Digitalization of Medical Services - A New Ally for Malpractice Risk Management

This paper examines the concept of digitalization of medical services as well as the role it plays in managing the risk of malpractice. Starting from the applications of technology in the medical field, this paper aims to analyze the possible effects and implications on the professional error committed in the exercise of the medical act that created damages. The article addresses a niche topic, in an area of strict specialty and interest. The authors debate aspects of the activity of providing medical assistance and the associated risk of malpractice in the context generated by new technologies. The research methodology used is mainly based on the review and synthesis of the existing literature.The conclusions are intended to be an invitation to academic dialogue and a starting point for researchers and practitioners

Congenital Abnormalities of the Fetal Heart

Congenital heart defects (CHDs) are the most frequent congenital malformations, the costliest hospital admissions for structural defects and the leading cause of infant general and malformations related mortality. Fetal echocardiography represents a skilled ultrasound examination, because of the complexity, physiological and structural particularities of the fetal heart. The efficiency of the cardiac scan is reported with great variation, depending on the scanning protocol, examiner experience and equipment quality but CHDs remains among the most frequently missed congenital abnormalities

The Relationship between Digital Technology and the Development of the Entrepreneurial Competencies of Young People in the Medical Field

Digital technology is an important tool that influences employees from the healthcare sector to manifest their intention to become an entrepreneur. Furthermore, the last pandemic crisis underlined the importance of digitalizing the relationship between medical staff and patients. The research aims to evaluate how digital technology influences the development of the entrepreneurial spirit of young people working in the medical field. The data were gathered from a sample of 395 young people with medical studies and analyzed with SMARTPLS4 using the PLS-SEM method. The motivation of young people with a background in the medical field to become entrepreneurs is strongly influenced by the objective assessment of the level of digitalization of the medical field. The usability and availability of new technology give people with a background in the medical field the desire to become an entrepreneur in this domain. The young people perceive their entrepreneurial potential in complementarity with the level of digitalization of the medical field. The research’s theoretical and practical contributions are underlined by the features of the young people that consider new technology as an omnipresent tool in their life. In the medical field, there are few theoretical papers and studies on the entrepreneurial spirit of young people with a background in healthcare, and our research underlines the importance of training the entrepreneurial competencies of young people in the medical field. The COVID-19 pandemic underlined the relevance of entrepreneurial competencies in building sustainable healthcare practices and identifying the deficiencies of healthcare systems to find timely solutions for the benefit of the patients. Therefore, the challenges related to the medical services market require a new approach to doctors’ entrepreneurial competencies

Learning deep architectures for the interpretation of first-trimester fetal echocardiography (LIFE) - a study protocol for developing an automated intelligent decision support system for early fetal echocardiography

Abstract Background Congenital Heart Disease represents the most frequent fetal malformation. The lack of prenatal identification of congenital heart defects can have adverse consequences for the neonate, while a correct prenatal diagnosis of specific cardiac anomalies improves neonatal care neurologic and surgery outcomes. Sonographers perform prenatal diagnosis manually during the first or second-trimester scan, but the reported detection rates are low. This project’s primary objective is to develop an Intelligent Decision Support System that uses two-dimensional video files of cardiac sweeps obtained during the standard first-trimester fetal echocardiography (FE) to signal the presence/absence of previously learned key features. Methods The cross-sectional study will be divided into a training part of the machine learning approaches and the testing phase on previously unseen frames and eventually on actual video scans. Pregnant women in their 12–13 + 6 weeks of gestation admitted for routine first-trimester anomaly scan will be consecutively included in a two-year study, depending on the availability of the experienced sonographers in early fetal cardiac imaging involved in this research. The Data Science / IT department (DSIT) will process the key planes identified by the sonographers in the two- dimensional heart cine loop sweeps: four-chamber view, left and right ventricular outflow tracts, three vessels, and trachea view. The frames will be grouped into the classes representing the plane views, and then different state-of-the- art deep-learning (DL) pre-trained algorithms will be tested on the data set. The sonographers will validate all the intermediary findings at the frame level and the meaningfulness of the video labeling. Discussion FE is feasible and efficient during the first trimester. Still, the continuous training process is impaired by the lack of specialists or their limited availability. Therefore, in our study design, the sonographer benefits from a second opinion provided by the developed software, which may be very helpful, especially if a more experienced colleague is unavailable. In addition, the software may be implemented on the ultrasound device so that the process could take place during the live examination. Trial registration The study is registered under the name „Learning deep architectures for the Interpretation of Fetal Echocardiography (LIFE)”, project number 408PED/2020, project code PN-III-P2–2.1-PED-2019. Trial registration: ClinicalTrials.gov , unique identifying number NCT05090306, date of registration 30.10.2020