AI Adoption in U.S. Health Care Won’t Be Easy

Health Highlights

James B. Rebitzer, Peter and Deborah Wexler Professor of Management, Boston University Questrom School of Business | Robert S. Rebitzer, National Advisor, Manatt Health

Editor’s Note: In an article recently published in Harvard Business Review, James Rebitzer and Robert Rebitzer—coauthors of the new book Why Not Better and Cheaper? Healthcare and Innovation—discuss the challenges the U.S. health care sector faces that could slow the adoption of artificial intelligence (AI). Summarized below, the article also shares three keys to accelerating adoption by building trust with providers, patients and the public. Click here to read the full article.

Manatt recently interviewed the authors and other thought leaders for its two-part webinar series exploring health care innovation and why it’s important for patients and society. Part 1 examines why valuable innovations sometimes go missing in health care and what to do about it. Part 2 focuses on how to get the most out of transformative technologies in health care. Click here to view the free webinars on demand and get more information about the new book Why Not Better and Cheaper? Healthcare and Innovation.

Artificial intelligence has the potential to improve every aspect of health care. History suggests, however, that the U.S. health sector struggles to put innovations like AI into practice. To accelerate adoption, health care innovators must build trust in AI with three critical constituencies: providers, patients and the public.

There are three things that innovators can do to build the requisite trust:

1. Change the narrative about the purpose of AI.

Instead of designing the new technologies to substitute for human decision making, innovators should aim toward new tools that complement and augment the expertise of providers. For example, AI applications have the potential to support the patient-provider relationship by relieving providers of rote tasks and enabling them to spend more time and attention on patients, problem-solving and communication.

2. Pay careful attention to how AI applications are implemented.

Prior to implementation, AI applications should demonstrably improve outcomes and provide better experiences for patients and providers. Payers, health systems and providers need to come to a common understanding about when it is appropriate to use an AI application, how it should be used, and how potential side effects will be identified and mitigated.

3. Assure patients and the public that AI applications serve their needs without threatening their rights.

Innovators should look to emerging frameworks that offer design principles for trustworthy AI, such as AI systems should be safe and effective, AI algorithms should be unbiased and promote equitable health care outcomes, and data privacy should be maintained. Patients should be informed when an automated system is being used, and they should be able to opt out of automated systems where appropriate.

The contrasting examples of two earlier transformative technologies—EHRs and minimally invasive gallbladder surgery—illustrate why it is necessary and urgent to reduce switchover disruptions for AI in health care.

In 1991, a report by the Institute of Medicine (IOM) of the National Academy of Sciences identified EHRs as an essential technology for health care. But by 2007 only 4% of physicians and less than 2% of hospitals reported having a fully functional EHR. In contrast, minimally invasive surgical removal of the gallbladder took just a few years from its first use in the United States in 1988 to nearly complete adoption.

Switchover disruptions were high for EHRs and low for the new surgical procedure. Why?

The introduction of EHRs required large initial expenditures on software and computers. Even more costly were training employees on the new system and the drop in productivity as they climbed the learning curve.

Additional cost and disruption came from the redesign of clinical and administrative workflows. The switchover to EHRs also involved hidden costs stemming from challenges to existing power relationships and professional identities.

Minimally invasive gallbladder surgery was also a big change from previous technology and required significant investment in costly new tools, training and processes. But the changes were primarily limited to the surgical suite. In addition, the idea of minimally invasive surgery was attractive to payers, patients and the public.

Some AI applications come with relatively low switchover disruptions. However, much of the current excitement about AI comes from large language models (LLMs), like ChatGPT, that have the potential to automate decision making about diagnoses and treatments. These AI applications are likely to come with large switchover disruptions.

Fortunately, AI is a new technology and attitudes are not yet written in stone. However, high switchover disruptions reduce the incentives for firms to adopt innovations, particularly in markets—like those for physician and hospital services and health insurance—that are highly concentrated and protected from external competition by regulatory and other barriers. Without action, the health sector may delay or forgo valuable AI applications much as it did EHRs.



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