home > Program - Contributed Paper Session > Answering information needs in workflow
Answering information needs in workflow
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Department of Biomedical Informatics, Columbia University, New York, NY, USA - USA
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Department of Biomedical Informatics, Columbia University, New York, NY, USA - USA
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Department of Biomedical Informatics, Columbia University, New York, NY, USA - USA
Answering information needs in workflow
During the process of patient care, clinicians frequently experience the need for information about treatment, diagnostic workup, disease progression and other aspects of patient management. This is especially true for physicians in training, e.g. when examining a patient or participating in rounds. In most of these situations, it is difficult or impossible for the clinician to immediately access appropriate information resources. Most information needs are never adequately articulated or recorded, and consequently are forgotten by the end of the day. Moreover, when clinicians do recall information needs, they often don’t act on them, due to the significant limitations of current retrieval systems and the exigencies of clinical practice.
This paper describes and discusses the architecture of an information system called CIQR (“seeker”): Context-Initiated Query and Response. CIQR enables clinicians working in the field to pose queries and receive
responses, without interrupting their workflow. CIQR is the outgrowth of several years of research in information retrieval with the goal of finding out what information the user really wants to know and delivering it when and where it is needed. Our previous efforts concentrated on the situations in which an information need arises while the user is viewing information in an electronic patient record. The patient record was used to establish a context that could be used to guide searches for information according to what is known about the specific patient, and what is know about patterns of queries. In the current work, we are supporting clinicians who are actively engaged in work, which is when most information needs arise. To accomplish this, we must capture the user’s question in the field in the easiest possible way, process the question centrally, and deliver a response at a later time when the user is free to review it. These tasks are implemented by the following three components: Question Analyzer
: identification of high-level information needs in natural language queries; Strategy Mediator: translation of high-level information needs into complex search strategies that adapt to user needs and capabilities of resources; and Response Generator: extraction of information from resources for delivery to users.
The Question Analyzer captures user questions as free-form speech on handheld devices, converts them to text centrally using automated speech recognition, and analyzes them semantically using natural language processing. The Strategy Mediator uses the semantic representation of the question to produce a multi-step, adaptive search strategy that exploits the both content and structure of semi-structured information resources, such as textbooks, guidelines and other reference materials. Finally, the system combines information from heterogeneous sources into short, coherent synthesis, adapted for different purposes. A unique aspect of this approach is that the user submits open-ended, multi-sen
tence questions, not just key words. This enables the user to provide contextual background related to the question, such as pertinent characteristics of the patient, the purpose of the query, and the kind of materials the user is seeking. These items provide vital clues for constructing search strategies that are better tuned to the user’s environment and emergent goals.