Document capture is often positioned as one of the easiest wins for AI in healthcare. Upload a document, extract the data, and move on. In practice, that assumption breaks down fast.
In this Office Hours session, Charu Nevatia walks through why AI-driven document capture is not a plug-and-play solution, even with modern LLMs and advanced extraction models. From inconsistent document formats and poor source quality to downstream posting rules, validation requirements, and EMR constraints, the real challenges emerge after the data is extracted.
Rather than focusing on algorithms or model performance alone, this conversation centers on the operational realities that determine success: how documents are classified, how confidence is measured, how exceptions are handled, and how extracted data is transformed into something systems can actually use without disrupting workflows.
This session is designed for healthcare leaders who are evaluating or already deploying AI document capture and want a clearer understanding of why outcomes vary so widely—and what practical steps make the difference between a promising pilot and a solution that scales.
Learning Objectives
- Understand why AI document capture challenges often arise after data extraction, not during it
- Identify common breakdown points in document capture workflows, including classification, validation, and posting
- Recognize why integration, rules, and exception handling are critical to scaling AI-driven document capture
- Ask more informed questions when evaluating document capture solutions beyond “does the model work?”