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Purpose: To assess the accuracy and precision of prosthetically driven implant placement achieved through static computer-aided implant surgery by analyzing the linear and angular deviations of implants. Materials and Methods: A total of 53 implants were included in the study. The implants were positioned using either tooth-supported templates or tooth-and-tissue-supported templates with fixation pins. Two distinct guided surgery approaches were used: pilot drill guided and fully guided. 3D data from the implant planning phase was superimposed with the 3D data from the final implant positions using the "Treatment Evaluation" tool within coDiagnostiX implant planning software (Straumann). This enabled the automatic calculation of deviations in implant placement accuracy. Results: The average angular deviation observed was 3.90 degrees. For linear deviations, the mean 3D deviation was 1.04 mm at the most coronal point of the implants and 1.56 mm at the implant apex. Conclusions: This research demonstrates the feasibility of a digital workflow for guided implant surgery, offering a promising treatment option. Nonetheless, it is important to note that deviations do occur, particularly in the apical region of the implant. Care should be taken, particularly in cases of limited bone availability. [ABSTRACT FROM AUTHOR]

The International Classification of Diseases, 11th Revision (ICD-11) has significantly improved the ability to navigate coding challenges beyond prior iterations of the ICD. Commonly encountered sources of complexity in clinical documentation include coding of uncertain and "ruled out" diagnoses. Assessing official international guidelines and rules, this paper documents extensive variation across countries in existing practices for coding and reporting unconfirmed and "ruled out" clinical concepts in ICD-10 (and modifications thereof). The design of ICD-11 is intended to mitigate these coding challenges by introducing postcoordination, expanding the range of codable clinical concepts, and offering clearer guidance in the ICD-11 Reference Guide. ICD-11 offers substantial progress towards more precise capture of uncertain and "ruled out" diagnoses, including international consensus on coding rules for these historically challenging clinical concepts. However, we identify the need for further clarification of the concepts of "provisional diagnosis" and "differential diagnosis."

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]

This column is intended to address the kinds of knotty problems and dilemmas with which many scholars grapple in studying health professions education. In this article, the authors address the challenges in proofreading a manuscript. Emerging researchers might think that someone in the production team will catch any errors. This may not always be the case. We emphasize the importance of guiding mentees to take the process of preparing a manuscript for submission seriously.

Kidney failure is particularly common in the United States, where it affects over 700,000 individuals. It is typically treated through repeated sessions of hemodialysis to filter and clean the blood. Hemodialysis requires vascular access, in about 70% of cases through an arteriovenous fistula (AVF) surgically created by connecting an artery and vein. AVF take 6 weeks or more to mature. Mature fistulae often require intervention, most often percutaneous transluminal angioplasty (PTA), also known as fistulaplasty, to maintain the patency of the fistula. PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF, and many patients undergo the procedure multiple times. Although PTA is important for AVF maturation and maintenance, research into predictive models of AVF function following PTA has been limited. Therefore, in this paper we hypothesize that based on patient-specific information collected during PTA, a predictive model can be created to help improve treatment planning. We test a set of rich, multimodal data from 28 patients that includes medical history, AVF blood flow, and interventional angiographic imaging (specifically excluding any post-PTA measurements) and build deep hybrid neural networks. A hybrid model combining a 3D convolutional neural network with a multi-layer perceptron to classify AVF was established. We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months. The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75, and AUROC of 0.75. These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA. These initial findings suggest that the hybrid model combining clinical data, imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis. Further study based on a large cohort is needed to refine the accuracy and model efficiency.

ObjectiveBlockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics.Materials and methodsThis systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized.ResultsFollowing screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility.DiscussionEthereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other.ConclusionWhile blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.

The Journal of Management Information Systems (JMIS) is celebrating its fifth decade of publication and is committed to publishing research papers that make significant contributions to the field of information systems. The journal's editors have an inclusive understanding of the field and aim to reflect and shape its growth and evolution. Over the years, JMIS has published papers on various topics, including the economics of IS, strategic deployment of IT, design research, neuroscience in IS research, behavioral cybersecurity, health informatics, and AI-human collaboration. The journal also recognizes its responsibility to support the well-being of society and explores the societal impacts of IT-driven change. The current issue features papers on topics such as the use of body-worn cameras in policing, technological innovation, crowdfunding, chatbots, sentiment analysis, cloud storage and security breaches, cybersecurity risk management, software architecture, and turnover of IT professionals. [Extracted from the article]

OBJECTIVES: This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. MATERIALS AND METHODS: We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. RESULTS: About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. DISCUSSION: The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. CONCLUSION: A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs without special effort will be crucial to ensure future technology robustness and durability.

Background: Asynchronous telepsychiatry (ATP) consultations are a novel form of psychiatric consultation. Studies comparing patient and provider satisfaction for ATP with that for synchronous telepsychiatry (STP) do not exist. Methods: This mixed-methods study is a secondary analysis of patients' and primary care providers' (PCPs) satisfaction from a randomized clinical trial of ATP compared with STP. Patients and their PCPs completed satisfaction surveys, and provided unstructured feedback about their experiences with either ATP or STP. Differences in patient satisfaction were assessed using mixed-effects logistic regression models, and the qualitative data were analyzed using thematic analysis with an inductive coding framework. Results: Patient satisfaction overall was high with 84% and 97% of respondents at 6 months reported being somewhat or completely satisfied with ATP and STP, respectively. Patients in the STP group were more likely to report being completely satisfied, to recommend the program to a friend, and to report being comfortable with their care compared with ATP (all p < 0.05). However, there was no difference between the patients in ATP and STP in perceived change in clinical outcomes (p = 0.51). The PCP quantitative data were small, and thus only summarized descriptively. Conclusions: Patients expressed their overall satisfaction with both STP and ATP. Patients in ATP reported more concerns about the process, likely because feedback after ATP was slower than that after STP consultations. PCPs had no apparent preference for STP or ATP, and reported implementing the psychiatrists' recommendations for both groups when such recommendations were made, which supports our previous findings. Trial Registration: ClinicalTrials.gov NCT02084979; https://clinicaltrials.gov/ct2/show/NCT02084979.

Objectives: The objectives of this retrospective study were to analyze telehealth utilization for two specialty care practices: oral medicine (OM) and oral and maxillofacial surgery (OMFS) during the first 2 years of the pandemic, its impact as a new treatment modality and on participating providers, as well as identify the type of patient visit that most readily adopted telehealth. Methods: Retrospective study of patients who sought specialty services, OM and OMFS, at an outpatient clinic in a university health system setting between March 1, 2019, and February 28, 2022. Source data were obtained from Epic, an electronic medical record application. Data were graphed using Tableau and Microsoft Excel software. Statistical analysis was performed utilizing chi-squared test and analysis of variance (ANOVA). Results: OMFS utilized telehealth 12% of the time, and OM 8% of the time. The majority (87%) of telehealth visits were for return patients (RPs). Compared with the first year of the pandemic, there was a decrease in the number of telehealth visits in the second year (p = 0.0001). As of August 2022, new patient (NP) telehealth encounters have largely returned to prepandemic levels (0-1.5%), whereas RP telehealth visits remained at an average level of 11.4% (9.4-12.4%). Surveyed providers consider telehealth as an effective complement to in-person care and will continue its use (4.2/5 Likert scale). Conclusions: Telehealth has become a viable pathway of care for OM and OMFS who previously did not utilize the remote platform to deliver healthcare. As a new treatment modality, telehealth is perceived as impactful in increasing access to specialty care by participating providers. NP visits are now almost completely in person, but telehealth continues for RPs. Ongoing demand for telehealth highlights urgency to develop appropriate standards and effective remote diagnostic/monitoring tools to maximize telehealth's capability to leverage finite health care resources and increase access to specialty care.

BackgroundVirtual activities, hybrid work and virtual mentoring have become part of the ongoing milieu of academic medicine. As the shift to remote mentoring continues to evolve, it is now possible to adapt, refine, and improve tools to support thriving mentoring relationships that take place virtually. This study explores strategies for virtual mentoring as a cornerstone for effective training programs among senior mentors participating in an ongoing mentoring program.MethodsWe conducted a qualitative study among prior and current participants of an ongoing "Mentoring the Mentors" program about key strategies for optimizing virtual mentoring. Data were coded and analyzed following a thematic analysis approach.ResultsRespondents were mostly female (62%), white (58%), and associate (39%) or full professors (32%). We found that, with the expansion of hybrid and fully remote work, there are now fewer opportunities for informal but important chance meetings between mentors and mentees; however, virtual mentoring provides opportunities to compensate for reduced interactivity normally experienced in the workplace. The heightened need to plan and be more deliberate in the virtual sphere was woven throughout narratives and was the foundation of most recommendations. Specifically, a central obstacle for respondents was that spontaneous conversations were harder to initiate because virtual conversations are expected to have set agendas.ConclusionsDeveloping new ways to maintain meaningful interpersonal relationships in a virtual training environment, including opportunities for serendipitous and informal engagement, is critical to the success of virtual mentoring programs.

BackgroundSmartphone apps may provide an opportunity to deliver mental health resources and interventions in a scalable and cost-effective manner. However, young people from marginalized and underserved groups face numerous and unique challenges to accessing, engaging with, and benefiting from these apps.ObjectiveThis study aims to better understand the acceptability (ie, perceived usefulness and satisfaction with an app) and feasibility (ie, the extent to which an app was successfully used) of mental health apps for underserved young people. A secondary aim was to establish whether adaptations can be made to increase the accessibility and inclusivity of apps for these groups.MethodsWe conducted 2 sequential studies, consisting of a systematic literature review of mental health apps for underserved populations followed by a qualitative study with underserved young male participants (n=20; age: mean 19). Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, an electronic search of 5 databases was conducted in 2021. The search yielded 18,687 results, of which 14 articles met the eligibility criteria.ResultsThe included studies comprised a range of groups, including those affected by homelessness, having physical health conditions, living in low- and middle-income countries, and those with sexual and gender minority identities. Establishing and maintaining user engagement was a pervasive challenge across mental health apps and populations, and dropout was a reported problem among nearly all the included studies. Positive subjective reports of usability, satisfaction, and acceptability were insufficient to determine users' objective engagement.ConclusionsDespite the significant amount of funding directed to the development of mental health apps, juxtaposed with only limited empirical evidence to support their effectiveness, few apps have been deliberately developed or adapted to meet the heterogeneous needs of marginalized and underserved young people. Before mental health apps are scaled up, a greater understanding is needed of the types of services that more at-risk young people and those in limited-resource settings prefer (eg, standard vs digital) followed by more rigorous and consistent demonstrations of acceptability, effectiveness, and cost-effectiveness. Adopting an iterative participatory approach by involving young people in the development and evaluation process is an essential step in enhancing the adoption of any intervention, including apps, in "real-world" settings and will support future implementation and sustainability efforts to ensure that marginalized and underserved groups are reached.Trial registrationPROSPERO CRD42021254241; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=254241.

BackgroundIn 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures.ObjectivesThe aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses.MethodsWe created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021.ResultsWe invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities.ConclusionCI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

There is increasing interest in understanding potential bias in medical education. We used natural language processing (NLP) to evaluate potential bias in clinical clerkship evaluations. Data from medical evaluations and administrative databases for medical students enrolled in third-year clinical clerkship rotations across two academic years. We collected demographic information of students and faculty evaluators to determine gender/racial concordance (i.e., whether the student and faculty identified with the same demographic). We used a multinomial log-linear model for final clerkship grades, using predictors such as numerical evaluation scores, gender/racial concordance, and sentiment scores of narrative evaluations using the SentimentIntensityAnalyzer tool in Python. 2037 evaluations from 198 students were analyzed. Statistical significance was defined as P

Cervical cancer, despite being preventable through primary and secondary prevention strategies, remains one of the leading causes of morbidity and mortality among women in Brazil. This study aimed to analyze the temporal, spatial, and space-time patterns of cervical cancer mortality in Brazil. An ecological study was conducted using temporal, spatial, and space-time analysis techniques, using death certificates with cervical cancer as the underlying cause or associated condition among females in Brazil from 2000 to 2021. Death certificate and population data were provided by the Department of Health Informatics of the Unified Health System (DATASUS) and the Brazilian Institute of Geography and Statistics (IBGE), respectively. A total of 123,306 deaths associated with cervical cancer among women were registered during the study period. A rising trend in mortality was detected since 2014 onwards, after 14 years of decline. Particularly, an increase in mortality was observed among the younger age groups, and in the North and Northeast regions regardless of age. Heterogeneity in the spatial distribution of cervical cancer mortality was observed, with high mortality clusters around the country, but mostly concentrated in the North and Northeast regions. These findings suggest a need and an opportunity to develop efficient and effective health policies targeting those regions and groups of women at higher risk which in turn will allow for fast and significant reductions in cervical cancer mortality in Brazil. [ABSTRACT FROM AUTHOR]

In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space. [ABSTRACT FROM AUTHOR]

Background: Antibiotic resistant infections cause over 700,000 deaths worldwide annually. As antimicrobial stewardship (AMS) helps minimise the emergence of antibiotic resistance resulting from inappropriate use of antibiotics in healthcare, we developed ePAMS+ (ePrescribing-based Anti-Microbial Stewardship), an ePrescribing and Medicines Administration (EPMA) system decision-support tool complemented by educational, behavioural and organisational elements. Methods: We conducted a non-randomised before-and-after feasibility trial, implementing ePAMS+ in two English hospitals using the Cerner Millennium EPMA system. Wards of several specialties were included. Patient participants were blinded to whether ePAMS+ was in use; prescribers were not. A mixed-methods evaluation aimed to establish: acceptability and usability of ePAMS+ and trial processes; feasibility of ePAMS+ implementation and quantitative outcome recording; and a Fidelity Index measuring the extent to which ePAMS+ was delivered as intended. Longitudinal semi-structured interviews of doctors, nurses and pharmacists, alongside non-participant observations, gathered qualitative data; we extracted quantitative prescribing data from the EPMA system. Normal linear modelling of the defined daily dose (DDD) of antibiotic per admission quantified its variability, to inform sample size calculations for a future trial of ePAMS+ effectiveness. Results: The research took place during the SARS-CoV-2 pandemic, from April 2021 to November 2022. 60 qualitative interviews were conducted (33 before ePAMS+ implementation, 27 after). 1,958 admissions (1,358 before ePAMS+ implementation; 600 after) included 24,884 antibiotic orders. Qualitative interviews confirmed that some aspects of ePAMS+ , its implementation and training were acceptable, while other features (e.g. enabling combinations of antibiotics to be prescribed) required further development. ePAMS+ uptake was low (28 antibiotic review records from 600 admissions; 0.047 records per admission), preventing full development of a Fidelity Index. Normal linear modelling of antibiotic DDD per admission showed a residual variance of 1.086 (log-transformed scale). Unavailability of indication data prevented measurement of some outcomes (e.g. number of antibiotic courses per indication). Conclusions: This feasibility trial encountered unforeseen circumstances due to contextual factors and a global pandemic, highlighting the need for careful adaptation of complex intervention implementations to the local setting. We identified key refinements to ePAMS+ to support its wider adoption in clinical practice, requiring further piloting before a confirmatory effectiveness trial. Trial registration: ISRCTN Registry ISRCTN13429325, 24 March 2022. [ABSTRACT FROM AUTHOR]

Background: Recently, deep learning has been increasingly applied in the field of dentistry. The aim of this study is to develop a model for the automatic segmentation, numbering, and state assessment of teeth on panoramic radiographs. Methods: We created a dual-labeled dataset on panoramic radiographs for training, incorporating both numbering and state labels. We then developed a fusion model that combines a YOLOv9-e instance segmentation model with an EfficientNetv2-l classification model. The instance segmentation model is used for tooth segmentation and numbering, whereas the classification model is used for state evaluation. The final prediction results integrate tooth position, numbering, and state information. The model's output includes result visualization and automatic report generation. Results: Precision, Recall, mAP50 (mean Average Precision), and mAP50-95 for the tooth instance segmentation task are 0.989, 0.955, 0.975, and 0.840, respectively. Precision, Recall, Specificity, and F1 Score for the tooth classification task are 0.943, 0.933, 0.985, and 0.936, respectively. Conclusions: This fusion model is the first to integrate automatic dental segmentation, numbering, and state assessment. It provides highly accurate results, including detailed visualizations and automated report generation. [ABSTRACT FROM AUTHOR]

Background: Medication reconciliation is essential for optimizing medication use. In part to promote effective medication reconciliation, the Department of Veterans Affairs (VA) invested substantial resources in health information exchange (HIE) technologies. The objectives of this qualitative study were to characterize VA clinicians' use of HIE tools for medication reconciliation in their clinical practice and to identify facilitators and barriers. Methods: We recruited inpatient and outpatient prescribers (physicians, nurse practitioners, physician assistants) and pharmacists at four geographically distinct VA medical centers for observations and interviews. Participants were observed as they interacted with HIE or medication reconciliation tools during routine work. Participants were interviewed about clinical decision-making pertaining to medication reconciliation and use of HIE tools, and about barriers and facilitators to use of the tools. Qualitative data were analyzed via inductive and deductive approaches using a priori codes. Results: A total of 63 clinicians participated. Over half (58%) were female, and the mean duration of VA clinical experience was 7 (range 0–32) years. Underlying motivators for clinicians seeking data external to their VA medical center were having new patients, current patients receiving care from an external institution, and clinicians' concerns about possible medication discrepancies among institutions. Facilitators for using HIE software were clinicians' familiarity with the HIE software, clinicians' belief that medication information would be available within HIE, and their confidence in the ability to find HIE medication-related data of interest quickly. Six overarching barriers to HIE software use for medication coordination included visual clutter and information overload within the HIE display; challenges with HIE interface navigation; lack of integration between HIE and other electronic health record interfaces, necessitating multiple logins and application switching; concerns with the dependability of HIE medication information; unfamiliarity with HIE tools; and a lack of HIE data from non-VA facilities. Conclusions: This study is believed to be the first to qualitatively characterize clinicians' HIE use with respect to medication reconciliation. Results inform recommendations to optimize HIE use for medication management activities. We expect that healthcare organizations and software vendors will be able to apply the findings to develop more effective and usable HIE information displays. [ABSTRACT FROM AUTHOR]

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user‐friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data‐sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science. [ABSTRACT FROM AUTHOR]

People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against 'Covid19vaccine' and 'Coronavirusvaccine' hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words 'Delhi heart', 'Lung institute', 'Gift', 'Unite2fightcorona', and 'Covid-19 Vaccine' are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards 'Covid-19 vaccines', gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine. [ABSTRACT FROM AUTHOR]

This article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain. [ABSTRACT FROM AUTHOR]

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as "lead," "bottled water," "water filters," "pneumonia," and "Legionnaires' Disease," we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of "Big Data," led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research. [ABSTRACT FROM AUTHOR]

Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) "Llama 2" to extract quantitative information from clinical text and evaluates its performance in identifying features of decompensated liver cirrhosis. The LLM identified five key clinical features in a zero- and one-shot manner from 500 patient medical histories in the MIMIC IV dataset. We compared LLMs of three sizes and various prompt engineering approaches, with predictions compared against ground truth from three blinded medical experts. Our pipeline achieved high accuracy, detecting liver cirrhosis with 100% sensitivity and 96% specificity. High sensitivities and specificities were also yielded for detecting ascites (95%, 95%), confusion (76%, 94%), abdominal pain (84%, 97%), and shortness of breath (87%, 97%) using the 70 billion parameter model, which outperformed smaller versions. Our study successfully demonstrates the capability of locally deployed LLMs to extract clinical information from free text with low hardware requirements. [ABSTRACT FROM AUTHOR]

This research letter explores the concept of "clinical momentum" in the care of older adults with advanced dementia. Clinical momentum refers to the tendency for interventions to be performed near the end of life, often resulting in excessive treatment. The study specifically focuses on the use of feeding tubes in patients with advanced dementia as an example of overtreatment. The researchers conducted a retrospective case-control study using electronic health records to identify patients with dementia who received a permanent feeding tube and compared them to controls who did not receive a feeding tube. The study aims to identify markers of clinical momentum in the medical records. The findings indicate that patients with advanced dementia who received a permanent feeding tube had longer hospital stays and more events such as discussions about goals of care and consultations for palliative care. The study suggests that prolonged exposure to a healthcare system that prioritizes interventions for isolated problems can result in care that does not align with guidelines. However, it is important to note that the study has limitations, including a small and nondiverse sample. [Extracted from the article]

The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available LLMs as symptom checkers for cardiological diseases by comparing their diagnostic capabilities in real disease cases. We employed a set of 9 models, including ChatGPT-4, OpenSource models, Google PaLM 2, and Meta's LLaMA, to assess their diagnostic accuracy, reliability, and safety across various clinical scenarios. Our methodology involved presenting these LLMs with symptom descriptions and test results in Spanish, requiring them to provide specialist diagnoses and recommendations in English. This approach allowed us to compare the performance of each model, highlighting their respective strengths and limitations in a healthcare context. The results revealed varying levels of accuracy, precision, and sensitivity among the models, demonstrating the potential of LLMs to enhance medical education and patient care. By analysing the capabilities of each model, our study contributes to a deeper understanding of artificial intelligence's role in medical diagnosis. We argue for the strategic implementation of LLMs in healthcare, emphasizing the importance of balancing sensitivity and realism to optimize patient outcomes. [ABSTRACT FROM AUTHOR]

Objective: To conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of international collaboration, tracking the emergence of research hotspots, and detecting trends through keyword bursts. Methods: In this bibliometric study, we identified and retrieved publications on AI applications in RDs spanning 2003 to 2023 from the Web of Science (WoS). We conducted a global research landscape analysis and utilized CiteSpace to perform keyword clustering and burst detection in this field. Results: A total of 1501 publications were included in this study. The evolution of AI applications in RDs progressed through three stages: the start-up period (2003–2010), the steady development period (2011–2018), and the accelerated growth period (2019–2023), reflecting this field's increasing importance and impact at the time of the study. These studies originated from 85 countries, with the United States as the leading contributor. "Mutation", "Diagnosis", and "Management" were the top three keywords with high frequency. Keyword clustering analysis identified gene identification, effective management, and personalized treatment as three primary research areas of AI applications in RDs. Furthermore, the keyword burst detection indicated a growing interest in the areas of "biomarker", "predictive model", and "data mining", highlighting their potential to shape future research directions. Conclusions: Over two decades, research on the AI applications in RDs has made remarkable progress and shown promising results in the development. Advancing international transboundary cooperation is essential moving forward. Utilizing AI will play a more crucial role across the spectrum of RDs management, encompassing rapid diagnosis, personalized treatment, drug development, data integration and sharing, and continuous monitoring and care. [ABSTRACT FROM AUTHOR]

Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and extreme gradient boosting) to predict total in-hospital cost for admission for each condition. Our models had good predictive performance, with testing R-squared values of 0.701-0.750 (mean of 0.713) for CHF; 0.694-0.724 (mean 0.709) for COPD; and 0.615-0.729 (mean 0.694) for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures, and elective/nonelective admission. Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online. [ABSTRACT FROM AUTHOR]

Background: Digital health offers unprecedented opportunities to enhance health service delivery across vast geographic regions. However, these benefits can only be realized with effective capabilities and clinical leadership of the rural healthcare workforce. Little is known about how rural healthcare workers acquire skills in digital health, how digital health education or training programs are evaluated and the barriers and enablers for high quality digital health education and training. Objective: To conduct a scoping review to identify and synthesize existing evidence on digital health education and training of the rural healthcare workforce. Inclusion criteria: Sources that reported digital health and education or training in the healthcare workforce in any healthcare setting outside metropolitan areas. Methods: We searched for published and unpublished studies written in English in the last decade to August 2023. The databases searched were PubMed, Embase, Scopus, CINAHL and Education Resources Information Centre. We also searched the grey literature (Google, Google Scholar), conducted citation searching and stakeholder engagement. The JBI Scoping Review methodology and PRISMA guidelines for scoping reviews were used. Results: Five articles met the eligibility criteria. Two case studies, one feasibility study, one micro-credential and one fellowship were described. The mode of delivery was commonly modular online learning. Only one article described an evaluation, and findings showed the train-the-trainer model was technically and pedagogically feasible and well received. A limited number of barriers and enablers for high quality education or training of the rural healthcare workforce were reported across macro (legal, regulatory, economic), meso (local health service and community) and micro (day-to-day practice) levels. Conclusions: Upskilling rural healthcare workers in digital health appears rare. Current best practice points to flexible, blended training programs that are suitably embedded with interdisciplinary and collaborative rural healthcare improvement initiatives. Future work to advance the field could define rural health informatician career pathways, address concurrent rural workforce issues, and conduct training implementation evaluations. Review registration number: Open Science Framework: https://doi.org/10.17605/OSF.IO/N2RMX. [ABSTRACT FROM AUTHOR]

The article discusses a concerning decline in childhood vaccination rates, which could lead to a resurgence of vaccine-preventable diseases such as measles and varicella. Topics discussed include the impact of the COVID-19 pandemic on vaccination coverage, the importance of early genetic testing for neurodevelopmental disorders, and recent advancements in treatments for conditions like Rett syndrome and metachromatic leukodystrophy.

With rapid development and mature of single‐cell measurements, single‐cell biology and pathology become an emerging discipline to understand the disease. However, it is important to address concerns raised by clinicians as to how to apply single‐cell measurements for clinical practice, translate the signals of single‐cell systems biology into determination of clinical phenotype, and predict patient response to therapies. The present Perspective proposes a new system coined as the clinical artificial intelligent single‐cell (caiSC) with the dynamic generator of clinical single‐cell informatics, artificial intelligent analyzers, molecular multimodal reference boxes, clinical inputs and outs, and AI‐based computerization. This system provides reliable and rapid information for impacting clinical diagnoses, monitoring, and prediction of the disease at the single‐cell level. The caiSC represents an important step and milestone to translate the single‐cell measurement into clinical application, assist clinicians' decision‐making, and improve the quality of medical services. There is increasing evidence to support the possibility of the caiSC proposal, since the corresponding biotechnologies associated with caiSCs are rapidly developed. Therefore, we call the special attention and efforts from various scientists and clinicians on the caiSCs and believe that the appearance of the caiSCs can shed light on the future of clinical molecular medicine. [ABSTRACT FROM AUTHOR]

Clinical Bioinformatics is a knowledge framework required to interpret data of medical interest via computational methods. This area became of dramatic importance in precision oncology, fueled by cancer genomic profiling: most definitions of Molecular Tumor Boards require the presence of bioinformaticians. However, all available literature remained rather vague on what are the specific needs in terms of digital tools and expertise to tackle and interpret genomics data to assign novel targeted or biomarker-driven targeted therapies to cancer patients. To fill this gap, in this article, we present a catalog of software families and human skills required for the tumor board bioinformatician, with specific examples of real-world applications associated with each element presented. [ABSTRACT FROM AUTHOR]

Background: The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health information and cybersecurity managers need to understand if, and to what extent, breach types and locations are associated with their organisation's business type. Objective: To investigate if breach type and breach location are associated with business type, and if so, investigate how these factors affect information systems and protected health information in for-profit versus non-profit organisations. Method: The quantitative study was performed using chi-square tests for association and post-hoc comparison of column proportions analysis on an archival data set of reported healthcare data breaches from 2020 to 2022. Data from the Department of Health and Human Services website was retrieved and each organisation classified as for-profit or non-profit. Results: For-profit organisations experienced a significantly higher number of breaches due to theft, and non-profit organisations experienced a significantly higher number of breaches due to unauthorised access. Furthermore, the number of breaches that occurred on laptops and paper/films was significantly higher in for-profit organisations. Conclusion: While the threat level of hacking techniques is the same in for-profit and non-profit organisations, certain breach types are more likely to occur within specific breach locations based on the organisation's business type. To protect the privacy and security of medical information, health information and cybersecurity managers need to align with industry-leading frameworks and controls to prevent specific breach types that occur in specific locations within their environments. [ABSTRACT FROM AUTHOR]

The aim of this research is to develop a method for creating an online service site in the field of health care. The object of research is the process of developing a medical website. The developed site of online services of the medical institution is a partial implementation of the medical information system. The creation of the methodology for the development of the site of online services in the field of health care was carried out on the basis of the development of API methods (Application Programming Interface methods) and functionality on the site server, which provides the opportunity to interact with eHealth and will allow integration with other medical systems. A feature of the created medical information system is interaction with the centralized eHealth system. API documentation, which is publicly available, is used to interact with the eHealth system. The choice of tools for the development of a medical information system is substantiated. For this, alternative variants of means of developing the client side of the site are determined and the importance of each means was calculated. Super Decisions software is used to determine the importance of criteria and compare alternative options. With the help of the Super Decisions program, the React framework is chosen for the development of the client side of the site. The structure of the website of online services in the medical field is developed, the client and server sides of the product are practically implemented. Development of the server part of the site was carried out based on integration with third-party systems and creation of API methods for exchanging information on the client side. Criteria are defined and programming languages for backend development are analyzed. PHP (Hypertext Preprocessor) language is chosen based on the analysis of reasonable criteria. Symfony is chosen as the PHP framework. A graphic representation of the structure and navigation of the site is developed. The procedure for setting up the environment and raising the project according to the selected technologies is proposed. Integration of the developed site with eHealth is proposed. Experimental testing of the developed site for online services in the field of health care is performed. [ABSTRACT FROM AUTHOR]

Background: Blurry images in teledermatology and consultation increased the diagnostic difficulty for both deep learning models and physicians. We aim to determine the extent of restoration in diagnostic accuracy after blurry images are deblurred by deep learning models. Methods: We used 19,191 skin images from a public skin image dataset that includes 23 skin disease categories, 54 skin images from a public dataset of blurry skin images, and 53 blurry dermatology consultation photos in a medical center to compare the diagnosis accuracy of trained diagnostic deep learning models and subjective sharpness between blurry and deblurred images. We evaluated five different deblurring models, including models for motion blur, Gaussian blur, Bokeh blur, mixed slight blur, and mixed strong blur. Main Outcomes and Measures: Diagnostic accuracy was measured as sensitivity and precision of correct model prediction of the skin disease category. Sharpness rating was performed by board-certified dermatologists on a 4-point scale, with 4 being the highest image clarity. Results: The sensitivity of diagnostic models dropped 0.15 and 0.22 on slightly and strongly blurred images, respectively, and deblurring models restored 0.14 and 0.17 for each group. The sharpness ratings perceived by dermatologists improved from 1.87 to 2.51 after deblurring. Activation maps showed the focus of diagnostic models was compromised by the blurriness but was restored after deblurring. Conclusions: Deep learning models can restore the diagnostic accuracy of diagnostic models for blurry images and increase image sharpness perceived by dermatologists. The model can be incorporated into teledermatology to help the diagnosis of blurry images. [ABSTRACT FROM AUTHOR]

Objective This study explores the perspectives of Australian community pharmacists regarding digital health interventions, examining adoption rates, advantages, and barriers. Methods A national voluntary online survey was conducted with Australian community pharmacists from September 2022 to June 2023. Results Of the 120 responses received, 118 were analysed. The main benefits identified included improved access to health records and patient care quality while major concerns included technical issues and data security. Suggestions for increased digital health adoption included digital health education and regulation. Conclusions Australian community pharmacists play a crucial role in adopting digital health yet face significant challenges. Despite the potential of telehealth, community pharmacists have not widely embraced its adoption in their practice. The digital healthcare transformation necessitates collaborative efforts and strict guideline adherence for effective integration. [ABSTRACT FROM AUTHOR]

Maternal morbidity and mortality remain significant challenges in the United States, with substantial burden during the postpartum period. The Centers for Disease Control and Prevention, in partnership with the National Association of Community Health Centers, began an initiative to build capacity in Federally Qualified Health Centers to (1) improve the infrastructure for perinatal care measures and (2) use perinatal care measures to identify and address gaps in postpartum care. Two partner health center-controlled networks implemented strategies to integrate evidence-based recommendations into the clinic workflow and used data-driven health information technology (HIT) systems to improve data standardization for quality improvement of postpartum care services. Ten measures were created to capture recommended care and services. To support measure capture, a data cleaning algorithm was created to prioritize defining pregnancy episodes and delivery dates and address data inconsistencies. Quality improvement activities targeted postpartum care delivery tailored to patients and care teams. Data limitations, including inconsistencies in electronic health record documentation and data extraction practices, underscored the complexity of integrating HIT solutions into postpartum care workflows. Despite challenges, the project demonstrated continuous quality improvement to support data quality for perinatal care measures. Future solutions emphasize the need for standardized data elements, collaborative care team engagement, and iterative HIT implementation strategies to enhance perinatal care quality. Our findings highlight the potential of HIT-driven interventions to improve postpartum care within health centers, with a focus on the importance of addressing data interoperability and documentation challenges to optimize and monitor initiatives to improve postpartum health outcomes. [ABSTRACT FROM AUTHOR]

Various hospitals have adopted digital technologies in the healthcare sector for various healthcare-related applications. Due to the effect of the Covid-19 pandemic, digital transformation has taken place in many domains, especially in the healthcare domain; it has streamlined various healthcare activities. With the advancement in technology concept of telemedicine evolved over the years and led to personalized healthcare and drug discovery. The use of machine learning (ML) technique in healthcare enables healthcare professionals to make a more accurate and early diagnosis. Training these ML models requires a massive amount of data, including patients' personal data, that need to be protected from unethical use. Sharing these data to train ML models may violate data privacy. A distributed ML paradigm called federated learning (FL) has allowed different medical research institutions, hospitals, and healthcare devices to train ML models without sharing raw data. This survey paper overviews existing research work on FL-related use cases and applications. This paper also discusses the state-of-the-art tools and techniques available for FL research, current shortcomings, and future challenges in using FL in healthcare. [ABSTRACT FROM AUTHOR]

Experiential theories of cognition propose that the external environment shapes cognitive processing, shifting emphasis from internal mechanisms to the learning of environmental structure. Computational modelling, particularly distributional models of lexical semantics (e.g., Landauer & Dumais, 1997) and models of lexical organization (e.g., Johns, 2021a), exemplifies this, highlights the influence of language experience on cognitive representations. While these models have been successful, comparatively less attention has been paid to the training materials used to train these models. Recent research has explored the role of social/communicatively oriented training materials on models of lexical semantics and organization (Johns, 2021a, 2021b, 2023, 2024), introducing discourse- and user-centred text training materials. However, determining the optimal training materials for these two model types remains an open question. This article addresses this problem by using experiential optimization (Johns, Jones, & Mewhort, 2019), which selects the materials that maximize model performance. This study will use experiential optimization to compare user-based and discourse-based corpora in optimizing models of lexical organization and semantics, offering insight into pathways towards integrating cognitive models in these areas. [ABSTRACT FROM AUTHOR]

[Display omitted] • This is the most systematic review of flavonoid drugs and clinical candidates to date. • 19 flavonoid drugs are approved for the market, while 36 candidates in clinical phases. • 52.6% of the marketed flavonoid drugs and 44.4% of clinical candidates are natural sources. • 30% of the marketed flavonoid drugs are used for the treatment of cardiovascular diseases. • 50% of the suspended clinical candidates are antineoplastic and immunomodulating agents. • Unexpectedly, flavonoid glycosides account for 36.8% of the marketed flavonoid drugs. Flavonoids are one of the most important metabolites with vast structural diversity and a plethora of potential pharmacological applications, which have drawn considerable attention in the laboratory. Nevertheless, it remains uncertain how many candidates were progressed to clinical application. We carried out a critical review of natural and semi-synthetic flavonoid drugs and candidates undergoing different clinical phases worldwide by applying an adequate search method and conducted a brief cheminformatic and bioinformatic analysis. It was expected that the obtained results might narrow the screening scope and reduce the cost of drug research and development. To our knowledge, this is the most systematic summarization of flavonoid-based drugs and clinical candidates to date. It was found that a total of 19 flavonoid-based drugs have been approved for the market, and of these, natural flavonoids accounted for 52.6%. Besides, a total of 36 flavonoid-based clinical candidates are undergoing or suspended in different phases, and of these, natural flavonoids account for 44.4%. Thus, natural flavonoids remain the best option for finding novel agents/active templates, and when investigated in conjunction with synthetic chemicals and biologicals, they offer the potential to discover novel structures that can lead to effective agents against a variety of human diseases. Additionally, flavonoid-based marketed drugs have been successful in cardiovascular treatment, and the related drugs account for more than 30% of marketed drugs. However, the use of flavonoids as antineoplastic and immunomodulating agents is not likely for approximately 50% of the candidates suspended in the clinical stage. Interestingly, the marketed drugs covered a broader range of chemical spaces based on size, polarity, and three-dimensional structure compared to the clinical candidates. In addition, flavonoid glycosides with poor oral bioavailability account for 36.8% of the marketed drugs, and thus, they could be thoroughly investigated. [ABSTRACT FROM AUTHOR]

Comprehensive health records (CHRs) that encompass patients' medical and dental information is here and now. CHRs enable health care providers to deliver personalized, precision medicine/dentistry effectively and efficiently. The objective of this report is to provide an overview of strengths, weaknesses, and opportunities associated with a comprehensive health care record system that integrates medical and dentalrecords. Strengths and benefits of CHRs (elevated patient safety, collaborative care, holistic patient care, enhanced research capabilities) are explained, and practical challenges associated with operating multiple record systems (specifically in medical and dental imaging), data migration and integration are explored. [ABSTRACT FROM AUTHOR]

The challenge of class imbalance in multiclass medical datasets is addressed in this study, through the proposal of modified classifiers premised on multiclass logistic regression. The principal aim is to augment the accuracy of medical diagnosis predictions by decisively managing imbalanced datasets with innovative methodologies. Performance evaluations are conducted on renowned multiclass medical datasets including thyroid, lymphography, dermatology, and ecoli. Prior to model development, Principal Component Analysis (PCA) is employed as a preprocessing measure to bolster data quality. The bespoke classifiers are trained via gradient descent optimization and evaluated through various metrics such as accuracy, precision, recall, and f1-score. A comparative analysis with preceding studies underscores the superior performance of the proposed model, accentuating its advantageous position over other algorithms. This research underscores the potential of the proposed model to furnish precise medical diagnosis predictions amidst class imbalance, capably distinguishing between minority and majority classes. In conclusion, this study delineates the promising potential of multiclass logistic regression for precise medical diagnoses in the realm of imbalanced datasets. INSET: c. [ABSTRACT FROM AUTHOR]

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As the capabilities of machine learning have developed, more researchers and health care providers are beginning to consider applications for health informatics to improve health care outcomes and address issues of health equity. The Centers for Medicare and Medicaid Services considers 30-day readmission rates to the Emergency Department (ED) to be an "outcome of care" measure. Such measures show how well a hospital is doing in preventing complications, educating patients about their care needs, and helping patients make a smooth transition from the hospital to home or other care facilities. While certain readmissions are medically necessary, hospitals usually aim to decrease the rate of 30-day ED readmissions by decreasing the number of avoidable unplanned revisits. This work is an evidential study that demonstrates the positive impact of integrating Community Health Workers (CHWs) and Social Determinants of Health (SDoH) in decreasing the 30-day unplanned hospital ED readmissions at Sinai Chicago. Using data from the Sinai Urban Health Institute, we compare predicting the readmissions of patients with and without data pertaining to CHWs and SDoH, characterize the improvement in predictions, and discuss lessons learned in the process. We show that when CHWs engage with patients, the predictive accuracy of the classifier is higher by 13.0%–15.2%. Importantly, we show that the features (data characteristics) related to the CHWs are important to the classification, pointing to the importance of the program. We optimize the classifier for engaged patients and demonstrate the improvement in predictive capabilities of the classier using multiple metrics and sets of features. We use our results to make recommendations for improving patient care, discuss limitations and future work. Notably, our work points directly to the human connection between patients and CHWs as an important feature predictive of readmission rate. [ABSTRACT FROM AUTHOR]

Background and Aim: Blockchain technology is transforming various industry sectors, including pharmaceuticals, due to its potential to revolutionize the drug system. Integrating blockchain into supply chain design decentralizes pharmaceutical supply chain management, improving workflow efficiency and reducing security threats. This study examines the impact of integrating blockchain technology into pharmaceutical supply chain practices on transparency and resilience. By adopting organizational information processing theory, a research model was developed to investigate the impact of blockchain features, including data quality, smart contracts, and traceability, on supply chain resilience and transparency. Additionally, the study examined the impact of supply chain transparency on resilience. Methods: Data were collected from 215 pharmaceutical industry personnel in Iran using an electronic survey. RStudio software was used for data analysis. Results: The results showed that data quality significantly affects the transparency and resilience of the pharmaceutical supply chain. While smart contracts are positively associated with supply chain transparency, they do not impact resilience. Traceability is positively associated with both transparency and resilience. Blockchainbased supply chain transparency positively impacts resilience. Conclusion: Integrating blockchain technology into the pharmaceutical supply chain can enhance transparency and resilience, enabling recovery from disruptive events. These insights can help pharmaceutical supply chain managers and stakeholders develop strategies to best utilize blockchain technology for enhancing transparency and resilience. [ABSTRACT FROM AUTHOR]

The research endeavours to map the studies conducted in the literature on the concept of telemedicine. Thus, the aim of the research is to make a systematic summary of the publications within the scope of Web of Science related to telehealth applications, which have become widespread in recent years due to the developments in technology, by using bibliometric analysis. The first work on telehealth was published in 1994 in the form of one article. Until 2010, the number of works published did not reach double digits, while the highest number of publications was 82 in 2022. The most academic studies have been produced in the fields of Health Sciences Services (92 publications), Medical Informatics (76 publications), and Engineering, Electrical-Electronics (72 publications). The highest number of publications in the field of telehealth are by Adrienne O'neil (6), Meikang Qiu (6) and Brian Oldenburg (5). The most cited authors were Amit Kumar Singh (283), Lindsey Anderson (264) and Rod S. Taylor (264). The most frequently used keywords in academic studies in the field of telehealth were telehealth (175 times), telemedicine (76 times) and COVID-19 (65 times). The number of publications produced on a country basis was as follows: USA (248), Australia (59), England (57) and India (57). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19. [ABSTRACT FROM AUTHOR]