David Carrell, PhD, is an assistant investigator whose most significant contributions to science entail the development and application of technologies for extracting rich information from unstructured clinical text, such a physician progress notes. This work uses state of the art clinical natural language processing (NLP) technologies in single- and multi-site settings.
Precision phenotyping, which is the process of using information from electronic health records (EHR) to identify patients with (or without) specific clinical characteristics, is a major focus of Dr. Carrell’s work. An example of this work is the development of an NLP system to identify women who have been diagnosed with recurrent breast cancer. Despite it being such a common and consequential clinical diagnosis, recurrent breast cancer is not a condition that can be identified from standardized diagnosis codes found in a person’s chart. Such codes appear in a chart to justify, for example, an imaging procedure when there is suspicion of possible recurrence or as part of a standard monitoring plan. In such cases, a code for recurrence will appear in the chart, but the patient’s chart notes will also state that the imaging study did not find evidence of disease.
For many years, researchers had desired a robust, automated method to identify women with breast cancer recurrences, yet it remained an unsolved problem because of the difficulty of isolating true positive cases (women with diagnosed recurrence) from false positive cases (women for whom there was suspicion of disease but none was found).
Our solution, supported by a grant from the National cancer institute (“Natural Language Processing for Cancer Research Network Surveillance Studies,” RC1CA146917, Carrell, PI), incorporated information from clinician progress notes, radiology reports, and pathology reports. The NLP algorithm achieved very high sensitivity (equal to that of expert manual chart abstractors). Published in the American Journal of Epidemiology, this was the first major report in that journal illustrating the power of clinical NLP methods.
Working with teams of researchers inside and outside KPWHRI, Dr. Carrell has applied similar precision phenotyping methods to identify patients with stenosis of the carotid artery, colon polyps, and problem use of prescription opioids. He is also using these methods to help health care systems evaluate the performance of physicians conducting screening colonoscopy examinations.