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Supported employment is a promising vocational rehabilitation intervention for improving disability employment. Its implementation, however, is not without challenges. Lack of funding and the heavy caseload of job coaches significantly constrain its impacts. A mobile software, avail® by CentralReach, was created based on the individual placement and support model for assisting individuals with disabilities to perform their job tasks. The purpose of this pilot study was to explore the feasibility and preliminary efficacy of avail. A total of 17 participants with disabilities were recruited in a Midwestern state from the United States to try avail for a period of time ranging from 40 to 348 days. Participants' experiences, including perceived usability, acceptance, and satisfaction, were collected to determine the feasibility of avail. In addition, a pre- and post-intervention analysis was conducted to identify its effects on users' job satisfaction, self-efficacy, and self-determination. Participants of this study expressed positive user experience for avail. Specifically, they felt that avail was helpful for their job performance. The results also showed a statistically significant result for improving participants' self-determination. avail is a feasible and potentially promising application for assisting workers with disabilities in their job performance. Directly available on mobile devices, avail has several advantages, such as real-time support and good portability. Suggestions and future directions were discussed for further software development. A job coach is needed in the individual placement and support model (IPS) to provide on-site job support for individuals with disabilities. However, job coaches were typically overwhelmed by the heavy caseloads. A job coach application such as the avail was created, and this study was to test its feasibility and preliminary efficacy. The study showed that avail was appropriate for individuals with disabilities to use at work, and they generally enjoyed using the application. The preliminary efficacy showed that avail improved their job performances and overall self-determination. [ABSTRACT FROM AUTHOR]

The inclusion of data analytics in the accounting curriculum is an important task that has been recommended by professional and academic accounting bodies as well as accrediting agencies. This research paper documents the results of a survey which investigated the extent to which data analytics is being included in various courses in the accounting curriculum and how universities are meeting the challenges of accomplishing this task. The survey was sent by email on October 20, 2019 to approximately 4,800 accounting instructors in the United States. The findings of the study indicated that most universities in the sample were including data analytics in their accounting curricula, and that AACSB accreditation seemed to influence the inclusion of data analytics. However, we found that some accounting programs were not including data analytics in their curriculum. The study indicated that AACSB-accredited schools were more likely to offer incentives to faculty to become knowledgeable about data analytics than other schools. [ABSTRACT FROM AUTHOR]

Background: Maternal mortality in the United States is a public health crisis and national emergency. Missed or delayed recognition of preventable life-threatening symptoms and untimely treatment of preventable high-risk medical conditions have been cited as key contributors to the nation's worsening mortality rates. Effective strategies are urgently needed to address this maternal health crisis, particularly for Black birthing populations. Morbidity and Mortality Assessment: Lifting Outcomes Via Education (MAMA LOVE) is a web-based platform that focuses on the identification of maternal morbidity and mortality risk factors. Objective: The purpose of this paper is to present the conceptualization, development, heuristics, and utility evaluation of the web-based maternal mortality risk assessment and educational tool MAMA LOVE. Methods: A user-centered design approach was used to gain feedback from clinical experts and potential end users to ensure that the tool would be effective among groups most at risk for maternal morbidity and mortality. A heuristic evaluation was conducted to evaluate usability and need within the current market. Algorithms describing key clinical, mental health, and social conditions were designed using digital canvas software (Miro) and incorporated into the final wireframes of the revised prototype. The completed version of MAMA LOVE was designed in Figma and built with the SurveyJS platform. Results: The creation of the MAMA LOVE tool followed three distinct phases: (1) the content development and creation of an initial prototype; (2) the feedback gathering and usability assessment of the prototype; and (3) the design, development, and testing of the final tool. The tool determines the corresponding course of action using the algorithm developed by the authors. A total of 38 issues were found in the heuristic evaluation of the web tool's initial prototype. Conclusions: Maternal morbidity and mortality is a public health crisis needing immediate effective interventions. In the current market, there are few digital resources available that focus specifically on the identification of dangerous symptoms and risk factors. MAMA LOVE is a tool that can address that need by increasing knowledge and providing resources and information that can be shared with health care professionals. [ABSTRACT FROM AUTHOR]

Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the findable, accessible, interoperable, reusable (FAIR) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with seven women, 11 men, and one nonbinary individual from seven states and DC in the United States with a range of professional backgrounds and roles. [ABSTRACT FROM AUTHOR]

The inclusion of data analytics in the accounting curriculum is an important task that has been recommended by professional and academic accounting bodies as well as accrediting agencies. This research paper is part of a survey which investigated how data analytics is being included in the accounting curriculum. This paper is an exploratory survey of the applications and software instructors are using in various accounting courses. The survey was sent by email to approximately 4,800 accounting instructors in the United States. [ABSTRACT FROM AUTHOR]

Background: Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity. ER is a fundamental challenge in data science, and a common barrier to ER research and development is that the data fields used for this fuzzy matching are personally identifiable information, such as name, address, and date of birth. The necessary restrictions on accessing and sharing these authentic data have slowed the work in developing, testing, and adopting new methods and software for ER. We recently released pseudopeople , a Python package that allows users to generate simulated datasets with configurable noise approaching the scale and complexity of the data on which large organizations and federal agencies, like the US Census Bureau regularly perform ER. With pseudopeople, researchers can develop new algorithms and software for ER of US population data without needing access to personal and confidential information., Methods: We created the simulated population data available for noising with pseudopeople using our Vivarium simulation platform. Our model simulates individuals and their families, households, and employment dynamics over time, which we observe through simulated censuses, surveys, and administrative data collection systems., Results: Our simulation process produced over 900 gigabytes of simulated censuses, surveys, and administrative data for pseudopeople, representing hundreds of millions of simulants. A sample simulated population of thousands of simulants is now openly available to all users of the pseudopeople package, and large-scale simulated populations of millions and hundreds of millions of simulants are also available by online request through GitHub. These simulated population data are structured for use by the pseudopeople package, which includes additional affordances to add various kinds of noise to the data to provide realistic, sharable challenges for ER researchers., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Haddock B et al.)

Epidemiological analyses of environmental exposures often benefit from including spatial splines in models to account for confounding by spatial location. Understanding how the number of splines relates to physical spatial differences is not always intuitive and can be context-dependent. To address this, we developed a R Shiny application, spconfShiny, that provides a user-friendly platform to calculate an effective bandwidth metric that quantifies the relationship between spatial splines and the range of implied spatial smoothing. spconfShiny can be accessed at https://g2aging.shinyapps.io/spconfShiny/. We illustrate the procedure to compute the effective bandwidth and demonstrate its use for different numbers of spatial splines across England, India, Ireland, Northern Ireland, and the United States. Using spconfShiny, we show the effective bandwidth increases with the size of the region and decreases with the number of splines. Including 10 splines on a 10km grid corresponds to effective bandwidths of 92.2km in Ireland and 927.7km in the United States., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Rainey, Keller. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Model-informed precision dosing (MIPD) optimizes drug doses based on pharmacokinetic (PK) model predictions, necessitating careful selection of models tailored to patient characteristics. This study evaluates the predictive performance of various vancomycin PK models across diverse age and BMI categories, drawing insights from a large multi-site database., Methods: Adults receiving vancomycin intravenous therapy at United States health systems between January 1, 2022, and December 31, 2023, were included. Patient demographics, vancomycin administration records, and therapeutic drug monitoring levels (TDMs) were collected from the InsightRX database. Age and body mass index (BMI)-based subgroups were formed to assess model performance, with predictions made iteratively. The optimal model for each age-BMI subgroup was chosen based on predefined criteria: models were filtered for mean percentage error (MPE) ≤ 20% and normalized root mean squared error (RMSE) < 8 mg/L, and then the most accurate among them was selected., Results: A total of 384,876 treatment courses across 155 US health systems were analyzed, contributing 841,604 TDMs. Eleven models were compared, showing varying accuracy across age-BMI categories (41%-73%), with higher accuracy observed once TDMs were available for Bayesian estimates of individual PK parameters. Models performed more poorly in younger adults compared to older adults, and the optimal model differed depending on age-BMI categories and prediction methods. Notably, in the a priori period, the Colin model performed best in adults aged 18-64 years across most BMI categories; the Goti/Tong model performed best in the older, non-obese adults; and the Hughes model performed best in many of the obese categories., Conclusion: Our study identifies specific vancomycin PK models that demonstrate superior predictions across age-BMI categories in MIPD applications. Our findings underscore the importance of tailored model selection for vancomycin management, especially highlighting the need for improved models in younger adult patients. Further research into the clinical implications of model performance is warranted to enhance patient care outcomes., (© 2024 The Author(s). Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy published by Wiley Periodicals LLC on behalf of Pharmacotherapy Publications, Inc.)

The US EPA ECOTOX database provides key ecotoxicological data that are crucial in environmental risk assessment. It can be used for computational predictions of toxicity or indications of hazard in a wide range of situations. There is no standardised or formalised method for extracting and subsetting data from the database for these purposes. Consequently, results in such meta-analyses are difficult to reproduce. The present study introduces the software package ECOTOXr, which provides the means to formalise data retrieval from the ECOTOX database in the R scripting language. Three cases are presented to evaluate the performance of the package in relation to earlier data extractions and searches on the website. These cases demonstrate that the package can reproduce data sets relatively well. Furthermore, they illustrate how future studies can further improve traceability and reproducibility by applying the package and adhering to some simple guidelines. This contributes to the FAIR principles, credibility and acceptance of research that uses data from the ECOTOX database., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system—design, hardware, and software—for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human–robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform. [ABSTRACT FROM AUTHOR]

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Motivation: Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation., Results: To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress., Availability and Implementation: More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR&#95;Metrics&#95;manuscript&#95;website., (© The Author(s) 2024. Published by Oxford University Press.)

Biomedical data are growing exponentially in both volume and levels of complexity, due to the rapid advancement of technologies and research methodologies. Analyzing these large datasets, referred to collectively as "big data," has become an integral component of research that guides experimentation-driven discovery and a new engine of discovery itself as it uncovers previously unknown connections through mining of existing data. To fully realize the potential of big data, biomedical researchers need access to high-performance-computing (HPC) resources. However, supporting on-premises infrastructure that keeps up with these consistently expanding research needs presents persistent financial and staffing challenges, even for well-resourced institutions. For other institutions, including primarily undergraduate institutions and minority serving institutions, that educate a large portion of the future workforce in the USA, this challenge presents an insurmountable barrier. Therefore, new approaches are needed to provide broad and equitable access to HPC resources to biomedical researchers and students who will advance biomedical research in the future., (Published by Oxford University Press 2024.)

Background: Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity and uniqueness of rare disease data. This creates a dilemma, as there is a lack of both methods and necessary data to create appropriate approaches initially. This work contributes to bridging this gap by providing synthetic datasets that can form the foundation for such developments., Methods: Using a hierarchical data generation approach parameterised with publicly available statistics, we generated datasets reflecting a random sample of rare disease patients from the United States (US) population. General demographics were obtained from the US Census Bureau, while information on disease prevalence, initial diagnosis, survival rates as well as race and sex ratios were obtained from the information provided by the US Centers for Disease Control and Prevention as well as the scientific literature. The software, which we have named SynthMD, was implemented in Python as open source using libraries such as Faker for generating individual data points., Results: We generated three datasets focusing on three specific rare diseases with broad impact on US citizens, as well as differences in affected genders and racial groups: Sickle Cell Disease, Cystic Fibrosis, and Duchenne Muscular Dystrophy. We present the statistics used to generate the datasets and study the statistical properties of output data. The datasets, as well as the code used to generate them, are available as Open Data and Open Source Software., Conclusion: The results of our work can serve as a starting point for researchers and developers working on methods and platforms that aim to improve the availability of rare disease data. Potential applications include using the datasets for testing purposes during the implementation of information systems or tailored privacy-enhancing technologies., (© 2024. The Author(s).)

The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software product lifecycle. To provide cytometry software developers, computational scientists, researchers, industry professionals, and diagnostic physicians/pathologists with an introduction to European Union (EU) and United States (US) regulatory frameworks. Informed by community feedback and needs assessment established during two international cytometry workshops, this article provides an overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry. Evolving regulatory frameworks are discussed, and specific examples regarding cytometry instruments, analysis software and clinical flow cytometry in-vitro diagnostic assays are provided. An important consideration for cytometry software development is the modular approach. As such, modules can be segregated and treated as independent components based on the medical purpose and risk and become subjected to a range of context-dependent compliance and regulatory requirements throughout their life cycle. Knowledge of regulatory and compliance requirements enhances the communication and collaboration between developers, researchers, end-users and regulators. This connection is essential to translate scientific innovation into diagnostic practice and to continue to shape the development and revision of new policies, standards, and approaches., (© 2024 International Clinical Cytometry Society.)

The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Deep convective thunderstorm tracking methodologies and software have become useful and necessary tools across many applications, from nowcasting to model verification. Despite many available options, many of these preexisting methods lack a customizable, fast, and flexible methodology that can track supercell thunderstorms within convective-allowing climate datasets with coarse temporal and spatial resolution. This project serves as one option to solve this issue via an all-in-one tracking methodology, built upon several open-source Python libraries, and designed to work with various temporal resolutions, including hourly. Unique to this approach is accounting for varying data availability of different model variables, while still sufficiently and accurately tracking specific convective features; in this case, supercells were the focus. To help distinguish supercells from ordinary cells, updraft helicity and other three-dimensional atmospheric data were incorporated into the tracking algorithm to confirm its supercellular status. Deviant motion from the mean wind was also used identify supercells. The tracking algorithm was tested and performed on a dynamically downscaled regional climate model dataset with 4-km horizontal grid spacing. Each supercell was tracked for its entire lifetime over the course of 26 years of model output, resulting in a supercell climatology over the central United States. Due to the tracking configuration and dataset used, the tracking performs most consistently for long-lived and strong supercells compared to weak and short-lived supercells. This tracking methodology allows for customizable open-source tracking of supercells in any downscaled convective-allowing dataset, even with coarse temporal resolution. [ABSTRACT FROM AUTHOR]

To enhance the water industry's body of knowledge on energy use in membrane filtration processes, AWWA is conducting multistage research on water utilities that have microfiltration/ultrafiltration systems. [ABSTRACT FROM AUTHOR]

The digital examination of scanned or measured 3D surface topography is referred to as Virtual Comparison Microscopy (VCM). Within the discipline of firearm and toolmark examination, VCM enables review and comparison of microscopic toolmarks on fired ammunition components. In the coming years, this technique may supplement and potentially replace the light comparison microscope as the primary instrument used for firearm and toolmark examination. This paper describes a VCM error rate and validation study involving 107 participants. The study included 40 test sets of fired cartridge cases from firearms with a variety of makes, models, and calibers. Participants used commercially available VCM software which allowed digital data distribution, specimen visualization, and submission of conclusions. The software also allowed participants to annotate areas of similarity and dissimilarity to support their conclusions. The primary cohort of 76 qualified United States and Canadian examiners that completed the study had an overall false‐positive error rate of 3 errors from 693 comparisons (0.43%) and a false‐negative error rate of 0 errors from 491 comparisons (0.0%). This accuracy is supplemented by the participant's provided surface annotations which provide insight into the cause of errors and the overall consistency across the independent examinations conducted in the study. The ability to obtain highly accurate conclusions on test fires from a wide range of firearms supports the hypothesis that VCM is a useful tool within the crime laboratory. [ABSTRACT FROM AUTHOR]

We report a new visualization tool for analysis of whole-genome assembly-assembly alignments, the Comparative Genome Viewer (CGV) (https://ncbi.nlm.nih.gov/genome/cgv/). CGV visualizes pairwise same-species and cross-species alignments provided by National Center for Biotechnology Information (NCBI) using assembly alignment algorithms developed by us and others. Researchers can examine large structural differences spanning chromosomes, such as inversions or translocations. Users can also navigate to regions of interest, where they can detect and analyze smaller-scale deletions and rearrangements within specific chromosome or gene regions. RefSeq or user-provided gene annotation is displayed where available. CGV currently provides approximately 800 alignments from over 350 animal, plant, and fungal species. CGV and related NCBI viewers are undergoing active development to further meet needs of the research community in comparative genome visualization., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Objectives: In 2018, CMS established reimbursement for the first Medicare-covered artificial intelligence (AI)-enabled clinical software: CT fractional flow reserve (FFRCT) to assist in the diagnosis of coronary artery disease. This study quantified Medicare utilization of and spending on FFRCT from 2018 through 2022 and characterized adopting hospitals, clinicians, and patients., Study Design: Analysis, using 100% Medicare fee-for-service claims data, of the hospitals, clinicians, and patients who performed or received coronary CT angiography with or without FFRCT., Methods: We measured annual trends in utilization of and spending on FFRCT among hospitals and clinicians from 2018 through 2022. Characteristics of FFRCT-adopting and nonadopting hospitals and clinicians were compared, as well as the characteristics of patients who received FFRCT vs those who did not., Results: From 2018 to 2022, FFRCT billing volume in Medicare increased more than 11-fold (from 1083 to 12,363 claims). Compared with nonbilling hospitals, FFRCT-billing hospitals were more likely to be larger, part of a health system, nonprofit, and financially profitable. FFRCT-billing clinicians worked in larger group practices and were more likely to be cardiac specialists. FFRCT-receiving patients were more likely to be male and White and less likely to be dually enrolled in Medicaid or receiving disability benefits., Conclusions: In the initial 5 years of Medicare reimbursement for FFRCT, growth was concentrated among well-resourced hospitals and clinicians. As Medicare begins to reimburse clinicians for the use of AI-enabled clinical software such as FFRCT, it is crucial to monitor the diffusion of these services to ensure equal access.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings., (© 2024 John Wiley & Sons Ltd.)

Introduction: In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study., Objective: We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification., Methods: We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification., Results: We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%)., Conclusion: The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results., (© 2024. The Author(s).)

The emergence of Lethal Autonomous Weapon Systems (LAWS) offers a comparatively low adoption-cost strategy to emerging great powers that would allow them to offset the conventional military advantage enjoyed by their rivals, which threatens the established global balance of power. This article critically analyses the role of China and the United States in the development of the "hardware" and "software" components of LAWS, concluding that, while the demonstration point has not yet been reached, the incubation of this emerging revolution in military affairs has already begun to have a geopolitical impact. [ABSTRACT FROM AUTHOR]

Introduction: Traumatic brain injury (TBI) is considered a signature injury from the fighting in Iraq and Afghanistan. Since the year 2000, over 370,000 U.S. active duty service members have been diagnosed with TBI. Although prior research has shown that even mild forms of TBI are associated with impaired cognitive performance, it is not clear which facets of cognition (computation, memory, reasoning, etc.) are impacted by injury.Method: In the present study, we compared active duty military volunteers (n = 88) with and without TBI on six measures of cognition using the Automated Neuropsychological Assessment Metric software.Results: Healthy volunteers exhibited significantly faster response times on the matching-to-sample, mathematical processing, and second round of simple reaction time tasks and had higher throughput scores on the mathematical processing and the second round of the simple reaction time tasks (P < 0.05).Conclusion: In this population, cognitive impairments associated with TBI influenced performance requiring working memory and basic neural processing (speed/efficiency). [ABSTRACT FROM AUTHOR]

Objective: The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk. Existing HIV risk prediction algorithms rely on manual construction of features and are limited in their application across diverse electronic health record systems. Furthermore, the accuracy of these models in predicting HIV in females has thus far been limited., Materials and Methods: We devised a pipeline for automatic construction of prediction models based on automatic feature engineering to predict HIV risk and tested our pipeline on a local electronic health records system and a national claims data. We also compared the performance of general models to female-specific models., Results: Our models obtain similarly good performance on both health record datasets despite difference in represented populations and data availability (AUC = 0.87). Furthermore, our general models obtain good performance on females but are also improved by constructing female-specific models (AUC between 0.81 and 0.86 across datasets)., Discussion and Conclusions: We demonstrated that flexible construction of prediction models performs well on HIV risk prediction across diverse health records systems and perform as well in predicting HIV risk in females, making deployment of such models into existing health care systems tangible., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Computer assisted surgical navigation systems are designed to improve outcomes by providing clinicians with procedural guidance information. The use of new technologies, such as mixed reality, offers the potential for more intuitive, efficient, and accurate procedural guidance. The goal of this study is to assess the positional accuracy and consistency of a clinical mixed reality system that utilizes commercially available wireless head-mounted displays (HMDs), custom software, and localization instruments., Methods: Independent teams using the second-generation Microsoft HoloLens © hardware, Medivis SurgicalAR © software, and localization instruments, tested the accuracy of the combined system at different institutions, times, and locations. The ASTM F2554-18 consensus standard for computer-assisted surgical systems, as recognized by the U.S. FDA, was utilized to measure the performance. 288 tests were performed., Results: The system demonstrated consistent results, with an average accuracy performance that was better than one millimeter (.75 ± SD .37 mm)., Conclusion: Independently acquired positional tracking accuracies exceed conventional in-market surgical navigation tracking systems and FDA standards. Importantly, the performance was achieved at two different institutions, using an international testing standard, and with a system that included a commercially available off-the-shelf wireless head mounted display and software., Competing Interests: Declaration of Conflict of InterestThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Authors with affiliation at the US Department of Veteran Affairs Palo Alto Health care System have no conflicts of interest to declare. Authors with affiliation at Medivis, Inc. Are employees and/or owners and declare stock ownership in the company.

The US FDA has given emergency use authorization to multiple Covid-19 drugs. We conducted disproportionality analysis to detect adverse reaction rates (ADRs) in the US FDA's Adverse Event Reporting System. We discovered not only severe ADRs but also unique ADRs and effectiveness that might be explained by pharmacokinetics. This shows that real-time safety reviews could be effective methods for comparing the safety and efficacy of fast-track authorized drugs.

Purpose: To survey pediatric dentists in the United States regarding adverse events during dental care for children. Methods: A self-administered, anonymous online survey was sent to American Academy of Pediatric Dentistry members (N equals 6,327) using REDCap ® software (between October and December 2019). The questionnaire (all items with radio-button numerical categories) included five items surveying pediatric adverse event occurrence and seven demographic items. Annualized occurrences of adverse events in US pediatric dental practices were extrapolated from the data collected. Results: The survey response was 11 percent (n equals 704), with 91 percent of respondents reporting that at least one child experienced an adverse event during dental treatment. The two most prevalent adverse events, each reported by 82 percent of respondents, were self-inflicted trauma to soft tissues after local anesthesia and nausea and vomiting, with annualized estimates of 7,816 and 7,003, respectively. Major adverse events (respiratory depression, cardiovascular depression, neurological damage, death) during pediatric dental treatment were reported by 14 percent of respondents (annualized estimate equals 443). "Wrong" errors (wrong tooth/wrong procedure/wrong patient) were reported by 24 percent of respondents (annualized estimate equals 600). Conclusions: Adverse events during pediatric dental care are of noticeable concern with some (wrong tooth/wrong procedure/wrong patient errors) that can be procedurally mitigated.

Search for information is now an integral part of healthcare. Searches are enabled by search engines whose objective is to efficiently retrieve the relevant information for the user query. When it comes to retrieving biomedical text and literature, Essie search engine developed at the National Library of Medicine (NLM) performs exceptionally well. However, Essie is a software system developed for NLM that has ceased development and support. On the other hand, Solr is a popular opensource enterprise search engine used by many of the world's largest internet sites, offering continuous developments and improvements along with the state-of-the-art features. In this paper, we present our approach to porting the key features of Essie and developing custom components to be used in Solr. We demonstrate the effectiveness of the added components on three benchmark biomedical datasets. The custom components may aid the community in improving search methods for biomedical text retrieval., (©2023 AMIA - All rights reserved.)

With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium. Most institutions participated in multiple programs/tools but reported relatively low usage (even when they participated, they frequently indicated that fewer than one individual/month benefitted from the platform to support research activities). Our findings suggest that investments in research data sharing have not yet achieved desired results., (©2023 AMIA - All rights reserved.)

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package (http://CRAN.R-project.org/), which simplifies IRTbased analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

As genomic and related data continue to expand, research biologists are often hampered by the computational hurdles required to analyze their data. The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Centers (BRC) to assist researchers with their analysis of genome sequence and other omics-related data. Recently, the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD), and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs merged to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) at https://www.bv-brc.org/ . The combined BV-BRC leverages the functionality of the original resources for bacterial and viral research communities with a unified data model, enhanced web-based visualization and analysis tools, and bioinformatics services. Here we demonstrate how antimicrobial resistance data can be analyzed in the new resource., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for software as a medical device and the unique regulatory designs for radiologic AI/machine learning algorithms to ensure their safety in clinical practice. Then the challenges in maximizing the efficacy of these tools posed by their clinical implementation are discussed., (© RSNA, 2024.)

Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters like fractional flow reserve computed tomography simulation (FFRCTS), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.

Objective: Apply natural language processing (NLP) to Amazon consumer reviews to identify adverse events (AEs) associated with unapproved over the counter (OTC) homeopathic drugs and compare findings with reports to the US Food and Drug Administration Adverse Event Reporting System (FAERS)., Materials and Methods: Data were extracted from publicly available Amazon reviews and analyzed using JMP 16 Pro Text Explorer. Topic modeling identified themes. Sentiment analysis (SA) explored consumer perceptions. A machine learning model optimized prediction of AEs in reviews. Reports for the same time interval and product class were obtained from the FAERS public dashboard and analyzed., Results: Homeopathic cough/cold products were the largest category common to both data sources (Amazon = 616, FAERS = 445) and were analyzed further. Oral symptoms and unpleasant taste were described in both datasets. Amazon reviews describing an AE had lower Amazon ratings (X2 = 224.28, P < .0001). The optimal model for predicting AEs was Neural Boosted 5-fold combining topic modeling and Amazon ratings as predictors (mean AUC = 0.927)., Discussion: Topic modeling and SA of Amazon reviews provided information about consumers' perceptions and opinions of homeopathic OTC cough and cold products. Amazon ratings appear to be a good indicator of the presence or absence of AEs, and identified events were similar to FAERS., Conclusion: Amazon reviews may complement traditional data sources to identify AEs associated with unapproved OTC homeopathic products. This study is the first to use NLP in this context and lays the groundwork for future larger scale efforts., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2023.)

Background: Patient-reported outcome (PRO) measures provide valuable evidence in clinical trials; however, poor compliance with PRO measures is a notable and long-standing problem, resulting in missing data that potentially impact the interpretation of trial results. Interactive, patient-centric platforms may increase participants' motivation to complete PRO measures over the course of a clinical trial. Thus, the aim of this study was to evaluate and optimize the usability of 3 popular consumer technologies-a traditional app-based interface, a chatbot interface, and a speech-operated interface-that may be used to improve user engagement and compliance with PRO measures., Methods: Participants aged 18-75 years from the general United States population tested the usability of 3 ePRO platforms: a traditional app-based interface using Datacubed Health Platform (Datacubed), a web-based chatbot interface using the Orbita platform, and a speech-operated Alexa interface using an Alexa Skill called "My Daily Wellness." The usability of these platforms was tested with 2 PRO measures: the EQ-5D-5 L and the SF-12v2 Health Survey (SF-12v2), Daily recall. Using a crossover design, 3 cohorts of participants tested each ePRO platform daily for 1 week. After testing, interviews were conducted regarding the participants' experience with each platform., Results: A total of 24 adults participated in the study. The mean age of participants was 45 years (range, 21-71 years), and half were female (n = 12; 50%). Overall, participants prioritized speed, ease of use, and device portability in selecting their preferred platform. The Datacubed app met these criteria and was the preferred platform among most participants (n = 20; 83%). Participants also suggested various modifications to the platforms, such as programmable notifications, adjustable speed, and additional daily reminders., Conclusions: These data demonstrate the importance of speed, ease of use, and device portability, features that are currently incorporated in the Datacubed app, in ePRO platforms used in future clinical trials. Additionally, the usability of ePRO platforms may be optimized by adding programmable notifications, adjustable speed, and increased daily reminders. The results of this study may be used to enhance the usability and patient centricity of these platforms to improve user compliance and engagement during clinical trials., (© 2023. The Author(s).)

Epidiolex, the first FDA-approved drug with cannabis extract, treats Dravet and Lennox-Gastaut syndromes. Using data from the FAERS database between 2018 and 2023, this study analyzed 13,275 Epidiolex-related adverse events. Through computational methods (ROR, PRR, BCPNN, EBGM), we found that real-world adverse reactions largely align with those in Epidiolex's drug leaflet. However, Seizure cluster, Blood ketone body decrease, Cortical visual impairment, Hyperactive pharyngeal reflex, and Poverty of speech emerged as potential new side effects not previously listed, warranting further attention for drug safety., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: To demonstrate the potential for real-time, three-dimensional (3D) surgical telementoring to enhance vitreoretinal surgical education., Methods: The 3D video feed from a high dynamic range surgical camera (NGENUITY) was run through a 4K video capture device (Magewell USB 4K) and set as the video input for a video conferencing application (Zoom). Remote surgical viewing was then performed in two-dimensions (2D) on a computer or in 3D with a virtual reality headset (Oculus Quest 2)., Results: Ten surgical cases were successfully live streamed in real time to two separate surgeons in the United States. Specific details of the case were visualized with low latency and interaction with the operating surgeon was possible without affecting the surgical display quality. Excluding the NGENUITY system and personal computers, ancillary equipment costs (video capture card and virtual reality headset) were kept to below $1,000., Conclusion: Our study demonstrates that 3D surgical video streaming can be achieved in real time with minimal latency through the use of low-cost video capture equipment and video conferencing/streaming software. The use of this technology gives educators the ability to mentor trainees without the traditional geographic and physical constraints of in-person surgical viewing.

In the future, the United States government can seek to limit the ownership of cyber weapons. The question is whether the Second Amendment to the United States Constitution gives a right to bear and own military-grade cyber weapons. The dual-use nature of cyber arms undermines the government's argument on this point. [ABSTRACT FROM AUTHOR]

Clinical decision support has been identified by the United States government as a method to decrease inappropriate imaging exams and promote judicious use of imaging resources. The adoption of this method will be incentivized by requiring appropriate use criteria to qualify for Medicare reimbursement starting in January 2020. While Medicare reimbursement is unlikely to directly impact pediatric imaging because of largely disparate patient populations, insurance providers typically use Medicare to benchmark their reimbursement guidelines. Therefore soon after their adoption these guidelines could become relevant to pediatric imaging. In this article we discuss how pediatric imaging was initially underrepresented in the clinical decision support realm, and how this was addressed by a subcommittee involving both American College of Radiology and Society for Pediatric Radiology members. We also present the experience of implementing clinical decision support software at two standalone pediatric hospitals and summarize the lessons learned from these deployments. [ABSTRACT FROM AUTHOR]

Information technology improves reliability, innovation, and efficiency in the medical care sector by assisting in coming up with electronic health records. Looking into the interoperability of software and databases is relevant from the perspective of electronic health records. The standardization of processes in the European Union and the United States is diverse, which makes it all the more important to discuss open standards. Software systems create patient-centric medical care services and a platform for management. Thus, they facilitate the formation of functional health information networks and the exchange of information. Therefore, this improves the value proposition for all stakeholders involved. Open-source standards have been found to be developed independently of any single party. They do not have any legal or technical closest that prevent any party to use them. Similarly, they do not have extensions or components with a dependency or being based on preparation standards. Additionally, they are available for full public assessment without any form of constraints. This paper discusses these open standards and how best they have been deployed in the United States and the European Union -- understanding that advantages and disadvantages of open standards are also imperative. [ABSTRACT FROM AUTHOR]

Background: There has been a surge in academic and business interest in software as a medical device (SaMD). SaMD enables medical professionals to streamline existing medical practices and make innovative medical processes such as digital therapeutics a reality. Furthermore, SaMD is a billion-dollar market. However, SaMD is not clearly understood as a technological change and emerging industry., Objective: This study aims to review the landscape of SaMD in response to increasing interest in SaMD within health systems and regulation. The objectives of the study are to (1) clarify the innovation process of SaMD, (2) identify the prevailing typology of such innovation, and (3) elucidate the underlying mechanisms driving the SaMD innovation process., Methods: We collected product information on 581 US Food and Drug Administration-approved SaMDs from the OpenFDA website and 268 company profiles of the corresponding manufacturers from Crunchbase, Bloomberg, PichBook.com, and other company websites. In addition to assessing the metadata of SaMD, we used correspondence and business process analysis to assess the distribution of intended use and how SaMDs interact with other devices in the medical process., Results: The current SaMD industry is highly concentrated in medical image processing and radiological analysis. Incumbents in the medical device industry currently lead the market and focus on incremental innovation, whereas new entrants, particularly startups, produce more disruptive innovation. We found that hardware medical device functions as a complementary asset for SaMD, whereas how SaMD interacts with the complementary asset differs according to its intended use. Based on these findings, we propose a regime map that illustrates the SaMD innovation process., Conclusions: SaMD, as an industry, is nascent and dominated by incremental innovation. The innovation process of the present SaMD industry is shaped by data accessibility, which is key to building disruptive innovation., (©Jiakan Yu, Jiajie Zhang, Shintaro Sengoku. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.11.2023.)

Background: The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app's effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses., Objective: This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods?, Methods: We searched the Food and Drug Administration's registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework., Results: We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy., Conclusions: This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions., (©Emily Grayek, Tamar Krishnamurti, Lydia Hu, Olivia Babich, Katherine Warren, Baruch Fischhoff. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 15.11.2023.)