43% of healthcare provider organizations report piloting or testing agentic AI, yet only 3% have deployed agents in live clinical workflows, according to research published in the January 2026 issue of NEJM AI by Microsoft and The Health Management Academy.
The gap between those two numbers is the actual state of healthcare AI right now. Everyone is testing something. Almost nobody has let an agent finish a workflow end to end without a person re-checking every step behind it.
This guide is part of Tecla's Agentic AI Use Cases series, covering agentic AI for healthcare specifically: the intake workflows, documentation patterns, and claims processes that have moved past the pilot stage.
What Is Agentic AI?
Agentic AI refers to goal-oriented systems that plan, execute, and adapt multi-step tasks with minimal human oversight, distinct from traditional automation or generative AI built to answer a single prompt.
Three capabilities define it: autonomy, deciding what actions to take and carrying them out; adaptability, adjusting the plan as new information arrives; and coordination, working across tools and systems to finish what no single tool could handle alone.
In a hospital, that difference shows up immediately. A rules engine flags a chart missing a required field.
An agentic system takes that chart, pulls context from registration, insurance eligibility, and prior visit history, and either resolves the gap itself or hands a staff member a fully investigated record instead of a red flag.
How Agentic Systems Work in Healthcare
Healthcare has run on automation for over a decade, first through rules-based clinical decision support, then through RPA bots handling scheduling, eligibility checks, and claims entry.
RPA automates a predefined, hand-coded sequence: if X, do Y. It is fast and auditable, but it breaks the moment a payer changes a form field or a chart doesn't match the expected template, and every new pattern means an engineer rewriting the workflow.
Agentic AI operates differently: it reasons instead of just executing a script. It reads the chart, claim, or alert, decides what context matters, and adjusts its handling as it goes, the way a skilled coordinator or biller would.
The trade-off is real and worth naming directly: RPA is more predictable and cheaper to audit; agentic systems are more adaptive and more expensive to run per case.
Most production healthcare deployments in 2026 are not one or the other.
They are structured workflows (routing, matching) for the repeatable 80% of cases, with agentic reasoning reserved for triage and investigation, and a human gate in front of anything that touches a diagnosis, a denial, or a patient's record directly.
Perceive chart, claim, alert
›
Retrieve EHR, payer, registry
›
Reason correlate, score
↓
Human gate
›
Act route, file, flag
›
Verify
Verify feeds back into Perceive, a continuous feedback loop, for the next cycle
Agentic AI Use Cases in Healthcare
Healthcare organizations don't adopt agentic AI as one system. They adopt it department by department, starting wherever the volume problem is worst.
Nine of those starting points are documented below, each at working depth. The architecture above stays abstract until it's tied to an actual trigger and an actual system.
What follows covers the agentic AI applications in healthcare that have moved past a slide deck and into daily use, not just what's technically possible.
Patient Intake and Records Digitization
Patient intake still means the same demographic and insurance details get keyed in more than once: once at scheduling, once at check-in, and again by whoever untangles the eligibility mismatch that surfaces two weeks later.
The workflow
What it does: classifies every incoming registration, validates
it against the payer's eligibility system, and matches it to existing records instead of leaving a
mismatch for a staff member to untangle later.
1
Patient registers via
portal, phone, or in-person intake
2
Demographic and
insurance data validated against the payer's eligibility system
3
Record matched
against prior visit history to avoid a duplicate patient file
4
Missing or
inconsistent fields flagged and resolved against the source document
5
Clean records routed
to scheduling; unresolved discrepancies routed to staff
6
Human gate: registration staff signs off on anything the system can't
resolve
The stack: a patient intake platform (Notable Health or
Qure4u), the EHR of record (Epic or Oracle Health), and an eligibility verification API (Availity or
Waystar).
Why it works: the volume problem is real, and intake
matching is pattern-matching at scale, exactly what routing does well.
Production concern: a mismatch resolved by creating a
new patient record instead of matching to the existing one produces a duplicate that can follow
a patient for years and complicate every visit after.
Clinical Documentation and Ambient Scribing
A Yale-led study published in JAMA Network Open found that burnout among clinicians using an ambient AI scribe dropped from 51.9% to 38.8% after 30 days, across 263 clinicians in six US health systems.
The workflow
What it does: listens to a patient visit with consent, drafts a
structured clinical note and matching orders, and hands the clinician a note to review instead of
leaving documentation for after hours.
1
Clinician obtains
patient consent and begins the visit with recording on
2
Conversation
transcribed and structured into a draft clinical note in real time
3
Draft matched
against the visit type's documentation and billing requirements
4
Orders and
follow-ups extracted and routed into the EHR workflow
5
Human gate: clinician reviews, edits, and signs the note before it
becomes part of the record
The stack: an ambient documentation platform (Abridge,
Nuance DAX Copilot, or Suki), integrated with the EHR of record (Epic or Oracle Health).
Why it works: converting a conversation into structured text is
exactly the kind of drafting task a language model handles well, and the clinician's sign-off
catches what the draft gets wrong.
Production concern: a fabricated detail in an otherwise
plausible note is easy to miss on a quick read, which is why review has to mean reading the
note, not just clicking sign.
Prior Authorization
The CMS Interoperability and Prior Authorization Final Rule requires health plans to decide urgent requests within 72 hours and standard requests within 7 days, effective January 1, 2026.
Today, only 12% of clinicians and 7% of administrators consistently see decisions in that window.
The workflow
What it does: pulls the clinical documentation behind a prior
authorization request, checks it against the payer's specific medical necessity criteria, and
clears the request or flags the exact gap instead of a generic denial.
1
Provider order
triggers a prior authorization request
2
Relevant clinical
documentation pulled from the chart automatically
3
Documentation
checked against the payer's specific medical necessity criteria
4
Requests meeting
criteria auto-approved within policy
5
Requests missing
criteria flagged with the specific gap for the provider to address
6
Human gate: reviewer signs off on anything auto-decisioning can't
resolve
The stack: an AI-native prior authorization platform (Cohere
Health's Cohere Unify), integrated with the EHR and the payer's utilization management
system.
Why it works: a scoring and matching task with a defensible
rationale attached, one of the higher-stakes but still well-bounded places for agentic
decisioning.
Production concern: a denial without a documented,
specific reason is exactly the kind of decision that turns into an appeal, a complaint, or a
regulatory inquiry.
Medical Coding and Claims Scrubbing
AI-based denial prediction is the second-highest-ROI AI investment health systems report, with 43% of implementers seeing 3x or greater returns, according to Eliciting Insights' February 2026 survey of 120 health systems.
The workflow
What it does: suggests codes from encounter documentation,
checks the claim against payer-specific edits before submission, and flags the claims likely to be
denied instead of finding out after the fact.
1
Encounter
documentation reviewed and codes suggested against payer-specific rules
2
Claim checked
against medical necessity and bundling edits before submission
3
High-risk claims
flagged and prioritized for review
4
Clean claims
submitted automatically within policy
5
Human gate: coder reviews flagged claims before
submission
The stack: a computer-assisted coding platform (Nym
Health, Fathom, or 3M/Solventum), integrated with the billing system and payer
clearinghouse.
Why it works: most claims genuinely follow a predictable
pattern, so routing frees coder time for the ones that actually need judgment.
Production concern: a coding pattern that consistently
leans toward higher-paying codes without clear documentation support is a False Claims Act
exposure, not just a billing error.
Clinical Literature and Evidence Synthesis
About 40% of US physicians, more than 430,000 people, now consult OpenEvidence for citation-backed answers to clinical questions, according to the company's own reporting.
The workflow
What it does: takes a clinical question, retrieves relevant
peer-reviewed literature and guidelines, and synthesizes a direct answer with inline citations
instead of a list of abstracts to read through.
1
Clinical question
submitted, often mid-visit or during case review
2
Peer-reviewed
literature and guidelines retrieved across indexed journals
3
Findings synthesized
into a direct answer with inline citations to source studies
4
Related or conflicting
evidence surfaced for context
5
Human gate: clinician verifies the citation and applies clinical judgment
before acting
The stack: a citation-grounded clinical retrieval platform
(OpenEvidence or a comparable tool), integrated with the EHR for patient context where
available.
Why it works: a research task with a verifiable citation trail,
the shape that makes retrieval-heavy agentic systems work well.
Production concern: a published systematic review found
accuracy varies in complex clinical scenarios even when the citations themselves are accurate,
which is exactly why the clinician's judgment stays the deciding factor.
Care Coordination and Care Management
A patient discharged after a cardiac event might need a cardiologist follow-up, a pharmacy refill, a home health referral, and a check-in call within a specific window, coordinated across systems that don't talk to each other by default.
The workflow
What it does: tracks a patient's care plan across referrals and
follow-ups, initiates the next step automatically, and flags what's fallen through instead of letting
a care gap go unnoticed.
1
Discharge or care
plan trigger detected
2
Referrals, follow-ups,
and care gaps identified against the care plan
3
Scheduling and
referral requests initiated across connected systems
4
Patient outreach
drafted for confirmation or reminder
5
Missed
appointments or unclosed gaps flagged for care manager follow-up
6
Human gate: care manager reviews high-risk patients before the plan
finalizes
The stack: a care management platform (such as Cohere
Health's care management suite), integrated with the EHR and the referral network.
Why it works: it correlates signals that usually sit in separate
systems, closing exactly the kind of gap a single-system view leaves open.
Production concern: a referral that bounces silently, say,
a specialist who doesn't accept the patient's plan, has to surface as a flag, not disappear into a
completed task.
Claims Denial Management and Appeals
Denial prediction and appeals sit on the same continuum as the coding workflow above: one tries to prevent the denial, the other handles it once it's already happened.
The workflow
What it does: parses a denial reason code, retrieves the
clinical documentation needed to contest it, and drafts an appeal citing the specific medical
necessity criteria instead of a generic template letter.
1
Denial received and
reason code parsed
2
Supporting clinical
documentation retrieved to build the appeal case
3
Appeal letter drafted
citing the specific medical necessity criteria
4
Similar historical
denials and outcomes checked for precedent
5
Human gate: billing staff reviews and submits the appeal
The stack: an AI-assisted appeals platform (Cohere Health's
appeals management product or a comparable revenue cycle tool), integrated with the claims
system and EHR.
Why it works: most of the appeal's strength comes from citing
the right documentation quickly, exactly what retrieval does well under a deadline.
Production concern: appeal deadlines are strict, often 30
to 180 days depending on the payer, and a missed deadline forfeits the appeal regardless of its
merit.
Agent Identity and Authentication for Healthcare Systems
HHS finalized AI transparency requirements for certified health IT in 2024, with a compliance date of January 1, 2026. A proposed follow-up rule, HTI-5, would roll back several of those same disclosure requirements before they take full effect.
The workflow
What it does: verifies and governs AI agents as first-class
non-human identities, since a valid credential plus authorized access no longer guarantees a
safe outcome once the requester is an autonomous agent touching protected health
information.
1
Agent registered as
a distinct identity with an owner and purpose
2
Request routed
through an identity gateway before touching PHI or clinical systems
3
Agent's permission
for the specific action verified against its authorized scope
4
Human gate: action allowed, blocked, or escalated based on
policy
5
Full action trail
logged, distinguishing agent from human activity
The stack: an identity platform layered onto the EHR's
existing access controls, with a business associate agreement covering the agent vendor's
handling of PHI.
Why it works: enforcement happens at the action level, not
just at login, the one control traditional identity systems were never built to provide.
Production concern: the federal transparency framework
for clinical AI is itself unsettled, which means an organization's own governance has to cover
ground the regulation hasn't finished deciding.
Radiology and Imaging Triage
A multi-condition CT triage model from Aidoc, cleared by the FDA in January 2026, reported a mean sensitivity of 97% and mean specificity of 98% across 14 conditions in its pivotal study.
A separate multicenter trial of Viz.ai's stroke detection algorithm, now deployed across more than 1,700 hospitals, found it cut diagnosis time by 44%, reduced time to treatment by 31 minutes, and reduced 90-day disability by 40%.
The workflow
What it does: analyzes an imaging study for time-critical
findings within seconds of acquisition, alerts the on-call specialist directly, and reprioritizes the
radiologist's worklist instead of leaving the study in a standard queue.
1
Imaging study
completed and sent to PACS
2
AI analyzes the
study for time-critical findings within seconds
3
Positive findings
trigger a real-time alert to the on-call specialist
4
Worklist reprioritized
so flagged studies move to the front of the queue
5
Human gate: radiologist reads and signs the final report
The stack: an FDA-cleared imaging triage platform (Aidoc or
Viz.ai), integrated with PACS and the hospital's on-call notification system.
Why it works: triage and prioritization are systematic tasks
suited to speed, while the diagnostic read and the report itself stay with the radiologist, exactly
where the accountability belongs.
Production concern: real-world specificity has been
shown to drop by as much as 24% outside the institution a model was trained in, which is why
FDA clearance alone isn't a substitute for local validation.
Implementation: Guardrails and Governance
Healthcare organizations are moving faster than governance can keep up with. The Microsoft and Health Management Academy research cited earlier found 60% of respondents cite reskilling and upskilling as a top challenge as agentic tools expand.
The same research found 77% expect agents to improve backend productivity, but far fewer have the workforce readiness, governance, and data infrastructure in place to support that at scale.
That gap is closed with the same guardrail layers that apply to any agentic system, made specific to healthcare.
| Layer |
What it does |
Healthcare-specific example |
| System prompt |
Sets the non-negotiables up front |
"Never finalize a denial or a diagnosis without a documented, reviewable reason" |
| Input filters |
Block or sanitize out-of-scope requests |
Treat patient-submitted forms and faxed records as untrusted content |
| Tool-call gatekeepers |
Cap what actions an agent can take |
Drafting and triage allowed; anything touching a diagnosis or denial needs a human |
| Output checks |
Scan before the action executes |
Block any output that doesn't carry a documented, traceable rationale |
| Human-in-the-loop |
Requires approval for high-impact actions |
A clinician signs every note; a reviewer signs every denial and appeal |
One risk is specific to this stack and worth naming directly: the documents an agent reads are also an attack surface.
A patient intake form or a faxed referral can carry embedded text designed to look like an instruction to the system reading it, the same class of prompt injection risk documented across other agentic use cases.
Every uploaded document should be treated as untrusted content, never as a set of commands to follow.
The Team Behind Production Agentic AI
Most healthcare AI programs stall on integration, not ambition. Connecting to an EHR customized over fifteen years is harder than building the model that will eventually plug into it.
Tecla's Agentic AI services design, build, and operate these workflows directly, the same intake, documentation, and claims systems above, running in your stack with the evals and guardrails production requires.
Or bring the expertise in-house: AI engineers who've worked on live healthcare systems, past the demo stage.
Tecla runs a network of senior engineers across the US and Latin America, built over more than a decade, with a top 3% acceptance rate and first candidates in 3 to 5 business days.
No company adopts all of this at once. Most teams start with whichever workflow addresses the most pressing problem.
Get free agentic AI audit →FAQ
What is an example of agentic AI in healthcare?
A concrete example is an ambient documentation agent: it listens to a patient visit with consent, drafts a structured clinical note and matching orders, and hands the clinician a note to review and sign, instead of leaving documentation for after hours.
How is agentic AI different from clinical decision support?
Traditional clinical decision support fires a fixed alert when a rule is met, like a drug interaction check. Agentic AI reasons across a patient's fuller context, gathers what a person would otherwise pull from several systems, and adapts its handling case by case rather than applying the same rule every time.
Can agentic AI replace clinicians or coders?
No. Production deployments use agents to absorb documentation, intake, and claims volume, while clinicians and coders supervise outcomes, handle ambiguous cases, and provide the sign-off any diagnosis, denial, or clinical record still requires.
How does agentic AI apply to claims and prior authorization?
The same shape applies across the revenue cycle. Prior authorization and claims scrubbing use routing for the repeatable requests and a human gate before anything auto-denies or auto-approves outside policy, the same pattern used in intake and documentation.
What are the risks of agentic AI in healthcare?
Unresolved AI transparency requirements for clinical algorithms, prompt injection through patient-submitted documents, and unclear accountability when an agent's output feeds a clinical or billing decision. Human sign-off on anything touching a diagnosis, denial, or record stays the primary control.
How do healthcare organizations start with agentic AI?
Start with the highest-volume, lowest-risk workflow: intake and records digitization, tied directly to administrative burden. It carries no direct clinical risk and a clear baseline to measure against, before expanding into documentation, claims, and care coordination.
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