A Natural Language Processing–Assisted Extraction System for Gleason Scores: Development and Usability Study

Abstract

Background Natural language processing (NLP) offers significantly faster variable extraction compared to traditional human extraction but cannot interpret complicated notes as well as humans can. Thus, we hypothesized that an “NLP-assisted” extraction system, which uses humans for complicated notes and NLP for uncomplicated notes, could produce faster extraction without compromising accuracy. Objective The aim of this study was to develop and pilot an NLP-assisted extraction system to leverage the strengths of both human and NLP extraction of prostate cancer Gleason scores. Methods We collected all available clinical and pathology notes for prostate cancer patients in an unselected academic biobank cohort. We developed an NLP system to extract prostate cancer Gleason scores from both clinical and pathology notes. Next, we designed and implemented the NLP-assisted extraction system algorithm to categorize notes into “uncomplicated” and “complicated” notes. Uncomplicated notes were assigned to NLP extraction and complicated notes were assigned to human extraction. We randomly reviewed 200 patients to assess the accuracy and speed of our NLP-assisted extraction system and compared it to NLP extraction alone and human extraction alone. Results Of the 2051 patients in our cohort, the NLP system extracted a prostate surgery Gleason score from 1147 (55.92%) patients and a prostate biopsy Gleason score from 1624 (79.18%) patients. Our NLP-assisted extraction system had an overall accuracy rate of 98.7%, which was similar to the accuracy of human extraction alone (97.5%; P=.17) and significantly higher than the accuracy of NLP extraction alone (95.3%; P<.001). Moreover, our NLP-assisted extraction system reduced the workload of human extractors by approximately 95%, resulting in an average extraction time of 12.7 seconds per patient (vs 256.1 seconds per patient for human extraction alone). Conclusions We demonstrated that an NLP-assisted extraction system was able to achieve much faster Gleason score extraction compared to traditional human extraction without sacrificing accuracy.

Digital Health Applications in Oncology: An Opportunity to Seize

“…Even when data fields that permit structured data entry are present in the EHR, such as stage and performance status, missing data are common. AI methods such as natural language processing and ML are increasingly applied in the research setting and in support of patient care and may aid in the extraction of important pathologic (eg, Gleason score), radiologic (eg, radiologist interpretation of progression), and cancer-specific (eg, performance status) covariates that are documented in consistent ways in structured text ( 54-56 ). Even with improved technology, natural language processing has a substantial error rate, and improving the capture and completeness of specific data in structured form should be encouraged and incentivized for clinicians who are entering clinical data and the EHR vendors who build the data capture tools to support clinician documentation and workflow.…”