Organizations missing key imperatives in implementing AI tools
Research delves into key overlooked factors that are essential for successful AI integration in healthcare settings.
Implementing artificial intelligence-based capabilities doesn’t begin and end with a technology application. It’s broader in scope, and many healthcare executives may be missing essential considerations that could risk success.
Healthcare executives are taking a “traditional data-focused approach to implementing generative AI” but that approach could be too narrow, according to analysis by Deloitte after its extensive survey of 60 healthcare executives for its 2024 Health Care Generative AI Outlook.
Deloitte’s analysts found that more than 70 percent of executives are “highly focused on data considerations, like data availability, quality, compliance, security and privacy during implementation.” But that will soon become table stakes, as efforts such as the National Artificial Intelligence Research Resource Pilot Project and other federated data initiatives are expected to increasingly answer data concerns.
Other considerations are being overlooked by healthcare executives. Deloitte says these “blind spots” are factors that fewer than 60 percent of executives are focused on, and they “may prevent healthcare organizations from successfully integrating generative AI into their workflows.”
Deloitte’s report suggests the following issues are being overlooked by healthcare executives.
Lack of effective governance. Oversight of data use and ensuring its accurate is crucial to an effective AI strategy to “ensure the effective use and quality of data, mitigate data bias for equitable design and safeguard patient privacy.” However, only 60 percent of executives are prioritizing a data governance model, and 45 percent are focusing on mitigating data biases.
Consumers’ priorities. Executives surveyed by Deloitte said they’re “less focused on building consumer trust in gen AI and improving data sharing (mentioned by 50 percent) and educating patients about AI and its risks (45 percent).” Assuaging consumers’ concerns about AI transparency on data use is crucial to build their trust.
Meeting workforce needs. Clinicians long have expressed fears about how AI will affect their jobs. Deloitte says its broader research into AI adoption shows “more value in using the technology to upskill and reskill their employees than reducing costs by eliminating jobs.” But healthcare executives aren’t prioritizing upskilling, addressing staff concerns and reassuring them, and change management in terms of shifting job roles and workforce composition.
Deloitte says healthcare organizations need effective strategies to accelerate adoption of AI while reassuring and supporting clinicians. Those strategies include:
- • Establishing effective governance. This can help establish “key decision makers and strategies, and then empower teams to test, learn and build.” Deloitte suggests the use of center of excellence models “to centralize expertise, helping to ensure that AI applications are developed and deployed with uniform standards for safety and adherence to emerging regulations.”
- • Building consumer trust and engagement. To gain traction and demonstrate value, “organizations should actively engage consumers to understand the most critical pain points and understand what AI solutions they are willing to use.” Focus groups can help assess new products.
- • Gaining workforce buy-in. Organizations need to emphasize casting gen AI as a support, putting more emphasis on workforce literacy and integration of gen AI as an ally, restoring trust and ameliorating workforce shortages.
- • Building scalable solutions. Single gen AI APIs “may face scaling challenges, and no single large language model will likely perform all tasks or use cases.” Organizations need to design for scalability upfront “to ensure that machine learning processes are dependable and efficient.”