85% Faster Submissions, 8+ FTEs Saved | Prior Authorization Automation
Learn how agentic AI freed 8+ FTEs for a leading U.S. health-tech platform.
Project Scope
Not so long ago, a fully comprehensive pain management practice network contacted us to solve one of their major bottlenecks - prior authorizations.
While they operate in over 15 clinics across two states, their RCM team needs to ensure timely reimbursements and smooth administrative workflows.
After all, pain management is a high-volume & high-reimbursements, as well as high-repetition & fast-burnout field.
That’s why they decided to invest in RCM automation.
Challenge | Volume & Timing
In this case, weekly volumes were rising faster than staffing capacity, and payer rules were shifting month-to-month, creating inconsistent turnaround times and unpredictable AR impact.
Each request required navigating multiple portals, reconciling conflicting requirements, and repeating data entry across disconnected systems.
Each week, their RCM team was losing hours to thousands of repeatable authorization requests across commercial payers like Medicare Advantage plans, and Medicaid MCOs.
This created natural variability in turnaround times and made operational planning increasingly difficult. High volume combined with payer diversity meant that even small inconsistencies slowed the overall cycle and created rising administrative load.
On the top of that, our RCM workshop excavated a few additional improvement fields including i.e. denials management automation.
As the company continued to scale, manual throughput could no longer keep pace with demand, prompting a shift toward automation to stabilize processing speed and ensure consistent output across all payers.
Every missing field meant more delays, more denials, and more rework. Hiring more staff wasn’t a smart solution due to training lag - it would only bloat Cost-To-Collect (CTC).
That’s when they turned to us for Agentic AI Automation.
Solution | Prior Authorization Automation
Since every insurer behaved differently our AI automation system had to scale predictably.
After mapping their operational flow and system, our team built a fully autonomous AI agent for prior authorization automation.
We built a modular structure with:
- unified GUI library,
- reusable objects for portal navigation,
- unified API interactions (AWS, registries, password vaults),
- client-specific activity library,
- automated logging & exception pipelines.
To top that off, our PA automation is capable of managing both submissions and follow-ups across fragmented payer systems.
Here are its two major components:
1. PA Data Intake & Submission Automation
Unlike old-school batch processing, we trained the AI agent to activate the moment a new order hits the EHR.
Unlike old-school batch processing, we trained the AI agent to activate the moment a new order hits the EHR. Before contacting any payer, the agent verifies provider identity and active status using the NPI Registry, then proceeds with payer-specific eligibility checks.
Once the data is verified, the agent securely logs into the payer portal. It navigates the interface and auto-fills provider details. It also transfers CPT/HCPCS strings, and ICD-10 codes with 100% accuracy.
Beyond data entry, the agent handles payer-specific prior authorization questions designed to assess medical necessity and utilization risk. This includes checking defined lookback windows for similar services, verifying prerequisite exams or procedures, and supplying supporting context required by each payer’s PA form.
Finally, it downloads the submission PDF and attaches it–along with the audit logs–directly to the client's CRM and EHR.
The real engineering challenge was portal instability.
Payer websites change constantly. To handle this, we avoided rigid scripts and built a Modular Activity Library instead. By using reusable objects for navigation and API calls, the system breaks less often.
This approach also made scaling much faster. In fact, it cut the time required to add a new payer workflow by 70% – saving 8+ FTEs.
How Does the PA Data Intake and Packet Submission Automation Works?
- Validates patient & provider data using NPI Registry.
- Logs into the payer portal and auto-fills all required fields.
- Transfers CPT/HCPCS & ICD-10 codes.
- Submits the PA request.
- Saves the submission PDF & attaches it to CRM/EHR.
- Updates PA status, timestamps, and audit logs.
- Flags only true exceptions for staff review.
2. Follow-Up Automation
The second AI agent solved the RCM team’s biggest distraction: the endless loop of checking for updates.
In a manual workflow, staff waste hours logging into portals just to see a "Pending" status. We automated that entirely.
The agent runs a multiple sweeps a day of all open cases.
It signs into the insurer portals and scans for changes. It doesn't just look for a status code; it reads the actual approval or denial messages.
Then, it writes that outcome directly into the CRM or EHR. If the claim is still processing, the agent notes it and moves on. If it finds a denial or a request for more info, it instantly alerts the staff.
This means your team stops chasing statuses and only touches a case when there is an actual problem to solve.
How Does the PA Follow-up Automation Works?
- Signs into insurer portals.
- Retrieves updated statuses.
- Reads approval/denial messages.
- Records the outcome in CRM/EHR.
- Alerts staff only when human review is needed.
3. Built In Enterprise-Grade Compliance
To top that off, every automated prior authorization request generated a complete, audit-ready record including: timestamps, and source-system logs.
All workflow actions are captured automatically.
Moreover, payer confirmation receipts are stored in AWS with immutable logging.
A PDF of each submission gets downloaded and attached directly to the CRM and EHR and every field change is tracked with full history; and all resubmissions or corrections are versioned automatically.
The result is a PA process that is fully transparent, fully traceable, and fully defensible during audits or payer reviews.
Results
Before automation, 9–16% of this clients' denials were directly linked to simple, avoidable mistakes like missing notes, incomplete demographics, coding mismatches, expired auth numbers, and inconsistent payer formatting.
These were operational misses, not medical-necessity disputes, and they directly prolonged reimbursement cycles across UHC, BCBS, Aetna, and multiple Medicaid MCOs.
The AI agent eliminated these inconsistencies by standardizing every submission across portals.
Clinical documents, codes, and eligibility data were validated automatically, and requests were filed on time, in the exact payer-specific structure required.
As a result, preventable PA-related denials fell to 4–7%, immediately improving clean-submission rates and shortening the revenue cycle.
Fewer avoidable denials meant fewer RFIs, fewer reworks, and a more predictable flow of payments–turning a chronic operational leak into a controlled, measurable process.
What once required entire teams now takes a few minutes to compute.
One AI agent can handle dozens of PAs simultaneously with no drop in accuracy.
Summary
In this particular project, shrinking the prior authorization processes from hours to minutes had a direct downstream effect on liquidity.
In many specialties, prior authorization routinely adds five or more days to the time it takes to begin treatment or schedule a procedure.
With administrative barriers removed, the average time-to-appointment dropped by 1.8 days, creating a tighter alignment between demand and capacity.
This operational shift pulled an estimated $40,000 to $60,000 in revenue recognition forward every week, simply because provider schedules were no longer held hostage by pending paperwork.
The stability of the schedule improved just as much as the cash flow. Last-minute cancellations and financial holds–often caused by stagnant portal requests–dropped significantly. Patients received care faster, and the clinic stopped losing slots to administrative friction.
With an autonomous core submission pipeline, our client is currently pivoting to high-complexity workflows.
The next phase involves our Denials Management Automation and a few additional minor automation including payments posting correspondence.
PA automation became the foundation of their new RCM operating model.
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