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Improved Identification of Venous Thromboembolism From Electronic Medical Records Using a Novel Information Extraction Software Platform

机译:使用小说提取软件平台改善了电子医疗记录静脉血栓栓塞的鉴定

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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 (P0.01).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.
机译:介绍:美国联邦政府报告报告静脉血栓栓塞(VTE),由医疗保健研究和质量患者安全指标12(AHRQ PSI-12)的机构定义,基于行政数据,其准确性尚未持续展示。我们使用了一种新颖的信息提取软件系统,以识别电子医疗记录的VTE,并评估其精度。方法:审查了从2009年到2014年入住的13,248名患者的医疗记录,从2009年到2014年被审查。患者遭遇被定义为医院入院,其中术后(脊柱,髋部或膝关节)和可以检测VTE的放射学诊断研究。医生手动报告的放射学报告并由理想X.Result分析:在2083个放射学报告中,理想-X正确识别了176/181个VTE事件,实现了97.2%的敏感性[95%置信区间(CI),93.7与手动审查相比,%-99.1%]和99.3%(95%CI,98.9%-99.7%)。在422个外科手术遭遇中,具有VTE的诊断放射线摄入研究,理想-X正确鉴定了42个VTE事件的41个,达到97.6%的敏感性(95%CI,87.4%-99.6%)和99.8%的特异性(95%CI,98.7 %-100.0%)。性能超过AHRQ PSI-12的灵敏度为92.9%(95%CI,80.5%-98.4%)和92.9%的特异性(95%CI,89.8%-95.3%),但只有差异特异性在统计学上显着(P <0.01)。结论:理想-X,一种新颖的信息提取软件系统,具有高精度的放射学报告中识别VTE,特异性超越AHRQ PSI-12。理想X可能会从电子医疗记录的自由文本中提高许多医疗状况的检测和监测。

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