AI’s next phase in health care: scale, governance, ROI

By Jay Bhatt, D.O., managing director, the Deloitte Center for Health Solutions, Deloitte Services LP, Bill Fera, MD, principal, and Rob Jacoby, principal, Deloitte Consulting LLP

A few years ago, attendees at the annual ViVE conference in Los Angeles seemed to view artificial intelligence (AI) with a combination of curiosity and caution. Its applications in health care were still largely untested. Fast forward to this year’s event in February, and the shift we observed was unmistakable. AI was everywhere—from keynote presentations to AI-powered solutions on the expo floor, to countless conversations in hallways and at our booth. AI and digital health are no longer viewed solely as areas of innovation. Instead, they’re increasingly recognized as indispensable tools for helping health care organizations stay competitive and financially viable. Still, AI is unlikely to reach its full potential if it’s used to accelerate fragmented or broken workflows and processes.

After piloting AI use cases, many health care executives appear focused on learning to effectively scale it, govern it, and deliver a measurable return on investment (ROI). However, questions remain about when AI will begin to generate meaningful savings. The ROI could be the result of increased productivity, cost reduction, and workforce stabilization. Realizing value will likely require some rethinking and redesigning of workflows. In addition, some of the AI solutions that were promoted at this year’s conference did not appear to address challenges that health care organizations are trying to solve.

A growing number of health care leaders appear to be focusing on integrating AI into day-to-day workflows, moving beyond pilots to pinpoint challenges that AI is best suited to solve. There also seems to be a growing appetite for using AI to improve administrative initiatives, such as revenue-cycle management, documentation summarization, workforce scheduling, care coordination, and prior authorization (see AI could help simplify prior authorization).

A recent Deloitte survey found that many health care executives intend to deploy AI in areas beyond back-office operations. More than 80% of health system executives said they are prioritizing agentic AI for clinical operations and care delivery, in addition to revenue-cycle management. Similarly, 70% of health plans are prioritizing agentic AI for utilization management, prior authorization processes, and claims management (see Health care leans into agentic AI). While generative AI primarily responds to prompts, agentic AI sequences tasks, independently responds to changing conditions, and orchestrates work across platforms to achieve clinical, administrative, and financial results.

Here’s a look at some of the trends we heard and saw at this year’s ViVE conference:

• Operations may be the center of gravity for AI: The early wins for AI are likely to be in operations where it can help to remove costs, improve workflows, and stabilize staffing. The technology is being used to help improve revenue-cycle management, care coordination, workforce scheduling, and prior authorizations. Some organizations are also using AI to improve their call centers.
• Fragmented diagnostics data can add costs, delay care: Using digital technology to seamlessly connect diagnostics data can create a more holistic view of the patient, resulting in more personalized care and earlier interventions. While digitization has become common in imaging, it still hasn’t truly been integrated into other diagnostic procedures. Fragmented platforms can cause clinicians to spend time trying to connect disparate diagnostic data streams (e.g., radiology, pathology, oncology, cardiology) to reconstruct a patient’s history. This can delay clinical decisions and result in what is essentially an operational tax for the health system.
• AI may be most effective once it disappears into clinical workflows: AI should be invisible. While the technology has the potential to reduce the workload on clinicians, staff may push back if they perceive it as an additional step in their work. Several sessions underscored that even the best technologies may falter if they can't be smoothly integrated into clinical and operational systems like electronic health records (EHRs), dashboards, analytics platforms or care workflows. Ambient documentation, for example, could help reduce the time clinical staff spend on administrative tasks and pull insight from conversations that doctors may have missed.
• Devices are becoming more consumer focused: Many of the emerging entrants we saw in the exhibit hall are pursuing consumer-focused devices such as wearables and at-home diagnostic tests. Data quality, governance and interoperability were repeatedly mentioned as prerequisites for wearables and diagnostics to be effective.
• Technology safety must be bolted on: The link between cyber security, patient safety, and medtech adoption was a noticeable undertone at the meeting. As systems adopt intelligent devices and connected platforms, leaders stressed that technology safety must be baked in, not bolted on, especially where connected devices generate or exchange clinical data.

Conclusion
One of the strongest messages we heard at this year’s conference was that value is unlikely to come from layering AI onto fragmented workflows or chasing shiny point solutions. Instead, value is likely to come from redesigning operations and care processes so AI can be governed, scaled, and measured for real productivity, cost, and workforce impact. Lasting change involves a long-term horizon to demonstrate value and build trust. The question for executives is, are your strategies and approaches driving toward scalability and sustainability? And are you driving bold innovations toward better outcomes?

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