Research delves into key overlooked factors that are essential for successful AI integration in healthcare settings
Implementing artificial intelligence-based capabilities in healthcare settings goes beyond just the technology application. A recent analysis by Deloitte has revealed that many healthcare executives may be missing essential considerations that are crucial for the successful integration of AI.
According to Deloitte’s 2024 Health Care Generative AI Outlook, more than 70 percent of healthcare executives are highly focused on traditional data considerations such as data availability, quality, compliance, security, and privacy during AI implementation. However, Deloitte warns that these considerations will soon become table stakes as data concerns are expected to be addressed by initiatives like the National Artificial Intelligence Research Resource Pilot Project.
The analysis also identified several key factors that are being overlooked by healthcare executives, which could hinder the successful integration of generative AI into their workflows. These “blind spots” include:
1. Lack of effective governance: Only 60 percent of executives are prioritizing a data governance model, and 45 percent are focusing on mitigating data biases. Effective oversight of data use is crucial for ensuring the quality of data, mitigating biases, and safeguarding patient privacy.
2. Consumers’ priorities: Executives are less focused on building consumer trust in generative AI, improving data sharing, and educating patients about AI and its risks. Building consumer trust in AI transparency is essential for gaining their confidence.
3. Meeting workforce needs: Healthcare executives are not prioritizing upskilling, addressing staff concerns, and reassurance in terms of shifting job roles and workforce composition. Deloitte suggests that AI can be used to upskill and reskill employees rather than eliminating jobs to reduce costs.
To address these challenges, Deloitte recommends the following strategies for healthcare organizations:
– Establishing effective governance: Creating key decision makers and strategies, empowering teams to test, learn, and build, and using center of excellence models to centralize expertise.
– Building consumer trust and engagement: Actively engaging consumers to understand their pain points and preferences for AI solutions.
– Gaining workforce buy-in: Emphasizing gen AI as a support, focusing on workforce literacy, and integrating gen AI as an ally to restore trust and address workforce shortages.
– Building scalable solutions: Designing for scalability upfront to ensure dependable and efficient machine learning processes.
By addressing these key factors and implementing effective strategies, healthcare organizations can accelerate the adoption of AI while reassuring and supporting clinicians in the integration process.