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<Paper uid="A97-1041">
  <Title>Language Generation for Multimedia Healthcare Briefings</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In a hospital setting it can be difficult for caregivers to obtain needed information about patients in a timely fashion. In a Cardiac Intensive Care Unit (ICU), communication regarding patient status is critical during the hour immediately following a coronary arterial bypass graft (CABG). It is at this critical point, when care is being transferred from the Operating Room (OR) to the ICU and monitoring is at a minimum, that the patient is most vulnerable to delays in treatment.</Paragraph>
    <Paragraph position="1"> During this time, there are a number of caregivers who need information about patient status and plans for care, including the ICU nurses who must prepare for patient arrival, the cardiologist who is off-site during the operation, and residents and attendings who will aid in determining postoperative care. The only people who can provide this information are those who were present during surgery and they are often too busy attending  to the patient to communicate much detail.</Paragraph>
    <Paragraph position="2"> To address this need, we are developing a multimedia briefing system, MAGIC (Multimedia Abstract Generation for Intensive Care), that takes as input online data collected during the surgical operation as well as information stored in the main databases at Columbia Presbyterian Medical Center (Roderer and Clayton, 1992). MAGIC generates a multimedia briefing that integrates speech, text, and animated graphics to provide an update on patient status (Dalal et al., 1996a). In this paper, we describe the issues that arise for language generation in this context: * Conciseness: The generation process must make coordinated use of speech and text to produce an overview that is short enough for time pressured caregivers to follow, but un-ambiguous in meaning.</Paragraph>
    <Paragraph position="3"> * Media specific tailoring: Generation must take into account that one output medium is speech, as opposed to the more usual written language, producing wording and sentence structure appropriate for spoken language. null * Coordination with other media: The language generation process must produce enough information so that speech and text can be coordinated with the accompanying graphics.</Paragraph>
    <Paragraph position="4"> In the following sections, we first provide an overview of the full MAGIC architecture and then describe the specific language generation issues that we address. We close with a discussion of our current directions.</Paragraph>
  </Section>
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