Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.
Author(s): Kostopoulou, Olga, Tracey, Christopher, Delaney, Brendan C
DOI: 10.1093/jamia/ocab025
Author(s): Apathy, Nate C, Vest, Joshua R, Adler-Milstein, Julia, Blackburn, Justin, Dixon, Brian E, Harle, Christopher A
DOI: 10.1093/jamia/ocab067
Successful technological implementations frequently involve individuals who serve as mediators between end users, management, and technology developers. The goal for this project was to evaluate the structure and activities of such mediators in a large-scale electronic health record implementation.
Author(s): Umstead, Claire N, Unertl, Kim M, Lorenzi, Nancy M, Novak, Laurie Lovett
DOI: 10.1093/jamia/ocab044
The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physicians and the U.S. public, we find that the public is significantly more likely to believe that physicians should be held responsible when an error occurs during care delivered with medical [...]
Author(s): Khullar, Dhruv, Casalino, Lawrence P, Qian, Yuting, Lu, Yuan, Chang, Enoch, Aneja, Sanjay
DOI: 10.1093/jamia/ocab055
To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.
Author(s): Melnick, Edward R, Ong, Shawn Y, Fong, Allan, Socrates, Vimig, Ratwani, Raj M, Nath, Bidisha, Simonov, Michael, Salgia, Anup, Williams, Brian, Marchalik, Daniel, Goldstein, Richard, Sinsky, Christine A
DOI: 10.1093/jamia/ocab011
The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.
Author(s): Klann, Jeffrey G, Estiri, Hossein, Weber, Griffin M, Moal, Bertrand, Avillach, Paul, Hong, Chuan, Tan, Amelia L M, Beaulieu-Jones, Brett K, Castro, Victor, Maulhardt, Thomas, Geva, Alon, Malovini, Alberto, South, Andrew M, Visweswaran, Shyam, Morris, Michele, Samayamuthu, Malarkodi J, Omenn, Gilbert S, Ngiam, Kee Yuan, Mandl, Kenneth D, Boeker, Martin, Olson, Karen L, Mowery, Danielle L, Follett, Robert W, Hanauer, David A, Bellazzi, Riccardo, Moore, Jason H, Loh, Ne-Hooi Will, Bell, Douglas S, Wagholikar, Kavishwar B, Chiovato, Luca, Tibollo, Valentina, Rieg, Siegbert, Li, Anthony L L J, Jouhet, Vianney, Schriver, Emily, Xia, Zongqi, Hutch, Meghan, Luo, Yuan, Kohane, Isaac S, , , Brat, Gabriel A, Murphy, Shawn N
DOI: 10.1093/jamia/ocab018
We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors.
Author(s): Dixon, Brian E, Grannis, Shaun J, McAndrews, Connor, Broyles, Andrea A, Mikels-Carrasco, Waldo, Wiensch, Ashley, Williams, Jennifer L, Tachinardi, Umberto, Embi, Peter J
DOI: 10.1093/jamia/ocab004
Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask.
Author(s): Mercaldo, Francesco, Santone, Antonella
DOI: 10.1093/jamia/ocab052
To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.
Author(s): Callahan, Alison, Polony, Vladimir, Posada, José D, Banda, Juan M, Gombar, Saurabh, Shah, Nigam H
DOI: 10.1093/jamia/ocab027
We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list.
Author(s): Wu, Patrick, Nelson, Scott D, Zhao, Juan, Stone, Cosby A, Feng, QiPing, Chen, Qingxia, Larson, Eric A, Li, Bingshan, Cox, Nancy J, Stein, C Michael, Phillips, Elizabeth J, Roden, Dan M, Denny, Joshua C, Wei, Wei-Qi
DOI: 10.1093/jamia/ocab019