Quantitative and qualitative methods advance the science of clinical workflow research.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad056
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad056
Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED).
Author(s): Moy, Amanda J, Hobensack, Mollie, Marshall, Kyle, Vawdrey, David K, Kim, Eugene Y, Cato, Kenrick D, Rossetti, Sarah C
DOI: 10.1093/jamia/ocad038
Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts.
Author(s): Krefman, Amy E, Ghamsari, Farhad, Turner, Daniel R, Lu, Alice, Borsje, Martin, Wood, Colby Witherup, Petito, Lucia C, Polubriaginof, Fernanda C G, Schneider, Daniel, Ahmad, Faraz, Allen, Norrina B
DOI: 10.1093/jamia/ocad028
Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and [...]
Author(s): Cagliero, Diana, Deuitch, Natalie, Shah, Nigam, Feudtner, Chris, Char, Danton
DOI: 10.1093/jamia/ocad022
Vaccines are crucial components of pandemic responses. Over 12 billion coronavirus disease 2019 (COVID-19) vaccines were administered at the time of writing. However, public perceptions of vaccines have been complex. We integrated social media and surveillance data to unravel the evolving perceptions of COVID-19 vaccines.
Author(s): Wang, Hanyin, Li, Yikuan, Hutch, Meghan R, Kline, Adrienne S, Otero, Sebastian, Mithal, Leena B, Miller, Emily S, Naidech, Andrew, Luo, Yuan
DOI: 10.1093/jamia/ocad029
To improve problem list documentation and care quality.
Author(s): Wright, Adam, Schreiber, Richard, Bates, David W, Aaron, Skye, Ai, Angela, Cholan, Raja Arul, Desai, Akshay, Divo, Miguel, Dorr, David A, Hickman, Thu-Trang, Hussain, Salman, Just, Shari, Koh, Brian, Lipsitz, Stuart, Mcevoy, Dustin, Rosenbloom, Trent, Russo, Elise, Ting, David Yut-Chee, Weitkamp, Asli, Sittig, Dean F
DOI: 10.1093/jamia/ocad020
Physicians' low adoption of diagnostic decision aids (DDAs) may be partially due to concerns about patient/public perceptions. We investigated how the UK public views DDA use and factors affecting perceptions.
Author(s): Nurek, Martine, Kostopoulou, Olga
DOI: 10.1093/jamia/ocad019
The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the [...]
Author(s): Dixon, Brian E, Staes, Catherine, Acharya, Jessica, Allen, Katie S, Hartsell, Joel, Cullen, Theresa, Lenert, Leslie, Rucker, Donald W, Lehmann, Harold
DOI: 10.1093/jamia/ocad033
This study aimed to assess Uganda's readiness for implementing a national Point-of-Care (PoC) electronic clinical data capture platform that can function in near real-time.
Author(s): Nabukenya, Josephine, Egwar, Andrew Alunyu, Drumright, Lydia, Semwanga, Agnes Rwashana, Kasasa, Simon
DOI: 10.1093/jamia/ocad034
Nonexercise algorithms are cost-effective methods to estimate cardiorespiratory fitness (CRF), but the existing models have limitations in generalizability and predictive power. This study aims to improve the nonexercise algorithms using machine learning (ML) methods and data from US national population surveys.
Author(s): Liu, Yuntian, Herrin, Jeph, Huang, Chenxi, Khera, Rohan, Dhingra, Lovedeep Singh, Dong, Weilai, Mortazavi, Bobak J, Krumholz, Harlan M, Lu, Yuan
DOI: 10.1093/jamia/ocad035