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Peter J Embi - One of the best experts on this subject based on the ideXlab platform.

  • A review of approaches to identifying patient phenotype cohorts using electronic health records
    Journal of the American Medical Informatics Association, 2014
    Co-Authors: Chaitanya Shivade, Preethi Raghavan, Peter J Embi, Stephen B Johnson, Noémie Elhadad, Eric Fosler-lussier, Albert M. Lai
    Abstract:

    Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

  • Clinical Research Informatics: Challenges, Opportunities and Definition for an Emerging Domain
    Journal of the American Medical Informatics Association : JAMIA, 2009
    Co-Authors: Peter J Embi, Philip R. O. Payne
    Abstract:

    Objectives: Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field. Design: Given the early stage of CRI knowledge and activity, we engaged in a series of qualitative studies with key stakeholders and opinion leaders to determine the range of challenges and opportunities facing CRI. These phases employed complimentary methods to triangulate upon our findings. Measurements: Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up survey with a larger group of self-identified CRI professionals, and 3) validation of our results via electronic peer-debriefing and member-checking with a group of CRI-related opinion leaders. Data were collected, transcribed, and organized for formal, independent content analyses by experienced qualitative investigators, followed by an iterative process to identify emergent categorizations and thematic descriptions of the data. Results: We identified a range of challenges and opportunities facing the CRI domain. These included 13 distinct themes spanning academic, practical, and organizational aspects of CRI. These findings also informed the development of a formal definition of CRI and supported further representations that illustrate areas of emphasis critical to advancing the domain. Conclusions: CRI has emerged as a distinct discipline that faces multiple challenges and opportunities. The findings presented summarize those challenges and opportunities and provide a framework that should help inform next steps to advance this important new discipline.

  • identifying challenges and opportunities in Clinical Research Informatics analysis of a facilitated discussion at the 2006 amia annual symposium
    American Medical Informatics Association Annual Symposium, 2007
    Co-Authors: Peter J Embi, Philip R. O. Payne, Stanley E Kaufman, Judith R Logan, Charles E Barr
    Abstract:

    Clinical Research Informatics (CRI) is a rapidly developing sub-domain of Biomedical Informatics that has seen considerable growth in recent years. While there are numerous activities and initiatives ongoing in this domain, systematic consideration and analysis of the challenges and opportunities that exist in this area are lacking. To begin to address this gap in knowledge and inform next steps in advancing this developing domain, we conducted a facilitated discussion among a diverse group of interested participants attending a meeting of the Clinical Research Informatics Working Group at the AMIA 2006 annual symposium. Findings from our analysis of these data are presented here and indicate a broad array of challenges and opportunities facing this developing area. These findings add new information to the limited literature regarding CRI and should provide direction for those working to set the CRI Research and development agenda.

Philip R. O. Payne - One of the best experts on this subject based on the ideXlab platform.

  • Clinical Research Informatics: Challenges, Opportunities and Definition for an Emerging Domain
    Journal of the American Medical Informatics Association : JAMIA, 2009
    Co-Authors: Peter J Embi, Philip R. O. Payne
    Abstract:

    Objectives: Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field. Design: Given the early stage of CRI knowledge and activity, we engaged in a series of qualitative studies with key stakeholders and opinion leaders to determine the range of challenges and opportunities facing CRI. These phases employed complimentary methods to triangulate upon our findings. Measurements: Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up survey with a larger group of self-identified CRI professionals, and 3) validation of our results via electronic peer-debriefing and member-checking with a group of CRI-related opinion leaders. Data were collected, transcribed, and organized for formal, independent content analyses by experienced qualitative investigators, followed by an iterative process to identify emergent categorizations and thematic descriptions of the data. Results: We identified a range of challenges and opportunities facing the CRI domain. These included 13 distinct themes spanning academic, practical, and organizational aspects of CRI. These findings also informed the development of a formal definition of CRI and supported further representations that illustrate areas of emphasis critical to advancing the domain. Conclusions: CRI has emerged as a distinct discipline that faces multiple challenges and opportunities. The findings presented summarize those challenges and opportunities and provide a framework that should help inform next steps to advance this important new discipline.

  • identifying challenges and opportunities in Clinical Research Informatics analysis of a facilitated discussion at the 2006 amia annual symposium
    American Medical Informatics Association Annual Symposium, 2007
    Co-Authors: Peter J Embi, Philip R. O. Payne, Stanley E Kaufman, Judith R Logan, Charles E Barr
    Abstract:

    Clinical Research Informatics (CRI) is a rapidly developing sub-domain of Biomedical Informatics that has seen considerable growth in recent years. While there are numerous activities and initiatives ongoing in this domain, systematic consideration and analysis of the challenges and opportunities that exist in this area are lacking. To begin to address this gap in knowledge and inform next steps in advancing this developing domain, we conducted a facilitated discussion among a diverse group of interested participants attending a meeting of the Clinical Research Informatics Working Group at the AMIA 2006 annual symposium. Findings from our analysis of these data are presented here and indicate a broad array of challenges and opportunities facing this developing area. These findings add new information to the limited literature regarding CRI and should provide direction for those working to set the CRI Research and development agenda.

Albert M. Lai - One of the best experts on this subject based on the ideXlab platform.

  • A review of approaches to identifying patient phenotype cohorts using electronic health records
    Journal of the American Medical Informatics Association, 2014
    Co-Authors: Chaitanya Shivade, Preethi Raghavan, Peter J Embi, Stephen B Johnson, Noémie Elhadad, Eric Fosler-lussier, Albert M. Lai
    Abstract:

    Objective To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. Materials and methods We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3years was assessed for inclusion in the review. Only articles using automated techniques were included. Results Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. Discussion We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. Conclusions There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

Charles E Barr - One of the best experts on this subject based on the ideXlab platform.

  • identifying challenges and opportunities in Clinical Research Informatics analysis of a facilitated discussion at the 2006 amia annual symposium
    American Medical Informatics Association Annual Symposium, 2007
    Co-Authors: Peter J Embi, Philip R. O. Payne, Stanley E Kaufman, Judith R Logan, Charles E Barr
    Abstract:

    Clinical Research Informatics (CRI) is a rapidly developing sub-domain of Biomedical Informatics that has seen considerable growth in recent years. While there are numerous activities and initiatives ongoing in this domain, systematic consideration and analysis of the challenges and opportunities that exist in this area are lacking. To begin to address this gap in knowledge and inform next steps in advancing this developing domain, we conducted a facilitated discussion among a diverse group of interested participants attending a meeting of the Clinical Research Informatics Working Group at the AMIA 2006 annual symposium. Findings from our analysis of these data are presented here and indicate a broad array of challenges and opportunities facing this developing area. These findings add new information to the limited literature regarding CRI and should provide direction for those working to set the CRI Research and development agenda.

Anil Jain - One of the best experts on this subject based on the ideXlab platform.

  • Patient characteristics associated with venous thromboembolic events: A cohort study using pooled electronic health record data
    Journal of the American Medical Informatics Association : JAMIA, 2012
    Co-Authors: David C. Kaelber, Wendy Foster, Jason R. Gilder, Thomas E. Love, Anil Jain
    Abstract:

    Objective To demonstrate the potential of de-identified Clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies. Materials and methods Data of 959 030 patients, pooled from multiple different healthcare systems with distinct EHR, were obtained. Data were standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore; Explorys Inc). Patients were 26 years or older seen in multiple healthcare systems from 1999 to 2011 with data from EHR. Results Comparing obese, tall subjects with normal body mass index, short subjects, the venous thromboembolic events (VTE) OR was 1.83 (95% CI 1.76 to 1.91) for women and 1.21 (1.10 to 1.32) for men. Weight had more effect then height on VTE. Compared with Caucasian, Hispanic/Latino subjects had a much lower risk of VTE (female OR 0.47, 0.41 to 0.55; male OR 0.24, 0.20 to 0.28) and African-Americans a substantially higher risk (female OR 1.83, 1.76 to 1.91; male OR 1.58, 1.50 to 1.66). This 13-year retrospective study of almost one million patients was performed over approximately 125 h in 11 weeks, part time by the five authors. Discussion As Research Informatics tools develop and more Clinical data become available in EHR, it is important to study and understand unique opportunities for Clinical Research Informatics to transform the scale and resources needed to perform certain types of Clinical Research. Conclusions With the right Clinical Research Informatics tools and EHR data, some types of very large cohort studies can be completed with minimal resources.