Formative evaluation of a patient-specific clinical knowledge summarization tool

To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed

Text summarization in the biomedical domain: A systematic review of recent research

The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain

A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports

<div><p>Large volumes of data are continuously generated from clinical notes and diagnostic studies catalogued in electronic health records (EHRs). Echocardiography is one of the most commonly ordered diagnostic tests in cardiology. This study sought to explore the feasibility and reliability of using natural language processing (NLP) for large-scale and targeted extraction of multiple data elements from echocardiography reports. An NLP tool, EchoInfer, was developed to automatically extract data pertaining to cardiovascular structure and function from heterogeneously formatted echocardiographic data sources. EchoInfer was applied to echocardiography reports (2004 to 2013) available from 3 different on-going clinical research projects. EchoInfer analyzed 15,116 echocardiography reports from 1684 patients, and extracted 59 quantitative and 21 qualitative data elements per report. EchoInfer achieved a precision of 94.06%, a recall of 92.21%, and an F1-score of 93.12% across all 80 data elements in 50 reports. Physician review of 400 reports demonstrated that EchoInfer achieved a recall of 92–99.9% and a precision of >97% in four data elements, including three quantitative and one qualitative data element. Failure of EchoInfer to correctly identify or reject reported parameters was primarily related to non-standardized reporting of echocardiography data. EchoInfer provides a powerful and reliable NLP-based approach for the large-scale, targeted extraction of information from heterogeneous data sources. The use of EchoInfer may have implications for the clinical management and research analysis of patients undergoing echocardiographic evaluation.</p></div

Summary on precision and recall for 21 different random data elements validated on multiple data sets of echocardiographic reports.

<p>Summary on precision and recall for 21 different random data elements validated on multiple data sets of echocardiographic reports.</p

Extraction of data elements and values into structured format from structured, semi-structured, and unstructured data from echocardiography reports.

<p>Extraction of data elements and values into structured format from structured, semi-structured, and unstructured data from echocardiography reports.</p

Examples of EchoInfer’s identification of data element and corresponding value structured output.

<p>Examples of EchoInfer’s identification of data element and corresponding value structured output.</p

Examples of non-standardized echocardiographic reporting that are not identified or extracted by EchoInfer.

<p>Examples of non-standardized echocardiographic reporting that are not identified or extracted by EchoInfer.</p

Precision and Recall for ten most frequent data elements identified in 15,116 echocardiograms.

<p>Precision and Recall for ten most frequent data elements identified in 15,116 echocardiograms.</p