Carolyn Wong Simpkins is a physician, health technology executive, and health system transformation leader. She joined the leadership team at Health Catalyst as Chief Medical Informatics Officer to lead the development of medical content and accelerate the infusion of clinical insight its next-generation suite of products, and is also helping to shape machine learning algorithms so they can best be used to influence important care decisions. She brings to this role insights from her experiences practicing medicine in diverse settings, from academic medical centers to critical access hospitals and community health centers, combined with a keen understanding of federal health policy and its systemic implications, gleaned from her time on the staff of the U.S. House of Representatives Ways and Means Health subcommittee and her observations from working globally on health system transformation programs and solutions for the UK based British Medical Journal. She is passionate about data, technology, design and disrupting healthcare paradigms to improve health outcomes for all. Carolyn is a Fellow of the second class of the Liberty Fellowship and a member of the Aspen Global Leadership Network.
As health systems nationwide advance in their analytics journey, many are ready to expand beyond traditional discrete data sources—“regular data”—and are cautiously curious about the hype around “big data.” In Gartner’s latest report on big data in healthcare, it identified clinical text (physician notes, or radiology and pathology reports) as the most relevant big data source for health systems. In fact, most health systems employ clinical chart abstractors, willing to endure the burden and delay of hiring a team of nurses to manually extract nuggets of information from the free-text content of their medical records because it is invaluable to evaluating medical performance. The work is difficult, time-consuming, costly, and unavoidably retrospective; because of the expert manual effort involved, the questions we are able to ask of free-text data is severely limited to the most essential, often dictated by reporting requirements.
Imagine being able to automate discovery of the left ventricular ejection fraction from the various free-text documents (diagnostic testing reports, cardiology notes), and transform this information into computable data that can be tracked and visualized to monitor progression of heart failure among your patients, enabling analysis of the impact of treatments and exacerbating events, and allowing for the triggering of interventions. Or, imagine we could begin to automate cardiac risk calculation in advance of surgeries by uncovering recent EKG changes, key findings from cardiac stress testing and any recent changes in chest pain symptomatology in addition to the already computable data in blood pressure, ICD-10 coded problem lists, and lab values. The presentation explores the possibilities of text analytics.