Our client is a cloud-based healthcare software company that provides electronic health record, practice management, patient engagement, and revenue cycle management solutions for independent medical practices.
Its appeal operations relied heavily on manual work, with each appeal taking two to four hours to process. Data was fragmented across systems, and the quality of appeal letters varied from case to case.
Manual errors in patient’s and coding data contributed to higher rejection rates and compliance risk while delayed submissions strained cash flow. High operating costs and 15-20% appeal drop-offs created further revenue leakage.
Our client engaged Emids to automate the appeal workflow end to end, standardize outputs, and establish a scalable, compliant operating model.
SOLUTION
Emids redesigned the appeal workflow to move each case from intake to submission in minutes, with the consistency, control, and auditability required for production.
Leveraging Pacca AI, Emids unique healthcare AI control plane, the team designed and implemented an architecture combining agentic AI with deterministic automation. Pacca AI coordinated the agents, models, workflows, enterprise systems, and governance controls across the process.
Emids team configured specialized AI agents to extract appeal data, assemble payer-specific packets, and submit completed appeals automatically. We first deployed Claude on Amazon Bedrock to interpret unstructured documents and support appeal content generation, then used UiPath to orchestrate rules-based steps and integrate the workflow with the client’s existing RCM systems.
Emids also encoded the business rules needed to validate data, apply compliance checks, remove duplicate records, and route exceptions for human review. Template-driven letter generation standardized outputs across more than 12 denial scenarios.
To keep the solution stable as the client’s systems evolved, the team built an abstraction layer that reduced dependency on API changes. Multi-office, multi-credential, and batch-processing capabilities allowed the workflow to scale across a complex operating environment.
Emids implemented Amazon S3 and PostgreSQL for document storage, audit trails, and execution tracking. The team also added Docker, centralized logging, and real-time monitoring, giving operations teams the visibility and control needed to run the workflow in production.
OUTCOMES
The automated workflow reduced processing time by 95%, from as much as four hours per appeal to about ten minutes. Our client can now submit appeals the same day, 30–60 days earlier than under the previous process. Earlier submission starts the review and recovery cycle sooner, reducing delays in collecting revenue from denied claims and improving cash flow.
