Dr. Ronald Rodriguez holds a unique title in healthcare. He’s professor of medical education and program director of the nation’s first MD/MS in Artificial Intelligence dual degree at The University of Texas Health Science Center at San Antonio. The five-year dual degree was launched in 2023.
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Rodriguez, who also holds a doctorate in cellular biology, is at the forefront of AI’s transformation of healthcare. He is well aware of all the positive ways AI and automation are benefiting healthcare already. But he also sees some aspects of the technology that should give pause to clinicians and IT executives.
This is part one of a two-part interview with Rodriguez. Here he points out matters of AI in healthcare that require great care by professionals – even places where he believes professionals are getting it wrong. Part two, coming soon, will be in video format and discuss the doctor’s groundbreaking work in healthcare AI education.
Q. What are some clinicians potentially doing wrong today with generative AI tools, and how can hospital and health system CIOs and other IT and privacy leaders do to make sure generative AI, today, is used correctly?
A. They are not protecting protected health information effectively. Many of the commercial large language model servers take the prompts and data uploaded to their servers and use it for further training later. In many cases, providers are cutting and pasting aggregate clinical data and asking the large language model to reorganize, summarize and provide an assessment.
Unfortunately, many times the patient’s PHI is contained in the lab reports, image reports or prior notes in ways that might not be readily apparent to the provider. Failure to eliminate the PHI is a tier 2 HIPAA violation. Each offense could potentially result in a separate fine. IT providers are able to tell when PHI is being cut and pasted and can warn users not to do it. Often this is already happening.
However, currently most of these systems are not enforcing compliance with these rules at the individual level. CIOs and technology leaders at hospitals and health systems can develop PHI removal tools that protect against these violations. Many of the LLM providers allow settings that prevent data sharing; however, enforcement of those settings is at the provider’s discretion and not ensured.
Q. You say: “Our current business model of AI use is an ecosystem where each prompt generates a cost based on the number of tokens. This incremental cost currently is modeled such that it is more likely to actually increase healthcare costs than reduce them.” Please explain what you mean by using a clear example that shows how costs go up.
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A. Let’s take DAX and Abridge, which are systems that take a recording of the patient-provider interaction, transcribe the interaction and summarize it for use in a note. The costs of these systems is based on actual usage.
The systems make life much easier for physicians, but there is no way to bill the patient for these extra costs through third-party payers. Instead, the only current option to pay for these incremental costs is for the providers to see more patients. Seeing more patients means third-party providers will see more claims, which ultimately will be reflected in higher premiums or lower benefits or both.
Other systems that automate answering patient questions using LLMs may provide immediate feedback to patients with simple questions but also comes at a cost which is incremental. Those costs currently also are not billable, and hence the result is pressure to see more patients.
Let’s consider a hospital system implementing one of these generative AI tools to assist physicians with clinical documentation. A single physician might interact with the AI engine multiple times per patient visit.
Now, multiply this across hundreds or thousands of physicians within a health system working across multiple shifts, and the cumulative cost of AI usage quickly skyrockets. Even if AI improves documentation efficiency, the operational expense of frequent AI queries may offset or even exceed the savings from reduced administrative work.
So far, AI usage models are pay-per-use and are not like traditional software with fixed licensing fees. So, the more an organization integrates AI into daily workflows, the higher the financial burden becomes.
Unless hospitals and healthcare providers negotiate cost-effective pricing structures, implement usage controls or develop in-house AI systems, they may find themselves in a situation where AI adoption leads to escalating operational costs rather than the anticipated savings.
Q. You told me: “Safeguards need to be put in place before we will ever realize a true improvement in our overall medical errors. Over-reliance on AI to correct mistakes could potentially result in different types of errors.” Please elaborate on the problem, and please discuss the needed safeguards, in your opinion.
A. LLMs are prone to hallucinations under certain situations. While some providers are very good at avoiding those situations – we actually teach our students how to avoid such situations – many are not aware. A new source of medical errors can be introduced if these errors are not caught. One way to safeguard against this is to use agentic specialty-specific AI LLMs.
These systems perform double checks on the information, confirm its veracity and use sophisticated methods to minimize errors. However, such systems are not built into the off-the-shelf LLMs like ChatGPT or Claud AI. They will cost more to use, and they will require a larger investment in infrastructure.
Investment in the infrastructure to protect privacy, prevent unintended sharing of PHI, and protect against predictable LLMs and misconceptions rampant in the internet data scraping used for pretraining of the foundational LLMs, will be required. Policies to enforce compliance will also be necessary.
Q. How should hospitals and health systems go about developing proper ethical policies, guidelines and oversight?
A. As AI technologies rapidly advance, major medical organizations need to provide guidance documents, and boilerplate policies that can help institutions adopt best practices. This can be accomplished at several levels.
Participation in oversight organizations and medical groups like the AMA, AAMC and governmental oversight committees can help solidify a common framework for ethical AI data access and use policies.
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