ABSTRACT
Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.
ABSTRACT
Background: Emergency department length of stay is a vital performance indicator for quality and efficiency in healthcare. This research aimed to evaluate the length of stay patterns in emergency departments across Saudi Arabia and to identify predictors for extended stays. The study used secondary data from the Ministry of Health's Ada'a program. Methods: Using a retrospective approach, the study examined data from the Ada'a program on emergency department length of stay from September 2019 to December 2021. These data covered 1,572,296 emergency department visits from all regions of Saudi Arabia. Variables analyzed included quality indicators, year of visit, shift time, hospital type, and data entry method. The analysis was conducted using multiple linear regression. Results: The study found that the median length of stay was 61 min, with significant differences among related predictors. All associations were significant with a value of p of less than 0.001. Compared to 2019, the length of stay was notably shorter by 28.5% in 2020 and by 44.2% in 2021. Evening and night shifts had a shorter length of stay by 5.9 and 7.8%, respectively, compared to the morning shift. Length of stay was lower in winter, summer, and fall compared to spring. Patients in levels I and II of the Canadian Triage and Acuity Scales had longer stays than those in level III, with those in level I reaching an increase of 20.5% in length of stay. Clustered hospitals had a longer length of stay compared to the non-clustered ones. Pediatric hospitals had a 15.3% shorter stay compared to general hospitals. Hospitals with data entered automatically had a 14.0% longer length of stay than those entered manually. Patients admitted to the hospital had a considerably longer length of stay, which was 54.7% longer compared to non-admitted patients. Deceased patients had a 20.5% longer length of stay than patients discharged alive. Conclusion: Data at the national level identified several predictors of prolonged emergency department length of stay in Saudi Arabia, including shift time, season, severity level, and hospital type. These results underline the necessity of continuous monitoring and improvement efforts in emergency departments, in line with policy initiatives aiming to enhance patient outcomes in Saudi Arabia.
