Documentation burden, defined as the excessive effort expended on healthcare documentation, is associated with a number of adverse outcomes, including clinician burnout, reduced quality of medical care, and disruption of clinical data contained in the electronic health record.[1] With the growing concern for the wellness of the clinical workforce, documentation burden is receiving national attention. The American Medical Informatics Association (AMIA) has taken the lead by establishing the 25 x 5 Task Force (“Task Force”) in December 2021, which aims to reduce clinician documentation burden to 25% of the current state in the coming 5 years. [2] Aligned with the timing of the Task Force launch, the AMIA Clinical Informatics Conference (CIC) 2022 co-chairs, Rosemary Kennedy (Connect America) and Paul Fu (City of Hope), conceptualized an opening plenary panel in a ‘fireside chat’ format focused on clinical documentation burden.[3] In this editorial, the authors describe the panel discussion, identify key themes from the panel, and offer recommendations to address documentation burden. The proceedings of the AMIA CIC 2022 Fireside Chat serve as an opportunity to acknowledge those who are engaged and passionate about addressing documentation burden from the vantage point of different stakeholders and institutions.

No abstract

This article synthesizes theoretical perspectives related to nurse cognition. We present a conceptual model that can be used by multiple stakeholders to study and contemplate how nurses use clinical decision support systems, and specifically, Barcode-Assisted Medication Administration, to make decisions during the delivery of care. Theoretical perspectives integrated into the model include dual process theory, the Cognitive Continuum Theory, human factors engineering, and the Recognition-Primed Decision model. The resulting framework illustrates the process of nurse cognition during Barcode-Assisted Medication Administration. Additionally, the model includes individual or human and environmental factors that may influence nurse cognition and decision making. It is important to consider the influence of individual, human, and environmental factors on the process of nurse cognition and decision making. Specifically, it is necessary to explore the impact of heuristics and biases on clinician decision making, particularly related to the development of alarm and alert fatigue. Aided by the proposed framework, stakeholders may begin to identify heuristics and cognitive biases that influence the decision of clinicians to accept or override a clinical decision support system alert and whether heuristics and biases are associated with inappropriate alert override.

This prognostic study performed external validation of a machine learning model to predict 6-month mortality among patients with advanced solid tumors.