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. 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. 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.  The explosive growth of health information has made it difficult for physicians to identify and locate clinically relevant information, required for providing state-of-the-art care, during workflow. One approach to delivering information to busy physicians at the point of care is to develop an automated system where clinicians pose questions in natural spoken language via voice capture during workflow and receive answers generated through automated searching, enabling immediate use in diagnosis, management, and treatment. The current study seeks to explore two issues that will inform the development of such a system. Specifically, we wish to understand the potential barriers and solutions for capture of information needs at the point of care as well as characterize the types and semantic content of questions being asked. Background Integral to the success of evidence-based practice is the timely acquisition of useful and relevant information during workflow. In an era of unparalleled explicit knowledge about the causes and effective treatments of disease, and outstanding electronic resources to access such knowledge, physicians continue to have difficulty in answering clinical questions that arise when caring for patients, 1-3 largely because of lack of time 4 and insufficient training in the use of electronic databases. 5, 6 Interns and residents are particularly vulnerable to gaps in knowledge, given their training status, especially in the hospital setting "after hours" when consultations with colleagues are less readily available. Many residents' clinical questions remain unanswered 7,8 even though "looking things up" clearly influences their clinical decision-making.  387 Given the current time demands on typical residents and the rapidly expanding medical knowledge base, a long-term goal might include development of systems of information retrieval and delivery that originate and terminate at the point of care. 10 An early approach was to make a computer workstation 11 available to the staff. As the technology advanced, an "evidence-cart," with access to information resources such as MEDLINE, followed clinical rounds to the bedside. 9 The cart has since been miniaturized in the form of a hand-held device using wireless technology to provide access to residents to search online databases. 12, 13 Enabling residents to search at the point of care does not circumvent two well-known obstacles to finding information on a timely basis. Given that an effective search often requires 10 -30 minutes to complete, the time available to perform a search seems to be a rate-limiting step. 14 -16 As long as residents must interrupt workflow to find information there will likely be continued under-use of available resources that could provide answers to information needs. Second, physicians have difficulty in achieving their information seeking goals and many lack the skills of effective and efficient information retrieval. One solution to providing information to busy residents at the point of care is to create an automated system which obviates the need for residents to perform their own searches. Our Context-Initiated Question Response (CIQR) project seeks to create a question-information retrieval system composed of three steps: residents ask questions in natural language which are communicated to a central server; a search strategy is generated based on the semantic content and intent of the questions; and relevant documents containing an "answer" are returned to the residents in a timely manner. The purpose of the current study was to explore several issues related to the first step of this proposed automated information retrieval system: capture of clinical questions in natural language at the point of care. Would residents be able to communicate questions in natural language via voice capture while in an inpatient setting? Inasmuch as there are no prior studies of voice capture of clinical questions, to our knowledge, we sought to determine if this form of communication was feasible in a busy clinical setting. A second goal of our study was to characterize the types of questions asked by the residents. Building a successful question-information retrieval system requires identifying the types of questions asked to direct an automated search. Prior studies suggest that the focus and format of questions may be useful in identifying target resources of a search. The focus usually falls into one of four major categories: diagnosis, treatment, management, and epidemiology (which includes prevalence and incidence, etiology, causation or association, risk factors, disease agents, genetics, and course or prognosis). 20, 21 Haynes and co-workers developed "filters" that would enable human searchers to explore a reduced search space for answers to questions with a particular focus, the "clinical queries" option. 22-25 A second characteristic is the format (background or foreground). Background questions seek general knowledge and are often posed by those, such as residents in training, unfamiliar with an area 26 and are usually answered in textbook level resources. 27 Foreground questions seek specific knowledge relating to the circumstances of an individual patient and are usually more complex 28 and more likely to be answered using MEDLINE or PubMed.
Our goal was to evaluate ASR systems on spontaneously spoken questions, inspired by a recent study REF in an inpatient setting demonstrating the feasibility of voice capturing medical residents' clinical questions in spontaneous natural language.
Voice Capture of Medical Residents' Clinical Information Needs During an Inpatient Rotation
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine