Voice Capture of Medical Residents' Clinical Information Needs During and Inpatient Rotation
Voice Capture of Medical Residents' Clinical Information Needs During and Inpatient Rotation
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DOI：
10.1197/jamia.m2940
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发表时间：
2009-05-01
影响因子：
6.4
通讯作者：
Mendonca, Eneida A.
中科院分区：
文献类型：
--
作者：
Chase, Herbert S.;Kaufman, David R.;Mendonca, Eneida A.
目的：为了确定一些挑战，医疗居民面临的问题，在解决他们的信息需求，在住院设置，通过研究如何语音捕获在自然语言的临床问题适合工作流程，并通过表征的重点，格式，语义内容和复杂性，他们的questions.Design：内科住院医师捕获的信息需求;在一家医院住院服务的数字记录仪，然后参加了半结构化的interviews.Measurements：访谈进行了分析，以确定紧急主题。分析记录的问题的焦点（诊断，治疗或流行病学）和格式，无论是前景（与个体患者相关的特定知识）或背景（关于病情的一般知识）。语义概念和类型确定使用元地图（UMLS -统一医学语言系统）和Manuel.Results：语音记录的问题出现，以揭露居民的潜在信息需求。虽然居民能够在工作流程中记录问题，但有一个延迟。时间问题具体化时，他们被记录。问题的焦点分布在诊断（32%），治疗（40%）和流行病学（28%），大多数问题是背景（69%）。问题语义复杂;前景和背景问题平均为12.6（SD 6.0）和9.1（SD 6.0）UMLS概念，当住院医师使用首字母缩略词或省略关键词时，MetaMap无法识别概念。结论：住院医师捕获临床问题是可行的;在工作流程中使用自然语言，记录问题可能会促使人们意识到以前未被识别的信息需求。然而，典型问题的语义复杂性和由于居民使用首字母缩略词和缩写而导致的映射失败对基于机器的语义内容提取提出了挑战。J Am Med Inform Assoc.2009;16：387-394，DOI 10.1197/jamia.M2940.
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 back ground (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. J Am Med Inform Assoc. 2009;16:387-394, DOI 10.1197/jamia.M2940.