Revenue cycle management and denial processing for healthcare providers
Med A/Rx operates a revenue cycle platform combining accounts receivable management, healthcare financial consulting, and denial management tooling. The tech stack reveals a company in mid-modernization: heavy use of legacy healthcare systems (Epic, Cerner, Meditech, HL7) paired with cloud infrastructure (AWS, Azure) and active adoption of containerization (Docker, Kubernetes) — indicating a push to decouple from monolithic EHR dependencies. Hiring is support-heavy (7 of 23 roles), signaling scaling of customer-facing operations alongside smaller engineering efforts to stabilize CI/CD and manage containerized workloads.
Med A/Rx was founded in 1988 and serves hospital and health system finance teams managing patient billing, claim denial recovery, and accounts receivable optimization. The company operates through three integrated units: PMAB handles outsourced collections with proprietary account-scoring algorithms; MBOC provides healthcare financial consulting; and Medspan Technologies delivers a web-based denial management platform. The platform integrates directly with major EHR systems (Epic, Cerner, Meditech) and processes healthcare claims data using HL7 messaging standards. Clients are predominantly U.S. healthcare organizations seeking to reduce claim denials and accelerate cash collection.
Epic Systems, Cerner, Meditech, HL7 for healthcare data; AWS and Azure for cloud infrastructure; MySQL, SQL Server, Snowflake for data; Java, Spring Boot, Angular for applications; Jira, Confluence for collaboration. Recently adopting Docker and Kubernetes.
Containerization of workloads (Docker/Kubernetes migration), automated build and deployment for Java applications, internal tools for support teams, and software implementation projects. Current pain points include claim denial analysis, maintaining reliable CI/CD pipelines, and managing accounts receivable.
Med A/Rx's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.