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

被引：34

作者：

Zheng, Le ^{[1
,2
]}

Wang, Yue ^{[2
,3
]}

Hao, Shiying ^{[2
]}

Shin, Andrew Y. ^{[2
]}

Jin, Bo ^{[4
]}

Ngo, Anh D. ^{[4
]}

Jackson-Browne, Medina S. ^{[4
]}

Feller, Daniel J. ^{[4
]}

Fu, Tianyun ^{[4
]}

Zhang, Karena ^{[2
]}

Zhou, Xin ^{[5
]}

Zhu, Chunqing ^{[4
]}

Dai, Dorothy ^{[4
]}

Yu, Yunxian ^{[6
]}

Zheng, Gang ^{[3
]}

Li, Yu-Ming ^{[5
]}

McElhinney, Doff B. ^{[2
]}

Culver, Devore S. ^{[7
]}

Alfreds, Shaun T. ^{[7
]}

Stearns, Frank ^{[4
]}

Sylvester, Karl G. ^{[2
]}

Widen, Eric ^{[4
]}

Ling, Xuefeng Bruce ^{[2
,6
]}

机构：

[1] Tsinghua Univ, Beijing, Peoples R China

[2] Stanford Univ, S370 Grant Bldg, Stanford, CA 94305 USA

[3] Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China

[4] HBI Solut Inc, Palo Alto, CA USA

[5] Pingjin Hosp Heart Ctr, Tianjin Key Lab Cardiovasc Remodeling & Target Or, Tianjin, Peoples R China

[6] Zhejiang Univ, Sch Med, Hangzhou, Zhejiang, Peoples R China

[7] HealthInfoNet, Portland, ME USA

来源：

JMIR MEDICAL INFORMATICS | 2016年 / 4卷 / 04期

关键词：

electronic medical record; natural language processing; diabetes mellitus; data mining; RISK SCORE; HYPERTENSION; DISEASE; OBESITY;

D O I：

10.2196/medinform.6328

中图分类号：

R-058 [];

学科分类号：

摘要：

Background: 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. Objective: This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). Methods: 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). Results: 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). Conclusions: 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.

引用

页码：38 / 50

页数：13

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