OBJECTIVE: To identify some of the challenges that medical residents face in addressing their information needs in an inpatient setting, by examining how voice capture in natural language of clinical questions fits into workflow, and by characterizing the focus, format, and semantic content and complexity of their questions. DESIGN: Internal medicine residents captured information needs on a digital recorder while on a hospital inpatient service and then participated in semi-structured interviews. MEASUREMENTS: Interviews were analyzed to identify emergent themes. Recorded questions were analyzed for focus (diagnosis, treatment, or epidemiology) and format, either foreground (specific knowledge relating to an individual patient) or background (general knowledge about a condition). Semantic concepts and types were identified using MetaMap (UMLS - Unified Medical Language System) and manually. RESULTS: Voice recording of questions appeared to unmask residents' latent information needs. Although residents were able to record questions during workflow, there was a delay from the time questions materialized to when they were recorded. Question focus was distributed among diagnosis (32%), treatment (40%), and epidemiology (28%), and the majority of questions were background (69%). Questions were semantically complex; foreground and background questions averaged 12.6 (SD 6.0) and 9.1 (SD 6.0) UMLS concepts, respectively. MetaMap failed to recognize concepts when residents used acronyms or abbreviations or omitted key terms. CONCLUSIONS: We found that it is feasible for residents to capture their clinical questions in natural language during workflow and that recording questions may prompt awareness of previously unrecognized information needs. However, the semantic complexity of typical questions and mapping failures due to residents' use of acronyms and abbreviations present challenges to machine-based extraction of semantic content.
OBJECTIVE: To identify some of the challenges that medical residents face in addressing their information needs in an inpatient setting, by examining how voice capture in natural language of clinical questions fits into workflow, and by characterizing the focus, format, and semantic content and complexity of their questions. DESIGN: Internal medicine residents captured information needs on a digital recorder while on a hospital inpatient service and then participated in semi-structured interviews. MEASUREMENTS: Interviews were analyzed to identify emergent themes. Recorded questions were analyzed for focus (diagnosis, treatment, or epidemiology) and format, either foreground (specific knowledge relating to an individual patient) or background (general knowledge about a condition). Semantic concepts and types were identified using MetaMap (UMLS - Unified Medical Language System) and manually. RESULTS: Voice recording of questions appeared to unmask residents' latent information needs. Although residents were able to record questions during workflow, there was a delay from the time questions materialized to when they were recorded. Question focus was distributed among diagnosis (32%), treatment (40%), and epidemiology (28%), and the majority of questions were background (69%). Questions were semantically complex; foreground and background questions averaged 12.6 (SD 6.0) and 9.1 (SD 6.0) UMLS concepts, respectively. MetaMap failed to recognize concepts when residents used acronyms or abbreviations or omitted key terms. CONCLUSIONS: We found that it is feasible for residents to capture their clinical questions in natural language during workflow and that recording questions may prompt awareness of previously unrecognized information needs. However, the semantic complexity of typical questions and mapping failures due to residents' use of acronyms and abbreviations present challenges to machine-based extraction of semantic content.
Authors: Oladimeji Farri; David S Pieckiewicz; Ahmed S Rahman; Terrence J Adam; Serguei V Pakhomov; Genevieve B Melton Journal: AMIA Annu Symp Proc Date: 2012-11-03
Authors: Jamie S Hirsch; Jessica S Tanenbaum; Sharon Lipsky Gorman; Connie Liu; Eric Schmitz; Dritan Hashorva; Artem Ervits; David Vawdrey; Marc Sturm; Noémie Elhadad Journal: J Am Med Inform Assoc Date: 2014-10-28 Impact factor: 4.497
Authors: Colleen E Crangle; Colin Bradley; Paul F Carlin; Robert J Esterhay; Roy Harper; Patricia M Kearney; Vera J C McCarthy; Michael F McTear; Eileen Savage; Mark S Tuttle; Jonathan G Wallace Journal: PLoS One Date: 2018-11-16 Impact factor: 3.240
Authors: Ellen J Bass; Justin Michael DeVoge; Linda A Waggoner-Fountain; Stephen M Borowitz Journal: J Am Med Inform Assoc Date: 2012-12-25 Impact factor: 4.497