A paper I really liked in the Student Finalist competition at AMIA 2008 was Jeff Friedlin’s project to use NLP processing to automate the identification of MRSA lab reports for state-level reporting. The Regenstrief Institute runs an electronic lab reporting system at the Indiana Network For Patient Care, which is a regional center that collects HL7 lab messages from hospitals throughout Indiana. The state of Indiana now requires that any positive MRSA result (not just invasive cases) be reported. The existing system had been using LOINC codes to identify positive cases. This was missing many positive reports because of lab systems that communicate in free text, usually with OBX segments in the HL7 message.
Dr. Friedlin sorted through all the types of lab messages received by the regional center and created an NLP system built on Regenstrief’s REX processor to identify those with MRSA positive results. He then tested his system with one year’s worth of data. To calculate accuracy he reviewed 64,554 messages himself to generate a gold standard. The results were fantastic, with a sensitivity of 99.96%, a specificity of 99.71%, and a PPV of 99.81%.
One side effect of this great work is that it led to a huge increase in positive MRSA reports for the state, because so many were being missed by the old system. He showed a slide with this increase during the presentation but I don’t have the numbers available. Reportedly his presentation later in the conference overflowed.
Friedlin J, Grannis S, Overhage JM. Using Natural Language Processing to Improve Accuracy of Automated Notifiable Disease Reporting. AMIA 2008 Symposium Proceedings. 2008. p.207-11. PMID 18999177.
Image Credit: estherase on Flickr
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