Applying a natural language processing tool to electronic health records to assess performance on colonoscopy quality measures

Abstract

Background Gastroenterology specialty societies have advocated that providers routinely assess their performance on colonoscopy quality measures. Such routine measurement has been hampered by the costs and time required to manually review colonoscopy and pathology reports. Natural Language Processing (NLP) is a field of computer science in which programs are trained to extract relevant information from text reports in an automated fashion. Objective To demonstrate the efficiency and potential of NLP-based colonoscopy quality measurement Design In a cross-sectional study design, we used a previously validated NLP program to analyze colonoscopy reports and associated pathology notes. The resulting data were used to generate provider performance on colonoscopy quality measures. Setting Nine hospitals in the UPMC health care system. Patients Study sample consisted of the 24,157 colonoscopy reports and associated pathology reports from 2008-9 Main Outcome Measurements Provider performance on seven quality measures Results Performance on the colonoscopy quality measures was generally poor and there was a wide range of performance. For example, across hospitals, adequacy of preparation was noted overall in only 45.7% of procedures (range 14.6% to 86.1% across nine hospitals), documentation of cecal landmarks was noted in 62.7% of procedures (range 11.6% to 90.0%), and the adenoma detection rate was 25.2% (range 14.9% to 33.9%). Limitations Our quality assessment was limited to a single health care system in Western Pennsylvania Conclusions Our study illustrates how NLP can mine free-text data in electronic records to measure and report on the quality of care. Even within a single academic hospital system there is considerable variation in the performance on colonoscopy quality measures, demonstrating the need for better methods to regularly and efficiently assess quality.

Anatomic and Advanced Adenoma Detection Rates as Quality Metrics Determined via Natural Language Processing

“…Our work adds to the sparse but growing literature demonstrating NLP of electronic health record data to measure and report quality metrics for colonoscopy ( 12,14,30 ). Quality measurement requires systems that are accurate, not burdensome, and allow comparison across practice settings.…”

A Strategy for Prioritizing Electronic Medical Records Using Structured Analysis and Natural Language Processing

“…In addition to medical information needs, other proposals aim to analyze administrative data and reports to assess complaints [25], emergency room use [26] or quality measures [27]. While these systems also apply NLP techniques, they do not match this analysis to medical knowledge bases or corpora because they are centered on administrative data.…”

Dense Annotation of Free-Text Critical Care Discharge Summaries from an Indian Hospital and Associated Performance of a Clinical NLP Annotator

“…Natural Language Processing (NLP) has the potential to mine unstructured textual data from document collections [10]. NLP has been used to extract biomedical data from narrative text documents such as pathology and radiology reports, progress notes, discharge summaries and procedure notes [11][12][13][14][15]. NLP has also been used in the identification of hospital acquired infections [16], extraction of medical meta data from social media [17] and in providing treatment recommendations to name a few [18].…”