In 2012, MD Anderson Cancer Center began a five-year collaboration with IBM to bring Watson's computing power into oncology care. The project ended without treating a single patient and cost the university roughly $62 million, according to a university audit that documented procurement problems, cost overruns and delays. A subsequent report in the Journal of the National Cancer Institute described the fundamental challenge: Watson struggled to interpret physician notes, medical shorthand and electronic records that held critical information.
The failure carried a clear operational lesson. Even impressive intelligence creates little value when it cannot understand the information around a process, participate in the work and hand the next action to the right system or person.
The same operating challenge has returned with agentic AI
Healthcare processes cross clinical information, coverage rules, providers, payers, care teams and multiple operating systems. Traditional interfaces can move data between systems without supplying all the business context an agent needs to operate across them. That is where the architecture problem begins.
Prior authorization illustrates the issue. From the outside, a request goes in and a decision comes back. Inside the enterprise, the process depends on eligibility, clinical documentation, coverage requirements, provider information and a review that may require professional judgment. Each component can function well while the overall process remains slow because the difficult work sits between systems.
Beginning in 2027, impacted payers face new CMS API requirements involving provider access, payer-to-payer exchange and prior authorization. The CMS Interoperability and Prior Authorization Final Rule requires impacted payers to implement certain FHIR APIs, with compliance dates generally starting January 1, 2027. The Prior Authorization API must support requests and responses and communicate whether a request is approved, denied with a specific reason or requires more information. Those connections create a foundation for a more integrated operating environment, though the information still has to enter a process that understands its context and knows where the work should go next.
Architecture should follow the work
For a healthcare CIO, that means understanding one complete process end to end. Which information does it depend on, where does it originate, who touches the work, which activities are repetitive and where does judgment enter? The architecture has to account for the system or person responsible for the next action. These operating questions expose the difference between automating an isolated task and changing how a complete workflow functions.
I organize that healthcare architecture around four layers: experience, intelligence, operations and secure data. Experience is where providers, payers, patients and care teams interact with the enterprise. Intelligence interprets information and can help understand what is happening or recommend an action. Operations carries that action through the organization by executing, routing, delegating or escalating the work. Secure data provides the records, rules and trusted information those functions depend on. Their value comes from how the layers connect around the process being performed.
In prior authorization, clinical and coverage information has to be available when needed. Intelligence may help determine whether the information is complete and what it means in context. Operations then moves the work according to the result. Missing information can route the request back for completion, an automated action may allow the process to continue and a case requiring clinical judgment has to reach the appropriate professional with enough context to make that judgment.
Human involvement should be designed into the architecture from the beginning. Healthcare includes administrative work that automation can handle and decisions where professional responsibility stays explicit. Security, privacy, governance, compliance and monitoring belong in the same operating design. As agents participate in more of the work, the organization should be able to understand which information was used, what action occurred and where responsibility sits afterward. This is where people-in-the-loop and agentic AI-in-the-loop become a useful operating model. The balance can vary across functions because the work itself varies.
Measure what changed
The measure I use for automation is what I call flex capacity. If one employee can handle 100 transactions and automation allows that role or team to handle 250, the organization has created additional capacity without adding people at the same rate. I have seen this in practice: work that previously required roughly 500 people has been handled by approximately 100 to 150 people as automation changed the process. Coding, testing, data entry and data analysis are examples of activities where technology can reduce repetitive effort.
I would apply that thinking to healthcare workflows by establishing the operating baseline before adding agents. That baseline should show how many people touch the process, the volume the team can handle, the time spent moving information and the points where people intervene because the system cannot determine the next step. After automation, the same operating measures show whether capacity actually changed. A large agent count provides much less information about the health of the operation.
Greater volume from the same organization is one useful result. Reduced administrative work is another, particularly when clinicians and experienced professionals gain more time for work that requires their judgment. The architecture has to support those outcomes across systems. An intelligent workflow may examine information, predict what could happen and recommend an action. Operations then carries the work to the appropriate destination. The path can involve software, a person or several systems depending on the process.
The 2027 CMS requirements give healthcare organizations a practical reason to improve important connections now. I would use that work to examine one complete operating process - from the information it depends on through the role intelligence can play and the points where professional judgment remains necessary. For professionals working with AI for Healthcare, understanding these process connections matters more than counting deployed agents. The earlier generation of healthcare AI taught how closely intelligence depends on operating architecture. Today's AI is considerably more capable, and healthcare interoperability continues to improve. CIOs have an opportunity to bring those developments together around the work itself.
Why this matters for healthcare professionals
The next advance in healthcare AI will be defined by how effectively the enterprise can put intelligence to work - not by the number of agents deployed. For teams handling prior authorization, claims and administrative workflows, the practical question is whether automation changes operating capacity. If people continue moving information manually and deciding where the work goes next, the organization has gained another intelligent capability without gaining much operating capacity. The architecture has to connect intelligence, operations and data around the complete process, with human judgment designed in from the start.
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