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Case Mix Index (CMI) represents a standard indicator of hospital disease seriousness at both the national and international level. CMI is structured to compute hospital payments and not to follow up disease seriousness. It is considerably related to the data accuracy as well as patients’ medical records. The main aim of the study was to quantify the factors influencing the differences in CMI depending on the types of healthcare facilities, their geographical location, as well as the procedural aspects within the Slovak healthcare system. We use data published by the Institute for Economic and Social Reforms, which continuously surveys the quality and outputs of healthcare provided in the Slovak Republic. The data comes from multiple datasets that are issued by healthcare authorities and stakeholders is the Slovak Republic such as insurance companies, the Ministry of Health of the Slovak Republic, the Ministry of Finance of the Slovak Republic, healthcare facilities, the National Health Information Centre etc. Regression analysis indicates that the positive impact on the Case Mix Index is possessed by the Operating experience of doctors and Overall inpatients satisfaction; whilst the negative impact on Case Mix Index values is documented for variables Total rehospitalisation within 30 d, Intensive care unit mortality, Economy, and Transparency. [ABSTRACT FROM AUTHOR]

Research background: As Artificial Intelligence (AI) weaves into the fabric of daily life, its societal and economic implications underscore the urgency of embracing an environment conducive to its informed adoption. This requires a sophisticated understanding of the societal perception and adaptability to AI, emphasizing the importance of developing comprehensive AI literacy. Purpose of the article: This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments. It allows us to define the basic determinants that influence the differences in the individual population structures. It also reveals the potential risks associated with the use of AI. Methods: This study investigates the awareness of Artificial Intelligence (AI) in daily lives of the Czech population, focusing on the influence of socio-demographic factors. Utilizing computer-assisted web interviewing, we surveyed 1,041 respondents in April 2023, ensuring representativeness by applying quotas for age, gender, education, region, and residential area size. Our investigation spanned AI applications in sectors like customer service, music playlist recommendation, email sorting, healthcare, online shopping, and home devices. Findings & value added: Findings taken from descriptive statistics reveal variable AI awareness levels across different domains, with younger demographics exhibiting notably lower awareness in several areas. Regression analysis highlighted that awareness is significantly associated with gender, age, and education level. Regression analysis showed that males, younger age groups and those with higher levels of education were more likely to correctly answer majority of questions about the role of AI in everyday life. These insights are crucial for stakeholders aiming to enhance AI literacy, tailor communication strategies, and develop digital platforms, offering guidance for policymakers and market analysts in optimizing AIrelated initiatives. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quality of higher education depends on the quality of scientific research of their employees. University scientific research and education are inseparable. Rating of their level is currently the subject of many scientific and expert discussions. At the present time, there is a prevailing pressure to create a categorization of universities on the basis of their scientific performance, which should translate also in the process of distribution of state subsidies for universities. In order to stop the process of stagnation in the quality of higher education it is necessary to identify the barriers to its growth as soon as possible and propose solutions for their elimination. For this reason, questions of objective and independent evaluation of the quality through multidimensional economic analysis are nowadays being brought more and more into focus. It was the ambition of our research. We aimed to evaluate the effectiveness of science and research at universities in the Czech Republic to find out the real state as well as the suitability of the applied method chosen for the evaluation process. We run Data Envelopment Analysis on data on faculty level. Our findings suggest, that efficiency level of faculties in global view is fair enough, but place for improvement still exists. Our analysis shows that 53% of the faculties in the Czech Republic operate on efficient way in term of Science and Research. Most equilibrated groups in term of efficiency is group of Engineering, followed by group of Architecture, group of Psychology and group of Informatics. Least equilibrated groups are group of Economics, group of Sociology and group of Law. [ABSTRACT FROM AUTHOR]