How athenaOne® unifies AI across the clinical workflow

Clinical AI creates more value when it works where clinicians work

Healthcare organizations are moving beyond the question of whether to adopt clinical AI and toward a more complex enterprise question: How do they deploy AI without adding another disconnected application, workflow, or data silo?

From reviewing a chart and documenting a visit to follow-up work, the many touch points that comprise a patient encounter can require multiple searches, application switches, and transfers of information between locations.

Each transition adds time and complexity to the clinician’s day and creates the risk of incomplete or missing information. A tool can perform its own task well while still creating extra work when information moves to the next task. For technology leaders evaluating clinical AI, the question is: what should they examine beyond the quality of the individual AI output?

The answer lies in the continuity of the overall workflow.

athenaOne builds AI into existing clinical workflows to help reduce the need to switch systems, rebuild context, or manage additional applications. Evaluating that approach means looking at what happens both within a task and when the work moves to the next person or step.

What an AI-native clinical workflow looks like in athenaOne

“AI-native” means AI is built into the places where clinicians review information and complete work. The tools use information already in the platform, including relevant patient history and recent changes, to support the task at hand.

There are two things to examine:

• Where is the AI available?
• What information can it use?

An AI assistant inside the patient chart may reduce the need to open a separate application. Its ability to answer a question using relevant chart information can also reduce the work of finding and assembling that information.

The following examples show how those connections can support clinical work and what enterprise leaders should look for when evaluating them.

Before the visit: Bring the relevant chart context forward

The EHR may hold a record of the patient’s health history over time, but a clinician will rarely have the time to comb through that information and understand the salient factors such as what’s changed since the last visit.

Sage™, athenaOne's AI assistant, helps bridge this knowledge gap by searching the patient chart to answer questions and generate relevant summaries.

For example, the clinician can ask Sage, “Have any of this patient’s medications changed since the last visit?”

Instead of searching through the chart to uncover new medications or altered doses, the clinician gets a summary from Sage, with the source information displayed below it. This gives the clinician a starting point for checking the answer against the chart before using it to prepare for the visit.

During the encounter: Turn conversation into actionable documentation

athenaOne’s AI-native encounter brings AI support into the clinical visit and documentation experience. Within that experience, athenaAmbient™, athenahealth’s ambient model, captures the conversation and creates a draft note for clinician review. Because the draft is created within athenaOne, the clinician can review, refine, and sign it without first transferring a note from a separate scribe application.

athenaAmbient can also suggest potential diagnoses and orders based on the conversation for clinicians to review. These suggestions give clinicians additional information to evaluate; clinicians remain responsible for deciding what belongs in the record and which actions are appropriate.

After the encounter: Keep information connected to the next task

Documentation created within athenaOne remains connected to the patient record and billing workflow, without becoming a separate document that staff must transfer.

That connection matters after the visit. A practice can’t start a claim until the encounter is closed, so helping clinicians review, finalize, and close encounters can support billing readiness.

Among early adopters, athenahealth reports that AI-native athenaOne has increased same-day chart completion — a measure of whether charts are completed on the day of the encounter – by up to 30%.¹

That gives leaders an outcome to examine alongside the workflow: are clinicians completing documentation sooner, or is work still accumulating after visits?

Across the clinical inbox: Reduce repetitive work

Documents continue to come in between visits, including consultation notes and test results, and pending tasks need attention. Advertisements and spam can clog inboxes and fax machines, taking staff time and attention away from that work.

With athenaOne’s Clinical Inbox, AI automatically redirects advertisement and spam faxes out of staff queues, reducing the routine sorting staff would otherwise need to do. Clinical documents still require attention, but staff have fewer irrelevant items to work through.

Across patient populations: Surface opportunities for follow-up

Insights on a population level can help practices identify patients who may need attention, such as those with missing or overdue recommended care.

athenaOne’s Data Explorer connects population-level reporting with access to individual patient charts. In its September 2026 announcement, athenahealth described population-level reporting, then being tested with users, that helps teams identify care gaps and review or document care in the relevant chart. Embedded AI explains why a patient has or has not met a quality measure.

For example, a quality team reviewing patients who have not met a measure can investigate the reason and open the relevant chart. The team can then determine whether the patient needs follow-up care or whether care already delivered is accurately reflected in the record.

Enterprise leaders should examine how easily users can move from a population finding to the patient information needed for follow-up. They should also confirm which capabilities are generally available and which remain in testing.

Why workflow continuity matters to enterprise healthcare leaders

The clinical examples reveal questions that a feature list alone can’t answer: what information is available, what work remains manual, and what happens after an AI output is generated?

Enterprise technology leaders can use the following to evaluate their clinical workflow:

• Integration: Where does information have to move between systems, and who maintains those connections? What must users transfer, reenter, or reconcile?
• Context: Which patient information is available when clinicians begin the next task, and what must they search for again?
• Oversight: Who can use each capability, review its output, and correct errors? How do users inspect supporting information and approve the result?
• Adoption: How much additional navigation or training does the complete workflow require?
• Expansion: What changes when the organization extends use of the technology across practices or specialties? What additional configuration, training, or support is required?

During a demonstration, ask to follow a representative task through to completion, including review and the next handoff. Seeing an AI-generated answer or draft is only part of the evaluation. Following the work further shows whether the capability reduces effort overall or shifts it to another user or step.

From individual AI tools to an AI-native platform

The value of clinical AI isn't the number of AI features available but whether intelligence, data, permissions, and clinical context operate as part of one platform and workflow experience.

For enterprise leaders, the practical test is whether clinicians and staff can complete connected tasks with less searching, transferring, and repeated work. Ask to see those connections in action — from the information the AI uses to the review and follow-up its output requires.

Request a custom athenaOne demo to see how AI-native capabilities work across the clinical workflow.