Could automation with AI be a solution to rising costs and the need for greater productivity in healthcare?
This was a major theme in a discussion at Becker’s 14th Annual Meeting, led by Randy Bush, principal, Deloitte Consulting LLP, and Jay Bhatt, DO, managing director, Deloitte Health Equity Institute and the Deloitte Center for Health Solutions. They also discussed the challenges associated with successful AI implementation.
Five key takeaways were:
1. Establishing effective governance is crucial for successful AI implementation. Creating the right guardrails upfront and monitoring them over time will determine how effective an AI strategy is. “Organizations must empower teams to develop and deploy AI applications with uniform standards for safety, adherence to emerging regulations and a risk-managed framework,” Mr. Bush said.
2. The importance of change management can’t be overlooked. The moment that organizations embark on an AI journey, they need to think about change management. “You can build a robust solution, but if you don’t know how your staff will use it, it will fail,” one participant said.
3. Building consumer trust and engagement is essential in AI adoption. Research has found that consumers want to know, for example, whether care teams are using Generative AI for care delivery, documentation or prior authorizations. “What matters most to consumers is important. We must understand their needs and preferences with regard to adoption, transparency and collaboration,” Dr. Bhatt said.
4. Workforce upskilling is necessary for AI fluency and successful implementation. Organizations should create learning and development programs to help employees build the skills that are essential for Generative AI, like prompt engineering. “Gaining workforce buy-in and fostering AI fluency is really important,” Dr. Bhatt said. "Positioning AI as a workforce ally can restore trust and address some of the challenges that may emerge in your organizations."
5. Designing AI solutions with scalability in mind is a must. Organizations can use AI to influence multiple levers, such as increasing revenue, reducing costs or improving customer satisfaction. “No single large language model can likely perform all tasks or use cases,” Mr. Bush said. Technology precursors to AI include data modernization and migration to the cloud.
In the near future, many believe that AI will revolutionize healthcare, making tasks easy, efficient and better for everyone.