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.
The Challenges of Information Review in Primary Care
Ideally, clinicians shouldn’t need to search for or surface information at all—because the information should never be lost to begin with. There are several chart review work flows prevalent in primary care, yet EHRs are not built to support them, or the workflows of any clinicians, beyond search and filter.
Stream - The Medical AI Scribe for Longitudinal Care & Value-Based Transformation
Stream is the first medical AI scribe designed for longitudinal care and value based care.
The Danger of Pre-Templated Information in Medical Records
Templating notes, exams, care plans, and histories can be bad for patient care, even if it's good for clinician efficiency. Clinical documentation ought to accurately reflect the hard work clinicians put into their care. Fortunately, large language models can help build better documentation that is reflective of the vibrancy of the patients they describe.
Redefining Ambient Clinical Intelligence
Written by: Alex Butler, MD, MS - Pediatrician & Chief of Product
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.
Task definition, annotated dataset, and supervised natural language processing models for symptom extraction from unstructured clinical notes
We present a clinically motivated task definition, dataset, and simple supervised natural language processing models to demonstrate the feasibility of building clinically applicable information extraction tools