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Concerns regarding the ethical justification for the use of single-encounter, procedure-based examinations on live patients for the licensure of dental hygienists and dentists in the United States persists despite decades of debate and publication on the subject. The purpose of this literature review was to summarize the specific ethical concerns and quantify recommendations in favor or against this examination methodology. A population, intervention, control or comparison, outcome (PICO) question was developed to review the topic as follows: \"For individuals receiving dental care as part of determination of candidates for competency and readiness for licensure, do patient-based licensure examinations, as compared to other assessment methods, violate or infringe upon ethical principles or ethical standards for health care or society?\" An electronic search was performed in three databases: PubMed/Medline, Scopus, and Embase. Key search terms and Medical Subject Headings (MeSH) included the following: ethics, clinical, competence, dental, dental hygiene, dentistry, education, licensure, live patient, and practice. Ethical concerns about the use of patient examinations have been published in the professional literature for over 35 years. Of the 29 selected or endpoint articles identified, 27 articles cited one or more ethical concerns relating to single-encounter patient-based examinations while 20 articles recommended the elimination of this type of examination with an additional 6 articles citing elimination as an option in resolving the ethical issues regarding this type of licensure examination. The literature holds a predominant, prevailing professional opinion that single-encounter, procedure-based examinations on live patients presents significant ethical concerns and should be eliminated as a method in initial dental hygiene and dental licensure. The literature also suggests that state dental boards should initiate corrective regulatory or legislative actions to expeditiously end recognition of live patient examinations in their licensure processes.

This paper will identify existing models of professional regulatory reform that could ensure a single licensing process that results in multi-jurisdictional licensure. The paper will also distinguish models or ad hoc arrangements that fail to achieve such a result. This is a timely topic in Canada because its professional regulatory framework is being examined for modernization. The Canadian professional regulatory framework for health care providers is a legacy of the Constitution of 1867. It can be characterized as a Federation of 13 different jurisdictional systems with each province or territory having exclusive jurisdiction over regulation of its health professionals. This results in differing entry to practice requirements, standards of practice, classes or categories of registration and transfer criteria for eligibility from other provinces. The United States nursing state board regulators, the Australian Commonwealth, and their state governments have moved from their original regulatory frameworks to modern ones. Their models are more supportive of mobility, cross-border virtual care, education, and health provider professional development and well-being. Aside from recent discussions in the 4 Canadian Atlantic provinces, there has been little will, effort, or advancement to modernize the regulatory framework in Canada to support multi-jurisdictional licensure. This paper aims to briefly describe 6 existing models that support multi-jurisdictional licensure. In the fall of 2022, the 4 Atlantic Premiers (akin to state Governors in the US) asked each of their medical regulatory authority (akin to State Medical Boards) to develop a licensing system such that physicians could practice in all 4 Atlantic provinces without the need to acquire multiple licenses. Two models will be discussed that meet this recently stated objective of the Atlantic Premiers while the others do not.

Background Large language models like ChatGPT have revolutionized the field of natural language processing with their capability to comprehend and generate textual content, showing great potential to play a role in medical education. This study aimed to quantitatively evaluate and comprehensively analysis the performance of ChatGPT on three types of national medical examinations in China, including National Medical Licensing Examination (NMLE), National Pharmacist Licensing Examination (NPLE), and National Nurse Licensing Examination (NNLE). Methods We collected questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. We applied direct instruction strategy via multiple prompts to force ChatGPT to generate the clear answer with the capability to distinguish between single-choice and multiple-choice questions. Results ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT’s performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening. Conclusions These results indicate ChatGPT failed the NMLE, NPLE and NNLE in China, spanning from year 2017 to 2021. but show great potential of large language models in medical education. In the future high-quality medical data will be required to improve the performance.

Twenty genetic therapies have been approved by the US Food and Drug Administration to date, a number that now includes the first CRISPR genome-editing therapy for sickle cell disease—CASGEVY (exagamglogene autotemcel, Vertex Pharmaceuticals). This extraordinary milestone is widely celebrated owing to the promise for future genome-editing treatments of previously intractable genetic disorders and cancers. At the same time, such genetic therapies are the most expensive drugs on the market, with list prices exceeding US$4 million per patient. Although all approved cell and gene therapies trace their origins to academic or government research institutions, reliance on for-profit pharmaceutical companies for subsequent development and commercialization results in prices that prioritize recouping investments, paying for candidate product failures and meeting investor and shareholder expectations. To increase affordability and access, sustainable discovery-to-market alternatives are needed that address system-wide deficiencies. Here we present recommendations of a multidisciplinary task force assembled to chart such a path. We describe a pricing structure that, once implemented, could reduce per-patient cost tenfold and propose a business model that distributes responsibilities while leveraging diverse funding sources. We also outline how academic licensing provisions, manufacturing innovation and supportive regulations can reduce cost and enable broader patient treatment. Implementation of new pricing and business structures and improved licensing and manufacturing processes could reduce the per-patient cost of gene therapy tenfold.

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. Med-PaLM, a state-of-the-art large language model for medicine, is introduced and evaluated across several medical question answering tasks, demonstrating the promise of these models in this domain.

Many health care organizations are experimenting with new ways of unleashing their workforce’s potential. Such approaches require reconfiguring of provider roles, but states and organizations often place restrictions on health professionals’ scope of practice.

This paper aims to contextualise the current state of medical registration for international medical graduates (IMGs) in Aotearoa New Zealand by providing a historical overview of medical licensing policies for IMGs since 1849. This paper and accompanying timeline were prepared from a document analysis of 306 historical and current medical licensing policy documents and other grey literature, including parliamentary Acts and Bills, annual reports, workforce surveys and media reports. Medical licensing policies originated in the colonial era and have historically privileged doctors from the United Kingdom, Ireland and other Commonwealth countries. The New Zealand Registration Examination pathway for IMGs who did not qualify or work in accepted countries was established in the 1990s, although its origins can be seen in policies from as early as 1905. Although medical licensing policies have been adapted over the past 175 years, these changes tend to follow a pattern of oscillation between stringency and leniency, rather than linear progression. As a result, there are striking similarities between contemporary and colonial medical licensing policies in the way IMGs are categorised and distinguished that could benefit from further clarification and consideration by policymakers.

To gain insight into how governments regulate their health laboratory sector, by reviewing health laboratory licensing legislation across different health-care systems in a diverse range of 18 countries worldwide. We selected countries for a diverse range of health-care systems, geography, income level, licensing legislation and standards adhered to. We selected aspects of health laboratory licensing that were consistently present in different countries and suitable for meaningful comparison, focusing on legislative approaches, certification and accreditation models, regulation, quality assurance, and biosafety and biosecurity requirements. Our analysis revealed that the licensing legislation for health laboratories typically encompasses two principal components: administrative procedural law and substantive law. We observed that the different ways in which countries regulate their health laboratories could be categorized within three distinct legislative approaches, namely: standalone licensing act, general licensing act and one based on a health insurance contract. Most countries used a two-step application process, comprising registration and licensing steps. License validity periods ranged over 1-5 years, with some countries opting for permanent licenses. Countries adopted diverse standards and requirements, with some mandating accreditation. Our findings highlight the diverse legislative approaches to health laboratory licensing, reflecting varying national capacities and regulatory priorities. Integrating robust quality standards, especially those aligned with International Organization for Standardization standard no. 15189, is essential for strengthening laboratory oversight and public health response. Effective licensing frameworks not only enhance domestic laboratory systems but also contribute to global health security through alignment with international obligations.

Aim To explore the strategies used by nursing students in passing the nursing licensure examination. Design This study uses a classic grounded theory design to explore the social processes influencing a nursing license examination. Methods Eight graduate students participated in this research study and were interviewed in‐depth twice. The Classic Grounded Theory method of Glaser was applied to collect and analyse the data until saturation was reached. Results The findings revealed that students who passed the nursing licensure examination described the strategies as a preliminary model comprising a core category, Reviewing (Phase 1), which consisted of two sub‐categories: Entering Time and Reviewing Styles. Additionally, two other main categories emerged: the Tutoring category (Phase 2) and the Testing Practice category (Phase 3). It was observed that each course (subject) does not necessarily follow a specific order in traversing these phases; they may move back and forth between them until the conclusion of the examination. Furthermore, it was found that the time allocated to Entering Time and completing the three phases significantly influences the successful passing of the nursing licensure examination.

Burgeoning technology holds the potential to improve the quality and reduce the cost of medical care. However, the evidence base for its use is currently limited. Research is needed to define areas of medicine in which the application of telehealth technology improves outcomes.