Perspectives on implementing models for decision support in clinical care.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad142
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocad142
Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system.
Author(s): Corbin, Conor K, Maclay, Rob, Acharya, Aakash, Mony, Sreedevi, Punnathanam, Soumya, Thapa, Rahul, Kotecha, Nikesh, Shah, Nigam H, Chen, Jonathan H
DOI: 10.1093/jamia/ocad114
Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources.
Author(s): Yao, Yao, Zhou, Hanchu, Cao, Zhidong, Zeng, Daniel Dajun, Zhang, Qingpeng
DOI: 10.1093/jamia/ocad116
We sought to learn from the experiences of women leaders in informatics by interviewing women in Informatics leadership roles. Participants reported career challenges, how they built confidence, advice to their younger selves, and suggestions for attracting and retaining additional women. Respondents were 16 women in leadership roles in academia (n = 9) and industry (n = 7). We conducted a thematic analysis revealing: (1) careers in informatics are serendipitous and nurtured by supportive communities [...]
Author(s): Payne, Velma L, Partridge, Brittany, Bozkurt, Selen, Nandwani, Anjali, Butler, Jorie M
DOI: 10.1093/jamia/ocad108
To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications.
Author(s): Peng, Cheng, Yang, Xi, Yu, Zehao, Bian, Jiang, Hogan, William R, Wu, Yonghui
DOI: 10.1093/jamia/ocad107
Author(s): Sutton, Reed T, Dhillon-Chattha, Pritma, Kumagai, Jason, Pitamber, Tiffany, Meurer, David P
DOI: 10.1055/s-0044-1779302
This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model.
Author(s): Laursen, Martin S, Pedersen, Jannik S, Hansen, Rasmus S, Savarimuthu, Thiusius R, Lynggaard, Rasmus B, Vinholt, Pernille J
DOI: 10.1055/a-2121-8380
Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.
Author(s): Zhang, Tianyi, Gephart, Sheila M, Subbian, Vignesh, Boyce, Richard D, Villa-Zapata, Lorenzo, Tan, Malinda S, Horn, John, Gomez-Lumbreras, Ainhoa, Romero, Andrew V, Malone, Daniel C
DOI: 10.1055/s-0043-1772686
According to Digital Health Canada 2013 eSafety Guidelines, an estimated one-third of patient safety incidents following implementation of clinical information systems (CISs) are technology-related. An eSafety checklist was previously developed to improve CIS safety by providing a comprehensive listing of system-agnostic, evidence-based configuration recommendations.
Author(s): Sutton, Reed T, Dhillon-Chattha, Pritma, Kumagai, Jason, Pitamber, Tiffany, Meurer, David P
DOI: 10.1055/s-0043-1771392
Novel record linkage (RL) methods have the potential to enhance clinical informatics by integrating patient data from multiple sources-including electronic health records, insurance claims, and digital health devices-to inform patient-centered care. Engaging patients and other stakeholders in the use of RL methods in patient-centered outcomes research (PCOR) is a key step in ensuring RL methods are viewed as acceptable, appropriate, and useful. The University of Colorado Record Linkage (CURL) platform [...]
Author(s): Reno, Jenna E, Ong, Toan C, Voong, Chan, Morse, Brad, Ytell, Kate, Koren, Ramona, Kwan, Bethany M
DOI: 10.1055/a-2105-6505