Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing
Automated Identification of Postoperative Complications Within an Electronic Medical Record Using Natural Language Processing
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DOI：
10.1001/jama.2011.1204
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发表时间：
2011-08-24
影响因子：
120.7
通讯作者：
Speroff, Theodore
中科院分区：
文献类型：
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作者：
Murff, Harvey J.;FitzHenry, Fern;Speroff, Theodore
背景目前大多数识别患者安全事件的自动化方法依赖于管理数据代码;然而，电子病历的自由文本搜索可以代表一种额外的监视方法。目的评估一种自然语言处理搜索方法，以识别综合电子病历中的术后手术并发症。设计，设置，和患者的横断面研究，包括1999年至2006年在6个退伍军人健康管理局（VHA）医疗中心接受住院外科手术的2974例患者。主要结果测量术后需要透析的急性肾功能衰竭，深静脉血栓形成，作为VA手术质量改进计划的一部分，通过病历审查确定的肺栓塞、败血症、肺炎或心肌梗死。我们确定了自然语言处理方法识别这些并发症的敏感性和特异性，并将其性能与使用出院编码信息的患者安全指标进行了比较。（39/1924）需要透析的急性肾衰竭，0.7%（18/2327）肺栓塞，1%（29/2327），7%（61/866）败血症，16%（222/1405）肺炎，2%（35/1822）心肌梗死。自然语言处理正确识别了82%（95%置信区间[CI]，67%-91%）的急性肾衰竭病例，而患者安全指标的正确识别率为38%（95% CI，25%-54%）。静脉血栓栓塞症也得到了类似的结果（59%，95% CI，44%-72% vs 46%，95% CI，32%-60%），肺炎（64%，95% CI，58%-70% vs 5%，95% CI，3%-9%），脓毒症（89%，95% CI，78%-94% vs 34%，95% CI，24%-47%）和术后心肌梗死（91%，95% CI，78%-97% vs 89%，95% CI，74%-96%）。两个自然语言处理和患者的安全指标是高度具体的，这些diagnosis.Conclusion在VA医疗中心，自然语言处理分析的电子病历，以确定术后并发症的患者进行住院手术的患者中，具有较高的敏感性和较低的特异性相比，基于出院编码的患者安全指标。美国医学会杂志2011;306（8）：848-855 www.jama.com
Context Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach.Objective To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.Design, Setting, and Patients Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006.Main Outcome Measures Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information.Results The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses.Conclusion Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding. JAMA. 2011;306(8):848-855 www.jama.com