Background: Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration of DTs in health care faces challenges such as fragmented applications, ethical concerns, and barriers to adoption. Objective: This study systematically reviews the existing literature on DT applications in health care with three objectives: (1) to map primary applications, (2) to identify key challenges and limitations, and (3) to highlight gaps that can guide future research. Methods: A meta-review was conducted in a systematic fashion, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and included 25 literature reviews published between 2021 and 2024. The search encompassed 5 databases: PubMed, CINAHL, Web of Science, Embase, and PsycINFO. Thematic synthesis was used to categorize DT applications, stakeholders, and barriers to adoption. Results: A total of 3 primary DT applications in health care were identified: personalized medicine, operational efficiency, and medical research. While current applications, such as predictive diagnostics, patient-specific treatment simulations, and hospital resource optimization, remain in their early stages of development, they highlight the significant potential of DTs. Challenges include data quality, ethical issues, and socioeconomic barriers. This review also identified gaps in scalability, interoperability, and clinical validation. Conclusions: DTs hold transformative potential in health care, providing individualized care, operational optimization, and accelerated research. However, their adoption is hindered by technical, ethical, and financial barriers. Addressing these issues requires interdisciplinary collaboration, standardized protocols, and inclusive implementation strategies to ensure equitable access and meaningful impact.

Assessing the performance of electrical impedance tomography (EIT) systems usually requires a phantom for validation, calibration, or comparison purposes. This paper describes a resistive mesh phantom to assess the performance of EIT systems while taking into account cabling stray effects similar to in vivo conditions. This phantom is built with 340 precision resistors on a printed circuit board representing a 2-D circular homogeneous medium. It also integrates equivalent electrical models of the Ag/AgCl electrode impedances. The parameters of the electrode models were fitted from impedance curves measured with an impedance analyzer. The technique used to build the phantom is general and applicable to phantoms of arbitrary shape and conductivity distribution. We describe three performance indicators that can be measured with our phantom for every measurement of an EIT data frame: SNR, accuracy, and modeling accuracy. These performance indicators were evaluated on our EIT system under different frame rates and applied current intensities. The performance indicators are dependent on frame rate, operating frequency, applied current intensity, measurement strategy, and intermodulation distortion when performing simultaneous measurements at several frequencies. These parameter values should, therefore, always be specified when reporting performance indicators to better appreciate their significance.

Purpose: This paper examines the influence of geopolitical tensions—operationalized as political divergence between governments—on firms’ foreign supply bases and the resulting effects on supply base complexity and sub-tier supplier sharing.Design/methodology/approach: The authors conduct panel data regression analyses over the period 2003–2019 to investigate whether political divergence affects foreign supply bases for 2,858 US firms sourcing from 99 countries and to examine how political divergence exposure impacts the supply network structures of 853 US firms.Findings: Firms reduce their supply bases in countries exposed to heightened geopolitical tensions. These supply chain adjustments are associated with increased supply base complexity and greater sub-tier supplier sharing.Originality/value: This study highlights the importance of state relations in global supply chain reconfiguration. Political divergence between governments provides a dual-view of political risk (i.e. buyer–supplier countries), which can help firms anticipate geopolitical disruptions. While reducing supply bases in foreign countries facing heightened geopolitical tensions is intended to mitigate disruptions, these supply base adjustments are linked to increased supply base complexity and sub-tier supplier sharing, thereby exposing firms to other types of supply disruptions. Additionally, this research contributes to understanding the effects of geopolitical tensions on supply base complexity through the lenses of transaction cost economics and resource dependence theory.

Background: Chronic diseases represent a significant global burden of mortality, exacerbated by behavioral risk factors. Artificial intelligence (AI) has transformed health promotion and disease reduction through improved early detection, encouraging healthy lifestyle modifications, and mitigating the economic strain on health systems. Objective: The aim of this study is to investigate how AI contributes to health promotion and disease reduction among Organization for Economic Co-operation and Development countries. Methods: We conducted a rapid review of the literature to identify the latest evidence on how AI is used in health promotion and disease reduction. We applied comprehensive search strategies formulated for MEDLINE (OVID) and CINAHL to locate studies published between 2019 and 2024. A pair of reviewers independently applied the inclusion and exclusion criteria to screen the titles and abstracts, assess the full texts, and extract the data. We synthesized extracted data from the study characteristics, intervention characteristics, and intervention purpose using structured narrative summaries of main themes, giving a portrait of the current scope of available AI initiatives used in promoting healthy activities and preventing disease. Results: We included 22 studies in this review (out of 3442 publications screened), most of which were conducted in the United States (10/22, 45%) and focused on health promotion by targeting lifestyle dimensions, such as dietary behavior (10/22, 45%), smoking cessation (6/22, 27%), physical activity (4/22, 18%), and mental health (3/22, 14%). Three studies targeted disease reduction related to metabolic health (eg, obesity, diabetes, hypertension). Most AI initiatives were AI-powered mobile apps. Overall, positive results were reported for process outcomes (eg, acceptability, engagement), cognitive and behavioral outcomes (eg, confidence, step count), and health outcomes (eg, glycemia, blood pressure). We categorized the challenges, benefits, and suggestions identified in the studies using a Strengths, Weaknesses, Opportunities, and Threats analysis to inform future developments. Key recommendations include conducting further investigations, taking into account the needs of end users, improving the technical aspect of the technology, and allocating resources. Conclusions: These findings offer critical insights into the effective implementation of AI for health promotion and disease prevention, potentially guiding policymakers and health care practitioners in optimizing the use of AI technologies in supporting health promotion and disease reduction.

In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given. To achieve this balance, there exist a wide variety of methods. In particular, several methods based on optimization models have been recently proposed in the causal inference literature. While these optimization-based methods empirically showed an improvement over a limited number of other causal inference methods in their relative ability to balance the distributions of covariates and to estimate causal effects, they have not been thoroughly compared to each other and to other noteworthy causal inference methods. In addition, we believe that there exist several unaddressed opportunities that operational researchers could contribute with their advanced knowledge of optimization, for the benefits of the applied researchers that use causal inference tools. In this review paper, we present an overview of the causal inference literature and describe in more detail the optimization-based causal inference methods, provide a comparative analysis of the prevailing optimization-based methods, and discuss opportunities for new methods.

Objectives: This study determined whether naturally occurring but significantly different outpatient follow-up frequencies are associated with clinical outcomes and service waiting times. Study Design: Longitudinal retrospective study. Methods: This study was conducted in an outpatient setting. Participants consisted of 340 patients with major depressive disorder who were randomly assigned to 4 psychiatrists and were followed at a variable frequency defined by the clinician. Patients were assessed at baseline and at every visit with structured interviews and self-reported questionnaires. These groups were also compared according to their baseline characteristics, treatment, and appointment frequencies. Little’s law was used to estimate the impact of modifying the appointment frequencies on the service waiting time. Results: The demographic variables, prescriptions, and depression severity at intake of patients across the 4 groups were similar. The mean times between appointments of the 4 groups were significantly different (87.0, 46.9, 67.9, and 61.5 days, respectively; ,P, < .001), but these differences in outpatient follow-up frequency were not associated with clinical outcomes (eg, mean last Quick Inventory of Depressive Symptomatology Self-Report score, 10.5, 10.0, 11.9, and 9.7; ,P, = .25). However, different outpatient follow-up frequencies had an estimated impact on waiting times for access to care; using Little’s law, it was observed that the waiting list would be eliminated by reducing by 23.9% the follow-up frequencies of the 3 psychiatrists with the highest frequencies. Conclusions: Although variations in appointment frequencies do not appear to have a major impact on clinical outcomes, they could be managed to achieve significant improvements in the accessibility of the clinic.