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Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.
Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.
Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.
All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.
The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
In 2019, a 42-year-old African man who works as an Ebola virus disease (EVD) researcher traveled from the Democratic Republic of Congo (DRC), near an ongoing EVD epidemic, to Philadelphia and presented to the Hospital of the University of Pennsylvania Emergency Department with altered mental status, vomiting, diarrhea, and fever. He was classified as a “wet” person under investigation for EVD, and his arrival activated our hospital emergency management command center and bioresponse teams. He was found to be in septic shock with multisystem organ dysfunction, including circulatory dysfunction, encephalopathy, metabolic lactic acidosis, acute kidney injury, acute liver injury, and diffuse intravascular coagulation. Critical care was delivered within high-risk pathogen isolation in the ED and in our Special Treatment Unit until a diagnosis of severe cerebral malaria was confirmed and EVD was definitively excluded.
This report discusses our experience activating a longitudinal preparedness program designed for rare, resource-intensive events at hospitals physically remote from any active epidemic but serving a high-volume international air travel port-of-entry.
Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.
The Crown stands at the heart of the New Zealand, British, Australian and Canadian constitutions as the ultimate source of legal authority and embodiment of state power. A familiar icon of the Westminster model of government, it is also an enigma. Even constitutional experts struggle to define its attributes and boundaries: who or what is the Crown and how is it embodied? Is it the Queen, the state, the government, a corporation sole or aggregate, a relic of feudal England, a metaphor, or a mask for the operation of executive power? How are its powers exercised? How have the Crowns of different Commonwealth countries developed? The Shapeshifting Crown combines legal and anthropological perspectives to provide novel insights into the Crown's changing nature and its multiple, ambiguous and contradictory meanings. It sheds new light onto the development of the state in postcolonial societies and constitutional monarchy as a cultural system.