BACKGROUND: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated
BACKGROUND: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated
Authors: Jyotishman Pathak; Sean P Murphy; Brian N Willaert; Hilal M Kremers; Barbara P Yawn; Walter A Rocca; Christopher G Chute Journal: AMIA Annu Symp Proc Date: 2011-10-22
Authors: John S Carter; Steven H Brown; Brent A Bauer; Peter L Elkin; Mark S Erlbaum; David A Froehling; Michael J Lincoln; S Trent Rosenbloom; Dietlind L Wahner-Roedler; Mark S Tuttle Journal: AMIA Annu Symp Proc Date: 2006
Authors: Omar Bouhaddou; Michael J Lincoln; Sarah Maulden; Holli Murphy; Pradnya Warnekar; Viet Nguyen; Siew Lam; Steven H Brown; Ferdinand J Frankson; Glen Crandall; Carla Hughes; Roger Sigley; Marcia Insley; Gail Graham Journal: AMIA Annu Symp Proc Date: 2006
Authors: Yehoshua Perl; James Geller; Michael Halper; Christopher Ochs; Ling Zheng; Joan Kapusnik-Uner Journal: Ann N Y Acad Sci Date: 2016-10-17 Impact factor: 5.691