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Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.

The trajectory of immune responses following the primary dose series determines the decline in vaccine effectiveness over time. Here we report on maintenance of immune responses during the year following a two-dose schedule of ChAdOx1 nCoV-19/AZD1222, in the absence of infection, and also explore the decay of antibody after infection. Total spike-specific IgG antibody titres were lower with two low doses of ChAdOx1 nCoV-19 vaccines (two low doses) (P = 0.0006) than with 2 standard doses (the approved dose) or low dose followed by standard dose vaccines regimens. Longer intervals between first and second doses resulted in higher antibody titres (P < 0.0001); however, there was no evidence that the trajectory of antibody decay differed by interval or by vaccine dose, and the decay of IgG antibody titres followed a similar trajectory after a third dose of ChAdOx1 nCoV-19. Trends in post-infection samples were similar with an initial rapid decay in responses but good persistence of measurable responses thereafter. Extrapolation of antibody data, following two doses of ChAdOx1 nCov-19, demonstrates a slow rate of antibody decay with modelling, suggesting that antibody titres are well maintained for at least 2 years. These data suggest a persistent immune response after two doses of ChAdOx1 nCov-19 which will likely have a positive impact against serious disease and hospitalization.

Hormesis refers to graded adaptive responses to harmful environmental stimuli where low-level toxicant exposures stimulate tissue growth and responsiveness while, in contrast, higher-level exposures induce toxicity. Although the intergenerational inheritance of programmed hormetic growth responses is described in plants and insects, researchers have yet to observe this phenomenon in mammals. Using a physiologically relevant mouse model, we demonstrate that chronic preconception paternal alcohol exposures program nonlinear, dose-dependent changes in offspring fetoplacental growth. Our studies identify an inverse j-shaped curve with a threshold of 2.4 g/Kg per day; below this threshold, paternal ethanol exposures induce programmed increases in placental growth, while doses exceeding this point yield comparative decreases in placental growth. In male offspring, higher paternal exposures induce dose-dependent increases in the placental labyrinth layer but do not impact fetal growth. In contrast, the placental hypertrophy induced by low-level paternal ethanol exposures associate with increased offspring crown-rump length, particularly in male offspring. Finally, alterations in placental physiology correlate with disruptions in both mitochondrial-encoded and imprinted gene expression. Understanding the influence of ethanol on the paternally-inherited epigenetic program and downstream hormetic responses in offspring growth may help explain the enormous variation observed in fetal alcohol spectrum disorder (FASD) phenotypes and incidence.

Virus neutralization assays measure neutralizing antibodies in serum and plasma, and the plaque reduction neutralization test (PRNT) is considered the gold standard for measuring levels of these antibodies for many viral diseases. We have developed procedures for the standard PRNT, microneutralization assay (MNA) and pseudotyped virus neutralization assay (PNA) for severe acute respiratory syndrome coronavirus 2. The MNA offers advantages over the PRNT by reducing assay time, allowing increased throughput and reducing operator workload while remaining dependent upon the use of wild-type virus. This ensures that all severe acute respiratory syndrome coronavirus 2 antigens are present, but Biosafety Level 3 facilities are required. In addition to the advantages of MNA, PNA can be performed with lower biocontainment (Biosafety Level 2 facilities) and allows for further increases in throughput. For each new vaccine, it is critical to ensure good correlation of the neutralizing activity measured using PNA against the PRNT or MNA. These assays have been used in the development and licensure of the ChAdOx1 nCoV-19 (AstraZeneca; Oxford University) and Ad26.COV2.S (Janssen) coronavirus disease 2019 vaccines and are critical for demonstrating bioequivalence of future vaccines.

To develop and implement a measure of how US hospitals contribute to community health with a focus on equity.Primary data from public comments and hospital surveys and secondary data from the IBM Watson Top 100 Hospitals program collected in the United States in 2020 and 2021.A thematic analysis of public comments on the proposed measure was conducted using an iterative grounded approach for theme identification. A cross-sectional survey of 207 hospitals was conducted to assess self-attestation to 28 community health best practice standards in the revised measure. An analysis of hospital rankings before and after inclusion of the new measure was performed.Public comment on the proposed measure was collected via an online survey, email, and virtual meetings in 2020. The survey of hospitals was conducted online by IBM in 2021. The analysis of hospital ranking compared the 2020 and 2021 IBM Watson Top 100 Hospitals program results.More than 650 discrete comments from 83 stakeholders were received and analyzed during measure development. Key themes identified in thematic analysis included equity, fairness, and community priorities. Hospitals that responded to a cross-sectional survey reported meeting on average 76% of applicable best practice standards. Least met standards included providing emergent buprenorphine treatment for opioid use disorder (53%), supporting an evidence-based home visiting program (53%), and establishing a returning citizens employment program (27%). Thirty-seven hospitals shifted position in the 100 Top Hospital rankings after the inclusion of the new measure.There is broad interest in measuring hospital contributions to community health with a focus on equity. Many highly ranked hospitals report meeting best practice standards, but significant gaps remain. Improving measurement to incentivize greater hospital contributions to community health and equity is an important priority.

Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors., Digital health technologies can effectively reach individuals within the context of their daily lives to improve health behaviors.

Background Alcohol (EtOH) is reported to adversely affect one of the most crucial roles of the blood-brain barrier (BBB), the regulation of its permeability, thereby compromising the stability of the homeostatic environment of the brain. The central component of the BBB, endothelial cells (ECs), regulates BBB transcellular transport, while their paracellular pathways are made virtually impermeable by molecular structures called tight junctions (TJs). These TJs are composed of proteins, such as claudin-5, a protein involved in the regulation of paracellular permeability and of key interest in this study. Methods and results The working hypothesis of this study postulated that the high levels of antioxidants (AOs) in the fermented Aspalathus linearis (Rooibos; Rf) tincture may protect the ECs of the BBB against oxidative stress induced by EtOH exposure. Cells were exposed for 24 hours to selected concentrations of EtOH (25 and 100 mM), Rf (containing an antioxidant equivalence of 1.9 nM Aspalathin), and cotreatments of EtOH and Rf. Cell viability, live cell number, and toxicity were analyzed using the trypan blue exclusion assay. RT-qPCR was implemented to quantify claudin-5 transcription. In addition, permeability (Transepithelial Electrical Resistance) of bEnd5 monolayers was measured. The experimental timeline for the above-mentioned parameters was 24 and 48 hours. Conclusions Our study showed that simultaneous exposure of Rf and EtOH was able to negate the effects of EtOH on cell viability and cell proliferation, but was not able to reverse or reduce the effects of EtOH on claudin-5 transcription and paracellular permeability. Furthermore, a novel finding in this study suggests that very low concentrations of AOs in tinctures such as Rooibos tea could profoundly alter the redox status of brain ECs.

Disparities in cardiovascular disease (CVD) and associated health and healthcare delivery outcomes have been partially attributed to differential risk factors, and to prevention and treatment inequities within racial and ethnic (including language) minority groups and low socioeconomic status (SES) populations in urban and rural settings. Digital health interventions (DHIs) show promise in promoting equitable access to high-quality care, optimal utilization, and improved outcomes; however, their potential role and impact has not been fully explored. The role of DHIs to mitigate drivers of the health disparities listed above in populations disproportionately affected by atherosclerotic-related CVD was systematically reviewed using published literature (January 2008–July 2020) from multiple databases. Study design, type and description of the technology, health disparities information, type of CVD, outcomes, and notable barriers and innovations associated with the technology utilized were abstracted. Study quality was assessed using the Oxford Levels of Evidence. Included studies described digital health technologies in a disparity population with CVD and reported outcomes. DHIs significantly improved health (eg, clinical, intermediate, and patient-reported) and healthcare delivery (eg, access, quality, and utilization of care) outcomes in populations disproportionately affected by CVD in 24 of 38 included studies identified from 2104 citations. Hypertension control was the most frequently improved clinical outcome. Telemedicine, mobile health, and clinical decision support systems were the most common types of DHIs identified. DHIs improved CVD-related health and healthcare delivery outcomes in racial/ethnic groups and low SES populations in both rural and urban geographies globally.

BACKGROUND The use of artificial intelligence (AI)–based tools in the care of individual patients and patient populations is rapidly expanding. OBJECTIVE The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. METHODS A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. RESULTS The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. CONCLUSIONS While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.