https://sloanreview.mit.edu/article/mayo-clinics-healthy-model-for-ai-success/
This column series looks at the biggest data and analytics challenges facing modern companies and dives deep into successful use cases that can help other organizations accelerate their AI progress.
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In 2022, we argued in the MIT Sloan Management Review AI in Action series that Mayo Clinic was probably the most aggressive adopter of AI among U.S. health care providers. Today, it’s time to review some of the factors that have made this health system successful with AI and its underlying components. One key: Mayo Clinic staff members see the data and AI team as enablers, not gatekeepers.
We were surprised in 2022 by the extent of AI activity underway at Mayo Clinic, but that’s explainable in part by the organization’s size: It’s the largest nonprofit integrated health system in the world. The system employs 76,000 staff and 7,300 physicians at three campuses in Minnesota, Arizona, and Florida.
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Moreover, with the organization’s long tradition of medical research, it makes sense that many of the organization’s clinicians and administrators have attempted to find ways to use AI to improve care. And as one of the most highly rated medical institutions in the world, Mayo Clinic would naturally want to improve its patient care and administrative processes with a revolutionary technology like AI.
Since we last wrote about Mayo Clinic, it has developed an infrastructure for enabling and facilitating AI development that has led to considerably increased activity (generative AI hasn’t hurt in that regard either). And since we are both interested in enablement (making it easier for users to do the right thing in building use cases with data, analytics, and AI) as opposed to the much more heavy-handed governance (“we professionals will tell you what you can do and what you can’t”), we were interested in revisiting an organization that has taken a strong enablement focus.
In speaking with Ajai Sehgal, Mayo Clinic’s chief data and analytics officer (CDAO), we learned about some new use cases — applications for specific tasks — that we weren’t aware of. On the clinical side, researchers have created an algorithm to identify certain heart pump problems (low ejection fraction, among others) from 12-lead echocardiogram (ECG) readings that were previously only detectable through stress tests. The AI algorithm can also be used to detect some heart diseases, including hypertrophic cardiomyopathy and cardiac amyloidosis. The algorithm was cleared by the Food and Drug Administration to be marketed as a medical device (by a Mayo Clinic spinoff called Anumana) and has already been modified to take Apple Watch single-lead ECG signals.
Mayo Clinic researchers have also created a new class of AI called hypothesis-driven AI that may help to improve interpretability of AI algorithms for health care treatments, particularly for cancer.
Sehgal says Mayo Clinic continues to work on administrative AI use cases. During the COVID pandemic, when capacity management was a major focus, a machine learning model was created to forecast the availability of beds in intensive care units. The same approach was later used to address capacity to treat RSV in the Children’s Center.
We’ve been advocating for companies to shift their focus from data and AI governance to enablement, by emphasizing the technology and services that make building AI applications easier and safer. Mayo Clinic is ahead of that game, and Sehgal extensively employs the enablement concept. It’s the primary approach to helping clinicians and administrators develop their own AI capabilities — a large-scale “citizen development” effort.