Clinical decision-making and artificial intelligence.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaf131
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaf131
To explore the performance of 4 large language model (LLM) chatbots for the analysis of 2 of the most commonly used tools for the advanced analysis of systematic reviews (SRs) and meta-analyses.
Author(s): Forero, Diego A, Abreu, Sandra E, Tovar, Blanca E, Oermann, Marilyn H
DOI: 10.1093/jamia/ocaf117
Recommendation systems have emerged as prevalent and effective tools. Investigating the impact of recommendation algorithms on users' health information adoption behavior can aid in optimizing health information services and advancing the construction and development of online health community platforms.
Author(s): Luo, Yaling, Zhao, Zerui, Xu, Xiaojuan, Zhao, Yueyan, Yang, Feng
DOI: 10.1093/jamia/ocaf115
This study aims to compare the diagnostic abilities of humans in wound image assessment with those of an AI-based model, examine how "expertise" affects clinicians' diagnostic performance, and investigate the heterogeneity in clinical judgments.
Author(s): Kücking, Florian, Hübner, Ursula H, Busch, Dorothee
DOI: 10.1093/jamia/ocaf116
To explore the potential effects of recent restrictions on discussions regarding diversity, equity, and inclusion (DEI) in the field of biomedical informatics.
Author(s): Hochheiser, Harry, Visweswaran, Shyam
DOI: 10.1093/jamia/ocaf107
This review paper comprehensively summarizes healthcare provider (HCP) evaluation of explanations produced by explainable artificial intelligence methods to support point-of-care, patient-specific, clinical decision-making (CDM) within medical settings. It highlights the critical need to incorporate human-centered (HCP) evaluation approaches based on their CDM needs, processes, and goals.
Author(s): Bauer, Jenny M, Michalowski, Martin
DOI: 10.1093/jamia/ocaf110
Evaluate the association between telemedicine intensity and ambulatory physician electronic health record (EHR) use following the COVID-19 pandemic.
Author(s): Kim, Seunghwan, Thombley, Robert, Eiden, Elise, Lou, Sunny, Adler-Milstein, Julia, Kannampallil, Thomas, Holmgren, A Jay
DOI: 10.1093/jamia/ocaf122
Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient's clinical notes to generate text-based explanations for risk prediction alerts.
Author(s): Nycklemoe, Samuel, Devarapu, Sriharsha, Gao, Yanjun, Carey, Kyle, Kuehnel, Nicholas, Munjal, Neil, Jani, Priti, Churpek, Matthew, Dligach, Dmitriy, Afshar, Majid, Mayampurath, Anoop
DOI: 10.1093/jamia/ocaf121
Real-world data (RWD) analyses primarily rely on structured clinical documentation collected through routine clinical care or driven by medical billing requirements. Patient-reported outcome measures (PROMs), integrated into electronic health records (EHRs), are an additional data source that could offer valuable insights into a patient's perspective and contribute to a more comprehensive understanding of health outcomes in RWD studies. This study aims to characterize agreement between PROMs symptoms and structured clinical [...]
Author(s): Castro, Victor M, Gainer, Vivian S, Crookes, Danielle M, Murphy, Shawn N, Manjourides, Justin
DOI: 10.1093/jamia/ocaf112
Clinicians retrieve data from electronic health record (EHR) systems and summarize them into clinical information to accomplish clinical reasoning and decision-making tasks. Visualization, using meaningful summarization methods and intuitive presentation approaches, can enhance this process. This systematic review examines how EHR data are summarized, visualized, and aligned with the 7 clinical reasoning and decision-making tasks shared by clinicians.
Author(s): Fan, Hao, Hardi, Angela, Yen, Po-Yin
DOI: 10.1093/jamia/ocaf103