When it comes to adopting healthcare AI, large, well-off hospitals are likely to frequently homer while smaller, struggling institutions go down looking. (Baseball analogy in honor of tonight’s Midsummer Classic.) As a result, some patients will benefit by AI while many others go wanting.
This won’t serve the whole of U.S. healthcare well.
Fortunately, there’s time to make sure AI implementation doesn’t unfold quite so “inequitably” between haves and have-nots. That’s the thrust of an opinion piece published in MedPage Today July 13.
Henry Bair, MD, MBA, and Mak Djulbegovic, MD, MSc, of Wills Eye Hospital and Jefferson Health in Philadelphia break down the challenge and offer prescriptions.
“As we continue to develop and experiment with AI technologies, equitable access to these technologies is crucial to prevent a widening divide in healthcare quality,” they write. “We must work to ensure that smaller clinics, community hospitals and underfunded institutions are not left behind.”
Bair and Djulbegovic suggest that pulling this off will take concerted efforts across three spheres of activity.
1. Government and policy interventions.
Government policies can play a critical role in promoting equitable AI implementation, the authors point out. “Policies should focus on providing funding, training grants and partnership mandates that encourage the adoption of AI in smaller, underfunded and community hospitals,” they add. “There are precedents for this; past initiatives have supported EHR and health IT adoption.” More:
‘Regulations should ensure that AI technologies address local health challenges and are equitably distributed across different regions.’
2. Education and training programs.
To reduce the educational gap, initiatives to enhance gen AI knowledge at all levels of medical education are essential, Bair and Djulbegovic write. “Professional associations such as the Association of American Medical Colleges have developed resources for this purpose and should continue to offer guidance on the design of medical school curricula and professional development programs.”
‘Healthcare systems can collaborate with academic and corporate organizations to create institution-specific AI training modules, as demonstrated by the abundance of existing online courses on gen AI use.’
3. Collaborative models.
Creating collaborative models for resource-sharing between AI-equipped hospitals and other hospitals has vast potential to reduce disparities, the authors note. “Well-resourced and innovation-focused hospitals should mentor and provide technical support to underfunded or smaller hospitals,” they add.
‘Establishing regional AI hubs that serve as centers of excellence can facilitate knowledge and resource distribution. Meanwhile, less AI-equipped hospitals ought to proactively consider how gen AI can benefit their workflows.’
Bair and Djulbegovic further recommend encouraging the private sector to invest in affordable AI solutions that can specifically serve hospitals with thinner resources at hand.
“By uplifting smaller, underfunded hospitals, the entire system becomes more resilient and capable of handling both public health crises and everyday medical issues alike,” they write.
Given how quickly AI tools are evolving, the authors underscore, “it is not premature to continue developing gen AI in an equitable manner. Neglecting to do so risks creating gaps that will be ever more difficult to bridge.” More:
‘By implementing the strategies outlined above, we can fulfill our ethical imperative to realize more inclusive healthcare systems in which AI technologies benefit all patients, regardless of where they are or the resources available to their healthcare providers.’
Read the whole thing.
