Identifying Precise Targets to Improve Child Mental Health Care Equity: Leveraging Advances in Clinical Research Informatics and Lived Experience.

To reduce child mental health disparities, it is imperative to improve the precision of targets and to expand our vision of social determinants of health as modifiable. Advancements in clinical research informatics and please state accurate measurement of child mental health service use and quality. Participatory action research promotes representation of underserved groups in informatics research and practice and may improve the effectiveness of interventions by informing research across all stages, including the identification of key variables, risk and protective factors, and data interpretation.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Advanced CKD detection through optimized metaheuristic modeling in healthcare informatics.

Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

Potential of Large Language Models in Health Care: Delphi Study.

BACKGROUND: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. OBJECTIVE: The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. METHODS: We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. RESULTS: The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. CONCLUSIONS: Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

Education in Health Informatics: Perspectives from the Italian Society for Biomedical Informatics (SIBIM).

The evolution of socio-technological habits together with the widespread demand of post-acute and chronic treatments outside hospital boundaries drove the increased demand of medical informatics experts to develop tools for and support healthcare professionals. The recent COVID-19 pandemic further highlighted the need of physicians able to manage diseases virtually and remotely. Moreover, healthcare professionals need to access to innovative techniques and procedures to manage biomedical data, cloud-based communication, and data sharing procedures, often connected to innovative devices to support an effective precision in the health treatments. In this paper we report the experiences of the Italian Biomedical Informatics Society (SIBIM), in the definition and promotion of eHealth educational topics in medical and health professions teaching programs, as well as in bioengineering schools, showing how SIBIM members' efforts have been applied towards increasing the level of eHealth contents in medical schools.

Opportunities for incorporating intersectionality into biomedical informatics.

Many approaches in biomedical informatics (BMI) rely on the ability to define, gather, and manipulate biomedical data to support health through a cyclical research-practice lifecycle. Researchers within this field are often fortunate to work closely with healthcare and public health systems to influence data generation and capture and have access to a vast amount of biomedical data. Many informaticists also have the expertise to engage with stakeholders, develop new methods and applications, and influence policy. However, research and policy that explicitly seeks to address the systemic drivers of health would more effectively support health. Intersectionality is a theoretical framework that can facilitate such research. It holds that individual human experiences reflect larger socio-structural level systems of privilege and oppression, and cannot be truly understood if these systems are examined in isolation. Intersectionality explicitly accounts for the interrelated nature of systems of privilege and oppression, providing a lens through which to examine and challenge inequities. In this paper, we propose intersectionality as an intervention into how we conduct BMI research. We begin by discussing intersectionality's history and core principles as they apply to BMI. We then elaborate on the potential for intersectionality to stimulate BMI research. Specifically, we posit that our efforts in BMI to improve health should address intersectionality's five key considerations: (1) systems of privilege and oppression that shape health; (2) the interrelated nature of upstream health drivers; (3) the nuances of health outcomes within groups; (4) the problematic and power-laden nature of categories that we assign to people in research and in society; and (5) research to inform and support social change.

Visualizing Study Profiles Using the GMDS Competency Catalog for Medical Informatics Programs.

BACKGROUND: Medical informatics programs cover a variety of topics. OBJECTIVES: To test the utility of the GMDS medical informatics competency catalog in comparing programs by developing study profiles. METHODS: Coverage of 234 competencies is recorded and visualized in a spider diagram. RESULTS: Spider diagrams allow visualizing various study profiles. CONCLUSION: The GMDS catalog seems useful for comparing medical informatics study programs, e.g., for interested students, employers, or accreditation reviewers.

Understanding IT in Healthcare: Relevance and Training Needs of IT in Private Medical Practice.

BACKGROUND: The integration of Information Technology (IT) into private medical practice is crucial in modern healthcare. Physicians managing office-related IT without proper knowledge risk operational inefficiencies and security. OBJECTIVES: This study determines the relevance of specific IT topics in medical practice and identifies the training needs of physicians for enhancing IT competencies in healthcare. METHODS: In March 2023 a cross-sectional online survey was conducted with physicians comprising nine IT-related topics in Tyrol, Austria. RESULTS: The survey results highlighted a strong perceived relevance and high demand for IT education among physicians working in their medical practice, especially in areas of core medical IT and security. The majority of responses indicated high relevance (76.7%) and high demand (69.7%) for IT topics in medical practice. CONCLUSION: The findings underscore a significant need for targeted IT training and support in medical practices, particularly in areas related to the medical practice and security. Addressing these needs could lead to improved healthcare delivery and better management of technological resources in the healthcare sector.

Taxonomy development methods regarding patient safety in health sciences - A systematic review.

BACKGROUND: Taxonomies are needed for automated analysis of clinical data in healthcare. Few reviews of the taxonomy development methods used in health sciences are found. This systematic review aimed to describe the scope of the available taxonomies relative to patient safety, the methods used for taxonomy development, and the strengths and limitations of the methods. The purpose of this systematic review is to guide future taxonomy development projects. METHODS: The CINAHL, PubMed, Scopus, and Web of Science databases were searched for studies from January 2012 to April 25, 2023. Two authors selected the studies using inclusion and exclusion criteria and critical appraisal checklists. The data were analysed inductively, and the results were reported narratively. RESULTS: The studies (n = 13) across healthcare concerned mainly taxonomies of adverse events and medication safety but little for specialised fields and information technology. Critical appraisal indicated inadequate reporting of the used taxonomy development methods. Ten phases of taxonomy development were identified: (1) defining purpose and (2) the theory base for development, (3) relevant data sources' identification, (4) main terms' identification and definitions, (5) items' coding and pooling, (6) reliability and validity evaluation of coding and/or codes, (7) development of a hierarchical structure, (8) testing the structure, (9) piloting the taxonomy and (10) reporting application and validation of the final taxonomy. Seventeen statistical tests and seven software systems were utilised, but automated data extraction methods were used rarely. Multimethod and multi-stakeholder approach, code- and hierarchy testing and piloting were strengths and time consumption and small samples in testing limitations. CONCLUSION: New taxonomies are needed on diverse specialities and information technology related to patient safety. Structured method is needed for taxonomy development, reporting and appraisal to strengthen taxonomies' quality. A new guide was proposed for taxonomy development, for which testing is required. Prospero registration number CRD42023411022.

Discovering the importance of health informatics education competencies in healthcare practice. A focus group interview.

BACKGROUND: As healthcare and especially health technology evolve rapidly, new challenges require healthcare professionals to take on new roles. Consequently, the demand for health informatics competencies is increasing, and achieving these competencies using frameworks, such as Technology Informatics Guiding Reform (TIGER), is crucial for future healthcare. AIM: The study examines essential health informatics and educational competencies and health informatics challenges based on TIGER Core Competency Areas. Rather than examine each country independently, the focus is on uncovering commonalities and shared experiences across diverse contexts. METHODS: Six focus group interviews were conducted with twenty-one respondents from three different countries (Germany (n = 7), Portugal (n = 6), and Finland (n = 8)). These interviews took place online in respondents' native languages. All interviews were transcribed and then summarized by each country. Braun and Clarke's thematic analysis framework was applied, which included familiarization with the data, generating initial subcategories, identifying, and refining themes, and conducting a final analysis to uncover patterns within the data. RESULTS: Agreed upon by all three countries, competencies in project management, communication, application in direct patient care, digital literacy, ethics in health IT, education, and information and knowledge management were identified as challenges in healthcare. Competencies such as communication, information and communication technology, project management, and education were identified as crucial for inclusion in educational programs, emphasizing their critical role in healthcare education. CONCLUSIONS: Despite working with digital tools daily, there is an urgent need to include health informatics competencies in the education of healthcare professionals. Competencies related to application in direct patient care, IT-background knowledge, IT-supported and IT-related management are critical in educational and professional settings are seen as challenging but critical in healthcare.

Factors impacting the adoption of big data in healthcare: A systematic literature review.

BACKGROUND: The term "big data" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare. METHODS: A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE. RESULTS AND CONCLUSION: The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.

Perspectives on the implementation of health informatics curricula frameworks.

BACKGROUND: The COVID-19 pandemic highlighted the necessity of equipping health professionals with knowledge and skills to effectively use digital technology for healthcare delivery. However, questions persist about the best approach to effectively educate future health professionals for this. A workshop at the 15th Nursing Informatics International Congress explored this issue. OBJECTIVE: To report findings from an international participatory workshop exploring pre-registration informatics implementation experiences. METHODS: A virtual workshop was held using whole and small group interactive methods aiming to 1) showcase international examples of incorporating health informatics into pre-registration education; 2) highlight essential elements and considerations for integrating health informatics into curricula; 3) identify integration models of health informatics; 4) identify core learning objectives, resources, and faculty capabilities for teaching informatics; and 5) propose curriculum evaluation strategies. The facilitators' recorded data and written notes were content analysed. RESULTS: Fourteen participants represented seven countries and a range of educational experiences. Four themes emerged: 1) Design: scaffolding digital health and technology capabilities; 2) Development: interprofessional experience of and engagement with digital health technology capabilities; 3) implementation strategies; and 4) Evaluation: multifaceted, multi-stakeholder evaluation of curricula. These themes were used to propose an implementation framework. DISCUSSION: Workshop findings emphasise global challenges in integrating health informatics into curricula. While course development approaches may appear linear, the learner-centred implementation framework based on workshop findings, advocates for a more cyclical approach. Iterative evaluation involving stakeholders, such as health services, will ensure that health professional education is progressive and innovative. CONCLUSIONS: The proposed implementation framework serves as a roadmap for successful health informatics implementation into health professional curricula. Prioritising engagement with health services and digital health industry is essential to ensure the relevance of implemented informatics curricula for the future workforce, acknowledging the variability in placement experiences and their influence on informatics exposure, experience, and learning.

Will Generative AI Tools Improve Access to Reliable Health Information?

This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and Virologist Davey Smith, head of the Division of Infectious Diseases and Global Public Health at the University of California, San Diego.