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
Author(s):
DOI: 10.1093/jamia/ocad044
The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata [...]
Author(s): Honerlaw, Jacqueline, Ho, Yuk-Lam, Fontin, Francesca, Gosian, Jeffrey, Maripuri, Monika, Murray, Michael, Sangar, Rahul, Galloway, Ashley, Zimolzak, Andrew J, Whitbourne, Stacey B, Casas, Juan P, Ramoni, Rachel B, Gagnon, David R, Cai, Tianxi, Liao, Katherine P, Gaziano, J Michael, Muralidhar, Sumitra, Cho, Kelly
DOI: 10.1093/jamia/ocad030
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
Prior authorization (PA) may be a necessary evil within the healthcare system, contributing to physician burnout and delaying necessary care, but also allowing payers to prevent wasting resources on redundant, expensive, and/or ineffective care. PA has become an "informatics issue" with the rise of automated methods for PA review, championed in the Health Level 7 International's (HL7's) DaVinci Project. DaVinci proposes using rule-based methods to automate PA, a time-tested strategy [...]
Author(s): Lenert, Leslie A, Lane, Steven, Wehbe, Ramsey
DOI: 10.1093/jamia/ocad016
We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients.
Author(s): Yasrebi-de Kom, Izak A R, Dongelmans, Dave A, de Keizer, Nicolette F, Jager, Kitty J, Schut, Martijn C, Abu-Hanna, Ameen, Klopotowska, Joanna E
DOI: 10.1093/jamia/ocad014
There are over 363 customized risk models of the American College of Cardiology and the American Heart Association (ACC/AHA) pooled cohort equations (PCE) in the literature, but their gains in clinical utility are rarely evaluated. We build new risk models for patients with specific comorbidities and geographic locations and evaluate whether performance improvements translate to gains in clinical utility.
Author(s): Xu, Yizhe, Foryciarz, Agata, Steinberg, Ethan, Shah, Nigam H
DOI: 10.1093/jamia/ocad017
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 21st Century Cures Act and the rise of telemedicine led to renewed focus on patient portals. However, portal use disparities persist and are in part driven by limited digital literacy. To address digital disparities in primary care, we implemented an integrated digital health navigator program supporting portal use among patients with type II diabetes. During our pilot, we were able to enroll 121 (30.9%) patients onto the portal. Of [...]
Author(s): Rodriguez, Jorge Alberto, Charles, Jean-Pierre, Bates, David W, Lyles, Courtney, Southworth, Bonnie, Samal, Lipika
DOI: 10.1093/jamia/ocad015
Increased social risk data collection in health care settings presents new opportunities to apply this information to improve patient outcomes. Clinical decision support (CDS) tools can support these applications. We conducted a participatory engagement process to develop electronic health record (EHR)-based CDS tools to facilitate social risk-informed care plan adjustments in community health centers (CHCs).
Author(s): Gunn, Rose, Pisciotta, Maura, Gold, Rachel, Bunce, Arwen, Dambrun, Katie, Cottrell, Erika K, Hessler, Danielle, Middendorf, Mary, Alvarez, Miguel, Giles, Lydia, Gottlieb, Laura M
DOI: 10.1093/jamia/ocad010