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Digital heritage portal interfaces are generally similar to digital library and search engine interfaces in displaying search results as a list of brief metadata records. The knowledge organization and search result display of these systems are item-centric, with little support for identifying relationships between items. This paper proposes a knowledge graph system and visualization interface as a promising solution for digital heritage systems to support users in browsing related items, understanding the relationships between items, and synthesizing a narrative on an issue. The paper discusses design issues for the knowledge graph, graph database, and graph visualization, and offers recommendations based on the authors' experience in developing three knowledge graph systems for archive and digital humanities resources: the Zubir Said personal archive collection at the Nanyang Academy of Fine Arts, Singapore; Singapore Pioneers social network; and Polyglot Medicine knowledge graph of Asian traditional and herbal medicine. Lessons learned from a small user study are incorporated in the discussion. [ABSTRACT FROM AUTHOR]

The article discusses the role of informatics in addressing challenges in cancer care and highlights the publication trends and readership interests of JCO Clinical Cancer Informatics (JCO CCI). The number of submissions to JCO CCI has increased over the years, with original manuscripts accounting for 80% of submissions in 2023. The top submission categories include artificial intelligence, electronic health records, patient care, applied research, and biostatistical models. The most cited articles in the journal cover topics such as patient care, imaging, genetics/genomics, and artificial intelligence. The readership survey reveals an interest in articles focused on artificial intelligence in clinical care, health equity, and palliative/supportive care. The future of JCO CCI aims to prioritize high-quality and timely research with real-world applicability to improve cancer care. [Extracted from the article]

Background: Our study aims to delineate the miRSNP–microRNA–gene–pathway interactions in the context of hypertrophic scars (HS) and keloids. Materials and Methods: We performed a computational biology study involving differential expression analysis to identify genes and their mRNAs in HS and keloid tissues compared to normal skin, identifying key hub genes and enriching their functional roles, comprehensively analyzing microRNA‐target genes and related signaling pathways through bioinformatics, identifying MiRSNPs, and constructing a pathway‐based network to illustrate miRSNP‐miRNA‐gene‐signaling pathway interactions. Results: Our results revealed a total of 429 hub genes, with a strong enrichment in signaling pathways related to proteoglycans in cancer, focal adhesion, TGF‐β, PI3K/Akt, and EGFR tyrosine kinase inhibitor resistance. Particularly noteworthy was the substantial crosstalk between the focal adhesion and PI3K/Akt signaling pathways, making them more susceptible to regulation by microRNAs. We also identified specific miRNAs, including miRNA‐1279, miRNA‐429, and miRNA‐302e, which harbored multiple SNP loci, with miRSNPs rs188493331 and rs78979933 exerting control over a significant number of miRNA target genes. Furthermore, we observed that miRSNP rs188493331 shared a location with microRNA302e, microRNA202a‐3p, and microRNA20b‐5p, and these three microRNAs collectively targeted the gene LAMA3, which is integral to the focal adhesion signaling pathway. Conclusions: The study successfully unveils the complex interactions between miRSNPs, miRNAs, genes, and signaling pathways, shedding light on the genetic factors contributing to HS and keloid formation. [ABSTRACT FROM AUTHOR]

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today's culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health. [ABSTRACT FROM AUTHOR]

Background: Large language model (LLM)-linked chatbots may be an efficient source of clinical recommendations for healthcare providers and patients. This study evaluated the performance of LLM-linked chatbots in providing recommendations for the surgical management of gastroesophageal reflux disease (GERD). Methods: Nine patient cases were created based on key questions addressed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) guidelines for the surgical treatment of GERD. ChatGPT-3.5, ChatGPT-4, Copilot, Google Bard, and Perplexity AI were queried on November 16th, 2023, for recommendations regarding the surgical management of GERD. Accurate chatbot performance was defined as the number of responses aligning with SAGES guideline recommendations. Outcomes were reported with counts and percentages. Results: Surgeons were given accurate recommendations for the surgical management of GERD in an adult patient for 5/7 (71.4%) KQs by ChatGPT-4, 3/7 (42.9%) KQs by Copilot, 6/7 (85.7%) KQs by Google Bard, and 3/7 (42.9%) KQs by Perplexity according to the SAGES guidelines. Patients were given accurate recommendations for 3/5 (60.0%) KQs by ChatGPT-4, 2/5 (40.0%) KQs by Copilot, 4/5 (80.0%) KQs by Google Bard, and 1/5 (20.0%) KQs by Perplexity, respectively. In a pediatric patient, surgeons were given accurate recommendations for 2/3 (66.7%) KQs by ChatGPT-4, 3/3 (100.0%) KQs by Copilot, 3/3 (100.0%) KQs by Google Bard, and 2/3 (66.7%) KQs by Perplexity. Patients were given appropriate guidance for 2/2 (100.0%) KQs by ChatGPT-4, 2/2 (100.0%) KQs by Copilot, 1/2 (50.0%) KQs by Google Bard, and 1/2 (50.0%) KQs by Perplexity. Conclusions: Gastrointestinal surgeons, gastroenterologists, and patients should recognize both the promise and pitfalls of LLM's when utilized for advice on surgical management of GERD. Additional training of LLM's using evidence-based health information is needed. [ABSTRACT FROM AUTHOR]

Objective: This article describes the first digital clinical information system tailored to support the operational needs of a forensic unit in Greece and to maintain its archives. Method: The development of our system was initiated towards the end of 2018, as a close collaboration between the Medical School of the University of Crete and the Forensic Medicine Unit of the University Hospital of Heraklion, Crete, where forensic pathologists assumed active roles during the specification and testing of the system. Results: The final prototype of the system was able to manage the life cycle of any forensic case by allowing users to create new records, assign them to forensic pathologists, upload reports, multimedia and any required files; mark the end of processing, issue certificates or appropriate legal documents, produce reports and generate statistics. For the first 4 years of digitised data (2017–2021), the system recorded 2936 forensic examinations categorised as 106 crime scene investigations, 259 external examinations, 912 autopsies, 102 post-mortem CT examinations, 804 histological examinations, 116 clinical examinations, 12 anthropological examinations and 625 embalmings. Conclusion: This research represents the first systematic effort to record forensic cases through a digital clinical information system in Greece, and to demonstrate its effectiveness, daily usability and vast potential for data extraction and for future research. [ABSTRACT FROM AUTHOR]

To analyze the reasons for refusal of aneuploidy screening in a multicultural Middle Eastern population. The study included patients delivering in a university hospital, who had refused aneuploidy screening during their pregnancy. We evaluated through a questionnaire submitted during the postpartum period the sociodemographic characteristics, beliefs, attitudes, and the main reason underpinning their choice. Religious, ethical, and financial factors, personal beliefs, medical information, perceived media information, and familial input were assessed through a Likert scale. Our pilot study included 70 patients. The main reason (33 %) was the refusal to terminate pregnancy if the screening tests ultimately led to a diagnosis of aneuploidy. Lack of adequate information on the availability and benefits of this screening method (28 %), religious beliefs (17 %), in addition to other minor reasons such as financial considerations, familial recommendations, late pregnancy follow-ups, and media influence were also identified as contributing factors. Aneuploidy screening is routinely offered to couples, with varying uptake rates observed worldwide. Sufficient information on prenatal screening and diagnosis should be provided to all pregnant women, presenting all available options, thus enabling them to make a free and informed choice during their pregnancy. [ABSTRACT FROM AUTHOR]

National data showed that the majority of deaths due to COVID-19 are elderly people with comorbidities such as diabetes, hypertension, and kidney failure who have a 3.5-fold risk of dying compared to non-elderly people. With an increasing number of elderly and a high prevalence of chronic diseases, home healthcare is important to enable people to be in their own homes rather than hospitals by taking advantage of telemedicine within limited distances. A cross-sectional quantitative study was conducted with 157 elderly respondents of the Chronic Disease Management Program (Prolanis) at Primary Healthcare Facilities participants in Semarang Regency by a validated questionnaire. Analyzed by SPSS version 21 and a chi-square test. The respondent knowledge level with fewer telemedicine-related categories is about 47.1%. The attitude regarding the development of telemedicine in Prolanis was positive (79.0%), and positively perceived the development of telemedicine by 58.3%. Age (p =.035; OR = 0.502) and gender (p =.010; OR = 2.605) were significantly related to respondents' knowledge. The educational background is significantly related to respondents' attitudes (p =.025; OR = 3.080). Health information technology (telemedicine) needs to be developed among elderly users, especially participants in the Prolanis (hypertension and diabetes mellitus type 2) in Health Service Facilities. [ABSTRACT FROM AUTHOR]

The landmark 1988 Institute of Medicine report The Future of Public Health served the public health community well by pointing to what needed to be done, fostering a sense of urgency, and offering concrete directions to be pursued. In this article, the impact of the 1988 report, and of the subsequent 2003 report on the Centers for Disease Control and Prevention (CDC), is considered by tracing the course of the ideas that influenced the consciousness of the public health community and subsequently catalyzed concrete action. Among these ideas was that "public health is in disarray." This assessment led to an awareness that something needed to be done. Further, by stating that the public health enterprise had 3 core functions (assessment, policy development, and assurance), the 1988 report set in motion policy development to address the "disarray." At a more fundamental level, both reports championed the need for governmental public health (particularly at the CDC) to take action to strengthen the capacity of local and state public health agencies to address a growing range of public health threats and emergencies. (Am J Public Health. 2024;114(5):489–494. https://doi.org/10.2105/AJPH.2024.307598) [ABSTRACT FROM AUTHOR]

Introduction: Patients with advanced age‐related macular degeneration (AMD) frequently experience loss to follow‐up (LTFU), heightening the risk of vision loss from treatment delays. This study aimed to identify factors contributing to LTFU in patients with advanced AMD and assess the effectiveness of telephone‐based outreach in reconnecting them with eye care. Methods: A custom reporting tool identified patients with advanced AMD who had not returned for eye care between 31 October 2021 and 1 November 2022. Potentially LTFU patients were enrolled in a telephone outreach programme conducted by a telehealth extender to encourage their return for care. Linear regression analysis identified factors associated with being LTFU and likelihood of accepting care post‐outreach. Results: Out of 1269 patients with advanced AMD, 105 (8.3%) did not return for recommended eye care. Patients LTFU were generally older (89.2 ± 8.9 years vs. 87.2 ± 8.5 years, p = 0.02) and lived farther from the clinic (25 ± 43 miles vs. 17 ± 30 miles, p = 0.009). They also had a higher rate of advanced dry AMD (26.7% vs. 18.5%, p = 0.04) and experienced worse vision in both their better‐seeing (0.683 logMAR vs. 0.566 logMAR, p = 0.03) and worse‐seeing (1.388 logMAR vs. 1.235 logMAR, p = 0.04) eyes. Outreach by a telehealth extender reached 62 patients (59%), 43 through family members or healthcare proxies. Half of the cases where a proxy was contacted revealed that the patient in question had died. Among those contacted directly, one third expressed willingness to resume eye care (20 patients), with 11 scheduling appointments (55%). Despite only two patients returning for in‐person eye care through the intervention, the LTFU rate halved to 4.4% by accounting for those patients who no longer needed eye care at the practice. Conclusions: There is a substantial risk that older patients with advanced AMD will become LTFU. Targeted telephone outreach can provide a pathway for vulnerable patients to return to care. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. [ABSTRACT FROM AUTHOR]

Background: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. Materials and methods: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. Results: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. Conclusions: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions. [ABSTRACT FROM AUTHOR]

Background and aim: Conditions such as hypertension, cardiovascular diseases, and hypercholesterolemia, are a major public health challenge. This study investigates the influence of oral health indicators, including gum bleeding, active dental caries, tooth mobility, and tooth loss, on their prevalence in Hungary, considering socioeconomic, demographic, and lifestyle factors. Materials and methods: Data from the 2019 Hungarian European Health Interview Survey with 5,603 participants informed this analysis. Data were accessed from the records maintained by the Department of Health Informatics at the University of Debrecen between September and November 2023. Variable selection employed elastic net regularization and k-fold cross-validation, leading to high-performing predictors for weighted multiple logistic regression models. Sensitivity analysis confirmed the findings' validity. Results: Significant links were found between poor oral health and chronic cardiac conditions. Multiple teeth extractions increased hypertension risk (OR = 1.67, 95% CI: [1.01–2.77]); dental prosthetics had an OR of 1.45 [1.20–1.75]. Gum bleeding was associated with higher cardiovascular disease (OR = 1.69 [1.30–2.21]) and hypercholesterolemia risks (OR = 1.40 [1.09–1.81]). Conclusions: Oral health improvement may reduce the risk of cardiac conditions. This underscores oral health's role in multidisciplinary disease management. [ABSTRACT FROM AUTHOR]

Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for materials informatics (MI) problems. However, there are only a few examples of the application of QNN to multivariate regression models, and little is known about how these models are constructed. This study aims to construct a QNN model to predict the melting points of metal oxides as an example of a multivariate regression task for the MI problem. Different architectures (encoding methods and entangler arrangements) are explored to create an effective QNN model. Shallow-depth ansatzs could achieve sufficient expressibility using sufficiently entangled circuits. The "linear" entangler was adequate for providing the necessary entanglement. The expressibility of the QNN model could be further improved by increasing the circuit width. The generalization performance could also be improved, outperforming the classical NN model. No overfitting was observed in the QNN models with a well-designed encoder. These findings suggest that QNN can be a useful tool for MI. [ABSTRACT FROM AUTHOR]

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications. Recommendations are provided with a means to engage in this mobilization effort. [ABSTRACT FROM AUTHOR]

Objective: Health literacy and its potential impacts on the wellbeing of patrons remain a highly regarded objective among health science and medical librarians when considering learning outcomes of patron communities. Librarians are positioned to champion literacy instruction activities. This study aimed to examine health information seeking attitudes and behaviors in an academic-based employee wellness program before and after health literacy workshops were developed and facilitated by an academic health sciences librarian. Methods: The intervention included instruction informed by Don Nutbeam's Health Literacy Framework and the Research Triangle Institute's Health Literacy Conceptual Framework. Sixty-five participants obtained through convenience sampling attended workshops and were invited to respond to pre- and post-session surveys. Using a quantitative quasiexperimental methodology, surveys collected health literacy indicators including preferred sources and handling practices of in-person and online health information. Results: Findings indicated workshops influenced information seeking behaviors as participants documented a decrease in social media use for health and wellness information (-36%) and medical information (-13%). An increase in the usage of consumer health databases (like Medline Plus) was also indicated post-workshop for health and wellness information (18%) and medical information (31%). Conclusion: Favorable impacts are evident in this small-scale study; however, more research is needed to confirm the influence of these methods on larger and more diverse populations. Librarians should continue to develop and disseminate theory-informed tools and methods aimed at engaging various communities in constructive health information seeking practices. [ABSTRACT FROM AUTHOR]

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. To report findings from an international participatory workshop exploring pre-registration informatics implementation experiences. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Aims: This systematic review aims to assess the prominence of medical informatics and biophysics in European medical schools, recognizing the growing importance of technology in healthcare. Methodology: A comprehensive search of Medline and PubMed databases was conducted by employing MeSH terms and relevant keywords following PRISMA guidelines. Results: The review reveals limited body of research on this topic, with 41 relevant studies identified. These studies underscore the value of medical informatics and biophysics in medical education. They emphasize the necessity of certification and reveal variations in curricula and training across European nations. Scientific novelty: This study contributes to existing literature by highlighting the scarcity of research on subject. Conclusions: The findings emphasize the significance of incorporating medical informatics and biophysics into medical education across European nations. Standardization, international guidelines, and accreditation procedures are imperative to ensure the competence of healthcare professionals in utilizing technology and information systems effectively. [ABSTRACT FROM AUTHOR]

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

Background and Objectives: To understand the relationship between Lean implementation in information technology (IT) departments and hospital performance, particularly with respect to operational and financial outcomes. Methods: Primary data were sourced from 1222 hospitals that responded to the National Survey of Lean (NSL)/Transformational Performance Improvement, which was fielded to 4500 general medical-surgical hospitals across the United States. Secondary sources included hospital performance data from the Agency for Healthcare Research and Quality (AHRQ) and the Centers for Medicare & Medicaid Services (CMS). We performed 2 sets of multivariable regressions using data gathered from US hospitals, linked to AHRQ and CMS performance outcomes. We examined 10 different outcomes measuring financial performance, quality of care, and patient experience, and their associations with Lean adoption within hospital IT departments. We then focused only on those hospitals that adopted Lean in IT to identify specific practices associated with performance. Results: Controlling for other factors, adoption of Lean IT management was associated with lower length of stay (b= -0.098, P= .018) and inpatient expense per discharge (b= -0.112, P= .090). Specifically, use of visual management tools (eg, A3 storyboards, status sheets) was associated with lower adjusted inpatient expense per discharge (b= -0.176, P= .034) and higher earnings before interest, taxes, depreciation, and amortization margin (b= 0.124, P= .042). Such tools were also associated with hospital participation in bundled payment programs (odds ratio = 2.326; P= .046; 95% confidence interval, 0.979-5.527) and percentage of net revenue paid on a shared risk basis (b= 0.188, P= .031). Conclusions: Lean IT management was associated with positive financial performance, particularly with hospital participation in value-based payment. More detailed study is needed to understand other influential factors and types of work processes, activities, or mechanisms by which high-functioning IT can contribute to financial outcomes. [ABSTRACT FROM AUTHOR]

This document discusses the use of biomedical data mining and machine learning technologies in smart healthcare systems for disease diagnosis and health informatics. These technologies analyze large medical datasets to identify patterns and correlations that may be missed by human observation, helping to predict disease progression and provide personalized treatment plans. The document also highlights the impact of the COVID-19 pandemic on global public health and the importance of early detection and diagnosis in monitoring the spread of the virus. Various detection techniques for COVID-19 are described, including RT-PCR, RT-LAMP, immunoassay antibody detection, radiography imaging, and acoustic detection. The document also addresses the long-term effects of COVID-19, such as long COVID, and the potential use of biomedical data mining and machine learning technologies in monitoring and treating these effects. Overall, the document emphasizes the role of these technologies in improving healthcare quality and rehabilitation outcomes. [Extracted from the article]

Background: Osteoarthritis (OA) is a major cause of chronic pain. Non-steroidal anti-inflammatory drugs (NSAIDs) are analgesics commonly used for musculoskeletal pain; however, NSAIDs can increase the risk of certain adverse events, such as gastrointestinal bleeding, edema, heart failure, and hypertension. Objective: The objective of this study was to characterize existing comorbidities among patients with OA. For patients with OA with and without a coexisting medical condition of interest (CMCOI), we estimated the prevalence of prescribing and dispensing NSAIDs pre-OA and post-OA diagnosis. Methods: Data from three large administrative claims databases were used to construct an OA retrospective cohort. Databases leveraged were IBM MarketScan Medicare Supplemental Database (MDCR), IBM MarketScan Commercial Database (CCAE), and Optum's de-identified Clinformatics® Data Mart Database (Optum CDM). The OA study population was defined to be those patients who had an OA diagnosis from an inpatient or outpatient visit with at least 365 days of prior observation time in the database during January 2000 through May 2021. Asthma, cardiovascular disorders, renal impairment, and gastrointestinal bleeding risks were the CMCOI of interest. Patients with OA were then classified as having or not having evidence of a CMCOI. For both groups, NSAID dispensing patterns pre-OA and post-OA diagnosis were identified. Descriptive analysis was performed within the Observational Health Data Sciences and Informatics framework. Results: In each database, the proportion of the OA population with at least one CMCOI was nearly 50% or more (48.0% CCAE; 74.4% MDCR; 68.6% Optum CDM). Cardiovascular disease was the most commonly observed CMCOI in each database, and in two databases, nearly one in four patients with OA had two or more CMCOI (23.2% MDCR; 22.6% Optum CDM). Among the OA population with CMCOI, NSAID utilization post-OA diagnosis ranged from 33.0 to 46.2%. Following diagnosis of OA, an increase in the prescribing and dispensing of NSAIDs was observed in all databases, regardless of patient CMCOI presence. Conclusions: This study provides real-world evidence of the pattern of prescribing and dispensing of NSAIDs among patients with OA with and without CMCOI, which indicates that at least half of patients with OA in the USA have a coexisting condition. These conditions may increase the risk of side effects commonly associated with NSAIDs. Yet, at least 32% of these patients were prescribed and dispensed NSAIDs. These data support the importance of shared decision making between healthcare professionals and patients when considering NSAIDs for the treatment of OA in patients with NSAID-relevant coexisting medical conditions. [ABSTRACT FROM AUTHOR]

Background: The Internet is a widely used source of health information, yet the accuracy of online information can be low. This is the case for low back pain (LBP), where much of the information about LBP treatment is poor. Methods: This research conducted a content analysis to explore what pain treatments for LBP are presented to the public on websites of Australian pain clinics listed in the PainAustralia National Pain Services Directory. Websites providing information relevant to the treatment of LBP were included. Details of the treatments for LBP offered by each pain service were extracted. Results: In total, 173 pain services were included, with these services linking to 100 unique websites. Services were predominantly under private ownership and located in urban areas, with limited services in non-urban locations. Websites provided detail on a median of six (IQR 3–8) treatments, with detail on a higher number of treatments provided by services in the private sector. Physical, psychological and educational treatments were offered by the majority of pain services, whereas surgical and workplace-focused treatments were offered by relatively few services. Most services provided details on multidisciplinary care; however, interdisciplinary, coordinated care characterised by case-conferencing was infrequently mentioned. Conclusions: Most websites provided details on treatments that were largely in-line with recommended care for LBP, but some were not, especially in private clinics. However, whether the information provided online is a true reflection of the services offered in clinics remains to be investigated. Information online about how low back pain should be treated is often not comprehensive and of low quality. Websites of Australian pain services endorsed by PainAustralia often offer treatments recommended for low back pain, but there is little information on the importance of interdisciplinary care and fewer pain services in non-urban areas. Patients are likely being educated about guideline-endorsed care for low back pain, but patients in non-urban areas are likely to experience difficulty in accessing this. [ABSTRACT FROM AUTHOR]

Hip fracture results in an older person's loss of independence. Limited healthcare resources make mobile Health (mHealth) an alternative. Engaging key stakeholders in health technology development is essential to overcome existing barriers. The aim of this study was to establish perspectives of older adults with hip fracture, family caregivers and health professionals (stakeholders) on the development of a mHealth system. Qualitative study guided by user-centered design principles with focus groups to engage stakeholders during the development. Seven focus groups were conducted [older adults with hip fracture (n = 2), caregivers (n = 3), and health providers (n = 2)] with 45 participants (14 older adults, 21 caregivers and 10 health providers). Inclusion criteria were older adults ≥ 65 years who sustained a hip fracture in the previous 3 months; family caregiver of a person with hip fracture; and health providers with 2+ years of clinical experience working older adults with hip fracture. We followed standard methods for focus groups, including recording sessions, transcription and conducting an inductive content analysis. The same moderator, with clinical and research experience, conducted all focus groups. Three themes were generated to consider for a future mHealth intervention: (1) user-friendly design; (2) content to include recovery and prevention information; and (3) implementation factors. Our mHealth system was developed based on feedback from participants. Co-creating mHealth technology with stakeholders is essential for uptake and adherence. We provide an overview of the development of ActiveHip+, an mHealth system for the clinical care of older adults with hip fracture. Designing mHealth tools through a co-creation process with the main stakeholders is a way of facilitating the use of health information and communication technology, especially for older adults. Creating a user-friendly and intuitive mobile application is a critical point for a feasible implementation. Including relevant information about the entire process of a hip fracture recovery and an easy way of communicating with health providers are important aspects for patients and caregivers' support. [ABSTRACT FROM AUTHOR]

The Health Information Technology for Economic and Clinical Health Act incentivized the adoption of electronic health records (EHRs). Health systems looked to leverage technology to assist in serving populations in health professional shortage areas. Qualitative research points to EHR usability as a source of health inequities in rural settings, making the challenges of EHR usage a subject of interest. Pennsylvania offers a model for investigating rural health infrastructure with it having the third largest rural population in the United States. This study analyzed the adoption of Electronic Prescribing in the 67 Pennsylvania (PA) counties. Physician adoption and usage data for PA and the United States were compared using a t-test to establish a basis for comparison. PA counties were categorized using the United States Department of Agriculture (USDA)'s Rural-Urban Commuting Areas (RUCAs) system. Surescript use percentages were plotted against the RUCA scores of each PA county to create a polynomial regression model. PA office-based physicians, on average, utilize e-prescription tools at the same rate as the national average with 59% of practices utilizing Surescripts as of 2013. There was no significant correlation between Surescript usage and the rural/urban classification of counties in Pennsylvania (R-squared value of 0.06). Pennsylvania was able to adopt health information technology (HIT) infrastructure at the same rate as the national average. Rural and metropolitan definitions do not correlate to meaningful use of HIT, thus usability of HIT cannot be tied to health outcomes. Future studies looking at specific forms of HIT and their ability to decrease the burden of administrative work for clinicians. [ABSTRACT FROM AUTHOR]

Purpose: To assess the quality and readability of online health information on vasectomy using validated readability and quality assessment tools. Materials and Methods: The top 50 search results for "vasectomy" on Google, Bing, and Yahoo were selected. Duplicate links, advertisements, blog posts, paid webpages, and information intended for healthcare providers were excluded. Flesch Reading Ease score, Flesch–Kincaid Grade level, Gunning Fog Index, and Simple Measure of Gobbledygook (SMOG) index were used to assess readability, with optimal readability level for online health information established as being at sixth grade reading level. DISCERN Instrument and JAMA Benchmark were used to assess the quality of selected webpages. Inter-assessment score correlation and results by webpage type were analyzed. Results: We analyzed 44 webpages, including 16 academic, 5 hospital-affiliated, 6 commercial, 13 non-profit health advocacy, and 4 uncategorized sources. The average readability of the evaluated webpages was at a 10th grade reading level as measured by the Flesch Kincaid Assessment tool, and an undergraduate reading level per the SMOG and Gunning Fog indices. Non-profit health advocacy webpages had the best reading level but still was not at the recommended level of grade 6 to 7. The overall DISCERN quality of the webpages was “fair”, with non-profit health advocacy pages performing best. Conclusions: The assessed webpages offer education on vasectomy in a language that is too complex for the general population to understand. Furthermore, several sources for online health information, such as non-profits, outperformed webpages by academic institutions. Increased healthcare collaboration and dedication to producing quality online patient resources is necessary to address these shortcomings and build trust among patients to increase utilization of vasectomy and decrease decisional regret [ABSTRACT FROM AUTHOR]

Objective: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine. Methods: We undertook a qualitative interview‐based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented. Results: Twenty‐five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years. Conclusions: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations. This qualitative study found that emergency clinicians in Australia and New Zealand were generally optimistic about the use of artificial intelligence (AI)‐enabled decision support tools in EDs. A number of issues were raised that require attention before full acceptance. AI‐enabled tools must operate as decision support only, be locally validated, be effectively integrated into existing workflows and take ethical concerns into consideration. [ABSTRACT FROM AUTHOR]

Background: Medication dosing calculation errors can cause significant harm to patients, especially in the pediatric population. Crushing tablets for dose division purposes may increase the risk of calculation errors, which can lead to incorrect dosing and compromised patient safety. This study aimed to develop a calculator to eliminate calculation errors associated with dose division. Methods: Using the Wix platform, a group of pharmacists created a user-friendly webpage "Dose 4 You." To enable accurate dose division calculations, the advanced language model Chat GPT and Visual Studio were used. The tool assists healthcare professionals through a step-by-step process, allowing them to enter the necessary dose and medication requirements. The Dose 4 You web page's reliability and feasibility were assessed using retrospective data and validated questionnaires, including the System Usability Scale (SUS), respectively and a Likert scale-based acceptance questionnaire. Results: The Dose 4 You website calculated the required amount of powdered tablet to achieve the desired dose with 100% accuracy. The obtained SUS score was 88.38, indicating excellent usability. The average score of all questions for acceptance was found to be 4.7 ± 0.15 indicating a strong agreement on the tool's usefulness and effectiveness. Conclusion: Dose 4 You is a reliable tool that improves patient safety by streamlining dose calculations and lowering calculation errors. The tool's ease of use, practicality in daily clinical practice, and potential to reduce medication errors are highlighted by the positive perception among healthcare professionals. Dose 4 You's successful implementation demonstrates the power of technology and collaboration in transforming medication administration and improving patient outcomes. Similar innovative solutions to optimize healthcare practices can be explored in future health informatics endeavors. [ABSTRACT FROM AUTHOR]

Background: High lactate to albumin ratio (LAR) has been reported to be associated to with poor prognosis in patients admitted to the intensive care unit (ICU). However, its role in predicting in-hospital mortality in AF patients admitted to ICU has not been explored. Methods: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to retrieve information on patients who had been diagnosed with AF. X-tile software was utilized to determine the optimal cut-off LAR. Area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA) were conducted to assess the prediction performance of LAR for in-hospital mortality. Results: Finally, 8,287 AF patients were included and 1,543 death (18.6%) occurred. The optimal cut-off value of LAR is 0.5. Patients in lower LAR (< 0.5) group showed a better in-hospital survival compared to patients in higher LAR (≥ 0.5) group (HR: 2.67, 95%CI:2.39–2.97, P < 0.001). A nomogram for in-hospital mortality in patients with AF was constructed based on multivariate Cox analysis including age, CCI, β blockers usage, APSIII, hemoglobin and LAR. This nomogram exhibited excellent discrimination and calibration abilities in predicting in-hospital mortality for critically ill AF patients. Conclusion: LAR, as a readily available biomarker, can predict in-hospital mortality in AF patients admitted to the ICU. The nomogram that combined LAR with other relevant variables performed exceptionally well in terms of predicting in-hospital mortality. [ABSTRACT FROM AUTHOR]

Highlights: Recent advances and design strategies for molecular machines working as collectives in building smart responsive materials and micro/nanoscale operations are summarized in this review. The modulation of collective behaviors characteristics and properties is summarized in focus, including reversibility, amplification, anisotropy and reconfigurability of smart materials, as well as reconfigurability, orthogonality and logical control of swarming. Experiences and paradigms in the field of micro/nanorobotics in collective construction, control strategies, and model transformations are expected to provide guidance for molecular machines to build reconfigurable, multi-dimensional, and multi-modularity advanced collectives. Molecular machines are key to cellular activity where they are involved in converting chemical and light energy into efficient mechanical work. During the last 60 years, designing molecular structures capable of generating unidirectional mechanical motion at the nanoscale has been the topic of intense research. Effective progress has been made, attributed to advances in various fields such as supramolecular chemistry, biology and nanotechnology, and informatics. However, individual molecular machines are only capable of producing nanometer work and generally have only a single functionality. In order to address these problems, collective behaviors realized by integrating several or more of these individual mechanical units in space and time have become a new paradigm. In this review, we comprehensively discuss recent developments in the collective behaviors of molecular machines. In particular, collective behavior is divided into two paradigms. One is the appropriate integration of molecular machines to efficiently amplify molecular motions and deformations to construct novel functional materials. The other is the construction of swarming modes at the supramolecular level to perform nanoscale or microscale operations. We discuss design strategies for both modes and focus on the modulation of features and properties. Subsequently, in order to address existing challenges, the idea of transferring experience gained in the field of micro/nano robotics is presented, offering prospects for future developments in the collective behavior of molecular machines. [ABSTRACT FROM AUTHOR]

Purpose Data are limited on utilizing a comprehensive scoring system in the electronic health record to help prioritize, align, and standardize clinical pharmacy services across multiple hospitals and practice models within a health system. The purpose of this article is to describe the development and implementation of an electronic scoring system to help inpatient pharmacists prioritize patient care activities and standardize clinical services across a diverse health system. Summary Inpatient pharmacists from all specialty areas across the health system partnered with health information technology pharmacists to develop a scoring system directly integrated into the electronic health record that would help triage patient care, identify opportunities for pharmacist intervention, and prioritize clinical pharmacy services. Individual variables were built based on documented patient parameters such as use of high-risk medications, pharmacy consults, laboratory values, disease states, and patient acuity. Total overall scores were assigned to patients based on the sum of the scores for the individual variables, which update automatically in real time. The total scores were designed to help inpatient pharmacists prioritize patients with higher scores, thus reducing the need for manual chart review to identify high-risk patients. Conclusion An electronic scoring system with a tiered point system developed for inpatient pharmacists creates a method to prioritize and align clinical pharmacy services across a health system with diverse pharmacy practice models. [ABSTRACT FROM AUTHOR]

Background Because poor health in youth risk affecting their entry in adulthood, improved methods for their early identification are needed. Health and welfare technology is widely accepted by youth populations, presenting a potential method for identifying their health problems. However, healthcare technology must be evidence-based. Specifically, feasibility studies contribute valuable information prior to more complex effects-based research. The current study assessed the process, resource, management, and scientific feasibility of the Youth Health Report System prototype, developed within a youth health clinic context in advance of an intervention study. Methods This mixed-methods feasibility study was conducted in a clinical setting. The process, resource, management, and scientific feasibility of the Youth Health Report System were investigated, as recommended in the literature. Participants were youth aged 16–23 years old, attending a youth health clinic, and healthcare professionals from three clinics. The youth participants used their smart phones to respond to Youth Health Report System health questions and healthcare professionals used their computer to access the results and for registration system entries. Qualitative data were collected from interviews with healthcare professionals, which were described with thematic analysis. Youth participants’ quantitative Youth Health Report System data were analyzed for descriptive statistics. Results Feasibility analysis of qualitative data from interviews with 11 healthcare professionals resulted in three themes: We expected it could be hard; Information and routines helped but time was an issue; and The electronic case report form was valuable in the health assessment. Qualitative data were collected from the Youth Health Report System. A total of 54 youth participants completed the evaluation questionnaire, and healthcare professionals retrieved information from, and made post-appointment system entries. Quantitative results revealed few missing items and acceptable data variability. An assessment template of merged qualitative and quantitative data guided a consensus discussion among the researchers, resulting in acceptable feasibility. Conclusions The process-, resource-, management-, and scientific feasibility aspects were acceptable, with some modifications, strengthening the potential for a successful Youth Health Report System intervention study. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively. [ABSTRACT FROM AUTHOR]

Organic field effect transistors (OFETs), used in the fabrication of nano‐sensors, are one of the most promising devices in organic electronics because of their lightweight, flexible, and low fabrication cost. However, the numerical modeling of such OFETs is still in an early stage due to the minimal analytical as well as numerical models presented in the literature. This research aims to demonstrate an experimentally verified machine‐learning model by investigating an OFET with polyaniline as a p‐type organic semiconductor. OFET's threshold voltage, on/off current ratio, subthreshold swing, and device mobilities are studied as the primary output chiasmatic parameters. The random‐forest machine learning model has shown the criticality of the doping effect on turning the OFET to depletion mode, with positive threshold voltage, under doping higher than 5×1014$$ 5\times {10}^{14} $$ cm−3. Additionally, the study highlights the effectiveness of the gate oxide thickness in controlling the OFET threshold voltage. A 50 nm oxide thickness showed sufficiency to have a non‐depleted OFET operation. [ABSTRACT FROM AUTHOR]

Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics. [ABSTRACT FROM AUTHOR]

Nonlinear curve fitting is an important process in laboratory medicine, particularly with the increased use of highly sensitive antibody-based assays. Although the process is often automated in commercially available software, it is important that clinical scientists and physicians recognize the limitations of the various approaches used and are able to select the most appropriate model. This article summarizes the key nonlinear functions and demonstrates their application to common laboratory data. Following this, a basic overview of the statistical comparison of models is presented and then a discussion of important algorithms used in nonlinear curve fitting. An accompanying Microsoft Excel workbook is available that can be used to explore the content of this article. [ABSTRACT FROM AUTHOR]

In the field of optical communications and quantum informatics, polarization and orbital angular momentum (OAM) offer a promising way to expand the dimension of information. However, detecting the polarization state and topological charge of the OAM beam on‐chip way can be challenging. To address this, an ultracompact metasurface is proposed and demonstrated. The metasurface is composed of six polarization‐sensitive metalenses that occupy 60°annular sectors each and are capable of simultaneous polarization state and orbital angular momentum detection. The metasurface focuses the coaxial polarized vortex beam into six locations, and the foci displacement is related to the value of the topological charge. The corresponding polarization state can be obtained through the reconstructed Stokes parameter using the six focus light intensities. For proof‐of‐concept, an ultracompact metasurface operating at a wavelength of 1064 nm is fabricated, which exhibits exceptional polarization and orbital angular momentum recognition ability. The proposed metasurface possesses the merits of ultra‐compact size and simultaneously entire polarization state and orbital angular momentum identification capability, which may encourage the development of on‐chip optical communication systems. [ABSTRACT FROM AUTHOR]

As interprofessional collaboration (IPC) in primary care receives increasing attention, the role of electronic medical and health record (EMR/EHR) systems in supporting IPC is important to consider. A scoping review was conducted to synthesize the current literature on the barriers and facilitators of EMR/EHRs to interprofessional primary care. Four online databases (OVID Medline, EBSCO CINAHL, OVID EMBASE, and OVID PsycINFO) were searched without date restrictions. Twelve studies were included in the review. Of six facilitator and barrier themes identified, the key facilitator was teamwork support and a significant barrier was data management. Other important barriers included usability related mainly to interoperability, and practice support primarily in terms of patient care. Additional themes were organization attributes and user features. Although EMR/EHR systems facilitated teamwork support, there is potential for team features to be strengthened further. Persistent barriers may be partly addressed by advances in software design, particularly if interprofessional perspectives are included. Organizations and teams might also consider strategies for working with existing EMR/EHR systems, for instance by developing guidelines for interprofessional use. Further research concerning the use of electronic records in interprofessional contexts is needed to support IPC in primary care. [ABSTRACT FROM AUTHOR]

Direct access testing (DAT) is an emerging care model that provides on-demand laboratory services for certain preventative, diagnostic, and monitoring indications. Unlike conventional testing models where health care providers order tests and where sample collection is performed onsite at the clinic or laboratory, most interactions between DAT consumers and the laboratory are virtual. Tests are ordered and results delivered online, and specimens are frequently self-collected at home with virtual support. Thus, DAT depends on high-quality information technology (IT) tools and optimized data utilization to a greater degree than conventional laboratory testing. This review critically discusses the United States DAT landscape in relation to IT to highlight digital challenges and opportunities for consumers, health care systems, providers, and laboratories. DAT offers consumers increased autonomy over the testing experience, cost, and data sharing, but the current capacity to integrate DAT as a care option into the conventional patient-provider model is lacking and will require innovative approaches to accommodate. Likewise, both consumers and health care providers need transparent information about the quality of DAT laboratories and clinical decision support to optimize appropriate use of DAT as a part of comprehensive care. Interoperability barriers will require intentional approaches to integrating DAT-derived data into the electronic health records of health systems nationally. This includes ensuring the laboratory results are appropriately captured for downstream data analytic pipelines that are used to satisfy population health and research needs. Despite the data- and IT-related challenges for widespread incorporation of DAT into routine health care, DAT has the potential to improve health equity by providing versatile, discreet, and affordable testing options for patients who have been marginalized by the current limitations of health care delivery in the United States. [ABSTRACT FROM AUTHOR]

Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

Objective: In the absence of access to surveillance system data, single-source administrative databases are often used to study health care utilization and health outcomes among people with sickle cell disease (SCD). We compared the case definitions from single-source administrative databases with a surveillance case definition to identify people with SCD. Materials and Methods: We used data from Sickle Cell Data Collection programs in California and Georgia (2016-2018). The surveillance case definition for SCD developed for the Sickle Cell Data Collection programs uses multiple databases, including newborn screening, discharge databases, state Medicaid programs, vital records, and clinic data. Case definitions for SCD in single-source administrative databases varied by database (Medicaid and discharge) and years of data (1, 2, and 3 years). We calculated the proportion of people meeting the surveillance case definition for SCD that was captured by each single administrative database case definition for SCD, by birth cohort, sex, and Medicaid enrollment. Results: In California, 7117 people met the surveillance case definition of SCD from 2016 through 2018; 48% of this group was captured by the Medicaid case definition and 41% by the discharge case definition. In Georgia, 10 448 people met the surveillance case definition of SCD from 2016 through 2018; 45% of this group was captured by the Medicaid case definition and 51% by the discharge case definition. These proportions differed by years of data, birth cohort, and length of Medicaid enrollment. Practice Implications: The surveillance case definition identified twice as many people with SCD as the single-source administrative database definitions during the same period, but trade-offs exist in using single administrative databases for decisions on policy and program expansion for SCD. [ABSTRACT FROM AUTHOR]

Medical ethics and professionalism are two essential parts of building up the identity of a competent physician. This study was conducted to determine the nature, content, and methods of medical ethics and professionalism education in Saudi public and private medical schools. It also sought to identify the challenges and obstacles in teaching and assessing medical ethics and professionalism and suggest appropriate changes. A cross-sectional study was carried out in Saudi private and public medical schools. To achieve the study's aim, an assessment tool in the form of a novel self-administered questionnaire was developed, piloted, and then used. A representative from each of the 28 Saudi medical schools participated in the study. Twenty-four (82.1%) responding medical schools have no medical ethics department. Most of the medical schools (64.2%) have 25% or less of their faculty staff who teach ethics holding a qualification in medical ethics. Most schools have a specific course for medical ethics and professionalism (85.7% and 57.1%, respectively). Multiple-choice questioning is the most popular assessment method in medical ethics and professionalism courses (89.3% and 60.7%, respectively). The need for more qualified staff and clear guidelines/resources is a significant drawback to the teaching of medical ethics. Therefore, the study recommends developing national guidelines dedicated to the undergraduate teaching curriculum from which courses would be designed to enhance medical ethics and medical professionalism. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic highlighted the United States' lack of a nationwide infrastructure for collecting, sharing, and using health data, especially for secondary uses (e.g., population health management and public health). The federal government is taking several important steps to upgrade the nation's health data ecosystem—notably, the Centers for Disease Control and Prevention's Data Modernization Initiative and the Office of the National Coordinator for Health Information Technology's Trusted Exchange Framework and Common Agreement. However, substantial barriers remain. Inconsistent regulations, infrastructure, and governance across federal and state levels and between states significantly impede the exchange and analysis of health data. Siloed systems and insufficient funding block effective integration of clinical, public health, and social determinants data within and between states. In this analytic essay, we propose strategies to develop a nationwide health data ecosystem. We focus on providing federal guidance and incentives to develop state-designated entities responsible for the collection, integration, and analysis of clinical, public health, social determinants of health, claims, administrative, and other relevant data. These recommendations include a regulatory clearinghouse, federal guidance, model legislation and templated regulation, funding to incentive enterprise architecture, regulatory sandboxes, and a 3-pronged research agenda. (Am J Public Health. 2024;114(2):209–217. https://doi.org/10.2105/AJPH.2023.307477) [ABSTRACT FROM AUTHOR]

Low tidal volume ventilation (LTVV; < 8 mL/kg predicted body weight [PBW]) is a well-established standard of care associated with improved outcomes. This study used data collated in multicenter electronic health record ICU databases from the United Kingdom and the United States to analyze the use of LTVV in routine clinical practice. What factors are associated with adherence to LTVV in the United Kingdom and North America? This was a retrospective, multicenter study across the United Kingdom and United States of patients who were mechanically ventilated. Factors associated with adherence to LTVV were assessed in all patients in both databases who were mechanically ventilated for > 48 h. We observed trends over time and investigated whether LTVV was associated with patient outcomes (30-day mortality and duration of ventilation) and identified strategies to improve adherence to LTVV. A total of 5,466 (Critical Care Health Informatics Collaborative [CCHIC]) and 7,384 electronic ICU collaborative research database [eICU-CRD] patients were ventilated for > 48 h and had data of suitable quality for analysis. The median tidal volume (V T) values were 7.48 mL/kg PBW (CCHIC) and 7.91 mL/kg PBW (eICU-CRD). The patients at highest risk of not receiving LTVV were shorter than 160 cm (CCHIC) and 165 cm (eICU-CRD). Those with BMI > 30 kg/m2 (CCHIC OR, 1.9 [95% CI, 1.7-2.13]; eICU-CRD OR, 1.61 [95% CI, 1.49-1.75]) and female patients (CCHIC OR, 2.39 [95% CI, 2.16-2.65]; eICU-CRD OR, 2.29 [95% CI, 2.26-2.31]) were at increased risk of having median V T > 8 mL/kg PBW. Patients with median V T < 8 mL/kg PBW had decreased 30-day mortality in the CCHIC database (CCHIC cause-specific hazard ratio, 0.86 [95% CI, 0.76-0.97]; eICU-CRD cause-specific hazard ratio, 0.9 [95% CI, 0.86-1.00]). There was a significant reduction in V T over time in the CCHIC database. There has been limited implementation of LTVV in routine clinical practice in the United Kingdom and the United States. V T > 8 mL/kg PBW was associated with worse patient outcomes. [ABSTRACT FROM AUTHOR]