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Educação, conhecimento, poder, tecnologia e inovação são conceitos cujos vínculos estão presentes em todas as relações humanas, em todos os momentos históricos e em todos os lugares. Um dos contextos nos quais a conexão entre esses conceitos demonstra grande relevância é o ambiente corporativo. Desta forma, o objetivo deste trabalho é promover reflexões sobre a possibilidade de ressignificação dos conceitos de técnica, tecnologia e inovação no âmbito da Educação Corporativa. Como método de pesquisa, foi empregada a revisão de literatura. Os resultados da pesquisa demonstram que esses conceitos podem ser empregados na gestão de pessoas e na gestão de conhecimento. Conclui-se que as reflexões a partir dessa temática podem alavancar os resultados das estratégias de ensino e aprendizagem voltadas para o desenvolvimento de pessoas e de organizações.

The study presented in this article aims to identify what are, in fact, the social missions of companies called “social companies” , and what are the benefits obtained from the use of ICTs by these companies. The research methods employed were bibliographic research and case study. The bibliographic research was elaborated from a systematic analysis and the use of a theoretical framework related to performance indicators, as proposed by Hutchinson and Molla (2009). The case study was carried out based on four social companies, with which observations were made in loco, application of interviews and analysis of the respective websites. The results indicated that the social mission of this type of company can be explained by the use of six indicators: access to markets, income generation, employment opportunities, social training, strengthening of relations with the sponsor and social outsourcing; and that these indicators require the use of ICTs to be developed. It is concluded that, if at least one of the indicators is present in these companies, ICTs can bring some kind of benefit.

As disciplinas de programação de computadores se posicionam entre as com maior índice de reprovação em graduações em computação. Porém, existem tecnologias com potencial para apoiar o processo de ensino e aprendizagem, permitindo aos discentes estudar extraclasse com uma abordagem diferente da tradicional, de aulas expositivas, tendo em seguida um feedback imediato por meio de resolução de problemas e avaliação automática das soluções construídas. A tecnologia Juiz On-line possui características fundamentadas na abordagem de ensino comportamental. A presente investigação objetiva analisar e discutir características da tecnologia, conectando-a com a abordagem comportamentalista, e propor sua aplicação em cursos superiores tecnológicos de computação.

Empresas brasileiras têm exigido novas competências de seus colaboradores. Para isso, novos modelos de gestão, aprendizagem e educação têm sido adotados, a fim de harmonizar os interesses das pessoas e suas carreiras com os objetivos estratégicos das corporações. Este artigo tem o objetivo de identificar relações entre gestão do conhecimento, aprendizagem organizacional e educação corporativa. A partir da abordagem dos conceitos em textos acadêmicos produzidos sobre os tópicos, os assuntos foram articulados de modo a trazer maior compreensão sobre o tema. Como resultado, foi possível analisar como o conhecimento é obtido, armazenado, disseminado e empregado pelas empresas, o que fomenta a aprendizagem no ambiente corporativo, viabilizando o planejamento e a implantação de programas de educação que não apenas qualificam os colaboradores, mas também os ajudam a exercer a liderança nos diversos grupos de trabalho das organizações, o que traz diversos benefícios para indivíduos, grupos, empresas, comunidades e para a sociedade.

As empresas brasileiras têm efetuado investimentos em educação para superarem crises, para adquirirem competividade para lidar com os desafios da economia atual e para alcançarem seus objetivos estratégicos. Este artigo tem como objetivo discutir a aplicação da teoria da aprendizagem significativa para o desenvolvimento de boas práticas de ensino na educação corporativa. Foi realizada uma pesquisa qualitativa, com base na revisão descritiva da literatura, a qual revelou estruturas e tendências a respeito da aplicabilidade dos pressupostos teóricos da aprendizagem significativa nos processos de ensino e aprendizagem nas empresas. Os resultados indicam que, para que ocorra aprendizagem significativa na educação corporativa, é necessário que os processos didáticos nas empresas sejam mais inclusivos em relação a novos conhecimentos, considerando a importância dos conhecimentos prévios dos indivíduos.

Este artigo faz uma análise da legislação ambiental brasileira, visando identificar suas referências à tecnologia da informação e dentro de suas exigências legais como ela influência a adoção de formas sustentáveis de uso da tecnologia da informação pelas empresas. Para tanto se procede a uma revisão bibliográfica da legislação promulgada até 2010, localizando-se as menções diretas e indiretas a tecnologia e analisando o contexto em que cada menção foi feita. A análise realizada indica que TIC é considerada pela legislação uma ferramenta fundamental para a preservação ambiental e como causadora de baixo impacto ambiental, dado que existem poucas referências as TIC neste sentido.

Objective: Determine how each organ component of the SOFA score differs in its contribution to mortality risk and how that contribution may change over time., Methods: We performed multivariate logistic regression analysis to assess the contribution of each organ component to mortality risk on Days 1 and 7 of an intensive care unit stay. We used data from two publicly available datasets, eICU Collaborative Research Database (eICU-CRD) (208 hospitals) and Medical Information Mart for Intensive Care IV (MIMIC-IV) (1 hospital). The odds ratio of each SOFA component that contributed to mortality was calculated. Mortality was defined as death either in the intensive care unit or within 72 hours of discharge from the intensive care unit., Results: A total of 7,871 intensive care unit stays from eICU-CRD and 4,926 intensive care unit stays from MIMIC-IV were included. Liver dysfunction was most predictive of mortality on Day 1 in both cohorts (OR 1.3; 95%CI 1.2 - 1.4; OR 1.3; 95%CI 1.2 - 1.4, respectively). In the eICU-CRD cohort, central nervous system dysfunction was most predictive of mortality on Day 7 (OR 1.4; 95%CI 1.4 - 1.5). In the MIMIC-IV cohort, respiratory dysfunction (OR 1.4; 95%CI 1.3 - 1.5) and cardiovascular dysfunction (OR 1.4; 95%CI 1.3 - 1.5) were most predictive of mortality on Day 7., Conclusion: The SOFA score may be an oversimplification of how dysfunction of different organ systems contributes to mortality over time. Further research at a more granular timescale is needed to explore how the SOFA score can evolve and be ameliorated.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably., (©Radha Nagarajan, Midori Kondo, Franz Salas, Emre Sezgin, Yuan Yao, Vanessa Klotzman, Sandip A Godambe, Naqi Khan, Alfonso Limon, Graham Stephenson, Sharief Taraman, Nephi Walton, Louis Ehwerhemuepha, Jay Pandit, Deepti Pandita, Michael Weiss, Charles Golden, Adam Gold, John Henderson, Angela Shippy, Leo Anthony Celi, William R Hogan, Eric K Oermann, Terence Sanger, Steven Martel. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.11.2024.)

Objectives: Septic shock is a common condition necessitating timely management including hemodynamic support with vasopressors. Despite the high prevalence and mortality, there is limited data characterizing patients who require three or more vasopressors. We sought to define the demographics, outcomes, and prognostic determinants associated with septic shock requiring three or more vasopressors., Design: This is a multicenter retrospective cohort of two ICU databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU-Clinical Research Database, which include over 400,000 patients admitted to 342 ICUs., Patients: Inclusion criteria entailed patients who were: 1) age 18 years old and older, 2) admitted to any ICU, 3) administered at least three vasopressors for at least 2 hours at any time during their ICU stay, and 4) identified to have sepsis based on the Sepsis-3 criteria., Interventions: None., Measurements and Main Results: A total of 3447 patients met inclusion criteria. The median age was 67 years, 60.5% were male, and 96.6% had full code orders at the time of the third vasopressor initiation. Septic shock requiring three or more vasopressors was associated with 57.6% in-hospital mortality. Code status changes occurred in 23.9% of patients following initiation of a third vasopressor. Elevated lactate upon ICU admission (odds ratio [95% CI], 2.79 [2.73-2.85]), increased duration of time between ICU admission and third vasopressor initiation (1.78 [1.69-1.87]), increased serum creatinine (1.61 [1.59-1.62]), and age above 60 years (1.47 [1.41-1.54]) were independently associated with an increased risk of mortality based on analysis of the MIMIC-IV database. Non-White race and Richmond Agitation-Sedation Scale scores were not associated with mortality., Conclusions: Septic shock requiring three vasopressors is associated with exceptionally high mortality. Knowledge of patients at highest risk of mortality in this population may inform management and expectations conveyed in shared decision-making., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score., Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report., Results: Five teams participated, three of which included both informaticians and clinicians. Most (4/5) used Python for analyses, the remaining team used R. Common analysis themes included relationship of the GOSSIS-1 prediction score with demographics and care related variables; relationships between demographics and outcomes; calibration and factors related to the context of care; and the impact of missingness. Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias., Discussion: Datathons are a promising approach for challenging developers and users to explore questions relating to unrecognized biases in medical machine learning algorithms.

Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them., Competing Interests: LAC is the Editor-In-Chief of PLOS Digital Health., (Copyright: © 2024 Nakayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems., (© 2024. The Author(s).)

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias., Competing Interests: Disclosure A.I. Wong holds equity and management roles in Ataia Medical. A.I. Wong is supported by the Duke CTSI by the National Center for Advancing Translational Sciences, United States (NCATS) of the National Institutes of Health, United States under UL1TR002553 and REACH Equity under the National Institute on Minority Health and Health Disparities, United States (NIMHD) of the National Institutes of Health under U54MD012530. All other authors have no conflicts to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: R.M.S has received royalties for patent or software licenses from iCAD, Philips, PingAn, ScanMed, Translation Holdings, and MGB as well as research support form CRADA with PingAn. The remaining authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Ejection fraction (EF) estimation informs patient plans in the ICU, and low EF can indicate ventricular systolic dysfunction, which increases the risk of adverse events including heart failure. Automated echocardiography models are an attractive solution for high-variance human EF estimation, and key to this goal are echocardiogram vector embeddings, which are a critical resource for computational researchers., Objectives: The authors aimed to extract the vector embeddings from each echocardiogram in the EchoNet dataset using a classifier trained to classify EF as healthy (>50%) or unhealthy (<= 50%) to create an embeddings dataset for computational researchers., Methods: We repurposed an R3D transformer to classify whether patient EF is below or above 50%. Training, validation, and testing were done on the EchoNet dataset of 10,030 echocardiograms, and the resulting model generated embeddings for each of these videos., Results: We extracted 400-dimensional vector embeddings for each of the 10,030 EchoNet echocardiograms using the trained R3D model, which achieved a test AUC of 0.916 and 87.5% accuracy, approaching the performance of comparable studies., Conclusions: We present 10,030 vector embeddings learned by this model as a resource to the cardiology research community, as well as the trained model itself. These vectors enable algorithmic improvements and multimodal applications within automated echocardiography, benefitting the research community and those with ventricular systolic dysfunction (https://github.com/Team-Echo-MIT/r3d-v0-embeddings)., Competing Interests: The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and our dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed by narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare., (© 2024. The Author(s).)

Competing Interests: CCo reports reimbursement for travel and lodging by Veracyte. The competing interests were outside the submitted work. LAC is funded by the National Institute of Health through R01 EB017205, DS-I Africa U54 TW012043-01 and Bridge2AI OT2OD032701, and the National Science Foundation through ITEST #2148451. The competing interests were outside the submitted work.

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a "diagnostic toolkit" consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, "black box" nature of the algorithms, and data storage/transfer concerns., Competing Interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health. Leo Anthony Celi is also funded by the National Institute of Health through R01 EB017205, DS-I Africa U54 TW012043-01 and Bridge2AI OT2OD032701, and the National Science Foundation through ITEST #2148451., (Copyright: © 2024 Chandra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process., Objective: This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative., Methods: This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05., Results: The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant., Conclusions: The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended., (©Simon Arunga, Katharine Elise Morley, Teddy Kwaga, Michael Gerard Morley, Luis Filipe Nakayama, Rogers Mwavu, Fred Kaggwa, Julius Ssempiira, Leo Anthony Celi, Jessica E Haberer, Celestino Obua. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.09.2024.)

Competing Interests: Leo Anthony Celi is the Editor-in-Chief of PLOS Digital Health and Miguel Ángel Armengol de la Hoz is a Section Editor of PLOS Digital Health. AR, CMJ, JLG, JCR, PH, ET, IT and MAA are associated with the Andalusian Regional Ministry of Health and Consumer Affairs.

Importance: Maneuvers assessing fluid responsiveness before an intravascular volume expansion may limit useless fluid administration, which in turn may improve outcomes., Objective: To describe maneuvers for assessing fluid responsiveness in mechanically ventilated patients., Registration: The protocol was registered at PROSPERO: CRD42019146781., Information Sources and Search: PubMed, EMBASE, CINAHL, SCOPUS, and Web of Science were search from inception to 08/08/2023., Study Selection and Data Collection: Prospective and intervention studies were selected., Statistical Analysis: Data for each maneuver were reported individually and data from the five most employed maneuvers were aggregated. A traditional and a Bayesian meta-analysis approach were performed., Results: A total of 69 studies, encompassing 3185 fluid challenges and 2711 patients were analyzed. The prevalence of fluid responsiveness was 49.9%. Pulse pressure variation (PPV) was studied in 40 studies, mean threshold with 95% confidence intervals (95% CI) = 11.5 (10.5-12.4)%, and area under the receiver operating characteristics curve (AUC) with 95% CI was 0.87 (0.84-0.90). Stroke volume variation (SVV) was studied in 24 studies, mean threshold with 95% CI = 12.1 (10.9-13.3)%, and AUC with 95% CI was 0.87 (0.84-0.91). The plethysmographic variability index (PVI) was studied in 17 studies, mean threshold = 13.8 (12.3-15.3)%, and AUC was 0.88 (0.82-0.94). Central venous pressure (CVP) was studied in 12 studies, mean threshold with 95% CI = 9.0 (7.7-10.1) mmHg, and AUC with 95% CI was 0.77 (0.69-0.87). Inferior vena cava variation (∆IVC) was studied in 8 studies, mean threshold = 15.4 (13.3-17.6)%, and AUC with 95% CI was 0.83 (0.78-0.89)., Conclusions: Fluid responsiveness can be reliably assessed in adult patients under mechanical ventilation. Among the five maneuvers compared in predicting fluid responsiveness, PPV, SVV, and PVI were superior to CVP and ∆IVC. However, there is no data supporting any of the above mentioned as being the best maneuver. Additionally, other well-established tests, such as the passive leg raising test, end-expiratory occlusion test, and tidal volume challenge, are also reliable., (© 2024. The Author(s).)

In the United States, there is a proposal to link hospital Medicare payments with health equity measures, signaling a need to precisely measure equity in healthcare delivery. Despite significant research demonstrating disparities in health care outcomes and access, there is a noticeable gap in tools available to assess health equity across various health conditions and treatments. The available tools often focus on a single area of patient care, such as medication delivery, but fail to examine the entire health care process. The objective of this study is to propose a process mining framework to provide a comprehensive view of health equity. Using event logs which track all actions during patient care, this method allows us to look at disparities in single and multiple treatment steps, but also in the broader strategy of treatment delivery. We have applied this framework to the management of patients with sepsis in the Intensive Care Unit (ICU), focusing on sex and English language proficiency. We found no significant differences between treatments of male and female patients. However, for patients who don't speak English, there was a notable delay in starting their treatment, even though their illness was just as severe and subsequent treatments were similar. This framework subsumes existing individual approaches to measure health inequities and offers a comprehensive approach to pinpoint and delve into healthcare disparities, providing a valuable tool for research and policy-making aiming at more equitable healthcare., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Leo Anthony Celi is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2024 Adams et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP., Competing Interests: In addition to his academic role, JS is an employee of GSK (a private company) and holds shares in the GSK group of companies. LAC is the Editor-in-Chief of PLOS Digital Health., (Copyright: © 2024 Muralidharan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight, and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility, and clinical applicability of LLM research in healthcare through comprehensive reporting., Coi: DSB: Editorial, unrelated to this work: Associate Editor of Radiation Oncology, HemOnc.org (no financial compensation); Research funding, unrelated to this work: American Association for Cancer Research; Advisory and consulting, unrelated to this work: MercurialAI. DDF: Editorial, unrelated to this work: Associate Editor of JAMIA, Editorial Board of Scientific Data, Nature; Funding, unrelated to this work: the intramural research program at the U.S. National Library of Medicine, National Institutes of Health. JWG: Editorial, unrelated to this work: Editorial Board of Radiology: Artificial Intelligence, British Journal of Radiology AI journal and NEJM AI. All other authors declare no conflicts of interest.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Charpignon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: There are known racial disparities in the organ transplant allocation system in the United States. However, prior work has yet to establish if transplant center decisions on offer acceptance-the final step in the allocation process-contribute to these disparities., Objective: To estimate racial differences in the acceptance of organ offers by transplant center physicians on behalf of their patients., Design: Retrospective cohort analysis using data from the Scientific Registry of Transplant Recipients (SRTR) on patients who received an offer for a heart, liver, or lung transplant between January 1, 2010 and December 31, 2020., Setting: Nationwide, waitlist-based., Patients: 32,268 heart transplant candidates, 102,823 liver candidates, and 25,780 lung candidates, all aged 18 or older., Measurements: 1) Association between offer acceptance and two race-based variables: candidate race and donor-candidate race match; 2) association between offer rejection and time to patient mortality., Results: Black race was associated with significantly lower odds of offer acceptance for livers (OR=0.93, CI: 0.88-0.98) and lungs (OR=0.80, CI: 0.73-0.87). Donor-candidate race match was associated with significantly higher odds of offer acceptance for hearts (OR=1.11, CI: 1.06-1.16), livers (OR=1.10, CI: 1.06-1.13), and lungs (OR=1.13, CI: 1.07-1.19). Rejecting an offer was associated with lower survival times for all three organs (heart hazard ratio=1.16, CI: 1.09-1.23; liver HR=1.74, CI: 1.66-1.82; lung HR=1.21, CI: 1.15-1.28)., Limitations: Our study analyzed the observational SRTR dataset, which has known limitations., Conclusion: Offer acceptance decisions are associated with inequity in the organ allocation system. Our findings demonstrate the additional barriers that Black patients face in accessing organ transplants and demonstrate the need for standardized practice, continuous distribution policies, and better organ procurement., Competing Interests: Conflicts of interest Authors declare that they have no conflicts of interests.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups., Methods: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used a ConvNext model trained during 50 epochs using a weighted cross entropy loss to avoid overfitting, with 70% training (20% validation), and 30% testing subsets. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Saliency maps were calculated for interpretability., Results: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 (± 18.26) years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. A ConvNext V2 model was trained and evaluated BRSET in four prediction tasks: "binary diabetic retinopathy diagnosis (Normal vs Diabetic Retinopathy)" (AUC: 97, F1: 89); "3 class diabetic retinopathy diagnosis (Normal, Proliferative, Non-Proliferative)" (AUC: 97, F1: 82); "diabetes diagnosis" (AUC: 91, F1: 83); "sex classification" (AUC: 87, F1: 70)., Discussion: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Nakayama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background Slow correction of severe hyponatremia has been historically recommended due to the risk of rare but catastrophic neurologic events with rapid correction. A recent study challenging this paradigm reported that rapid correction is associated with shorter hospital length of stay, but that study did not control for admission diagnosis. The objective of this study was to determine whether rapid correction is associated with shorter length of stay when controlling for admission diagnosis. Methods This retrospective cohort study is based on the fourth edition of the Medical Information Mart for Intensive Care, MIMIC-IV, a deidentified, publicly available clinical research database which includes admissions from 2008-2019. Patients were identified who presented to the hospital with initial sodium <120 mEq/L and were categorized according to total sodium correction achieved in the first day (<6 mEq/L; 6-10 mEq/L; >10 mEq/L). Linear regression was used to assess for an association between correction rate and hospital length of stay, and to determine if this association was significant when controlling for admission diagnosis classifications based on diagnosis related groups (DRGs). Results There were 419 patients with severe hyponatremia (<120 mEq/L) included in this study, of whom 374 survived to discharge. Median [IQR] hospital length of stay was 6 [4, 11] days. In a univariable linear regression, there was a trend towards a significant association between the highest rate of correction (>10 mEq/L) and shorter length of stay, as compared with a moderate rate of correction (coef. -2.764, 95% CI [-5.791, 0.263], p=0.073), but the association was not significant when controlling for admission diagnosis group (coef. -1.561, 95% CI [-4.398, 1.276], p=0.280). There was a significant association in the survivor subset (coef. -3.455, 95% CI [-6.668, -0.242], p=0.035), but it was also not significant when controlling for admission diagnosis group (coef. -2.200, 95% CI [-5.144, 0.743], p=0.142). Conclusions Rapid correction is not associated with shorter length of stay when controlling for admission diagnosis, suggesting that the disease state confounds this association. Findings from prior and future studies reporting this association should not drive clinical decision making if the confounding effect of hospital admission diagnosis and competing risk of death are not fully accounted for.

Digital health is increasingly promoting open health data. Although this open approach promises a number of benefits, it also leads to tensions with Indigenous data sovereignty movements led by Indigenous peoples around the world who are asserting control over the use of health data as a part of self-determination. Digital health has a role in improving access to services and delivering improved health outcomes for Indigenous communities. However, we argue that in order to be effective and ethical, it is essential that the field engages more with Indigenous peoples´ rights and interests. We discuss challenges and possible improvements for data acquisition, management, analysis, and integration as they pertain to the health of Indigenous communities around the world., (© 2024. The Author(s).)

Background: Interventional therapies (ITs) are an emerging treatment modality for pulmonary embolism (PE); however, the degree of racial, sex-based, and sociodemographic disparities in access and timing is unknown., Objectives: To investigate barriers to access and timing of ITs for PE across the United States., Methods: A retrospective cohort study utilizing the Nationwide Inpatient Sample from 2016-2020 included adult patients with PE. The use of ITs (mechanical thrombectomy and catheter-directed thrombolysis) was identified via International Classification of Diseases 10th revision codes. Early IT was defined as procedure performed within the first 2 days after admission., Results: A total of 27 805 273 records from the 2016-2020 Nationwide Inpatient Sample database were examined. There were 387 514 (1.4%) patients with PE, with 14 249 (3.6%) of them having undergone IT procedures (11 115 catheter-directed thrombolysis, 2314 thrombectomy, and 780 both procedures). After multivariate adjustment, factors associated with less use of IT included Black race (odds ratio [OR], 0.90; 95% CI, 0.86-0.94; P < .01), Hispanic race (OR, 0.73; 95% CI, 0.68-0.79; P < .01), female sex (OR, 0.88; 95% CI, 0.85-0.91; P < .01), treatment in a rural hospital (OR, 0.49; 95% CI, 0.44-0.54; P < .01), and lack of private insurance (Medicare OR, 0.77; 95% CI, 0.73-0.80; P < .01; Medicaid OR, 0.65; 95% CI, 0.61-0.69; P < .01; no coverage OR, 0.87; 95% CI, 0.82-0.93; P < .01). Among the patients who received IT, 11 315 (79%) procedures were conducted within 2 days of admission and 2934 (21%) were delayed. Factors associated with delayed procedures included Black race (OR, 1.12; 95% CI, 1.01-1.26; P = .04), Hispanic race (OR, 1.52; 95% CI, 1.28-1.80; P < .01), weekend admission (OR, 1.37; 95% CI, 1.25-1.51; P < .01), Medicare coverage (OR, 1.24; 95% CI, 1.10-1.40; P < .01), and Medicaid coverage (OR, 1.29; 95% CI, 1.12-1.49; P < .01)., Conclusion: Significant racial, sex-based, and geographic barriers exist in overall access to IT for PE in the United States., Competing Interests: Declaration of competing interests There are no competing interests to disclose., (Copyright © 2024 International Society on Thrombosis and Haemostasis. Published by Elsevier Inc. All rights reserved.)

We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation. Complications include total hospital and ICU length of stay and in-hospital death. We hope this dataset will inspire collaborative research and development in perioperative medicine and serve as a reproducible external validation dataset to improve surgical outcomes., (© 2024. The Author(s).)

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health., (© 2024. The Author(s).)

Competing Interests: Competing interests: None declared.

Competing Interests: DSB reports being an Associate Editor of Radiation Oncology at HemOnc.org (no financial compensation, unrelated to this work, and recieving funding from American Association for Cancer Research, unrelated to this work. HJWLA reports advising and consulting for Onc.AI, Love Health, Sphera, Editas, AstraZeneca, and Bristol Myers Squibb, unrelated to this work. RHM reports being on an Advisory Board for ViewRay and AstraZeneca; Consulting for Varian Medical Systems and Sio Capital Management; and honorarium from Novartis and Springer Nature. JL reports research funding from Viewray, NH Theraguix, and Varian. ML reports advisory and consulting for Pfizer, Gilead, Novartis, and AstraZeneca, unrelated to this work. BHK reports research funding from Botha-Chan Low Grade Glioma Consortium (National institutes of Health [NIH]-USA K08DE030216-01). All other authors declare no competing interests. The authors acknowledge financial support from the Woods Foundation (DSB, RHM, BHK, and HJWLA) NIH (NIH-USA U54CA274516-01A1 (SC, MG, BHK, HJWLA, GKS, and DSB), NIH-USA U24CA194354 (HJWLA), NIH-USA U01CA190234 (HJWLA), NIH-USA U01CA209414 (HJWLA), and NIH-USA R35CA22052 (HJWLA), NIH-NIDA R01DA051464 (MA), R01GM114355 (GKS), NIH-USA R01LM012973 (TM and MA), NIH-USA R01MH126977 (TM), NIH-USA U54 TW012043-01 (JG and LAC), NIH-USA OT2OD032701 (JG and LAC), NIH-USA R01EB017205 (LAC), and the EU European Research Council (HJWLA 866504), all outside of the submitted work. All data collected and generated in this study, after de-identification, are available at https://github.com/AIM-Harvard/OncQA. SC: conceptualisation, data curation, formal analysis, investigation, methodology, visualisation, and writing (original draft, review, and editing). MG: conceptualisation, data curation, and formal analysis. SM, FH EH, BHK, FEC, JL: data curation, investigation, and methodology. RHM: data curation, investigation, methodology, and writing (review and editing). HJWLA: investigation, methodology, resources, and writing (review and editing). JG: formal analysis, investigation, methodology, visualisation, and writing (review and editing). TM and GKS: formal analysis, investigation, methodology, and writing (review and editing). ML data curation, formal analysis, investigation, and methodology. LAC formal analysis, investigation, supervision, and writing (review and editing). MA: conceptualisation, data curation, formal analysis, investigation, methodology, supervision, and writing (review and editing). DSB: conceptualisation, data curation, formal analysis, investigation, methodology, supervision, visualisation, resources, and writing (original draft, review, and editing). SC and DSB directly accessed and verified the underlying data reported in the manuscript. All authors have full access to all the data in the study and accept responsibility to submit for publication.

Pulse oximeters measure peripheral arterial oxygen saturation (SpO 2 ) noninvasively, while the gold standard (SaO 2 ) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO 2 and SaO 2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70-100%. Minority racial and ethnic groups account for ~25% of the data - a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions., (© 2024. The Author(s).)

Objective: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations., Materials and Methods: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists. The ChatGPT-generated responses were analyzed statistically in a disease-drug matrix, considering disease-medication associations (Q-value) and expert evaluation., Results: ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. We evaluated its drug suggestions using the Q-value, showing that human expert validation agreement surpassed Q-value cutoff-based agreement. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes., Conclusion: ChatGPT provides medication recommendations as a second opinion in dermatology treatment, but its reliability and comprehensiveness need refinement for greater accuracy. In the future, integrating a medical domain-specific knowledge base for training and ongoing optimization will enhance the precision of ChatGPT's results., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)