RESUMO
A new era has begun in the treatment of cardiovascular disorders as a direct result of the significant developments that have been made in robotics and artificial intelligence (AI). This abstract investigates the potential and impact that AI algorithms and robotic systems may have in the diagnosis and treatment of cardiovascular problems. The field of cardiovascular treatments has been completely transformed by robotically assisted surgeries, which have enabled minimally invasive procedures with increased patient safety and decreased recovery time. The incorporation of AI algorithms into cardiovascular care has made early abnormality identification, risk classification, and tailored treatment planning significantly easier. However, problems including patient safety, data privacy, and smooth integration into existing healthcare systems need to be solved. This abstract places an emphasis on the necessity of collaboration and responsible implementation in order to fully harness the promise of robotics and AI in cardiovascular care, which will ultimately lead to improved patient outcomes and an enhanced quality of life.
RESUMO
Introduction Motivation is a power that directs employees toward achieving their special goals and the organization's objectives in general. Recently, motivation has been considered one of the most important issues in the workplace since each organization wants to get the most out of its resources, including its manpower. In Saudi Arabia, the Aseer Health Directorate like the other regions, has a monthly reward containing certification and online announcements of employee excellence via email and website. The annual ministry program has three levels: 10% in salary for employees who have received excellence certification from the general director, 20% if they add research, and 30% if they register a patent. Methods A cross-sectional study was conducted in the Aseer Central Hospital among healthcare providers (HCP) of all categories using structured interviews with three parts: socio-demographic, knowledge about criteria for excellence in the program, and opinion toward the program and its effects. The sample size was calculated using the total number of employees in the hospital, with 77 being the bare minimum required. The sampling method used was systematic random sampling with a list of statistical analyses from the SPSS statistical package (IBM Corp., Armonk, NY, USA). The statistical analysis description for all variables was based on frequency and presence, followed by bivariate analysis. Results In the study, 103 participants who were physicians made up 28.2% of the sample, followed by nurses at 32%, and other health professionals at 39.8%. Males made up 64.1% and females 35.9%. While 55.3% of individuals claimed to be aware of the standards of excellence allowance, 44.7% of participants denied doing so. Around 7.8% of employees missed work or arrived late, compared to 92.2% who didn't, and 75% of those who had proof in their files received a pay cut or other penalty. The average employee's age was 33.5 years old, and 4.05% of them believed they performed better than their coworkers. An average of 4.43 employees believe that there will be more competition when the number of allowances increases. While the excellence allowance has a favorable effect as indicated by the 4.55 mean. With a mean of 2.47 and 2.45, the majority of employees, respectively, did not agree with the transparent and fair selection process. Conclusion The purpose of the study was to demonstrate the significance of rewarding and encouraging employees, as well as the positive impact of this system on increasing their performance and productivity in order to implement the health organization's strategy and improve healthcare delivery and quality. There is a high percentage of employees who are dissatisfied with the transparency and fairness of the selection criteria, and some of them believe that team performance should be improved rather than individual performance. So the recommendation is to improve the selection criteria, make them more specific and transparent, and increase the number of teams and individuals who can be enrolled to improve the healthcare delivery system.
