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Physicians: explore 3 source-linked works published from 2026 to 2026, with original documents and citations.

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Protocol for project FIERCE: A randomized controlled trial to evaluate a positive emotion-focused meditation intervention for physicians with elevated stress.

INTRODUCTION: Nearly 80% of healthcare providers experience adverse psychological symptoms (e.g., depression, burnout, sleep disturbance) stemming from workplace stressors. Elevated levels of stress have been associated with unfavorable occupational, patient, and provider-related outcomes, imposing a heavy burden on a strained system. Given the impact of stress on both employee and patient health, effective interventions are urgently needed to reduce distress and promote well-being among healthcare professionals. Mindfulness-based interventions show promise for addressing these challenges. We developed a six-week, remotely delivered mindfulness intervention, the Building Emotional Strength Training (BEST) program, based on the Buddhist Four Immeasurables practice to cultivate the distinct emotional qualities of loving-kindness, compassion, joy, and equanimity. The present study aims to evaluate the feasibility and efficacy of a Four Immeasurables-based mindfulness intervention on perceived stress (primary outcome), burnout, depressive symptoms, and inflammatory biomarkers, while enhancing psychological well-being and sleep quality (secondary outcomes) in physicians. We will also investigate potential mediators of intervention effects, including compassion, positive affect, equanimity, and mindfulness. METHOD: We will enroll 90 full-time physicians in a remote, two-arm randomized controlled trial with 1:1 allocation to either the meditation intervention or waitlist control. Participants will complete self-report questionnaires and provide blood samples at baseline, mid-course, and post-intervention to assess outcomes and mediators. DISCUSSION: The project aims to advance the study of mindfulness-based interventions that reduce distress and promote well-being through practices that cultivate prosocial and altruistic feelings toward oneself and others. While mindfulness interventions have gained considerable interest, none have specifically drawn from the Four Immeasurables practice to target loving-kindness, compassion, joy, and equanimity. This novel investigation could expand our understanding of practices that foster kindness and compassion to reduce distress in an at-risk population. TRIAL REGISTRATION: ClinicalTrials.gov NCT07283744, registered on 2025/10/14. The Open Science Framework, registered on 2026/06/26.

Humans

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial.

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown. Methods: To examine how physicians and laypeople in expert-based and lay-juror legal systems evaluate physicians' acceptance or rejection of AI recommendations, particularly when those recommendations deviate from standard care, we designed a randomized vignette study: a 2 &#xd7; 2 factorial design varying the AI recommendation (standard vs. nonstandard care) and a fictional physician's decision (accept vs. reject). The study was conducted online in 2023 among nationally representative samples of U.S. and German adults and from 2023 to 2024 among German physicians. In total, 387 German physicians, 2291 U.S. adults, and 2283 German adults participated; those not completing the survey or failing attention checks were excluded per preregistered criteria. Participants were randomly assigned to 1 of 4 vignettes, varying the AI recommendation (standard vs. nonstandard care) and physician's decision (accept vs. reject). The reasonableness of the fictional physician's decision was measured, rated by participants on a Likert scale. Results: Analysis, following preregistered exclusion criteria, included 248 German physicians, 1202 U.S. adults, and 1358 German adults. Physicians accepting standard-care AI recommendations were rated more reasonable than those rejecting them (U.S. laypeople: t = 5.36; 95% CI, 0.45-0.97; P < 0.001; German physicians: t = 2.47; 95% CI, 0.14-1.30; P = 0.02; German laypeople: t = 4.14; 95% CI, 0.27-0.76; P < 0.001). Ratings of physicians accepting versus rejecting AI nonstandard-care recommendations were statistically equivalent. Equivalence was tested at an &#x3b1;-value of 0.05 using a two 1-sided tests procedure, reported with 90% CIs per standard convention (U.S. laypeople: t = -4.90; 90% CI, -0.1 to 0.36; P < 0.001; German physicians: t = -1.76; 90% CI, -0.12 to 0.67; P = 0.04; German laypeople: t = 5.35; 90% CI, -0.35 to 0.06; P < 0.001). Conclusion: Across the United States and Germany, samples representative of lay jurors and court-appointed experts viewed accepting standard-care AI advice as more reasonable, whereas accepting or rejecting nonstandard-care AI advice was judged similarly. Contrary to predictions, malpractice liability regimes do not necessarily pose a barrier to AI use in precision medicine.

Artificial Intelligence

A Sentiment-Based Comparison of AI- and Physician-Generated Empathic Statements in Palliative Care.

CONTEXT: Empathic communication promotes trust in patient-provider relationships. As healthcare integrates artificial intelligence (AI) into patient communication, we have yet to understand how these models' communication compares to that of physicians. OBJECTIVES: Our primary objectives were to examine patient preferences for AI-generated vs. palliative care physician-generated empathic statements addressing fear and anxiety around cancer treatment, and to analyze associations between linguistic features and patient preferences. METHODS: We conducted a secondary analysis of the PALL-AI trial, a randomized controlled survey comparing cancer patients' preferences of AI- to physician-generated empathic statements. Physicians and AI were provided the same prompt with a maximum sentence length. Patient preferences for each statement were measured in blinded surveys. We analyzed sentiment of the statements using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and the National Research Council Canada (NRC) Emotion Lexicon. We evaluated associations between sentiment scores and patient preferences using Spearman's correlation coefficients. RESULTS: A total of 105 patients completed blinded surveys, preferring the AI-generated statement 72.4% of the time. VADER sentiment analysis showed all three AI statements displayed positive sentiment, while all three physician statements displayed negative sentiment. Controlling for statement length, AI statements used twice as many positive words as human statements. However, they contained a similar number of negative words. Of the eight NRC emotions, "trust" and "joy" demonstrated the strongest correlations with patient preference. CONCLUSION: Patients preferred AI-generated statements around cancer care over those from palliative care physicians when standardized for prompt and statement length. Analysis shows AI-generated statements contain more positive language which may be the factor driving patient preference toward AI.

Humans
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