Elizabeth Marshall, MD, MBA, is Associate Director, Clinical Analytics at Linguamatics, an IQVIA company. Marshall completed her fellowship training in informatics at the Medical University of South Carolina and served as an assistant professor and clinical manager for a study focused on decreasing veteran suicidality. She served in the United States Air Force, then later became a research physician dedicated to the development of informatics solutions to improve the effectiveness of mental health treatments for military veterans. In honor of her work advancing the treatment of suicidality and PTSD for veterans while a clinical research health scientist at the Ralph H. Johnson Veterans Administration Medical Center, she was awarded the Research Training Institute Scholar Award from the ICRC-S Injury Control Research Center for Suicide Prevention (2013).
To reduce time-consuming manual searches and improve their findings, many organizations are turning to artificial intelligence technologies such as natural language processing. Clinical and research institutions are adopting NLP to capture unstructured information from EHRs that helps with diagnosis.
To reduce the systemic health inequities that have put so many individuals at higher risk of getting sick and dying from Covid-19, healthcare organizations must leverage advanced technologies such as NLP to identify SDoH and other critical information from unstructured notes.
Munck Wilson Mandala Partner Greg Howison shared his perspective on some of the legal ramifications around AI, IP, connected devices and the data they generate, in response to emailed questions.
To help clinicians diagnose rare disease more quickly and accurately, many healthcare organizations are embracing technology solutions like natural language processing (NLP) tools that can create augmented intelligence workflows that facilitate the rapid search of unstructured clinical data from multiple data sources.