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Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin – the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as ‘revolutionary’ and ‘disruptive’, is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is ‘fit-for-purpose’ is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Biomedical image analysis challenges have increased in the last ten years, but common practices have not been established yet. Here the authors analyze 150 recent challenges and demonstrate that outcome varies based on the metrics used and that limited information reporting hampers reproducibility.

Digital health technologies (ie, the integration of digital technology and health information) aim to increase the efficiency of health care delivery; they are rapidly adapting to health care contexts to provide improved medical services for citizens. However, contrary to expectations, their rapid adoption appears to have led to health inequities, with differences in health conditions or inequality in the distribution of health care resources among different populations. This scoping review aims to identify and describe the inequities of health care services brought about by the adoption of digital health technologies. The factors influencing such inequities, as well as the corresponding countermeasures to ensure health equity among different groups of citizens, were also studied. Primary studies and literature, including articles and reviews, published in English between 1990 and 2020 were retrieved using appropriate search strategies across the following three electronic databases: Clarivate Analytics' Web of Science, PubMed, and Scopus. Data management was performed by two authors (RY and WZ) using Thomson Endnote (Clarivate Analytics, Inc), by systematically screening and identifying eligible articles for this study. Any conflicts of opinion were resolved through discussions with the corresponding author. A qualitative descriptive synthesis was performed to determine the outcomes of this scoping review. A total of 2325 studies were collected during the search process, of which 41 (1.76%) papers were identified for further analysis. The quantity of literature increased until 2016, with a peak in 2020. The United States, the United Kingdom, and Norway ranked among the top 3 countries for publication output. Health inequities caused by the adoption of digital health technologies in health care services can be reflected in the following two dimensions: the inability of citizens to obtain and adopt technology and the different disease outcomes found among citizens under technical intervention measures. The factors that influenced inequities included age, race, region, economy, and education level, together with health conditions and eHealth literacy. Finally, action can be taken to alleviate inequities in the future by government agencies and medical institutions (eg, establishing national health insurance), digital health technology providers (eg, designing high-quality tools), and health care service recipients (eg, developing skills to access digital technologies). The application of digital health technologies in health care services has caused inequities to some extent. However, existing research has certain limitations. The findings provide a comprehensive starting point for future research, allowing for further investigation into how digital health technologies may influence the unequal distribution of health care services. The interaction between individual subjective factors as well as social support and influencing factors should be included in future studies. Specifically, access to and availability of digital health technologies for socially disadvantaged groups should be of paramount importance.

The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) are widely used to examine health care technology acceptance among older adults. However, existing literature exhibits considerable heterogeneity, making it difficult to determine consistent predictors of acceptance and behavior. We aimed to (1) determine the influence of perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) on the behavioral intention (BI) to use health care technology among older adults and (2) assess the moderating effects of age, gender, geographic region, type of health care technology, and presence of visual demonstrations. A systematic search was conducted across Google Scholar, Web of Science, Scopus, IEEE Xplore, and ProQuest databases on March 15, 2024, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Of the 1167 initially identified studies, 41 studies (11,574 participants; mean age 67.58, SD 4.76 years; and female:male ratio=2.00) met the inclusion criteria. The studies comprised 12 mobile health, 12 online or telemedicine, 9 wearable, and 8 home or institution hardware investigations, with 23 studies from Asia, 7 from Europe, 7 from African-Islamic regions, and 4 from the United States. Studies were eligible if they used the TAM or UTAUT, examined health care technology adoption among older adults, and reported zero-order correlations. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the Newcastle-Ottawa Scale, evaluating selection, comparability, and outcome assessment with 34% (14/41) of studies rated as good quality and 66% (27/41) as satisfactory. Random-effects meta-analysis revealed significant positive correlations for PU-BI (r=0.607, 95% CI 0.543-0.665; P<.001), PEOU-BI (r=0.525, 95% CI 0.462-0.583; P<.001), and SI-BI (r=0.551, 95% CI 0.468-0.624; P<.001). High heterogeneity was observed across studies (I²=95.9%, 93.6%, and 95.3% for PU-BI, PEOU-BI, and SI-BI, respectively). Moderator analyses revealed significant differences based on geographic region for PEOU-BI (Q=8.27; P=.04), with strongest effects in Europe (r=0.628) and weakest in African-Islamic regions (r=0.480). Technology type significantly moderated PU-BI (Q=8.08; P=.04) and SI-BI (Q=14.75; P=.002), with home or institutional hardware showing the strongest effects (PU-BI: r=0.736; SI-BI: r=0.690). Visual demonstrations significantly enhanced PU-BI (r=0.706 vs r=0.554; Q=4.24; P=.04) and SI-BI relationships (r=0.670 vs r=0.492; Q=4.38; P=.04). Age and gender showed no significant moderating effects. The findings indicate that PU, PEOU, and SI significantly impact the acceptance of health care technology among older adults, with heterogeneity influenced by geographic region, type of technology, and presence of visual demonstrations. This suggests that tailored strategies for different types of technology and the use of visual demonstrations are important for enhancing adoption rates. Limitations include varying definitions of older adults across studies and the use of correlation coefficients rather than controlled effect sizes. Results should therefore be interpreted within specific contexts and populations.

Objective Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies. Methods We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research. Results We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and ‘omics’-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects. Discussion Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.