Description
Migration from a historical to a new EHR system at Mayo Clinic was associated with deterioration of performance of a rule-based NLP for identification of PAD cases from clinical narratives, installed in the institutional near-real time NLP infrastructure. After refining the algorithm and keywords for the new EHR system, the revised PAD-NLP system had accuracy of 98%, sensitivity of 96%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 96.15%.
Learning Objective: Migration of EHR systems significantly impacts operation and performance of analytic tools installed in the near real-time NLP infrastructure. This study describes the process to restore functionality and for further refinement of the NLP algorithm mitigating the impact of the EHR migration.
Authors:
Sungrim Moon (Presenter)
Mayo Clinic
Vinod Kaggal, Mayo Clinic
Sunghwan Sohn, Mayo Clinic
Hongfang Liu, Mayo Clinic
Rajeev Chaudhry, Mayo Clinic
Adelaide Arruda-Olson, Mayo Clinic
Presentation Materials: