Electronic medical record (EMR) utilization for public health surveillance.
BACKGROUND
Public health surveillance systems need to be refined. We intend to use a generic approach for early identification of patients with severe influenza-like illness (ILI) by calculating a score that estimates a patients disease-severity. Accordingly, we built the Intelligent Severity Score Estimation Model (ISSEM), structured so that the inference process would reflect experts decision-making logic. Each patients disease-severity score is calculated from numbers of respiratory ICD9 encounters, and laboratory, radiologic, and prescription-therapeutic orders in the EMR. Other ISSEM components include chronic disease evidence, probability of immunodeficiency, and the providers general practice-behavior patterns.


RESULTS
Sensitivity was determined from 200 randomly selected patients with upper- and lower-respiratory tract ILI; specificity, from 300 randomly selected patients with URI only. For different age groups, ISSEM sensitivity ranged between 90% and 95%; specificity was 72% to 84%.


CONCLUSIONS
Our preliminary assessment of ISSEM performance demonstrated 93.5% sensitivity and 77.3% specificity across all age groups.
Mnatsakanyan
Zaruhi R
ZR
Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Mollura
Daniel J
DJ
Ticehurst
John R
JR
Hashemian
Mohammad R
MR
Hung
Lang M
LM
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D006801:Humans; D007251:Influenza, Human / Q000145:classification / Q000175:diagnosis*; D008396:Maryland; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index