The analysis of this research is based on the comparative technical efficiency processes of computed tomography (CT) and magnetic resonance imaging (MRI) service delivery in the seven corresponding regions of the National Health Service (NHS) in England. Two inputs—estimated numbers of radiographers and scanner systems—and one output—the volume of exams performed (2022/23)—were used in a Data Envelopment Analysis (DEA). Four distinct DEA models: CCR, BCC Input-Oriented, BCC Output-Oriented, and the Additive model were used to compute the efficiency. The outcome of the analysis was that the majority of the NHS regions witnessed the relative technical efficiency of a high level. For the MRI services, London and the East of England turned out to be the most efficient decision-making units (DMUs). The East of England and the Midlands scored the highest in efficiency for CT services. This implies that there might be certain factors like economies of scale, specialized high-throughput centers, and optimal resource allocation strategies that substantially contribute to such performance. The identified benchmarks, although performance is overall strong, provide health policy with insights. The results point out specific regions whose operational practices can act as a model for others, implying that targeted efficiency improvement is possible without the need for increasing the resource levels but rather through better use of existing assets.

The technological efficiency of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) services within eight Australian states and territories is focused on within this particular study. Data Envelopment Analysis (DEA) was applied with two inputs—the number of radiographers and scanners estimated—and one output—MBS (Medicare Benefits Schedule) services per 1,000 people. The efficiency was calculated with the help of four standard DEA models, i.e., CCR, BCC Input-Oriented, BCC Output-Oriented, and the Additive model. The result of the study was a counter-intuitive finding: most models identified the Northern Territory (NT), a large and sparsely populated region, as the most effective unit for MRI and CT services. On the contrary, states with larger populations such as Victoria, Queensland, and New South Wales were still in the poor relative performance rankings. The Australian Capital Territory (ACT) was also among the top but Tasmania had medium efficiency. The outcomes indicate that small and resource-limited systems can reach a very high technical efficiency by using the limited infrastructure extensively. Nevertheless, this is probably an outcome of the models' concentration on volume-based output and may not reflect performance dimensions such as case-mix complexity or equity of access, among others. The discoveries are a milestone for the choice of action of policymakers, pointing out that one should examine the factors causing inefficiency in the large and more complex state health systems.

This paper assesses the comparative technical efficiency of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) service delivery in all U.S. states and the District of Columbia, totaling 50. Data Envelopment Analysis (DEA) was applied and two inputs were used—the estimated number of scanners and radiographers—and one output—the estimated annual volume of exams. Efficiency was calculated according to the CCR, BCC Input-Oriented, BCC Output-Oriented, and Additive models. The investigation disclosed that almost every U.S. region performs relatively efficiently in a technical way to a great extent for MRI and CT services. Wyoming and Montana stood out as the best-evaluated decision-making units for MRI. In the case of CT, Montana once more was the winner. Consequently, these states with a small population manage to get the best results from their resources. The high scores all over the country signal a healthcare facility that is very good at turning imaging resources into the volume of service. The U.S. is rated highly in technical efficiency with respect to diagnostic imaging, but this is probably the result of a system that rewards high-volume service production. The study reveals an important difference between technical efficiency and wider value, implying that high performance in volume output is not automatically equal to cost-effectiveness or best patient outcomes. Quality and cost metrics should be incorporated in the future research.

In Indonesia, the fast uptake of high-end medical imaging has made it important to do proper performance evaluations. These evaluations will help in the efficient use of very expensive resources. This research is based on Data Envelopment Analysis (DEA) as a tool for determining the relative efficiency of different hospitals in the use of Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) throughout the operation of the above-mentioned techniques. The methods employed are CCR, BCC (in both cases, the analysis is made either from the standpoint of inputs or outputs), and Additive models. The Overall results point to a scenario characterized by an overwhelmingly high level of technical efficiency albeit with substantial differences. Under the CCR model, the aforementioned hospitals leading in private MRI services (Siloam Hospitals ASRI, Mayapada, Medistra) turn out to be completely efficient while the major public referral centers (RSUP Dr. Sardjito, RSUP Dr. Hasan Sadikin) also record outstanding performance. In the CT domain, BIMC Hospital and the major public hospitals are the most efficient, while a single radiology clinic was detected to be significantly less efficient. The difference between CCR and BCC results implies that some institutions are struggling with scale inefficiency rather than management problems. It has been highlighted that the type of hospital and the operational context are the major factors affecting the efficiency. The issue of efficient service delivery through the use of high technology that hospital administrators and policymakers face can be analyzed through the implementation of the differentiated strategies focusing on scale optimization and improved resource utilization.

This research applies TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, to evaluate and rank the performance of telemedicine in the seven Chinese cities—Hangzhou, Wuhan, Chengdu, Shanghai, Beijing, Guangzhou, and Shenzhen. The evaluation was based on five major criteria: Infrastructure, Healthcare System Integration, Accessibility & Equity, Service Breadth & Quality and Regulatory Environment. In a noteworthy finding, Hangzhou was at the top all the time, while Shenzhen was at the bottom despite its technological might. The result was very strong, being the same even when the cost criterion was given 50% weight which showed that the price factor alone does not overpower other performance disparities. The outcomes are a surprise to the common understanding that very high-quality medical institutions in a city and very good technology infrastructure are the only factors that determine the success of telemedicine. Rather, a city’s ability to build an integrated, user-centered digital ecosystem determines the leadership in telemedicine. The first position of Hangzhou indicates the benefit of unrestricted healthcare accessibility through a platform-based system whereas the last position of Shenzhen points out the drawbacks of an expensive and inequitable system. This study shifts the paradigm for future telemedicine cities from "Medical Resource" to "Integrated Ecosystem" model, thus, posing significant implications for the decision makers who strive to ensure effective and equal access to digital health services.