Matching a sample of 4,581 US hospitals over 13 years with 67,915 ambulatory care entities, we investigate the spillover effects of regional ambulatory electronic health record (EHR) adoption on inpatient costs at a focal hospital. We find that a one standard deviation (SD) increase in nearby ambulatory EHR adoption results in $1.60 million in savings in inpatient costs for the focal hospital. These savings are amplified when ambulatory entities and hospitals engage in health information exchange (HIE), particularly when they belong to unaffiliated health systems, highlighting the economic benefits of information sharing among competing entities in healthcare referral networks. Additionally, we analyze the reciprocal spillover effects of hospital EHR on focal rural clinics and again find significant cost savings. Together, the results underscore that spillover effects from regional EHR adoptions, materialized through improved electronic information exchange with unaffiliated providers across different levels of care delivery, can substantially lower costs at the focal healthcare organizations. Our empirical evidence extends the current understanding of the economic value of regional inter-organizational information sharing generated by distinct EHR and HIE applications used at hospitals and clinics.

The concept of the Metaverse, an immersive simulated world with parallels to reality, has gained significant prominence in recent times. Initially popularized through gaming, the Metaverse is now poised to infiltrate various aspects of human life. Intelligent transportation systems represent a promising yet challenging domain for Metaverse integration. Alternative implementations can create challenges in different dimensions. A comprehensive evaluation that takes challenges and opportunities for the different dimensions into account is required in decision making process of choosing the best implementation method. This study presents the development of a novel evaluation model, the Fermatean Fuzzy Operational Competitiveness Rating (OCRA) model, which incorporates the Fermatean Fuzzy Distance Measure (FF-DM) and Relative Closeness Coefficient (FF-RCC) techniques. The model is tested in a case to rank three alternative approaches, considering criteria of four key dimensions: managerial, safety, user, and urban mobility. In the first stage, the FF-DM and FF-RCC-based tool is employed to determine the criteria weights. In the second stage, an enhanced version of the Fermatean Fuzzy OCRA model, utilizing FF-DM and FF-RCC, is employed to rank the alternatives. The findings indicate that policymakers' decisions in traffic management hold the potential to shape the trajectory of the Metaverse movement, representing an unparalleled opportunity with implications that extend beyond our current comprehension.

This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventional methods that rely on predefined ranks for criteria weighting coefficients, the proposed rough OPA method employs an aggregated rough linguistic matrix, enabling a more precise and unbiased calculation of interval values. Moreover, the model addresses inherent uncertainties by incorporating nonlinear aggregation functions, accommodating decision makers' risk attitudes for flexible decision-making. To validate the model's efficacy, a large-scale post-EV test drive survey is conducted, enabling the determination of relative criterion importance. Sensitivity analysis confirms the robustness of the model, demonstrating that marginal changes in parameters do not alter the ranking order. The results unveil the significance of the reliability criterion and reveal that vehicle-related characteristics outweigh economic and environmental attributes in the decision-making process. Overall, this innovative MCDM model contributes to a more accurate and objective analysis, enhancing the understanding of users' preferences and supporting informed decision-making in EV purchases.