What does a truly AI-assisted clinical workflow look like?

Why clinicians need more than AI-powered documentation

For many clinicians, the workday starts long before the first patient arrives. They may need to review charts, locate outside records and piece together lab results, imaging reports and specialist notes before the day’s first appointment. Once clinic begins, documentation, coding, inbox management and follow-up tasks continue long after the last patient leaves, often spilling into evenings and weekends.

According to athenahealth's 2026 Physician Sentiment Survey, conducted by The Harris Poll, 41 percent of physicians report experiencing burnout weekly, with administrative work remaining one of the biggest contributors.¹ At the same time, healthcare organizations are increasingly looking to AI for relief. A 2024 MGMA survey found that 42 percent of medical group leaders already use ambient AI, while 80 percent are likely to implement or upgrade an ambient AI solution within the next year.²

But adopting AI doesn't automatically create a better workflow. If clinicians have to switch between multiple applications or manually move information from one system to another, technology risks adding complexity instead of reducing it. 

A truly AI-assisted clinical workflow supports clinicians throughout the day, helping them prepare for appointments, document visits, manage incoming information and complete follow-up work within a single, connected workflow. The goal isn't to replace clinical judgment but to reduce repetitive administrative work so clinicians can spend more time focused on patient care.

Get the information that matters

Preparing for an appointment often means searching through dozens of pages of documentation. Hospital discharge summaries, specialist letters, imaging reports, laboratory results and previous encounter notes may all contain information needed for today's visit, but finding it takes time. 

That challenge becomes even greater when patients receive care across multiple organizations. Clinicians may need to review external records alongside the clinical data already available in their organization’s EHR to understand what has changed since the patient's last visit.

An AI-assisted workflow can reduce that manual effort by surfacing recent diagnoses, medications, procedures, test results and significant clinical events from both internal and external records. How useful AI is depends not only on the models behind it but on the breadth, quality and continuity of the clinical information it can help organize. As care becomes increasingly distributed across specialists, hospitals, urgent care centers and virtual settings, AI can be more useful when it can help clinicians synthesize information across those encounters rather than treating each visit as an isolated event. Rather than replacing clinical judgment, AI helps clinicians quickly sift through large volumes of patient data, surfacing the right information at the right time to support more informed decisions throughout the patient journey.    

The value of AI extends beyond efficiency. By surfacing relevant information from prior encounters, outside records, medications, procedures and recent clinical events, it can help create a more complete, longitudinal view of the patient. That can improve continuity of care while allowing clinicians to spend less time reconstructing a patient's history and more time applying their expertise to the decisions that matter most.

Keep the focus on the patient—not the keyboard 

Clinicians often balance meaningful conversation with accurate documentation, yet typing throughout the encounter can interrupt eye contact, affect rapport and make visits feel less personal.

Ambient AI documentation is changing that dynamic. By listening to the conversation and generating a structured draft note, ambient documentation allows clinicians to engage more naturally with patients while reducing the amount of manual note-taking required during the visit. Clinicians remain responsible for reviewing, editing and approving every note, while the amount of manual effort required to create the initial draft is reduced.

Customer experiences illustrate the kinds of workflow improvements ambient documentation can support, although results vary by organization and clinician. At OrthoLoneStar, clinicians using Ambient Notes reduced after-hours documentation time by 36 percent, increased same-day encounter close rates by 12 percent and saved an average of 3.6 minutes per patient encounter—more than an hour each day for a clinician seeing 20 patients. At Springfield Clinic, one physician increased same-day note completion from fewer than 10 percent of visits to nearly every encounter while creating enough additional patient capacity to offset the cost of the technology by seeing just two or three more patients each day.

Beyond improving efficiency, reducing documentation during the visit allows clinicians to give patients more attention while making same-day chart completion more achievable. In an athenahealth customer survey, 86 percent of clinicians said Ambient Notes helped them focus more fully on patients³, while 90 percent reported that it helped reduce their feelings of burnout.⁴

Turn documentation into downstream impact 

Completing the note is only one step in closing the loop on patient care. Orders still need to be placed, diagnoses documented accurately, coding finalized and follow-up instructions prepared. Traditionally, much of this work requires clinicians to revisit information they've already documented during the encounter.

An AI-assisted workflow reduces that duplication by using information captured during the visit to help support documentation and coding. AI can also identify and flag potential diagnosis gaps based on information already in the patient's record, surfacing them to clinicians as they determine whether additional documentation or follow-up is appropriate. More complete documentation supports quality reporting, risk adjustment and reimbursement, particularly for practices participating in value-based care models. It can also help surface chronic or unresolved conditions for clinician review and follow-up.

Reduce the burden of incoming documents 

For many clinicians, the workday doesn't end when the final patient leaves. New laboratory results, imaging reports, hospital discharge summaries, referral letters and patient messages continue arriving throughout the workday, creating an administrative backlog that competes with direct patient care.

AI can automatically classify incoming documents, extract key clinical information and present concise summaries, allowing staff and clinicians to identify what requires action without reviewing every document in full.

The efficiency gains can be significant. According to athenahealth data, practices using Document Services AI workflows have seen a 91% reduction in document processing time.⁵ Although results will vary between organizations, reducing repetitive administrative work allows staff to spend more time supporting patients and less time managing paperwork.

Because information is reviewed and routed more efficiently, important clinical updates are less likely to be delayed, helping practices reduce administrative bottlenecks.

Measuring success: Focus on outcomes, not features

Practices can evaluate the impact of an AI-assisted clinical workflow by tracking measures such as: 

• Less time spent documenting visits 
• Higher rates of same-day note completion 
• Reduced after-hours EHR work 
• Faster processing of incoming clinical documents 
• More complete documentation for quality reporting and reimbursement 
• Changes in clinician satisfaction and perceived administrative burden 
• Changes in appointment capacity and clinician workload

Together, these capabilities illustrate a broader shift. AI in healthcare is becoming less about automating individual tasks and more about reducing friction across the clinical workflow, helping clinicians move more efficiently from one stage of care to the next.

For practice leaders, improvements in documentation and administrative efficiency may also affect measures such as patient capacity, billing workflow efficiency and after-hours clinician work.

Connecting AI across the clinical day 

The future of AI-assisted clinical workflows isn't defined by a single capability. It's defined by how seamlessly clinical information, insights and administrative tasks move from one stage of care to the next. As these tools evolve, their usefulness will increasingly depend on one thing. How well they connect information and reduce repetitive work across the clinical day, while keeping clinicians in control of clinical decisions.

Learn how athenahealth is helping practices reduce administrative burden with AI-powered clinical workflows by exploring our AI Resource Center.