Healthcare institutions are rapidly adopting AI technologies, yet many are not adequately preparing for the integration of AI as a colleague in the workplace.
Traditionally, the healthcare sector has viewed AI primarily as a tool to support human tasks. For instance, ambient documentation software aids doctors in completing notes, while virtual assistants field patient inquiries. Similarly, prior authorization tools streamline administrative processes. These technologies have been straightforward to understand, aligning with the conventional model where AI assists humans, who maintain responsibility for decision-making and governance.
However, a shift is occurring. Increasingly, healthcare organizations are entrusting AI agents with responsibilities that once necessitated human input. These agents are evolving beyond mere productivity boosters; they are becoming digital team members that need proper governance, oversight, and established operational boundaries. AI is no longer just an assistant; it is now an active participant in workflows.
This transformation marks a significant shift in how healthcare envisions AI's role.
The Transition
Many healthcare organizations are still evaluating AI on a case-by-case basis, asking whether these tools can alleviate documentation burdens, enhance call center performance, or accelerate prior authorizations. While these queries are vital, they underestimate the rapid evolution taking place. Historically, healthcare systems have centered around human actions, leading to governance frameworks and operational processes that cater to human interactions.
As AI agents take on more independent roles, healthcare organizations must adapt to a new operational model, different from anything they’ve managed before.
AI agents follow distinct operational rules. Unlike their human counterparts, these agents work continuously and can scale almost instantaneously. They manage information at machine speed, which may soon lead to a situation where digital agents outnumber human workers within healthcare systems.
With capabilities such as simultaneous task execution and multitasking across a variety of systems, AI agents challenge longstanding assumptions about operational capacities, governance structures, and oversight.
This evolution transcends a mere technological challenge; it also presents a significant workforce management issue.
The Data Challenge
AI agents rely heavily on data to function effectively, whether for clinical documentation or care coordination. As organizations deploy numerous AI agents—potentially hundreds or even thousands—the volume of data accessed and processed will surge. However, the real challenge lies not just in managing increased data but in overseeing the significant operational activity occurring in systems originally designed for human users.
Each AI agent acts as a participant, requesting data, starting workflows, making decisions within set guidelines, and interfacing with enterprise applications. This influx creates new demands for governance, security, identity management, and data integrity across the organization.
Leaders in healthcare often ponder how many AI tools they can deploy. A more pertinent question is: How many digital agents can your organization effectively supervise? Organizations that start considering this question now will be in a better position than those still focused on deploying AI in isolated use cases.
The Governance Oversight
Healthcare possesses established methods to govern human users—employees have credentials, defined permissions, and training, and their actions can be audited. Yet, AI systems introduce a different set of governance considerations:
- What information should each agent access? - Who is accountable for the outputs of an AI agent? - What processes are in place for granting or revoking permissions for agents? - How can an organization monitor thousands of AI workers without overwhelming existing systems?
These questions pertain to governance but also represent broader leadership challenges. While models exist for managing human employees, healthcare organizations have yet to develop robust frameworks to govern AI agents that can make decisions at machine speeds.
Recognizing this gap allows organizations to begin formulating the policies, accountability guidelines, and operational practices necessary for appropriately managing their AI workforce.
As organizations transition from pilot programs to widespread adoption, these issues will become increasingly critical.
The Leadership Imperative
Those organizations that successfully navigate this evolution will not necessarily be the ones with the most extensive AI deployments. They will be the ones that understand AI agents as more than just technological implementations, viewing them as integral participants in daily operations.
Instead of fearing these AI colleagues, healthcare must acknowledge that leading them requires a different mindset than simply deploying new technologies. Installing technology is manageable, but leading a workforce demands strategic vision and guidance.
Sagnik Bhattacharya, CEO of Rhapsody, emphasizes the importance of building the necessary infrastructure to support AI and connected care within healthcare organizations. This evolution is an opportunity for leaders to rethink governance and operational models in line with the changing landscape.
