Accreditation is documented and reported by the external evaluation organization that the health facility provides services at certain standards. While on-site survey practices are being carried out by external evaluation organizations, there has been a trend toward new survey approaches using digital technologies as a result of the research carried out to ensure efficiency in surveys as well as improved effectiveness. With the emergence of the Covid-19 epidemic, external evaluation organization in all sectors has been forced to work remotely and adopt digital technology. Shared remote survey experience results reported its benefits as also some problems. The increase in the adaptation of digital technologies in quality and accreditation surveys showed that the use of technology in the survey structure will develop further in the future. Can artificial intelligence technologies be the next digital technology that will be adapted to surveys? In addition to the benefits of using artificial intelligence technologies, there are potential problems to consider and some requirements for using them. external evaluation organizations must be prepared to develop their organizational capacity to ensure that quality and accreditation surveys are responsive to changing industry needs and must make the necessary investments to make the data, which is the most important source of digital technologies, accessible and usable. Graphical Abstract

Clinical quality, as a technical result quality of health services, is a concept that outlines how health system inputs are transformed into health outcomes. The aim of the study is to develop a model in which the relative clinical quality levels of the patients are evaluated with the Network Data Envelopment Analysis (NDEA) method by using the structure, process, and outcome measures of the Coronary Artery Bypass Graft (CABG) surgery. The research was conducted in a tertiary training and research hospital as a prospective, cross-sectional and registry research. Clinical quality levels of patients who underwent CABG surgery were evaluated with NDEA (two-stage) method in managerial and clinical efficiency stages. NDEA showed that 3 patients had the best clinical quality level. The patient profile with a low clinical quality level was created in managerial and clinical stages and quality improvement points were determined. The NDEA model enabled the analysis of all the structure, process and outcome measures simultaneously and was used to evaluate clinical quality with multiple measures. Using this data, the CABG surgical process profile was created. Intensive care unit (ICU) and postoperative inpatient day, cardiopulmonary bypass (CPB) and cross-clamp (CC) duration, and the use of fresh frozen plasma (FFP) were determined as the CABG surgery points requiring quality improvement.

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Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare. Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies. Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions. Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.

Dental education requires students to acquire a certain skill set in addition to academic-based theoretical education. Changes in the education method, during the Covid-19 pandemic, have had different effects on dentistry students. The e-learning method offers advantages such as removing physical limits and supporting self-learning and creativity. However, clinical learning of dental education and the psychological state of the students affected negatively all around the world. Digital Simulation Technologies (DSTs) including augmented reality (AR), virtual reality (VR), and haptic simulation, have been a valuable resource coping with adverse situations in dental education due to the impact of the Covid-19 pandemic. DSTs need to be developed in the future on finger support, tactile sensation, force feedback, high screen resolution, depth perception in stereoscopic images, accurate deformation simulation, different training difficulty levels, big data technology in dental skills training.