A dollar-shaped stethoscope.
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There are high hopes for healthcare AI, but promises alone don't pay the bills. Bain and McKinsey research reports that U.S. healthcare leaders expect and even demand positive returns from their AI investments. However, demonstrating ROI will be difficult and will remain a significant barrier to adoption for some time.
Potential benefits and costs of AI
Healthcare organizations can leverage AI to improve quality of care, improve patient and staff experiences, accelerate research, extract more insights from data, and more.
You can also use AI to directly increase revenue through increased volume, faster throughput, better risk adjustment and service level coding, and improved revenue cycle management. Meanwhile, AI has the potential to reduce costs by reducing staffing needs, reducing turnover, and improving supply chain efficiency.
Of course, AI also increases costs. Evaluating different AI products takes time and effort, and organizations need to consider opportunity costs (AI may distract from other activities) and reputational risks (due to possible adverse events).
Additionally, implementing AI is complex, resource-intensive, and fraught with potential pitfalls. Productivity is often temporarily reduced (“switching costs”). Once deployed, there will be ongoing expenditures for software, monitoring, and data infrastructure.
Why analyzing AI ROI is so difficult
Translating abstract concepts like quality, efficiency, and productivity into numbers is difficult and requires asking many difficult questions.
First, from whose perspective is ROI evaluated?The interests of various healthcare stakeholders do not always align. For example, nurse managers may be attracted to AI to triage patients because it reduces staffing needs, but patients may resent having to do more work themselves. No.
Second, who will pay for AI and who will it affect? Often, the people who decide to buy AI are different from the people it will most impact. For example, executives may promote AI tools that enhance risk coding, but physicians may resist it if it requires changes to the way they document care.
Third, what is the timeline? History shows that organizations initially used technology to streamline existing processes, limiting the overall benefits. It takes years to unlock new and better ways to produce goods and services. Today, healthcare systems are rushing to adopt AI, allowing clinicians to write the same (usually poor) notes faster, rather than completely rethinking clinical documentation. We are witnessing this.
Fourth, what is baseline performance? Most organizations know little about the time and effort that clinicians and staff spend on various tasks (e.g., writing discharge summaries) We know even less about quality (e.g., is the summary accurate and easy to read?).
Finally, which metrics best assess the impact of AI? Healthcare data is siled and incomplete, making it difficult to measure and assign value to constructs such as quality and clinician health. is.
Why positive ROI is confusing
Jim Covello, head of global equity research at Goldman Sachs, said: “The high costs of developing and implementing AI technology mean that AI applications are extremely important for companies to achieve an adequate return on investment.'' “This means we have to solve complex and important problems.” However, it may be too much to expect that today's AI will solve complex and important medical problems.
First, generative AI tools are typically too unreliable and error-prone to be applied to high-value tasks. As such, most organizations use them to alleviate “grunt work” such as creating clinical records and completing prior authorizations. However, AI can paradoxically make these tasks more difficult. For example, physicians at UC San Diego Health used ChatGPT to respond to patient messages and, paradoxically, spent 22% more time on this task than physicians who did not use AI. I did.
An AI solution that saves you time doesn't necessarily increase your productivity. Consistent with Parkinson's law that 'work expands until it fills the time allotted for its completion', three in four UK clinicians intend to spend time caring for patients freed up by AI. I reported that there is no. The same could be said of American doctors, who are now deploying tools like AI scribes.
Similarly, AI does not work in isolation. To realize the benefits of AI, organizations must alleviate various downstream constraints. For example, if a doctor's schedule is already full, automating patient scheduling will not improve access. Similarly, an algorithm that identifies hospitalized patients who are ready for discharge is useless if there is no place to send the patient after admission.
Payment models also come with additional challenges. Most healthcare payments are fee-for-service, and payers rarely reimburse AI software. Therefore, organizations implementing AI often need to significantly increase service volumes to break even financially, given operating margins in the low single digits. However, most AI tools (for example, those that summarize clinical records, identify patients at high risk of deterioration, or help detect precancerous colon polyps) No.
Across industries, technology transformation programs typically deliver less than one-third of their expected value. AI in healthcare is no exception.
Looking to the future
None of this is to say that AI in healthcare is without value. AI can help make care more accessible, effective and sustainable. Still, as the AI hype (and accompanying FOMO) fades, the pressure on ROI will increase.
As a result, AI will penetrate most rapidly into back-office finance areas such as revenue cycle management. Startups in this space are already experiencing the highest maturity rates, valuations, and exits.
Similarly, many AI products for clinicians will also extend to activities that directly impact finances. For example, documentation and summarization tools will start recommending risk codes and fees.
For other AI products, organizations may require clinical teams to see more patients or reduce headcount. Importantly, while AI holds great promise and has the potential to transform care in the long term, there is no free lunch.
