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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.Objectives: This paper describes the scale-up efforts at the VA Salt Lake City Health Care System to address processing large corpora of clinical notes through a natural language processing (NLP) pipeline. The use case described is a current project focused on detecting the presence of an indwelling uri-nary catheter in hospitalized patients and subsequent catheter-associated urinary tract infections.Methods: An NLP algorithm using v3NLP was developed to detect the presence of an indwelling urinary catheter in hospitalized patients. The algorithm was tested on a small corpus of notes on patients for whom the presence or absence of a catheter was already known (reference standard). In planning for a scale-up, we estimated that the original algorithm would have taken 2.4 days to run on a larger corpus of notes for this project (550,000 notes), and 27 days for a corpus of 6 million records representative of a national sample of notes. We approached scaling-up NLP pipelines through three techniques: pipeline replication via multi-threading, intra-annotator threading for tasks that can be further decomposed, and remote annotator services which enable annotator scale-out.Results: The scale-up resulted in reducing the average time to process a record from 206 milliseconds to 17 milliseconds or a 12-fold increase in performance when applied to a corpus of 550,000 notes.Conclusions: Purposely simplistic in nature, these scale-up efforts are the straight forward evolution from small scale NLP processing to larger scale extraction without incurring associated complexities that are inherited by the use of the underlying UIMA framework. These efforts represent generalizable and widely applicable techniques that will aid other computationally complex NLP pipelines that are of need to be scaled out for processing and analyzing big data.

Objective: Clinical information systems typically present patient data in chronologic order, organized by the source of the information (e.g., laboratory, radiology). This study evaluates the functionality and utility of a knowledge-based system that generates concept- oriented views (organized around clinical concepts such as disease or organ system) of clinical data. Design: The authors have developed a system that uses a knowledge base of interrelationships between medical concepts to infer relationships between data in electronic medical records. They use these inferences to produce summaries, or views, of the data that are relevant to a specific concept of interest. They evaluated the ability of the system to select relevant information, reduce information overload, and support physician information retrieval. Measurements: The sensitivity and specificity of the system for identifying relevant patient information were calculated. Effect on information overload was assessed by comparing the amount of information in each view with the amount of information in the entire record. Information retrieval accuracy and cost (time) were used to measure the effect of using concept- oriented views on the efficiency and effectiveness of retrievals. Results: The sensitivity and specificity of the system for identifying relevant clinical information were generally in the range of 70 to 80 percent. Concept-oriented views are effective in reducing the amount of information retrieved (over 80 percent reduction) and, compared with source- oriented views, are able to improve physician retrieval accuracy (p = 0.04). Conclusion: Computer-generated, concept-oriented views can be used to reduce clinician information overload and improve the accuracy of clinical data retrieval.

Background The development of consumer health information applications such as health education websites has motivated the research on consumer health vocabulary (CHV). Term identification is a critical task in vocabulary development. Because of the heterogeneity and ambiguity of consumer expressions, term identification for CHV is more challenging than for professional health vocabularies. Objective For the development of a CHV, we explored several term identification methods, including collaborative human review and automated term recognition methods. Methods A set of criteria was established to ensure consistency in the collaborative review, which analyzed 1893 strings. Using the results from the human review, we tested two automated methods—C-value formula and a logistic regression model. Results The study identified 753 consumer terms and found the logistic regression model to be highly effective for CHV term identification (area under the receiver operating characteristic curve = 95.5%). Conclusions The collaborative human review and logistic regression methods were effective for identifying terms for CHV development.