TY  - JOUR
AU  - Zheng, Le
AU  - Wang, Yue
AU  - Hao, Shiying
AU  - Shin, Andrew Y
AU  - Jin, Bo
AU  - Ngo, Anh D
AU  - Jackson-Browne, Medina S
AU  - Feller, Daniel J
AU  - Fu, Tianyun
AU  - Zhang, Karena
AU  - Zhou, Xin
AU  - Zhu, Chunqing
AU  - Dai, Dorothy
AU  - Yu, Yunxian
AU  - Zheng, Gang
AU  - Li, Yu-Ming
AU  - McElhinney, Doff B
AU  - Culver, Devore S
AU  - Alfreds, Shaun T
AU  - Stearns, Frank
AU  - Sylvester, Karl G
AU  - Widen, Eric
AU  - Ling, Xuefeng Bruce
PY  - 2016
DA  - 2016/11/11
TI  - 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
JO  - JMIR Med Inform
SP  - e37
VL  - 4
IS  - 4
KW  - electronic medical record
KW  - natural language processing
KW  - diabetes mellitus
KW  - data mining
AB  - 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. 
SN  - 2291-9694
UR  - http://medinform.jmir.org/2016/4/e37/
UR  - https://doi.org/10.2196/medinform.6328
UR  - http://www.ncbi.nlm.nih.gov/pubmed/27836816
DO  - 10.2196/medinform.6328
ID  - info:doi/10.2196/medinform.6328
ER  -