JAMIA Article
Working together to transform health and health care.
January 1, 2016
Author(s): Payne, Thomas H, Fridsma, Doug B
DOI: 10.1093/jamia/ocv193
Author(s): Payne, Thomas H, Fridsma, Doug B
DOI: 10.1093/jamia/ocv193
To determine the impact of tethered personal health record (PHR) use on patient engagement and intermediate health outcomes among patients with coronary artery disease (CAD).
Author(s): Toscos, Tammy, Daley, Carly, Heral, Lisa, Doshi, Riddhi, Chen, Yu-Chieh, Eckert, George J, Plant, Robert L, Mirro, Michael J
DOI: 10.1093/jamia/ocv164
Author(s): Tang, Charlotte, Lorenzi, Nancy, Harle, Christopher A, Zhou, Xiaomu, Chen, Yunan
DOI: 10.1093/jamia/ocv198
Author(s): Ohno-Machado, Lucila
DOI: 10.1093/jamia/ocv205
To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD).
Author(s): Mamykina, Lena, Heitkemper, Elizabeth M, Smaldone, Arlene M, Kukafka, Rita, Cole-Lewis, Heather, Davidson, Patricia G, Mynatt, Elizabeth D, Tobin, Jonathan N, Cassells, Andrea, Goodman, Carrie, Hripcsak, George
DOI: 10.1093/jamia/ocv169
Author(s): Fridsma, Doug B
DOI: 10.1093/jamia/ocv163
Biomedical Informatics is a growing interdisciplinary field in which research topics and citation trends have been evolving rapidly in recent years. To analyze these data in a fast, reproducible manner, automation of certain processes is needed. JAMIA is a "generalist" journal for biomedical informatics. Its articles reflect the wide range of topics in informatics. In this study, we retrieved Medical Subject Headings (MeSH) terms and citations of JAMIA articles published [...]
Author(s): Han, Dong, Wang, Shuang, Jiang, Chao, Jiang, Xiaoqian, Kim, Hyeon-Eui, Sun, Jimeng, Ohno-Machado, Lucila
DOI: 10.1093/jamia/ocv157
Mobile sensor data-to-knowledge (MD2K) was chosen as one of 11 Big Data Centers of Excellence by the National Institutes of Health, as part of its Big Data-to-Knowledge initiative. MD2K is developing innovative tools to streamline the collection, integration, management, visualization, analysis, and interpretation of health data generated by mobile and wearable sensors. The goal of the big data solutions being developed by MD2K is to reliably quantify physical, biological, behavioral [...]
Author(s): Kumar, Santosh, Abowd, Gregory D, Abraham, William T, al'Absi, Mustafa, Beck, J Gayle, Chau, Duen Horng, Condie, Tyson, Conroy, David E, Ertin, Emre, Estrin, Deborah, Ganesan, Deepak, Lam, Cho, Marlin, Benjamin, Marsh, Clay B, Murphy, Susan A, Nahum-Shani, Inbal, Patrick, Kevin, Rehg, James M, Sharmin, Moushumi, Shetty, Vivek, Sim, Ida, Spring, Bonnie, Srivastava, Mani, Wetter, David W
DOI: 10.1093/jamia/ocv056
Author(s): Bourne, Philip E, Bonazzi, Vivien, Dunn, Michelle, Green, Eric D, Guyer, Mark, Komatsoulis, George, Larkin, Jennie, Russell, Beth
DOI: 10.1093/jamia/ocv136
Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).
Author(s): Mo, Huan, Thompson, William K, Rasmussen, Luke V, Pacheco, Jennifer A, Jiang, Guoqian, Kiefer, Richard, Zhu, Qian, Xu, Jie, Montague, Enid, Carrell, David S, Lingren, Todd, Mentch, Frank D, Ni, Yizhao, Wehbe, Firas H, Peissig, Peggy L, Tromp, Gerard, Larson, Eric B, Chute, Christopher G, Pathak, Jyotishman, Denny, Joshua C, Speltz, Peter, Kho, Abel N, Jarvik, Gail P, Bejan, Cosmin A, Williams, Marc S, Borthwick, Kenneth, Kitchner, Terrie E, Roden, Dan M, Harris, Paul A
DOI: 10.1093/jamia/ocv112