Using natural language processing to improve accuracy of automated notifiable disease reporting.
We examined whether using a natural language processing (NLP) system results in improved accuracy and completeness of automated electronic laboratory reporting (ELR) of notifiable conditions. We used data from a community-wide health information exchange that has automated ELR functionality. We focused on methicillin-resistant Staphylococcus Aureus (MRSA), a reportable infection found in unstructured, free-text culture result reports. We used the Regenstrief EXtraction tool (REX) for this work. REX processed 64,554 reports that mentioned MRSA and we compared its output to a gold standard (human review). REX correctly identified 39,491(99.96%) of the 39,508 reports positive for MRSA, and committed only 74 false positive errors. It achieved high sensitivity, specificity, positive predicted value and F-measure. REX identified over two times as many MRSA positive reports as the ELR system without NLP. Using NLP can improve the completeness and accuracy of automated ELR.
Friedlin
Jeff
J
Regenstrief Institute, Inc, Indianapolis, IN, USA.
Grannis
Shaun
S
Overhage
J Marc
JM
D001185:Artificial Intelligence; D018563:Disease Notification / Q000379:methods*; D004282:Documentation / Q000379:methods*; D007196:Indiana; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D009323:Natural Language Processing; D014956:Writing