Samantha Adams Festschrift: How to be a Student and How to Mentor Students-A Remembrance of Dr. Samantha Adams, Who Did These and Everything Else So Well.
Author(s): Craven, Catherine K, Adams, Martha
DOI: 10.1055/s-0038-1666798
Author(s): Craven, Catherine K, Adams, Martha
DOI: 10.1055/s-0038-1666798
Author(s): Solomonides, Anthony
DOI: 10.1055/s-0038-1666799
Author(s): DeMuro, Paul R, Novak, Laurie L, Petersen, Carolyn
DOI: 10.1055/s-0038-1654701
Author(s): Novak, Laurie L, Kuziemsky, Craig, Kaplan, Bonnie
DOI: 10.1055/s-0038-1656524
Author(s): Aarts, Jos
DOI: 10.1055/s-0038-1656523
Author(s): Pierce, Robin L, Berti Suman, Anna, Koops, Bert-Jaap, Leenes, Ronald
DOI: 10.1055/s-0038-1641596
To explore perceptions of critical care providers about a novel collaborative inpatient health information technology (HIT) in a pediatric intensive care unit (PICU) setting.
Author(s): Asan, Onur, Holden, Richard J, Flynn, Kathryn E, Murkowski, Kathy, Scanlon, Matthew C
DOI: 10.1093/jamiaopen/ooy020
Most determinants of health originate from the "contexts" in which we live, which has remained outside the confines of the U.S. healthcare system. This issue has left providers unprepared to operate with an ample understanding of the challenges patients may face beyond their purview. The recent shift to value-based care and increasing prevalence of Electronic Health Record (EHR) systems provide opportunities to incorporate upstream contextual factors into care. We discuss [...]
Author(s): Estiri, Hossein, Patel, Chirag J, Murphy, Shawn N
DOI: 10.1093/jamiaopen/ooy025
The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT.
Author(s): Ronquillo, Jay G, Erik Winterholler, J, Cwikla, Kamil, Szymanski, Raphael, Levy, Christopher
DOI: 10.1093/jamiaopen/ooy019
The growing availability of rich clinical data such as patients' electronic health records provide great opportunities to address a broad range of real-world questions in medicine. At the same time, artificial intelligence and machine learning (ML)-based approaches have shown great premise on extracting insights from those data and helping with various clinical problems. The goal of this study is to conduct a systematic comparative study of different ML algorithms for [...]
Author(s): Tang, Fengyi, Xiao, Cao, Wang, Fei, Zhou, Jiayu
DOI: 10.1093/jamiaopen/ooy011