Browsing by Subject "clinical informatics"

Now showing 1 - 5 of 5
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    Characteristics of the National Applicant Pool for Clinical Informatics Fellowships (2016-2017)
    (AMIA, 2018) Bell, Douglas S.; Baldwin, Kevin; Bell, Elijah J.; Lehmann, Christoph U.; Webber, Emily C.; Mohan, Vishnu; Leu, Michael G.; Hoffman, Jeffrey M.; Kaelber, David C.; Landman, Adam B.; Hron, Jonathan; Silverman, Howard D.; Levy, Bruce; Elkin, Peter L.; Poon, Eric; Luberti, Anthony A.; Finnell, John T.; Safran, Charles; Palma, Jonathan P.; Forman, Bruce H.; Kileen, James; Arvin, David; Pfeffer, Michael; Pediatrics, School of Medicine
    We conducted a national study to assess the numbers and diversity of applicants for 2016 and 2017 clinical informatics fellowship positions. In each year, we collected data on the number of applications that programs received from candidates who were ultimately successful vs. unsuccessful. In 2017, we also conducted an anonymous applicant survey. Successful candidates applied to an average of 4.2 and 5.5 programs for 2016 and 2017, respectively. In the survey, unsuccessful candidates reported applying to fewer programs. Assuming unsuccessful candidates submitted between 2-5 applications each, the total applicant pool numbered 42-69 for 2016 (competing for 24 positions) and 52-85 for 2017 (competing for 30 positions). Among survey respondents (n=33), 24% were female, 1 was black and none were Hispanic. We conclude that greater efforts are needed to enhance interest in clinical informatics among medical students and residents, particularly among women and members of underrepresented minority groups.
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    Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
    (IEEE, 2019) Fan, Ziwei; Burgun, Evan; Ren, Zhiyun; Schleyer, Titus; Ning, Xia; Medicine, School of Medicine
    Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of test cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in.
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    Improving “Desktop medicine” efficiency using Guided Inquiry Learning in an Electronic Health Records System
    (2018-07-18) Purkayastha, Saptarshi; Naliyatthaliyazchayil, Parvati Ravindranathan Menon; Surapaneni, Asha Kiranmayee; Kowkutla, Ashwini; Maity, Pallavi
    Recent studies have shown that more than 50% of physician time is spent on “desktop medicine” – the practice of medicine that requires the use of computers and other technology. Providers are trained in other medical practices through elaborate course work, but don’t get enough training and follow-up training on the desktop medicine practices such as efficient use of an electronic health record (EHR) system. By putting in practice theories from learning sciences, human-computer interaction and evaluation in an undergraduate health information management (HIM) course, we developed a module called Student Team Learning Monitor (STLM) in an open-source EHR.
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    Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system
    (Oxford Academic, 2019-01-16) Embi, Peter J.; Richesson, Rachel; Tenenbaum, Jessica; Kannry, Joseph; Friedman, Charles; Sarkar, Indra Neil; Smith, Jeff; Medicine, School of Medicine
    Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and populations.
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    Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system
    (Oxford Academic, 2019-01-16) Embi, Peter J.; Richesson, Rachel; Tenenbaum, Jessica; Kannry, Joseph; Friedman, Charles; Sarkar, Indra Neil; Smith, Jeff; Medicine, School of Medicine
    Abstract. The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and populations