: The long-term goal of this proposal is to enhance the manner in which physicians access, process and marshal medical information by providing them with an automatically generated, comprehensive, and up-to date summary of the information appearing in a patient record. At the point of patient care, physicians must often rapidly process a potentially overwhelming quantity of information pertaining to a patient. Failure to do so effectively may lead to provision of suboptimal care. Some electronic health record systems provide an automatically produced """"""""cover sheet"""""""" geared to help physicians with a broad overview of a given patient, but the information is derived from the structured data fields in the patient record, ignoring the valuable narrative text entered by clinicians over time. We are building upon our prior work in summarization and natural language processing and leveraging our expertise in cognitive research studying information needs and decision making of clinicians to build a patient record summarizer that gathers information narrative (unstructured) as well as structured parts in the record. We focus on producing a summary for patients with kidney disease, as they often have a complex medical history with numerous conditions, procedures and medications. Providing a holistic, up-to-date summary of their chart would prove valuable to physicians in general and nephrologists in particular. The following three aims will be carried out: (1) conduct a formative study to determine how physicians prioritize and mentally represent relevant information when reviewing a patient chart;(2) create a set of automated methods to select salient pieces of information in the patient record and organize them into a coherent summary;and (3) evaluate the efficacy, efficiency and physician-user satisfaction associated with the use of the summarizer. A primary strength of this proposal is that we are addressing the problem of information overload, a bottleneck in the use of electronic health records, and evaluate the impact of our solution on clinicians'actions and patients'health outcomes. Furthermore, we propose to use novel natural language processing, knowledge-based and data mining methods to extract and organize salient information. Finally, we contribute to informatics research by extending the electronic health record functionalities to go beyond a simple documentation-entry system towards a useful reference and decision-making tool for physicians
We propose to design an automatically generated, comprehensive, and up-to-date summary of the information appearing in a patient record. Such a summary would enhance the manner in which both patients and their physicians access, process and marshal medical information.
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| Pivovarov, Rimma; Elhadad, Noémie (2015) Automated methods for the summarization of electronic health records. J Am Med Inform Assoc 22:938-47 |
| Walsh, Colin; Elhadad, Noémie (2014) Modeling clinical context: rediscovering the social history and evaluating language from the clinic to the wards. AMIA Jt Summits Transl Sci Proc 2014:224-31 |
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| Pivovarov, Rimma; Albers, David J; Hripcsak, George et al. (2014) Temporal trends of hemoglobin A1c testing. J Am Med Inform Assoc 21:1038-44 |
| Pivovarov, Rimma; Albers, David J; Sepulveda, Jorge L et al. (2014) Identifying and mitigating biases in EHR laboratory tests. J Biomed Inform 51:24-34 |
| Perotte, Adler; Pivovarov, Rimma; Natarajan, Karthik et al. (2014) Diagnosis code assignment: models and evaluation metrics. J Am Med Inform Assoc 21:231-7 |
| Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie (2013) Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies. BMC Bioinformatics 14:10 |
| Pivovarov, Rimma; Elhadad, Noémie (2012) A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts. J Biomed Inform 45:471-81 |
| Van Vleck, Tielman T; Elhadad, Noémie (2010) Corpus-Based Problem Selection for EHR Note Summarization. AMIA Annu Symp Proc 2010:817-21 |
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