Improved Identification of Venous Thromboembolism From Electronic Medical Records Using a Novel Information Extraction Software Platform. Issue 9 (September 2018)

• Record Type: Journal Article
• Authors: Dantes, Raymund B.; Zheng, Shuai; Lu, James J.; Beckman, Michele G.; Krishnaswamy, Asha; Richardson, Lisa C.; Chernetsky-Tejedor, Sheri; Wang, Fusheng
• Is Part Of: Medical care. Volume 56:Issue 9(2018)
• Publication Date: 2018-09
• Subjects: venous thromboembolism -- natural language processing -- machine learning -- quality improvement; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000000831
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Introduction: The United States federally mandated reporting of venous thromboembolism (VTE), defined by Agency for Healthcare Research & Quality Patient Safety Indicator 12 (AHRQ PSI-12), is based on administrative data, the accuracy of which has not been consistently demonstrated. We used IDEAL-X, a novel information extraction software system, to identify VTE from electronic medical records and evaluated its accuracy. Methods: Medical records for 13, 248 patients admitted to an orthopedic specialty hospital from 2009 to 2014 were reviewed. Patient encounters were defined as a hospital admission where both surgery (of the spine, hip, or knee) and a radiology diagnostic study that could detect VTE was performed. Radiology reports were both manually reviewed by a physician and analyzed by IDEAL-X. Results: Among 2083 radiology reports, IDEAL-X correctly identified 176/181 VTE events, achieving a sensitivity of 97.2% [95% confidence interval (CI), 93.7%–99.1%] and specificity of 99.3% (95% CI, 98.9%–99.7%) when compared with manual review. Among 422 surgical encounters with diagnostic radiographic studies for VTE, IDEAL-X correctly identified 41 of 42 VTE events, achieving a sensitivity of 97.6% (95% CI, 87.4%–99.6%) and specificity of 99.8% (95% CI, 98.7%–100.0%). The performance surpassed that of AHRQ PSI-12, which had a sensitivity of 92.9% (95% CI, 80.5%–98.4%) and specificity of 92.9% (95% CI, 89.8%–95.3%), though only the difference in specificity was statistically significant ( P <0.01). Conclusion: IDEAL-X, a novel information extraction software system, identified VTE from radiology reports with high accuracy, with specificity surpassing AHRQ PSI-12. IDEAL-X could potentially improve detection and surveillance of many medical conditions from free text of electronic medical records.