My mom got diagnosed with cancer through the MyChart app.
Author(s): Shapiro, Aaron
DOI: 10.1093/jamia/ocab193
Author(s): Shapiro, Aaron
DOI: 10.1093/jamia/ocab193
During the coronavirus disease 2019 (COVID-19) pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers.
Author(s): Romero, Lisa, Carneiro, Pedro B, Riley, Catharine, Clark, Hollie, Uy, Raymonde, Park, Michael, Mawokomatanda, Tebitha, Bombard, Jennifer M, Hinckley, Alison, Skapik, Julia
DOI: 10.1093/jamia/ocab233
To evaluate the International Classification of Health Interventions (ICHI) in the clinical and statistical use cases.
Author(s): Fung, Kin Wah, Xu, Julia, Ameye, Filip, Burelle, Lisa, MacNeil, Janice
DOI: 10.1093/jamia/ocab220
Author(s): Yang, Jiannan, Xu, Zhongzhi, Wu, William Ka Kei, Chu, Qian, Zhang, Qingpeng
DOI: 10.1093/jamia/ocab214
This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19).
Author(s): Hong, Peter, Herigon, Joshua C, Uptegraft, Colby, Samuel, Bassem, Brown, D Levin, Bickel, Jonathan, Hron, Jonathan D
DOI: 10.1093/jamia/ocab231
The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.
Author(s): Li, Kathy, Urteaga, Iñigo, Shea, Amanda, Vitzthum, Virginia J, Wiggins, Chris H, Elhadad, Noémie
DOI: 10.1093/jamia/ocab182
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab274
Hospital-acquired infections (HAIs) are associated with significant morbidity, mortality, and prolonged hospital length of stay. Risk prediction models based on pre- and intraoperative data have been proposed to assess the risk of HAIs at the end of the surgery, but the performance of these models lag behind HAI detection models based on postoperative data. Postoperative data are more predictive than pre- or interoperative data since it is closer to the [...]
Author(s): Yang, Haoyu, Tourani, Roshan, Zhu, Ying, Kumar, Vipin, Melton, Genevieve B, Steinbach, Michael, Simon, Gyorgy
DOI: 10.1093/jamia/ocab229
Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes.
Author(s): Martin, Jacob A, Crane-Droesch, Andrew, Lapite, Folasade C, Puhl, Joseph C, Kmiec, Tyler E, Silvestri, Jasmine A, Ungar, Lyle H, Kinosian, Bruce P, Himes, Blanca E, Hubbard, Rebecca A, Diamond, Joshua M, Ahya, Vivek, Sims, Michael W, Halpern, Scott D, Weissman, Gary E
DOI: 10.1093/jamia/ocab248
To characterize variation in clinical documentation production patterns, how this variation relates to individual resident behavior preferences, and how these choices relate to work hours.
Author(s): Gong, Jen J, Soleimani, Hossein, Murray, Sara G, Adler-Milstein, Julia
DOI: 10.1093/jamia/ocab253