利用自然语言处理技术在电子病历中自动识别术后并发症。

Automated identification of postoperative complications within an electronic medical record using natural language processing.

机构信息

Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.

出版信息

JAMA. 2011 Aug 24;306(8):848-55. doi: 10.1001/jama.2011.1204.

DOI:10.1001/jama.2011.1204

PMID:21862746

Abstract

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.

摘要

背景

目前，大多数用于识别患者安全事件的自动化方法都依赖于管理数据代码；然而，电子病历中的自由文本搜索可能代表了一种额外的监测方法。

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、地点和患者：横断面研究，涉及 1999 年至 2006 年期间在 6 家退伍军人事务部（VA）医疗中心接受住院手术的 2974 名患者。

主要结果测量

术后发生需要透析的急性肾衰竭、深静脉血栓形成、肺栓塞、败血症、肺炎或心肌梗死的情况，这些并发症是通过医疗记录审查作为 VA 手术质量改进计划的一部分确定的。我们确定了自然语言处理方法识别这些并发症的灵敏度和特异性，并将其性能与基于出院编码信息的患者安全指标进行了比较。

结果

每个样本的术后事件比例为：需要透析的急性肾衰竭为 2%（1924 例中的 39 例），肺栓塞为 0.7%（2327 例中的 18 例），深静脉血栓形成为 1%（2327 例中的 29 例），败血症为 7%（866 例中的 61 例），肺炎为 16%（1405 例中的 222 例），心肌梗死为 2%（1822 例中的 35 例）。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（59%，95%CI，44%-72%与 46%，95%CI，32%-60%）、肺炎（64%，95%CI，58%-70%与 5%，95%CI，3%-9%）、败血症（89%，95%CI，78%-94%与 34%，95%CI，24%-47%）和术后心肌梗死（91%，95%CI，78%-97%与 89%，95%CI，74%-96%）的结果类似。自然语言处理和患者安全指标对这些诊断均具有较高的特异性。