Prior authorization · Production

Seguros Bolívar cuts prior authorization from three to four weeks to near real time with n8n and Gemini AI

The problem

Seguros Bolívar's prior authorization process required clinical staff to manually review each medical order, verify policy coverage and exclusions, and authorize or deny requests—a process taking three to four weeks per case. Orders arrived in inconsistent formats across images, PDFs, and other document types, and the company's 20-year-old legacy core system made automation through traditional development costly, rigid, and error-prone.

First attempt

Modifying the legacy core system to address the bottleneck was ruled out as not viable; traditional software development on the 20-year-old core was described as costly, inflexible, and error-prone.

Workflow diagram · grounded in source
1
Medical order arrives
trigger
“Orders came in across inconsistent formats: images, PDFs, and different document types, all requiring manual interpretation”
2
Gemini AI interprets and extracts data
ai_action
“the n8n workflow integrated with Gemini AI nodes to interpret incoming medical orders, extract relevant data, apply business rules, and generate authorizations in near real time”
3
Patient and policy data queried
integration
“n8n workflows connect to internal databases and APIs, read business rules, and process transactions without requiring any changes to the 20-year-old core application”
4
Business rules applied and approval chain executed
validation
“The workflow reads the business logic, queries patient and policy information, and executes the full approval chain that previously required manual human intervention”
5
Authorization generated near real time
output
“many authorizations are generated on the spot, creating a radically better service experience”
Reported outcome

The prior authorization process went from three to four weeks to near real time, with many authorizations now generated on the spot.
Over 300 active n8n workflows run across the organization with 3,000 employees enabled, and the operational cost of the authorization process dropped drastically. Clinical staff can now focus on medical judgment rather than repetitive administrative tasks.

Reported metrics
Prior authorization processing timethree to four weeks to near real time
Active n8n workflowsover 300
Employees enabled on n8n3,000
Operational cost of authorization processdropped drastically
Reported stack
n8nGemini AI
Source
https://n8n.io/case-studies/seguros-bolivar/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The prior authorization process went from three to four weeks to near real time, with many authorizations now generated on the spot.

What tools did this team use?

n8n, Gemini AI.

What results were reported?

Prior authorization processing time: three to four weeks to near real time; Active n8n workflows: over 300; Employees enabled on n8n: 3,000; Operational cost of authorization process: dropped drastically (source-reported, not independently verified).

What failed first in this deployment?

Modifying the legacy core system to address the bottleneck was ruled out as not viable; traditional software development on the 20-year-old core was described as costly, inflexible, and error-prone.

How is this prior authorization AI workflow structured?

Medical order arrives → Gemini AI interprets and extracts data → Patient and policy data queried → Business rules applied and approval chain executed → Authorization generated near real time.

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