Perceived Gaps in Knowledge for the Advanced Heart Failure and Transplant Cardiology Fellow: the Results of a National Needs-Assessment Survey.

BACKGROUND: The field of Advanced Heart Failure and Transplant Cardiology has evolved greatly since it was first established. We sought to elicit program directors' and fellows' viewpoints on potential curricular deficits so we can better meet the educational goals of current and future fellows. METHODS AND RESULTS: We surveyed advanced heart failure and transplant cardiology program directors and fellows concerning their perceptions of the current adequacy of training and their desire for additional training needed to achieve medical competency in advanced heart failure and transplant cardiology at their institutions, as defined by the 2017 ACC Advanced Training Statement. Survey results identified key competencies deemed to be inadequately addressed during training and those in which a moderate or significant additional amount of training was desired. These competencies were identified within the 4 main domains of the fellowship: heart failure, pulmonary hypertension, mechanical circulatory support, and heart transplantation. CONCLUSIONS: This study highlights key medical-knowledge competencies that are inadequately addressed by current fellowship training in advanced heart failure and transplant cardiology. Fellowship programs should develop curricula that focus on the integration of these competencies into training to ensure that fellows are well equipped to care for patients.

Enhancing Human Papillomavirus Vaccination Rates through Better Knowledge? Insights from a Survey among German Medical Students.

INTRODUCTION: Vaccination against human papillomavirus (HPV) significantly reduces the risk for malignant diseases like cervix, anal, or penile cancer. However, although vaccination rates are rising, they are still too low mirroring a lack of disease awareness in the community. This study aims to evaluate knowledge about HPV vaccination as well as the vaccination rate among German medical students. MATERIAL AND METHODS: Medical students were surveyed during a German medical students' sports event. The self-designed survey on HPV vaccination consisted of 24 items. The data collection was anonymous. RESULTS: Among 974 participating medical students 64.9% (632) were women, 335 (34.4%) were male and 7 (0.7%) were nonbinary. Mean age was 23.1 ± 2.7 (± standard deviation; range 18-35) years. Respondents had studied mean 6.6 ± 3.3 (1-16) semesters and 39.4% (383) had completed medical education in urology. 613 (64%) respondents reported that HPV had been discussed during their studies. 7.6% (74) had never heard of HPV. In a multivariate model female gender, the knowledge about HPV, and having worked on the topic were significantly associated with being HPV-vaccinated. Older students were vaccinated less likely. CONCLUSIONS: Better knowledge and having worked on the topic of HPV were associated with a higher vaccination rate. However, even in this highly selected group the knowledge about HPV vaccination was low. Consequently, more information and awareness campaigns on HPV vaccination are needed in Germany to increase vaccination rates.

Proposing a new history of grief's medicalisation: A critical discourse analysis.

Conceptualisations of grief have transformed significantly in recent decades, from an experience accepted and expressed in community spaces to a diagnosable clinical phenomenon. Narratives of this transformation tend to focus on grief's relationship to major depression, or on recent nosological changes. This paper examines the possibility of a new narrative for medicalisation by grounding in the networks of language and power created around 'grief' through a critical discourse analysis of psy-discipline articles (n = 70) published between 1975 and 1995. Focusing on shifts in definitions of, methods used to approach, and rationales motivating study of the experience, it posits that the psy-disciplines exerted exclusive expertise over grief decades before its creation as a diagnosis. By reconceptualising grief in the terms of psy-specific symptoms and functional performance and by approaching it with the decontextualising and interventionist methods of an increasingly scientific psy-discipline, the psy-community medicalised grief between 1975 and 1995. Identifying neoliberal and other cultural influences shaping this process of medical construction and reconsidering narratives of grief's history mindful of the powers exerted in medicalisation, this paper establishes that these moments played a critical role in the development of the present's grief.

Diagnosing social ills: Theorising social determinants of health as a diagnostic category.

Medicine, as an institution and discipline, has embraced social determinants of health as a key influence on clinical practice and care. Beyond simply acknowledging their importance, most recent versions of the International Classification of Diseases explicitly codify social determinants as a viable diagnostic category. This diagnostic shift is noteworthy in the United States, where 'Z-codes' were introduced to facilitate the documentation of illiteracy, unemployment, poverty and other social factors impacting health. Z-codes hold promise in addressing patients' social needs, but there are likely consequences to medicalising social determinants. In turn, this article provides a critical appraisal of Z-codes, focussing on the role of diagnoses as both constructive and counterproductive sources of legitimacy, knowledge and responsibility in our collective understanding of health. Diagnosis codes for social determinants are powerful bureaucratic tools for framing and responding to psychosocial risks commensurate with biophysiological symptoms; however, they potentially reinforce beliefs about the centrality of individuals for addressing poor health at the population level. I contend that Z-codes demonstrate the limited capacity of diagnoses to capture the complex individual and social aetiology of health, and that sociology benefits from looking further 'upstream' to identify the structural forces constraining the scope and utility of diagnoses.

What have we learned about constructed response short-answer questions from students and faculty? A multi-institutional study.

PURPOSE: The purpose of this study was to enrich understanding about the perceived benefits and drawbacks of constructed response short-answer questions (CR-SAQs) in preclerkship assessment using Norcini's criteria for good assessment as a framework. METHODS: This multi-institutional study surveyed students and faculty at three institutions. A survey using Likert scale and open-ended questions was developed to evaluate faculty and student perceptions of CR-SAQs using the criteria of good assessment to determine the benefits and drawbacks. Descriptive statistics and Chi-square analyses are presented, and open responses were analyzed using directed content analysis to describe benefits and drawbacks of CR-SAQs. RESULTS: A total of 260 students (19%) and 57 faculty (48%) completed the survey. Students and faculty report that the benefits of CR-SAQs are authenticity, deeper learning (educational effect), and receiving feedback (catalytic effect). Drawbacks included feasibility, construct validity, and scoring reproducibility. Students and faculty found CR-SAQs to be both acceptable (can show your reasoning, partial credit) and unacceptable (stressful, not USMLE format). CONCLUSIONS: CR-SAQs are a method of aligning innovative curricula with assessment and could enrich the assessment toolkit for medical educators.

ChatGPT's adherence to otolaryngology clinical practice guidelines.

OBJECTIVES: Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines. METHODS: We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses. RESULTS: The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests. CONCLUSIONS: Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

Knowledge sharing among academics from Egyptian medical schools during the COVID-19 pandemic.

BACKGROUND: Sharing knowledge among scientists during global health emergencies is a critical issue. So, this study investigates knowledge-sharing behavior and attitude among staff members of 19 Medical schools in Egyptian universities during the COVID-19 pandemic. METHODS: Across-sectional study was conducted using a web-based questionnaire. A total of 386 replies from the 10,318 distributed questionnaires were analyzed. Descriptive statistics were computed using SPSS (version 22) to summarize the demographic data. Inferential statistics such as the independent and chi-square test were used to achieve the study aims. RESULTS: More than half of the respondents (54.4%) indicated that their levels of knowledge of COVID-19 were good. Most participants (72.5%) reported that scientific publications and international websites were the most reliable source of their knowledge concerning COVID-19. More than 46% stated they sometimes share their knowledge. The lack of time to share and organizational culture were the most important factors that could affect their knowledge sharing. Additionally, about 75% of participants shared knowledge about treatment.

The use of specialized (medical) knowledge in the criminal process: the practice of the European Court of Human Rights.

OBJECTIVE: Aim: This article is aimed at raising awareness and stimulating scientific discussion on the necessity of involving qualified medical professionals in conducting criminal procedural actions that involve intervention in human somatic rights, in order to further improve the legal instruments ensuring compliance with the European Court of Human Rights (hereinafter referred to as the ECHR) standards in this field. PATIENTS AND METHODS: Materials and Methods: In preparing the article, the following issues were worked out: the provisions of international legal acts; legal positions of the ECHR related to the use of medical knowledge in the criminal process; scientific studies of various aspects of the use of medical knowledge in the criminal process. The methodological basis of the research is dialectical, comparative-legal, systemic-structural, analytical, synthetic, complex research methods. CONCLUSION: Conclusions: The use of medical knowledge in the criminal process generally takes two forms: (a) expert and (b) ancillary. The expert form, particularly forensic medical examination, must adhere to a set of criteria reflected in the practice of the ECHR. Personal searches involving penetration into human body cavities generally align with the requirements of the he European Convention on Human Rights (hereinafter referred to as the Convention), provided certain conditions are met, including medical considerations. The criterion for the admissibility of coercive collection of biological samples for examination is the existence of samples independent of the individual's will.

[The body in old age: Pernicious medical and materialist thinking]. / Le corps de la vieillesse : une pensée médicale et matérialiste pernicieuse.

This article looks at the historical construction of knowledge about the elderly body from a medical perspective that is concerned with the materiality of the body and associated losses. It recalls, presents and analyses the paradigm of loss, decline and failure that dominates the way care is provided, and examines the issues associated with this domination. By presenting old age as a social construct produced by language and subject to values relating to a certain performance of the body, the author invites us to shift our perspective and take a finer, more complex and broader view of the body and the experience of being old.

MKCL: Medical Knowledge with Contrastive Learning model for radiology report generation.

Automatic radiology report generation has the potential to alert inexperienced radiologists to misdiagnoses or missed diagnoses and improve healthcare delivery efficiency by reducing the documentation workload of radiologists. Motivated by the continuous development of automatic image captioning, more and more deep learning methods have been proposed for automatic radiology report generation. However, the visual and textual data bias problem still face many challenges in the medical domain. Additionally, do not integrate medical knowledge, ignoring the mutual influences between medical findings, and abundant unlabeled medical images influence the accuracy of generating report. In this paper, we propose a Medical Knowledge with Contrastive Learning model (MKCL) to enhance radiology report generation. The proposed model MKCL uses IU Medical Knowledge Graph (IU-MKG) to mine the relationship among medical findings and improve the accuracy of identifying positive diseases findings from radiologic medical images. In particular, we design Knowledge Enhanced Attention (KEA), which integrates the IU-MKG and the extracted chest radiological visual features to alleviate textual data bias. Meanwhile, this paper leverages supervised contrastive learning to relieve radiographic medical images which have not been labeled, and identify abnormalities from images. Experimental results on the public dataset IU X-ray show that our proposed model MKCL outperforms other state-of-the-art report generation methods. Ablation studies also demonstrate that IU medical knowledge graph module and supervised contrastive learning module enhance the ability of the model to detect the abnormal parts and accurately describe the abnormal findings. The source code is available at: https://github.com/Eleanorhxd/MKCL.

A method for structuring complex clinical knowledge and its representational formalisms to support composite knowledge interoperability in healthcare.

INTRODUCTION: The use and interoperability of clinical knowledge starts with the quality of the formalism utilized to express medical expertise. However, a crucial challenge is that existing formalisms are often suboptimal, lacking the fidelity to represent complex knowledge thoroughly and concisely. Often this leads to difficulties when seeking to unambiguously capture, share, and implement the knowledge for care improvement in clinical information systems used by providers and patients. OBJECTIVES: To provide a systematic method to address some of the complexities of knowledge composition and interoperability related to standards-based representational formalisms of medical knowledge. METHODS: Several cross-industry (Healthcare, Linguistics, System Engineering, Standards Development, and Knowledge Engineering) frameworks were synthesized into a proposed reference knowledge framework. The framework utilizes IEEE 42010, the MetaObject Facility, the Semantic Triangle, an Ontology Framework, and the Domain and Comprehensibility Appropriateness criteria. The steps taken were: 1) identify foundational cross-industry frameworks, 2) select architecture description method, 3) define life cycle viewpoints, 4) define representation and knowledge viewpoints, 5) define relationships between neighboring viewpoints, and 6) establish characteristic definitions of the relationships between components. System engineering principles applied included separation of concerns, cohesion, and loose coupling. RESULTS: A "Multilayer Metamodel for Representation and Knowledge" (M*R/K) reference framework was defined. It provides a standard vocabulary for organizing and articulating medical knowledge curation perspectives, concepts, and relationships across the artifacts created during the life cycle of language creation, authoring medical knowledge, and knowledge implementation in clinical information systems such as electronic health records (EHR). CONCLUSION: M*R/K provides a systematic means to address some of the complexities of knowledge composition and interoperability related to medical knowledge representations used in diverse standards. The framework may be used to guide the development, assessment, and coordinated use of knowledge representation formalisms. M*R/K could promote the alignment and aggregated use of distinct domain-specific languages in composite knowledge artifacts such as clinical practice guidelines (CPGs).

Hearts in their hands-Physicians' gestures embodying shared professional knowledge around the world.

The biomedical approach to medical knowledge is widely accepted around the world. This article considers whether the incorporated aspects of physician-patient interaction have become similarly common across the globe by comparing the gestures that physicians use in their interactions with patients. Up to this point, there has been little research on physicians' use of gestures in health-care settings. We explore how-in four university hospitals in Turkey, the People's Republic of China, The Netherlands and Germany-physicians use gesture in their discussions with simulated patients about the condition of heart failure. Our analysis confirms the importance of gestures for organising both the personal interaction and the knowledge transfer between physician and patient. From the perspective of global comparison, it is notable that physicians in all four hospitals used similar gestures. This demonstrates the globality of biomedical knowledge in an embodied mode. Physicians used gestures for a range of purposes, including to convey the idea of an 'anatomical map' and for constructing visual models of (patho-)physiological processes. Since biomedical language is rife with metaphor, it was not surprising that we also identified an accompanying metaphorical gesture which has a similar form in the various locations that were part of the study.

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning.

BACKGROUNDS: Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios. METHODS: To address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients. RESULTS: Experimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction. CONCLUSIONS: Our KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.

Differential disease diagnoses of epistaxis based on dynamic uncertain causality graph.

PURPOSE: Epistaxis is a common symptom and can be caused by various diseases, including nasal diseases, systemic diseases, etc. Many misdiagnosis and missed diagnosis of epistaxis are caused by lack of clinical knowledge and experience, especially some interns and the clinicans in primary hospitals. To help inexperienced clinicans improve their diagnostic accuracies of epistaxis, a computer-aided diagnostic system based on Dynamic Uncertain Causality Graph (DUCG) was designed in this study. METHODS: We build a visual epistaxis knowledge base based on medical experts' knowledge and experience. The knowledge base intuitively expresses the causal relationship among diseases, risk factors, symptoms, signs, laboratory checks, and image examinations. The DUCG inference algorithm well addresses the patients' clinical information with the knowledge base to deduce the currently suspected diseases and calculate the probability of each suspected disease. RESULT: The model can differentially diagnose 24 diseases with epistaxis as the chief complaint. A third-party verification was performed, and the total diagnostic precision was 97.81%. In addition, the DUCG-based diagnostic model was applied in Jiaozhou city and Zhongxian county, China, covering hundreds of primary hospitals and clinics. So far, the clinicians using the model have all agreed with the diagnostic results. The 432 real-world application cases show that this model is good for the differential diagnoses of epistaxis. CONCLUSION: The results show that the DUCG-based epistaxis diagnosis model has high diagnostic accuracy. It can assist primary clinicians in completing the differential diagnosis of epistaxis and can be accepted by clinicians.

Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform.

BACKGROUND: This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. METHOD: This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels-medical institutional level and public level-and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. RESULTS: (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. CONCLUSIONS: The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

Perceptions of a virtual education platform: how plastic surgery education has progressed during the COVID-19 pandemic at one academic institution.

BACKGROUND: To continue education during the COVID-19 pandemic, we implemented a Virtual Education Platform (VEP) and Virtual Visiting Professorship (VVP) in March 2020 into our plastic surgery residency curriculum. This study investigated resident and guest speaker perceptions of the VEP since the start of the pandemic. METHODS: The VEP consists of weekly VVP lectures and usual conferences held over Zoom. In May 2020, residents and speakers completed surveys that assessed the perceptions of the VEP using a 5-point Likert scale and open-ended responses. In August 2021, residents also completed follow-up surveys. RESULTS: A total of 19 (100%) residents and 10 (100%) speakers responded to the 2020 surveys and 15 (88.2%) residents responded to the 2021 follow-up survey. Speakers represented nine academic institutions, one international. 74% of residents responded that they learned a lot or a great deal from the VVP. In 2021, 100% of residents agreed that virtual conferences should remain a core component in PRS residency education, even after social distancing requirements subside. The VVP lectures were mentioned as the most helpful lectures in both years. Easy accessibility without travel time was the most mentioned advantage of the VEP in both years, with significantly more residents citing this benefit in 2021 (p = 0.0076). The most reported disadvantage for residents was the lack of social interaction and community in both years, with significantly more residents in 2021 citing this as a disadvantage (p = 0.0307). Residents' attitudes also shifted such that significantly more residents liked and were satisfied with the VVP lectures from 2020 to 2021 (p = 0.04). CONCLUSION: Over a year into the COVID-19 pandemic, resident perceptions of a virtual education platform and virtual visiting professorship were very positive. The quick development, implementation, and high efficacy of these educational experiences underscore that learning is possible in alternative forms in unprecedented times.

From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights.

Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.

Intellectual property protection for traditional medical knowledge in China's context: a round peg in a square hole?

This article is an examination of the extent to which traditional medical knowledge in China can be protected by intellectual property laws. The analysis begins by providing a global picture with regard to the historic origin of intellectual property, exploring the reasons why China does not have indigenous counterparts to the western system of intellectual property rights protecting its traditional knowledge (including traditional medical knowledge) and stating the problems of transplanting western intellectual property standards in China. A discussion follows on how China, under foreign pressure, has made efforts to comply with the changing standards mandated by various international, regional, and bilateral arrangements related to intellectual property, with examples of the development of China's patent law. China's approach towards the protection of traditional medical knowledge in various international fora related to intellectual property is explored. Finally, there is a specific examination of the compatibilities between the western system of intellectual property rights and traditional medical knowledge in China, at the national and community levels. This article argues that the system of intellectual property rights does not easily fit with China's traditional medical knowledge because of China's unique cultural traits, distinctive historical context and wide ethnic, religious, and local community diversity.

[Overview of the application of knowledge graphs in the medical field].

With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.

Solidarity and the ethics of exposing others to risk in medical research.

The ethical justifiability of the invitation of others to participate in research and their deliberate exposure to risks of harm is not a common topic in bioethics. If, however, some offers ought not to be made and the corresponding actions ought not to be facilitated, invitations to, and the conduct of, a medical study involving humans needs justification. This paper addresses this issue by linking the search for medical knowledge with solidarity. The argument begins with the observation that scientific research is aimed at general knowledge, which is a necessary condition of the social value of research. The applicability of this knowledge to many makes it potentially a public good; that is, a good that is available freely to all. For knowledge to be a public good, a social decision to make it freely available to all needs to be made. It is proposed that this decision be grounded in society's, and so in both researchers' and potential research participants', commitment to solidarity and its obligations of provision, sharing, support, and loyalty. These obligations imply, among other things, an imperfect obligation to participate in research and the corresponding entitlement of the investigators to invite others to participate in research, and so to expose them to its risks during implementation. This entitlement is exercised in an environment shaped by the standards and protections of research ethics and the relevant institutional arrangements.