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
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
Author(s): Yang, Jiannan, Xu, Zhongzhi, Wu, William Ka Kei, Chu, Qian, Zhang, Qingpeng
DOI: 10.1093/jamia/ocab214
To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.
Author(s): Ripperger, Michael, Lotspeich, Sarah C, Wilimitis, Drew, Fry, Carrie E, Roberts, Allison, Lenert, Matthew, Cherry, Charlotte, Latham, Sanura, Robinson, Katelyn, Chen, Qingxia, McPheeters, Melissa L, Tyndall, Ben, Walsh, Colin G
DOI: 10.1093/jamia/ocab218
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
The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research [...]
Author(s): Meystre, Stéphane M, Heider, Paul M, Kim, Youngjun, Davis, Matthew, Obeid, Jihad, Madory, James, Alekseyenko, Alexander V
DOI: 10.1093/jamia/ocab186
Over a 31-year span as Director of the US National Library of Medicine (NLM), Donald A.B. Lindberg, MD, and his extraordinary NLM colleagues fundamentally changed the field of biomedical and health informatics-with a resulting impact on biomedicine that is much broader than its influence on any single subfield. This article provides substance to bolster that claim. The review is based in part on the informatics section of a new book [...]
Author(s): Miller, Randolph A, Shortliffe, Edward H
DOI: 10.1093/jamia/ocab245
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocab249
Author(s): Perez-Pozuelo, Ignacio, Spathis, Dimitris, Gifford-Moore, Jordan, Morley, Jessica, Cowls, Josh
DOI: 10.1093/jamia/ocab198
Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.
Author(s): Orenstein, Evan W, Kandaswamy, Swaminathan, Muthu, Naveen, Chaparro, Juan D, Hagedorn, Philip A, Dziorny, Adam C, Moses, Adam, Hernandez, Sean, Khan, Amina, Huth, Hannah B, Beus, Jonathan M, Kirkendall, Eric S
DOI: 10.1093/jamia/ocab179