artificial intelligence ((AI)) might unlock efficiencies for patients and medical systems. But to transform science fiction into reality, AI applications in the sector must prove that they can improve business profitability to deliver returns for investors.

Innovation is a powerful force for change in healthcare, and AI is well-suited to shake up the sector. Healthcare systems hold huge amounts of data that can be mined for insight. Pharmaceutical companies are always searching for ways to speed up long drug-development processes. In theory, AI could help promote more effective management across the industry, helping healthcare firms get the right drugs in the right quantities to the right patients.

But there's a vast distance from theory to practice, especially with nascent technologies. Bridging the gap will require business proof, more than scientific pizazz. Here's how we think investors should start thinking about the future of AI in healthcare across four broad areas.

1. Research and development: AI could potentially be used to improve success rates for drugs in clinical trials. However, this will take years. Even if AI does eventually help boost drug-development success rates, it might not create competitive advantages. If the technology is adopted across the sector to speed up time to market, the competitive benefits will diminish, and pricing may be pressured. Expect plenty of PR in this area, but it may not turn into profitability.
2. Clinical trials: The logistics of clinical trials are cumbersome. Companies must find the right sites, with the right pool of patients, as fast as possible. We think AI could help companies find clinical sites that enroll patients faster for trials, and identify underperforming sites, to help rectify them quicker.
3. Commercial development: Even successful drugs face big hurdles to get to market. AI could help companies identify doctors and specialists who might be prime candidates for a new product. It could also help companies target the most effective events for generating a buzz around a new drug. This might not sound as exciting as using AI for R&D, but it is an essential element of the business formula for any pharmaceutical company.
4. The human experience: Most consumers won't know or care whether a drug trial is accelerated by AI. But every patient wants to get the best diagnosis from their doctor. We think AI could make a huge difference here. Imagine a smart physician's AI assistant that can help a doctor zoom in on a rare disorder that a patient might have based on a given set of symptoms. These are the types of tangible benefits that consumers will be willing to pay more for - and can help contribute meaningfully to the bottom line of healthcare providers.

In all these areas, we believe AI's success will be measured by its ability to produce better healthcare outcomes. For example, UnitedHealth Group ( UNH ) says AI can help reduce the time taken to turn data into insights, enabling employers and insurers to better understand all the factors impacting a person's health. When implemented strategically, this can lead to better healthcare decision-making and lower costs for healthcare businesses.

Companies like Veeva Systems ( VEEV ), based in the US, and Icon, based in Ireland, are already helping to introduce AI in commercial tools, with more advancements expected on the clinical side as well. Intuitive Surgical ( ISRG ), which manufactures robotic surgical systems, collects data from millions of procedures to help address anomalies and complications. Google ( GOOG ) ( GOOGL ) and Northwestern Medicine are working on an AI model that may be capable of detecting lung cancer earlier than current diagnostic tools, increasing the chances of effective treatment.

How Can Investors Evaluate AI in Healthcare?

Companies that put cash to work by investing in innovation are signaling efforts to secure consistent long-term profitability. But investors shouldn't try to predict which AI initiatives will be transformative. We believe investors in healthcare companies must always stay focused on business - not science. Just as we apply this principle to drug development, which is notoriously difficult to predict, we don't think investors have any advantage in forecasting how AI technology will shape the future of healthcare.

The history of disruptive technology is littered with failure. When the dot-com boom dazzled investors, countless early darlings died on the vine. Yet the technology itself eventually transformed the world we live in beyond recognition and spawned many new profitable industries and businesses.

Similarly, the AI revolution will take time and will progress in fits and starts. But technological wizardry doesn't equal business success. In fact, if a successful AI application becomes commoditized, it could even reduce profitability. When a healthcare company unveils a shiny new robot, investors must ask: how will it make money? How long will it take to get to market? What's your competitive strategy versus peers?

Keeping these questions front and center is crucial to navigating the AI craze. Investors in healthcare companies should always focus on durable businesses that reinvest above their cost of capital, in our view. If a company with these attributes also delivers on a promising AI plan, investors will benefit further. But if the AI initiative fails, investors will still have a profitable business as a cushion.

The views expressed herein do not constitute research, investment advice, or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams, and are subject to revision over time.

References to specific securities are presented to illustrate the application of our investment philosophy only and are not to be considered recommendations by AB. The specific securities identified and described do not represent all of the securities purchased, sold, or recommended for the portfolio, and it should not be assumed that investments in the securities identified were or will be profitable.

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Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.

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