Healthcare Application Maintenance Case Study | Orthopedic Care
Explore this case study to see the challenge, solution architecture, and measurable impact.
Read Case StudyHow a U.S. healthcare revenue cycle company — with a lean analytics team of 8 — used generative AI solutions to eliminate 88% of manual reporting effort and put real-time insights in front of every team leader, on demand.
This U.S.-based healthcare company provides revenue cycle management solutions for physicians, hospitals, and health systems — handling patient billing, claims processing, coding, collections, and back-office operations at scale.
The analytics team — just 8 people supporting 500+ staff across the organisation — spent the majority of their time manually pulling reports and writing narrative summaries for department leaders. By the time a report reached a decision-maker, the data was often hours or days old.
JanBask designed and deployed RESOLV — a custom AI-powered operations intelligence assistant built on Microsoft Azure AI — that lets any team leader query their business data in plain English and get a structured, narrative answer in seconds.
RESOLV, a custom NLP assistant trained on healthcare RCM terminology — from "denial management" to "days in AR."
Semantic search across SharePoint and SQL Server — finds the right report in seconds regardless of where it lives.
AI writes plain-English summaries of charts and trend data — the work that previously consumed analyst hours.
Live answers to operational questions — claim rejection rates, coding profiles, payer transactions — without batch delays.
Bi-directional sync with Salesforce Health Cloud so patient and payer context is always present in every query result.
Deployed natively in Microsoft Teams, supporting queries in multiple languages across all time zones and devices.
Healthcare data doesn't forgive generic approaches. Revenue cycle terminology is specialised, acronym-heavy, and context-dependent in ways that off-the-shelf models get wrong.
Mapped 3,000+ reports in SharePoint, 14 SQL Server databases, and 6 months of Salesforce records. Identified that 3 question categories accounted for 61% of analyst requests.
Built RESOLV on Azure AI, training on 5,000+ support tickets and a custom healthcare RCM glossary. Achieved 93% intent accuracy with domain-specific disambiguation.
Soft-launched to 3 department heads, then company-wide. By week 8, RESOLV handled 94% of ad hoc analytics requests without analyst involvement.
All metrics tracked against pre-implementation baselines across the full organisation. RESOLV went live company-wide in week 11 of the engagement.
A modern, enterprise-grade technology stack chosen for healthcare integration, real-time processing, and scalability. Built to handle the complexity of revenue cycle data across multiple systems, time zones, and user roles — while keeping every query HIPAA-conscious and every answer actionable.
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