Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records.
Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing-Based Algorithm With Statewide Electronic Medical Records.
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DOI：
10.2196/medinform.6328
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发表时间：
2016-11-11
影响因子：
3.2
通讯作者：
Ling XB
中科院分区：
文献类型：
--
作者：
Zheng L;Wang Y;Hao S;Shin AY;Jin B;Ngo AD;Jackson-Browne MS;Feller DJ;Fu T;Zhang K;Zhou X;Zhu C;Dai D;Yu Y;Zheng G;Li YM;McElhinney DB;Culver DS;Alfreds ST;Stearns F;Sylvester KG;Widen E;Ling XB
基于结构化病历的糖尿病病例发现并不能完全识别出与糖尿病相关的病史以自由文本形式提供的糖尿病患者。已使用手动图表审核，但人工成本高且延迟时间长。本研究使用结构化和非结构化电子病历 (EMR) 开发并测试了基于网络的糖尿病病例查找算法。这项研究基于健康信息交换 (HIE) 电子病历数据库，该数据库覆盖了美国缅因州几乎所有的医疗机构。使用叙述性临床记录，回顾性地（2012 年 7 月 1 日至 2013 年 6 月 30 日）使用 HIE 相关设施的随机子集开发了基于网络的自然语言处理 (NLP) 病例查找算法，然后与其余设施进行了盲测。基于NLP的算法随后被集成到HIE数据库中并进行了前瞻性验证（2013年7月1日至2014年6月30日）。在前瞻性队列中的 935,891 名患者中，仅使用诊断代码就识别出 64,168 例糖尿病病例。我们基于 NLP 的病例查找算法前瞻性地发现了额外的 5756 个未编码病例（5756/64,168，增加 8.97%），阳性预测值为 0.90。在通过这两种方法识别的 21,720 名糖尿病患者中，在结构化 EMR 中记录糖尿病诊断之前，基于 NLP 的算法识别出 6616 名患者（6616/21,720，30.46%）（平均时间差 = 48 天）。在线NLP算法可有效实时识别未编码的糖尿病病例，从而显着提高糖尿病病例发现率。基于 NLP 的病例查找算法成功集成到缅因州 HIE 数据库中，表明应用这种新方法来实现更完整的糖尿病诊断确定的巨大潜力。
Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency. This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). This study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.
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影响因子：
16.2
作者：
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DOI：
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发表时间：
2014-07
期刊：
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影响因子：
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作者：
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Krumholz HM
DOI：
10.3122/jabfm.2012.04.110289
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影响因子：
2.9
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影响因子：
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作者：
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影响因子：
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作者：
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通讯作者：
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