Hospital and health system CIOs told Becker's that healthcare organizations are hesitant to create a generative AI strategy due to technical, financial, regulatory and ethical considerations.
According to a Bain & Co. survey, 75 percent of health system executives believe generative AI has reached a turning point in the healthcare industry, but only 6 percent reported having a generative AI strategy for their organization.
Becker's asked five CIOs: What's holding organizations back from creating a generative AI strategy?
Sunil Dadlani. Executive Vice President and Chief Information and Digital Transformation Officer of Atlantic Health System (Morristown, N.J.): The gap between healthcare leaders' enthusiasm for the promise of generative AI and the scant adoption of AI strategies within organizations can be attributed to a range of obstacles.
These hurdles span from technical issues, such as a lack of specialized skills and data protection concerns, to financial constraints like hefty startup costs and ambiguous returns on investment.
Regulatory uncertainties involving compliance and ethical considerations are also deterrents. In addition, organizations must overcome any inherent resistance to change or lack of trust in new technologies, and strategic uncertainties like unproven use cases can also be contributing factors.
Will Landry. Senior Vice President and CIO of Franciscan Missionaries of Our Lady Health System (Baton Rouge, La.): Fear of the unknown. This includes tech, cyber and compliance fears, as well as lack of talent and technical knowledge in the ability to control and manage generative AI solutions.
Prasanna Menta. CIO of Sheppard Pratt (Baltimore): The disparity between the acknowledged promise of generative AI in healthcare and its limited deployment can be attributed to a confluence of challenges.
Technological complexities necessitate specialized expertise, robust data infrastructure, and seamless integration with legacy systems. Financial barriers include substantial initial investments and ambiguous returns on investment. Regulatory and ethical considerations, such as compliance with data privacy laws and ethical governance, further complicate matters.
Moreover, the rapidly evolving AI landscape, an absence of industry-wide best practices, and apprehensions about vendor reliability and exclusivity contribute to organizational hesitancy. Addressing these multifaceted issues demands a holistic strategy that encompasses technical, financial, ethical and organizational dimensions for successful AI assimilation in healthcare settings.
David Higginson. Executive Vice President and Chief Innovation Officer of Phoenix Children's: It's important to recognize generative AI is a relatively new technology and this type of generational change takes time to be fully understood, strategized and integrated into existing workflows.
Most realize the impact of this kind of technology will be broad and transformative, affecting traditional tasks in innovative ways. As a result, spending time to understand and correctly operationalize this new form of AI is critical to avoid following the oversold hype cycle of machine learning.
Even the tech industry with all its resources is still in the process of learning how to effectively incorporate generative AI into products in a safe, secure, cautious way. Industry giants like Microsoft, who has the exclusive license for the OpenAI's GPT platform, are still taking cautious steps with initial releases such as the "co-pilot technology" to ensure they realize the true value of large language models. Some hospitals are taking a centralized approach, aiming to validate the technology's accuracy through committees before sanctioning its use.
This approach seems a bit reactionary and risks limiting the broader opportunities offered by generative AI. The reality is unless hospitals are currently blocking computer and smartphone access to ChatGPT, it's likely employees are already utilizing the technology.
Darrell Bodnar. CIO of North Country Healthcare (Berlin, N.H.): Healthcare organizations traditionally lag other industries when it comes to adopting new technologies. Generative AI is not much different, but I feel there are some reasons for being a bit more aggressive when it comes to generative AI.
There are some very quick wins with minimal risk for really any industry. Low-risk benefits in the healthcare industry include office automation, document creation, policy development, marketing initiatives, summarization of data, media development, training and education materials, and just about any administrative task. After creating a framework and guidelines including policies, procedures and controls, I see no real reason not to move forward with generative AI tools.
Clinically, the creation of patient letters and discharge instructions are low-hanging fruit as well, but they really need to have specific guidelines and should not include PHI. Clinical use beyond that needs to proceed cautiously with security, privacy, and compliance top of mind. I feel this is where most healthcare organizations are waiting for the trusted development of the technology through their established business partners like EMR vendors, healthcare software and technology developers, and large technology partners like Microsoft and Google. The next 12 to 18 months is going to see rapid growth of generative AI in healthcare. Healthcare organizations need to prepare now and have a sound strategy as well as a documented road map for adoption.