Integration of Virtual Technology and Artificial Intelligence Improves Satisfaction, Patient Safety, and Nursing Workforce Efficiency.

Background: Virtual care technology including artificial intelligence (AI) may augment nursing functions creating flexibility in staffing that reduces workforce shortages and enhances patient safety.

Local Problem: A health system experienced nursing workforce shortages and patient safety concerns.

Methods: Quality improvement methodology was used to evaluate the impact of implementing virtual care technology with AI.

Neural, Motivational, and Psychological Measures of Pain Avoidance Predict Future Alcohol Use in Adult Drinkers.

Drinking as a coping method in response to pain is a complex behaviour, involving multiple neural, motivational, and psychological factors. Among these factors, pain sensitivity and pain-related drinking motive can significantly promote alcohol use. In contrast, proactive avoidance - a beneficial strategy of initiating overt actions to avoid negative outcomes - reduces harmful consumption.

Pro-survival roles for p21(Cip1/Waf1) in non-small cell lung cancer.

Background: Quiescence is reversible proliferative arrest. Multiple mechanisms regulate quiescence that are not fully understood. High expression of the CDK inhibitor p21 correlates with a poor prognosis in non-small cell lung cancer (NSCLC) and, in non-transformed cells, p21 promotes quiescence after replication stress.

The Effect of Implicit Bias on the OB/GYN Residency Application Process.

Medical students from racially minoritized backgrounds receive negative evaluations more frequently, possibly from implicit bias. To determine if a preference for White applicants over Asian and Black applicants exists among resident and attending reviewers of obstetrics and gynecology (OB/GYN) residency applications. In 2021, we sampled academic OB/GYN physicians from 6 academic institutions to randomly receive questionnaire form A or B, each containing 4 contrived Electronic Residency Application Service profiles stratified by United States Medical Licensing Examination scores; institutions attended; and strength of recommendation letters into top-, mid-, and low-tiers.