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.
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.
Tsinghua University, Beijing, China.|Stanford University, Stanford, CA, United States.
Le
http://orcid.org/0000-0003-4744-367X
L
Zheng
Stanford University, Stanford, CA, United States.|Zhejiang University, Hangzhou, China.
Yue
http://orcid.org/0000-0002-0981-935X
Y
Wang
Stanford University, Stanford, CA, United States.
Shiying
http://orcid.org/0000-0002-7527-2460
S
Hao
Stanford University, Stanford, CA, United States.
Andrew Y
http://orcid.org/0000-0001-6713-0609
AY
Shin
HBI Solutions Inc, Palo Alto, CA, United States.
Bo
http://orcid.org/0000-0001-9268-7134
B
Jin
HBI Solutions Inc, Palo Alto, CA, United States.
Anh D
http://orcid.org/0000-0003-0593-8420
AD
Ngo
HBI Solutions Inc, Palo Alto, CA, United States.
Medina S
http://orcid.org/0000-0003-0219-0744
MS
Jackson-Browne
HBI Solutions Inc, Palo Alto, CA, United States.
Daniel J
http://orcid.org/0000-0002-1258-7107
DJ
Feller
HBI Solutions Inc, Palo Alto, CA, United States.
Tianyun
http://orcid.org/0000-0001-8103-8841
T
Fu
Stanford University, Stanford, CA, United States.
Karena
http://orcid.org/0000-0003-4065-2882
K
Zhang
Tianjin Key Laboratory of Cardiovascular Remodeling and Target Organ Injury, Pingjin Hospital Heart Center, Tianjin, China.
Xin
http://orcid.org/0000-0003-1395-7103
X
Zhou
HBI Solutions Inc, Palo Alto, CA, United States.
Chunqing
http://orcid.org/0000-0003-0649-4403
C
Zhu
HBI Solutions Inc, Palo Alto, CA, United States.
Dorothy
http://orcid.org/0000-0001-9778-198X
D
Dai
School of Medicine, Zhejiang University, Hangzhou, China.
Yunxian
http://orcid.org/0000-0002-3394-9832
Y
Yu
Zhejiang University, Hangzhou, China.
Gang
http://orcid.org/0000-0001-5864-2944
G
Zheng
Tianjin Key Laboratory of Cardiovascular Remodeling and Target Organ Injury, Pingjin Hospital Heart Center, Tianjin, China.
Yu-Ming
http://orcid.org/0000-0003-1981-8120
YM
Li
Stanford University, Stanford, CA, United States.
Doff B
http://orcid.org/0000-0001-7242-0934
DB
McElhinney
HealthInfoNet, Portland, ME, United States.
Devore S
http://orcid.org/0000-0001-5826-2079
DS
Culver
HealthInfoNet, Portland, ME, United States.
Shaun T
http://orcid.org/0000-0003-1752-4823
ST
Alfreds
HBI Solutions Inc, Palo Alto, CA, United States.
Frank
http://orcid.org/0000-0003-2049-9163
F
Stearns
Stanford University, Stanford, CA, United States.
Karl G
http://orcid.org/0000-0002-8559-0155
KG
Sylvester
HBI Solutions Inc, Palo Alto, CA, United States.
Eric
http://orcid.org/0000-0002-8999-4659
E
Widen
Stanford University, Stanford, CA, United States.|School of Medicine, Zhejiang University, Hangzhou, China.
Xuefeng Bruce
http://orcid.org/0000-0002-5386-3884
XB
Ling
data mining; diabetes mellitus; electronic medical record; natural language processing