Insights from the Front Lines of Medical Documentation
We explore the root causes of information chaos, designing for clarity, and the thoughtful application of AI in medicine.
Too Much Information, Too Little Time
Information overload is more than a nuisance—it’s a major contributor to clinical burnout. When alerts, messages, and chart clutter pile up without prioritization, cognitive load skyrockets. In part two of our Information Chaos series, we break down how overload disrupts clinical reasoning—and what we’re doing about it.
The Real Cost of Note Bloat: Clinical Clarity at Risk
Medical documentation is a clinical act, not just a clerical task. Learn why note bloat undermines patient care and how clinicians can rebuild clean, useful, clinically-driven notes through thoughtful, evidence-informed practices.
The Future of Live Documentation - Addressing the Growing Problem of Medical Documentation Overload
As AI continues to transform healthcare, many assume it can fix the growing issue of documentation overload. While AI offers just-in-time summaries and automation, relying solely on it without improving how data is structured leads to bloated, disorganized charts. In our latest post, we explore why better organization—through problem-oriented documentation and structured data—is key to streamlining workflows, reducing costs, and enhancing patient care.
Prevalence and Sources of Duplicate Information in the Electronic Medical Record
The prevalence of information duplication (copy-paste) in electronic medical records (EMRs) suggests that it is an adaptive behavior requiring further investigation so that improved documentation systems can be developed.
A Fully Collaborative, Noteless Electronic Medical Record Designed to Minimize Information Chaos: Software Design and Feasibility Study
We propose and build a prototype of the world's first noteless electronic health record, which is built to optimize collaboration and minimize information chaos.
A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning
Incidental findings are a common medical problem that are prone to falling through the cracks of the medical system. Building safety net systems to identify, track, and to help manage these potentially dangerous findings can decrease the cognitive burden on physicians and lead to better outcomes for patients. In this manuscript, we present a software system designed to identify adrenal incidentalomas and track them over time.