Medicoda provides an AI‑driven platform that automatically codes patient cases (ICD & OPS) at expert level and highlights revenue‑relevant documentation gaps for hospitals. By reading the full medical record—including poorly scanned documents—and integrating via standard HL7 or archive interfaces, the system accelerates case processing, improves billing accuracy, and remains fully GDPR‑compliant on European servers.
Funding
Funding not disclosed
Founders
Product
Problem
Hospitals face increasing pressure from complex coding regulations, rising personnel costs, and a shortage of experienced medical coders, leading to missed revenue opportunities and documentation gaps.
Solution
Medicoda offers an AI-driven platform that automatically reads complete patient records—including poorly scanned documents—to generate expert‑level ICD and OPS codes. The system identifies potential DRG and OPS adjustments, provides justified code changes, and flags length‑of‑stay concerns, enabling hospitals to capture additional revenue while reducing coding errors. All processing occurs on European servers with full GDPR compliance, ensuring data privacy. Results are delivered via email, the hospital information system, or a web application, allowing staff to focus on complex cases and clinical care.
Target Audience
Primary customers are hospital medical‑controlling departments and coding teams in public, private, and non‑profit hospitals seeking to improve coding efficiency and revenue capture.
Features
- End‑to‑end AI analysis of entire patient files, extracting data from scanned documents and unstructured text
- Automatic pre‑coding of cases to expert‑level quality with over 10,000 historical reference cases
- Real‑time detection of revenue‑relevant code changes (DRG/OPS) with documented justifications
- Length‑of‑stay monitoring and indication checks to support appropriate stay extensions or reductions
- Integration options via simple document access or HL7 interfaces for seamless workflow embedding
- GDPR‑compliant processing on European servers with no use of hospital data for external model training