Mark S. Erlbaum

VA national drug file reference terminology: A cross-institutional content coverage study

BACKGROUND Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminologys (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 OTCs 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

Evaluating RECONSIDER: a computer program for diagnostic prompting

RECONSIDER, a computer program designed to perform as a diagnostic prompting aid, was evaluated for its ability to include the correct diagnosis in an ordered computed list of candidate diseases. The study was performed using 100 consecutive first admissions to the medical service of a university hospital, where the individuals entering the data into the program were blind to all but a limited set of findings known at time of admission. Each person entering the data created one or more lists of diagnostic possibilities (versions) using the program. The program suggested the correct diagnosis within the first 40 on its list 61% (498/797) of the time; the correct diagnosis was present with the first 40 in at least one version 93% (98/105) of the time. Performance was found to be best with cases having a single diagnosis and when more terms were entered into the program.

Collaborative conversational interfaces

This paper proposes a method of designing human-computer speech interfaces based on principles of human conversation. It argues that conversation is the primary mode of language use and that it is fundamentally collaborative. Speech interfaces should therefore be designed to recreate the collaborative nature of natural conversations. The paper presents five strategies for designingcollaborative conversational interfaces, and it describes the principles of human-language use that underly these strategies. The paper also argues that collaborative conversational interfaces have a crucial advantage over other kinds of interfaces in that they are readily adaptive to different levels of experience and styles of use. The paper gives examples of collaborative conversational interfaces that we have developed, and discusses the ways in which these interfaces have been made adaptive.

Knowledge architectures for patient access to breast-cancer information

To provide resourceful information about breast-cancer diagnosis and treatment, the authors worked with frequently-asked question (FAQ) files to create an effective breast-cancer knowledge server. They discuss the challenge of using FAQs to accommodate breast-cancer patients and their diverse information needs.