Recomendaciones para la realización de cirugías bariátricas electivas en el contexto de pandemia por COVID-19

This document updates and replaces the recommendations made in May 2020. These recommendations may be rapidly modified, so they should be continually checked for possible updates. They constitute a guideline but are not intended to replace medical judgment in any way. These recommendations have been made based on the current knowledge of the COVID-19 pandemic, supported by expert recommendations and society consensus1-27 and in accordance with the local situation, where the performance of the health institutions that have adapted to the infection has already been assessed.

Medical Decision-Making in the Physician Hierarchy: A Pilot Pedagogical Evaluation

Purpose: Recently, the American College of Graduate Medical Education included medical decision-making as a core competency in several specialties. To date, the ability to demonstrate and measure a pedagogical evolution of medical judgment in a medical education program has been limited. In this study, we aim to examine differences in medical decision-making of physician groups in distinctly different stages of their postgraduate career. Methods: The study recruited physicians with a wide spectrum of disciplines and levels of experience to take part in 4 medical simulations divided into 2 categories, abdominal pain (biliary colic [BC] and renal colic [RC]) or chest pain (cardiac ischemia with ST-segment elevation myocardial infarction [STEMI] and pneumothorax [PTX]). Evaluation of medical decision-making used the Medical Judgment Metric (MJM). The targeted selection criteria for the physician groups are administrative physicians (APs), representing those with the most experience but whose current duties are largely administrative; resident physicians (RPs), those enrolled in postgraduate medical or surgical training; and mastery level physicians (MPs), those deemed to have mastery level experience. The study measured participant demographics, physiological responses, medical judgment scores, and simulation time to case resolution. Outcome differences were analyzed using Fisher exact tests with post hoc Bonferroni-adjusted z tests and single-factor analysis of variance F tests with post hoc Tukey honestly significant difference, as appropriate. The significance threshold was set at P < .05. Effect sizes were determined and reported to inform future studies. Results: A total of n = 30 physicians were recruited for the study with n = 10 participants in each physician group. No significant differences were found in baseline demographics between groups. Analysis of simulations showed a significant ( P = .002) interaction for total simulation time between groups RP: 6.2 minutes (±1.58); MP: 8.7 minutes (±2.46); and AP: 10.3 minutes (±2.78). The AP MJM scores, 12.3 (±2.66), for the RC simulation were significantly ( P = .010) lower than the RP 14.7 (±1.15) and MP 14.7 (±1.15) MJM scores. Analysis of simulated patient outcomes showed that the AP group was significantly less likely to stabilize the participant in the RC simulation than MP and RP groups ( P = .040). While not significant, all MJM scores for the AP group were lower in the BC, STEMI, and PTX simulations compared with the RP and MP groups. Conclusions: Physicians in distinctly different stages of their respective postgraduate career differed in several domains when assessed through a consistent high-fidelity medical simulation program. Further studies are warranted to accurately assess pedagogical differences over the medical judgment lifespan of a physician.

Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskeletal injuries, etc. A perspective skill could be obtained from the increased amount of data and a range of possible options could be obtained from the AI though they are difficult to detect with the human eye. Experts, who occupy as a spearhead in the field of medicine in the digital era, could gather the information of the AI into healthcare. But the field of radiology includes many considerations such as diagnostic communication, medical judgment, policymaking, quality assurance, considering patient desire and values, etc. Through AI, doctors could easily gain the multidisciplinary clinical platform with more efficiency and execute the value-added task.

Theory

The conceptual framework for the Physicianship Curriculum is described in this chapter. The crucial participants are depicted in an “educational triangle,” a diagrammatic representation illustrating the roles and functional relationships of these participants. The chapter introduces the concept of the attending teacher, who is at once a clinician, teacher, and role model. We draw an explicit parallel between clinical care and medical education; it leads us to consider student-centered education as the pedagogical analogue to person-centered care. The text addresses the nature of medical judgment and the significant feature of uncertainty that is part of the experiences of all the relevant actors. The second half of the chapter explicates the constructs of epistēme, techné, and phronēsis, originating from Aristotle, whose framework underpins the philosophic armature of the Physicianship Curriculum.