This project focuses on designing and evaluating methods to improve clinician decision-making by generating clinician-tailored and patient-specific knowledge summaries. Knowledge summaries will consist of semantic fragments (i.e., small units of text that provide meaningful information) that are relevant to a clinician's patient specific information needs. This kind of decision support is important because clinicians often raise information needs in the course of patient care and these needs are largely unmet. Unmet information needs are missed opportunities for self-directed learning and improved patient care. Although answers to clinicians'questions can often be found in online health knowledge resources, significant barriers limit the use of these resources for patient care. An increasingly popular approach to lowering these barriers is to provide context-sensitive """"""""infobutton"""""""" links within electronic health record (EHR) systems. Based on the clinical context, infobuttons anticipate clinicians'information needs and provide relevant links to knowledge resources. Infobuttons do a good job helping clinicians'meet simple information needs, but are less optimal when (i) answers cannot be easily found without substantial cognitive effort scanning the information retrieved;and (ii) the information need is associated with data not displayed on the EHR screen. In the proposed study, we will address limitations of previous approaches leveraging significant preliminary research, state-of-the-art information extraction and text summarization tools, and increasingly adopted EHR standards. The research will be guided by a foundation of information-seeking behavior theories. The study has the following aims and hypotheses: * Generate knowledge summaries leveraging patients'EHR data. H1: Knowledge summaries are more efficacious and efficient than manual search for finding answers to patient-specific questions. * Identify contextual and cognitive factors that contribute to clinicians'information needs and information-seeking behavior. H1: (i) Contextual and cognitive factors are associated with the type of information need;and (ii) with the decision to pursue an information need. * Transform knowledge summaries into tailored knowledge summaries based on clinician's contextual and cognitive factors. H1: Clinician tailored knowledge summaries are more efficacious and efficient than knowledge summaries for meeting clinicians'information needs and improving decision-making.

Public Health Relevance

The proposed research addresses a significant problem related to the large frequency of information needs that clinicians raise in the course of care and are not met. Unmet information needs are among the main causes of medical errors and are missed opportunities for self-directed learning. In this study, we will address this problem by providing clinicians with patient-specific and clinician-tailored knowledge summaries. The proposed system will be developed with a set of open source tools and resources, in a standards-compliant software architecture. Thus, our research has the potential to be replicated on a national scale and to play a significant role in the overall improvement of health and health care.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
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University of Utah
Schools of Medicine
Salt Lake City
United States
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Del Fiol, Guilherme; Mostafa, Javed; Pu, Dongqiuye et al. (2016) Formative evaluation of a patient-specific clinical knowledge summarization tool. Int J Med Inform 86:126-34
Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana et al. (2016) Classification of clinically useful sentences in clinical evidence resources. J Biomed Inform 60:14-22
Duc An Bui, Duy; Del Fiol, Guilherme; Hurdle, John F et al. (2016) Extractive text summarization system to aid data extraction from full text in systematic review development. J Biomed Inform :
Bui, Duy Duc An; Del Fiol, Guilherme; Jonnalagadda, Siddhartha (2016) PDF text classification to leverage information extraction from publication reports. J Biomed Inform 61:141-8
Raja, Kalpana; Dasot, Naman; Goyal, Pawan et al. (2016) Towards Evidence-based Precision Medicine: Extracting Population Information from Biomedical Text using Binary Classifiers and Syntactic Patterns. AMIA Jt Summits Transl Sci Proc 2016:203-12
Morid, Mohammad Amin; Jonnalagadda, Siddhartha; Fiszman, Marcelo et al. (2015) Classification of Clinically Useful Sentences in MEDLINE. AMIA Annu Symp Proc 2015:2015-24
Bui, Duy Duc An; Jonnalagadda, Siddhartha; Del Fiol, Guilherme (2015) Automatically finding relevant citations for clinical guideline development. J Biomed Inform 57:436-45
Mishra, Rashmi; Bian, Jiantao; Fiszman, Marcelo et al. (2014) Text summarization in the biomedical domain: a systematic review of recent research. J Biomed Inform 52:457-67
Zhang, Mingyuan; Del Fiol, Guilherme; Grout, Randall W et al. (2013) Automatic identification of comparative effectiveness research from medline citations to support clinicians' treatment information needs. Stud Health Technol Inform 192:846-50
Mishra, Rashmi; Del Fiol, Guilherme; Kilicoglu, Halil et al. (2013) Automatically extracting clinically useful sentences from UpToDate to support clinicians' information needs. AMIA Annu Symp Proc 2013:987-92

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