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Recent technological advances in data science hold great promise in medicine. Large-sized high-quality datasets are essential but often difficult to obtain due to privacy, cost, and practical challenges. Here, we discuss synthetic data's generation, evaluation, and regulation, highlighting its current applications and limits., (© 2024 médecine/sciences – Inserm.)

For many, the World Health Organization's (WHO) definition of health does not reflect their own understanding of health, because it lacks aspects such as spiritual wellbeing. Responding to these concerns, the WHO called in 2023 for a vision of health that integrates physical, mental, psychological, emotional, spiritual, and social wellbeing. To date, medical practitioners are often reluctant to consider spiritual aspects, because of a perceived lack of statistical evidence about the strength of relations. Research on this topic is emerging. A recent study among 800 young people living with HIV in Zimbabwe showed how study participants navigated three parallel, at times contradicting health systems (religious, traditional, medical). Conflicting approaches led to multifaceted dilemmas (= spiritual struggles), which were significantly related to poorer mental and physical health. This illustrates the need for inclusion of spiritual aspects for health and wellbeing in research, and of increased collaboration between all stakeholders in healthcare., Competing Interests: The author declares that they do not have any conflicts of interest., (Copyright © 2024 Wüthrich-Grossenbacher.)

Importance: There is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost., Observations: Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks., Conclusions and Relevance: The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model 1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM 2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA 3 , MedMCQA 4 , PubMedQA 5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics 6 ), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications., (© 2023. The Author(s).)

We introduce a new logic, called Temporal Cohort Logic (TCL), for cohort specification and discovery in clinical and population health research. TCL is created to fill a conceptual gap in formalizing temporal reasoning in biomedicine, in a similar role that temporal logics play for computer science and its applications. We provide formal syntax and semantics for TCL and illustrate the various logical constructs using examples related to human health. Relationships and distinctions with existing temporal logical frameworks are discussed. Applications in electronic health record (EHR) and in neurophysiological data resource are provided. Our approach differs from existing temporal logics, in that we explicitly capture Allen's interval algebra as modal operators in a language of temporal logic (rather than addressing it in the semantic structure). This has two major implications. First, it provides a formal logical framework for reasoning about time in biomedicine, allowing general (i.e., higher-levels of abstraction) investigation into the properties of this approach (such as proof systems, completeness, expressiveness, and decidability) independent of a specific query language or a database system. Second, it puts our approach in the context of logical developments in computer science, allowing potential translation of existing results into the setting of TCL and its variants or subsystems so as to illuminate opportunities and computational challenges involved in temporal reasoning for biomedicine., (©2022 AMIA - All rights reserved.)

Background: In France, the progressive use of emergency departments (EDs) by primary care providers (PCPs) as a point of access to hospitalization for nonurgent patients is one of the many causes of their overcrowding. To increase the proportion of direct hospital admissions, it is necessary to improve coordination between PCPs and hospital specialists. The objective of our work was to describe the design and implementation of an electronic referral system aimed at facilitating direct hospital admissions., Methods: This initiative was conducted in a French area (Hauts-de-Seine Sud) through a partnership between the Antoine-Béclère University Hospital, the Paris-Saclay University Department of General Medicine and the local health care network. The implementation was carried out in 3 stages, namely, conducting a survey of PCPs in the territory about their communication methods with the hospital, designing and implementing a web-based application called "SIPILINK" (Système d'Information de la Plateforme d'Intermédiation Link) and an innovative organization for hospital management of the requests, and analysing through descriptive statistics the platform use 9 months after launch., Results: The e-referral platform was launched in November 2019. First, a PCP filled out an electronic form describing the reason for his or her request. Then, a hospital specialist worked to respond within 72 h. Nine months after the launch, 132 PCPs had registered for the SIPILINK platform, which represented 36.6% of PCPs in this area. Of the 124 requests made, 46.8% corresponded to a hospitalization request (conventional or day hospitalization). The most requested specialty was internal medicine (48.4% of requests). The median time to first response was 43 min, and 43.5% of these requests resulted in direct admission (conventional or day hospitalization)., Conclusions: This type of system responds to a need for coordination in the primary-secondary care direction, which is less often addressed than in the secondary-primary care direction. The first results show the potential of the system to facilitate direct admissions within a short time frame. To make the system sustainable, the next step is to extend its use to other hospitals in the territory., (© 2022. The Author(s).)

Introduction: Rational medicine use is an appropriate prescribing, dispensing, and patient use of medicines for the diagnosis, prevention, and treatment of diseases. It is affected by several factors. Irrational use of medicine is a widespread problem at all levels of care. This review is aimed at assessing the medicine use pattern in health facilities of Ethiopia using the medicine use pattern developed by WHO/INRUD., Methods: Relevant literature was searched from Google Scholar, PubMed, Hinari, Web of Science, and Scopus using inclusion and exclusion criteria. A systematic review was used to summarize the medicine use pattern in health facilities of Ethiopia, and that WHO core drug use indicators were employed., Result: From 188 searched studies, 30 literatures were reviewed. The average number of drugs per encounter was 2.11. The percentage of encounters with antibiotics and injection was 57.16% and 22.39%, respectively. The percentage of drugs prescribed by generic name and from an essential drug list was 91.56% and 90.19%, respectively. On average, patients spent 5.14 minutes for consultation and 106.52 seconds for dispensing. From prescribed drugs, 67.79% were dispensed, while only 32.25% were labeled adequately. The availability of key essential medicines was 64.87%. The index of rational drug use value was 7.26. Moreover, the index of rational drug prescribing, index of rational patient-care drug use, and index of rational facility-specific drug use were 3.74, 2.51, and 1.01, respectively., Conclusion: Ethiopian health facilities were faced with antibiotic overprescribing, short consultation, and dispensing times, poor labeling of medicines, poor availability of key drugs, and nonadherence to the essential drug list. Routine, multidisciplinary awareness creation, and regulation should be implemented to promote rational medicine use at a national level., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2021 Solomon Ahmed Mohammed and Abebe Getie Faris.)

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objectives: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, many US clinics have shifted some or all of their practice from in-person to virtual visits. In this study, we assessed the use of telehealth among primary care and specialty clinics, by targeting healthcare administrators via multiple channels., Methods: Using an online survey, we assessed the use of, barriers to, and reimbursement for telehealth. Respondents included clinic administrators (chief executive officers, vice presidents, directors, and senior-level managers)., Results: A total of 85 complete responses were recorded, 79% of which represented solo or group practices and 63% reported a daily patient census >50. The proportion of clinics that delivered ≥50% of their consults using telehealth increased from 16% in March to 42% in April, 35% in May, and 30% in June. Clinics identified problems with telehealth reimbursement; although 63% of clinics reported that ≥75% of their telehealth consults were reimbursed, only 51% indicated that ≥75% of their telehealth visits were reimbursed at par with in-person office visits. Sixty-five percent of clinics reported having basic or foundational telehealth services, whereas only 9% of clinics reported advanced telehealth maturity. Value-based care participating clinics were more likely to report advanced telehealth services (27%), compared with non-value-based care clinics (3%)., Conclusions: These findings highlight the adaptability of clinics to quickly transition and adopt telehealth. Uncertainty about reimbursement and policy changes may make the shift temporal, however.

One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a "black box." Machine Learning (ML) can assist in reading radiological, endoscopic and histological pictures, suggesting diagnosis and predict disease outcome, and even recommending therapy and surgical decisions. However, clinical adoption of these AI tools has been slow because of a lack of trust. Besides clinician's doubt, patients lacking confidence with AI-powered technologies also hamper development. While they may accept the reality that human errors can occur, little tolerance of machine error is anticipated. In order to implement AI medicine successfully, interpretability of ML algorithm needs to improve. Opening the black box in AI medicine needs to take a stepwise approach. Small steps of biological explanation and clinical experience in ML algorithm can help to build trust and acceptance. AI software developers will have to clearly demonstrate that when the ML technologies are integrated into the clinical decision-making process, they can actually help to improve clinical outcome. Enhancing interpretability of ML algorithm is a crucial step in adopting AI in medicine., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

There is a global interest in optimizing post-surgical tissue repair strategies, leading to better patient outcomes and fewer complications, most ideally with reduced overall cost. In this regard, in recent years, the interest in autologous biological treatments in orthopedic surgery and sports medicine has increased greatly, and the addition of platelet-rich plasma (PRP) to the surgical armamentarium is of particular note. Unfortunately, the number of PRP preparation devices has also grown immensely over the recent decades, raising meaningful concern for the considerable variation in the qualities of currently available PRP preparations. The lack of consensus on the standardization of PRP preparation and of agreement on condition specific PRP formulations is largely responsible for the sometimes contradictory outcomes in the literature. Furthermore, the full potential of PRP technology, the concept of individualized treatment protocols based on bioformulation options, and platelet dosing, angiogenesis, and antimicrobial and painkilling effects of PRP relevant to orthopedic surgery have rarely been addressed. In this review, we will discuss recent developments regarding PRP preparations and potential therapeutic effects. Additionally, we present a synopsis of several published data regarding PRP applications in orthopedic surgery for treating tendon injuries, inducing bone repair, strengthening spinal fusion outcomes, and supporting major joint replacements.

Background: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce., Objective: This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable., Methods: Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded., Results: In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001)., Conclusions: Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards., (©Simon Lennartz, Thomas Dratsch, David Zopfs, Thorsten Persigehl, David Maintz, Nils Große Hokamp, Daniel Pinto dos Santos. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.02.2021.)

The importance of diversity is self-evident in medicine and medical research. Not only does diversity result in more impactful scientific work, but diverse teams of researchers and clinicians are necessary to address health disparities and improve the health of underserved communities. MD/PhD programs serve an important role in training physician-scientists, so it is critical to ensure that MD/PhD students represent diverse backgrounds and experiences. Groups who are underrepresented in medicine and the biomedical sciences include individuals from certain racial and ethnic backgrounds, individuals with disabilities, individuals from disadvantaged backgrounds, and women. However, underrepresented students are routinely discouraged from applying to MD/PhD programs due to a range of factors. These factors include the significant cost of applying, which can be prohibitive for many students, the paucity of diverse mentors who share common experiences, as well as applicants' perceptions that there is inadequate support and inclusion from within MD/PhD programs. By providing advice to students who are underrepresented in medicine and describing steps programs can take to recruit and support minority applicants, we hope to encourage more students to consider the MD/PhD career path that will yield a more productive and equitable scientific and medical community.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

Introduction: Clinical decision-making is a daily practice conducted by medical practitioners, yet the processes surrounding it are poorly understood. The influence of 'shortcuts' in clinical decision-making, known as heuristics, remains unknown. This paper explores heuristics and the valuable role they play in medical practice, as well as offering potential solutions to minimize the risk of incorrect decision-making., Method: The quasi-systematic review was conducted according to modified PRISMA guidelines utilizing the electronic databases Medline, Embase and Cinahl. All English language papers including bias and the medical profession were included. Papers with evidence from other healthcare professions were included if medical practitioners were in the study sample., Discussion: The most common decisional shortcuts used in medicine are the Availability, Anchoring and Confirmatory heuristics. The Representativeness, Overconfidence and Bandwagon effects are also prevalent in medical practice. Heuristics are mostly positive but can also result in negative consequences if not utilized appropriately. Factors such as personality and level of experience may influence a doctor's use of heuristics. Heuristics are influenced by the context and conditions in which they are performed. Mitigating strategies such as reflective practice and technology may reduce the likelihood of inappropriate use., Conclusion: It remains unknown if heuristics are primarily positive or negative for clinical decision-making. Future efforts should assess heuristics in real-time and controlled trials should be applied to assess the potential impact of mitigating factors in reducing the negative impact of heuristics and optimizing their efficiency for positive outcomes.

The historical reliance of biological research on the use of animal models has sometimes made it challenging to address questions that are specific to the understanding of human biology and disease. But with the advent of human organoids - which are stem cell-derived 3D culture systems - it is now possible to re-create the architecture and physiology of human organs in remarkable detail. Human organoids provide unique opportunities for the study of human disease and complement animal models. Human organoids have been used to study infectious diseases, genetic disorders and cancers through the genetic engineering of human stem cells, as well as directly when organoids are generated from patient biopsy samples. This Review discusses the applications, advantages and disadvantages of human organoids as models of development and disease and outlines the challenges that have to be overcome for organoids to be able to substantially reduce the need for animal experiments.

Contemporary medicine is characterized by an increasing subspecialization and the acquisition of a greater knowledge about the interaction among the different body structures (biosemiotics), both in health and disease. This article proposes a new conceptualization of the body based on considering it as a biological space (cells, tissues, and organs) and a biosemiotic space (exchange of signs among them). Its development would lead to a new subspecialty focused on the study and interference of disease biosemiotics (biosemiotic medicine), which would trigger a processbased medicine centered on early diagnosis and management of disease., Competing Interests: None, (Sociedad Argentina de Pediatría.)

The 2019 Entomological Society of America annual meeting was held in St. Louis, Missouri, just blocks away from the iconic Gateway Arch. Representing a 'gateway to the West', this inspired the theme of the Highlights in Medical Entomology to reflect on the accomplishments of the past year as we move into a 'new frontier' of vector biology research. Papers were selected broadly across arthropods that influence public health, focusing on topics ranging from West Nile virus transmission, ticks and tick-borne disease, to advances in genetics and 'big data' studies. This included current perspectives on West Nile virus ecology and epidemiology, which has now been endemic in the United States for 20 yr. Additional topics such as the advantages of citizen science and the importance of scientific communication were also discussed. Together, these papers demonstrate the achievements of the vector community while emphasizing the challenges that we collectively face to reduce the burden of vector-borne disease., (© The Author(s) 2020. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Gender inequality exists in advanced faculty and leadership positions at academic medical centers; however, despite growing awareness, how to best approach and rectify the issue is unknown. To energize the conversation on gender inequality at one academic medical center, chairs and women faculty were surveyed to identify barriers faced by women navigating their careers. A symposium with short talks to increase awareness, a panel with University leaders to discuss issues and successful strategies to overcome gaps, and focus groups to delve further into key areas that underlie inequity through an active café style format were planned and implemented. This multifaceted approach resulted in a wealth of knowledge. The symposium and panel highlighted important relevant issues and offered personal strategies for successful career advancement, while the focus group discussions further identified barriers and inspired ongoing efforts across departments and novel approaches to overcome three key issues (work-life integration, deliberate promotion of mentor/sponsor relationships, and overcoming unconscious bias) identified through the initial surveys. Compiled data were then disseminated to participants and University leaders to enhance awareness of available programs and prompt action in critical areas lacking support. Overall, the approach indicated that securing support from leaders and the academic community alike are pertinent to emphasize actions needed to overcome issues affecting women in academic medicine. Moreover, bringing leaders and faculty together for an informational session and brainstorming appears to energize the conversation. Such efforts can ultimately instill change and establish an inclusive environment where all members of the academic medicine community can thrive.

Neointimal formation and atherogenesis are major vascular complications following percutaneous coronary intervention, and there is lack of pharmacological therapy. This study was aimed to examine the effect of forskolin (FSK), a cyclic adenosine monophosphate (cAMP)-elevating agent, on vascular response to angioplasty wire injury and on atherogenesis in mice. Forskolin treatment reduced neointima formation at 7 and 28 days after wire injury. Early morphometrics of the injured vessels revealed that FSK treatment enhanced endothelial repair and reduced inflammatory cell infiltration. In vitro treatment of primary aortic cells with FSK, at 3-100 μmol/L, increased endothelial cell proliferation, whereas FSK, at 30-100 μmol/L, inhibited smooth muscle cell proliferation. FSK inhibited lipopolysaccharide-induced leucocyte-endothelial interaction in vitro and in vivo. In a mouse model of atherosclerosis driven by dyslipidaemia and hypertension, FSK administration increased endothelial repair and reduced atherosclerotic plaque formation, without affecting blood pressure, plasma lipids or aortic aneurysms formation. In summary, FSK, at doses relevant to human therapeutic use, protects against neointimal hyperplasia and atherogenesis, and this is attributable to its activities on pro-endothelial repair and anti-inflammation. This study raises a potential of clinical use of FSK as an adjunct therapy to prevent restenosis and atherosclerosis after percutaneous coronary intervention., (© 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Competing Interests: The authors declare no competing interest.

Introduction: Competency-based medical education (CBME) hinges on robust assessment. However, integrating regular workplace-based assessment within demanding and sometimes chaotic clinical environments remains challenging. Many faculty lack assessment expertise, and some programs lack the infrastructure and faculty numbers to fulfill CBME's mandate. Recognizing this, we designed and implemented an assessment innovation that trains and deploys a cadre of faculty to assess in specialties outside their own. Specifically, we explored trainees' perceptions of and receptiveness to this novel assessment approach., Methods: Within Western University's Surgical Foundations program, 27 PGY‑1 trainees were formatively assessed by trained non-surgeons on a basic laparoscopic surgical skill. These assessments did not impact trainees' progression. Four focus groups were conducted to gauge residents' sentiments about the experience of cross-specialty assessment. Data were then analyzed using a thematic analysis approach., Results: While a few trainees found the experience motivating, more often trainees questioned the feedback they received and the practicality of this assessment approach to advance their procedural skill acquisition. What trainees wanted were strategies for improvement, not merely an assessment of performance., Discussion: Trainees' trepidation at the idea of using outside assessors to meet increased assessment demands appeared grounded in their expectations for assessment. What trainees appeared to desire was a coach-someone who could break their performance into its critical individual components-as opposed to an assessor whose role was limited to scoring their performance. Understanding trainees' receptivity to new assessment approaches is crucial; otherwise training programs run the risk of generating more assessments without added value.

Sustainability, i.e. the goal of maintaining a human-ecosystem equilibrium, is a comprehensive topic and suggestive ideal prompted by many current threats from and to humanity, such as climate change, environmental pollution, fatal drug reactions in modern medicine, and the like. Today, sustainable concepts are desperately needed, also in terms of medical treatment. Homeopathy offers an approach of rational and yet innocuous therapeutics, methodically not being reliant on prior animal testing and mass production of drugs, avoiding contamination of soil, air, or water, and toxic side-effects. It is based on a concept of specifically empowering the life-force of the patient to rid itself from pathogenic influences. Homeopathy, as outlined by its founder Samuel Hahnemann, may indeed be understood in a broader sense than just medicinal, and applied in a pedagogical, psychological, and political context as well. A similar methodically related approach may be found in Mahatma Gandhi's strategy of Satyagraha (holding onto truth) which also aims to specifically prompt and compel people to renounce their vices in a sustainable way. Both ways of healing in a moral sense, however, rest on premises whose plausibility has increasingly been questioned in the recent past. Thus, the waning appreciation of Hahnemann's and Gandhi's mindset is mirroring unsettling changes in the world's socioeconomic constitution rather than indicating its putative ineptitude to achieve sustainability on a global scale., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: It is challenging to add rigorous, competency-based communication skills training to existing clerkship structures. The authors embedded a simulation-based mastery learning (SBML) curriculum into a medicine subinternship to demonstrate feasibility and determine the impact on the foundational skill of breaking bad news (BBN)., Method: All fourth-year students enrolled in a medicine subinternship at Northwestern University Feinberg School of Medicine from September 2017 through August 2018 were expected to complete a BBN SBML curriculum. First, students completed a pretest with a standardized patient using a previously developed BBN assessment tool. Learners then participated in a 4-hour BBN skills workshop with didactic instruction, focused feedback, and deliberate practice with simulated patients. Students were required to meet or exceed a predetermined minimum passing standard (MPS) at posttest. The authors compared pretest and posttest scores to evaluate the effect of the intervention. Participant demographic characteristics and course evaluations were also collected., Results: Eighty-five students were eligible for the study, and 79 (93%) completed all components. Although 55/79 (70%) reported having personally delivered serious news to actual patients, baseline performance was poor. Students' overall checklist performance significantly improved from a mean of 65.0% (SD = 16.2%) items correct to 94.2% (SD = 5.9%; P < .001) correct. There was also statistically significant improvement in scaled items assessing quality of communication, and all students achieved the MPS at mastery posttest. All students stated they would recommend the workshop to colleagues., Conclusions: It is feasible to embed SBML into a required clerkship. In the context of this study, rigorous SBML resulted in uniformly high levels of skill acquisition, documented competency, and was positively received by learners.

Objective. To review the literature regarding the use of learning style frameworks in health science education, with particular attention to learning outcomes and use for self-awareness. Findings. Of the 415 articles identified in an initial search of the literature, 31 articles involving learning style frameworks were included after screening titles, abstracts, and full texts. Multiple learning style frameworks, including VARK, Kolb Learning Style Inventory, Honey and Mumford Learning Style Questionnaire, and Pharmacist Inventory of Learning Styles, have been used in various health science education disciplines, including medicine, nursing, and pharmacy. Most publications were descriptive in nature, reporting the learning styles of the given student cohort. Most studies that attempted to find a correlation between learning style and learning outcomes found none. In cases where a correlation was found, it was weak or inconsistent with findings from other published studies. No identified studies described use of learning style frameworks for increasing self-awareness in learners. Summary. While several different learning style inventories have been used to assess health science education students, their utility for predicting learning outcomes appears to be weak. Using learning style inventories to improve learner self-awareness is an unexplored area of education and research., (© 2020 American Association of Colleges of Pharmacy.)

Iron is essential for all living organisms. Many iron-containing proteins and metabolic pathways play a key role in almost all cellular and physiological functions. The diversity of the activity and function of iron and its associated pathologies is based on bond formation with adjacent ligands and the overall structure of the iron complex in proteins or with other biomolecules. The control of the metabolic pathways of iron absorption, utilization, recycling and excretion by iron-containing proteins ensures normal biologic and physiological activity. Abnormalities in iron-containing proteins, iron metabolic pathways and also other associated processes can lead to an array of diseases. These include iron deficiency, which affects more than a quarter of the world's population; hemoglobinopathies, which are the most common of the genetic disorders and idiopathic hemochromatosis. Iron is the most common catalyst of free radical production and oxidative stress which are implicated in tissue damage in most pathologic conditions, cancer initiation and progression, neurodegeneration and many other diseases. The interaction of iron and iron-containing proteins with dietary and xenobiotic molecules, including drugs, may affect iron metabolic and disease processes. Deferiprone, deferoxamine, deferasirox and other chelating drugs can offer therapeutic solutions for most diseases associated with iron metabolism including iron overload and deficiency, neurodegeneration and cancer, the detoxification of xenobiotic metals and most diseases associated with free radical pathology., Competing Interests: The authors declare no conflicts of interest.

Suicide is among the most important causes of mortality in medicine as it is the most common cause of death due to illness from the teenage years into middle age. Yet our approach to mental health research aimed at suicide prevention has often diverged from accepted practices in other areas of medicine. This includes the exclusion of those at highest risk of suicide from clinical trials and the recent emphasis on prediction. In this Viewpoint, we propose that comparing our approach to that of other medical specialties would help us to avoid strategic errors and discuss the implications for the field of suicide prevention.