Not the medical informatics of our founding mothers and fathers, or is it?
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocz027
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocz027
Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2 × 2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose [...]
Author(s): Goldstein, Benjamin A, Phelan, Matthew, Pagidipati, Neha J, Holman, Rury R, Pencina, Michael J, Stuart, Elizabeth A
DOI: 10.1093/jamia/ocy188
Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market.
Author(s): Kim, Yoojung, Lee, Bongshin, Choe, Eun Kyoung
DOI: 10.1093/jamia/ocz003
Growth in big data and its potential impact on the healthcare industry have driven the need for more data scientists. In health care, big data can be used to improve care quality, increase efficiency, lower costs, and drive innovation. Given the importance of data scientists to U.S. healthcare organizations, I examine the qualifications and skills these organizations require for data scientist positions and the specific focus of their work.
Author(s): Meyer, Melanie A
DOI: 10.1093/jamia/ocy181
In biomedicine, there is a wealth of information hidden in unstructured narratives such as research articles and clinical reports. To exploit these data properly, a word sense disambiguation (WSD) algorithm prevents downstream difficulties in the natural language processing applications pipeline. Supervised WSD algorithms largely outperform un- or semisupervised and knowledge-based methods; however, they train 1 separate classifier for each ambiguous term, necessitating a large number of expert-labeled training data, an [...]
Author(s): Pesaranghader, Ahmad, Matwin, Stan, Sokolova, Marina, Pesaranghader, Ali
DOI: 10.1093/jamia/ocy189
Author(s):
DOI: 10.1093/jamia/ocz017
The study sought to assess awareness, perceptions, and value of telehealth in primary care from the perspective of patients.
Author(s): Liaw, Winston R, Jetty, Anuradha, Coffman, Megan, Petterson, Stephen, Moore, Miranda A, Sridhar, Gayathri, Gordon, Aliza S, Stephenson, Judith J, Adamson, Wallace, Bazemore, Andrew W
DOI: 10.1093/jamia/ocy182
This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets.
Author(s): Grannis, Shaun J, Xu, Huiping, Vest, Joshua R, Kasthurirathne, Suranga, Bo, Na, Moscovitch, Ben, Torkzadeh, Rita, Rising, Josh
DOI: 10.1093/jamia/ocy191
The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies.
Author(s): Chung, Arlene E, Shoenbill, Kimberly, Mitchell, Sandra A, Dueck, Amylou C, Schrag, Deborah, Bruner, Deborah W, Minasian, Lori M, St Germain, Diane, O'Mara, Ann M, Baumgartner, Paul, Rogak, Lauren J, Abernethy, Amy P, Griffin, Ashley C, Basch, Ethan M
DOI: 10.1093/jamia/ocy169
Health information technology (HIT) interventions include electronic patient records, prescribing, and ordering systems. Clinical pathways are multidisciplinary plans of care that enable the delivery of evidence-based healthcare. Our objective was to systematically review the effects of implementing HIT-supported clinical pathways.
Author(s): Neame, Matthew T, Chacko, Jerry, Surace, Anna E, Sinha, Ian P, Hawcutt, Daniel B
DOI: 10.1093/jamia/ocy176