In this paper we describe an algorithm called ConText for determining whether clinical conditions mentioned in clinical reports are negated, hypothetical, historical, or experienced by someone other than the patient. The algorithm infers the status of a condition with regard to these properties from simple lexical clues occurring in the context of the condition. The discussion and evaluation of the algorithm presented in this paper address the questions of whether a simple surface-based approach which has been shown to work well for negation can be successfully transferred to other contextual properties of clinical conditions, and to what extent this approach is portable among different clinical report types. In our study we find that ConText obtains reasonable to good performance for negated, historical, and hypothetical conditions across all report types that contain such conditions. Conditions experienced by someone other than the patient are very rarely found in our report set. A comprehensive solution to the problem of determining whether a clinical condition is historical or recent requires knowledge above and beyond the surface clues picked up by ConText.

The use of NLP for information extraction from free-text colonoscopy and pathology reports creates opportunities for large scale, routine quality measurement, which can support quality improvement in colonoscopy care.

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.

This paper describes a probabilistic case detection system (CDS) that uses a Bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. The diagnostic accuracy of CDS for these conditions, as measured by the area under the ROC curve, was 0.97, and the overall accuracy for NLP employed in CDS was 0.91.

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