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Background: Patient care is a public health issue all over the world, with nursing errors in hospitals being a significant source of harm to patients and a hindrance to the healthcare system's efficiency. Purpose: The purpose of this study was to investigate health teams' perceptions of strategies used to reduce nursing error that affects patient safety. Patients and Methods: A descriptive study was conducted from January to March 2022 at Najran University Hospital and KKH to better understand the issue. The study included 400 healthcare team members, including nurses, physicians, nurse aides, health workers, and others. The data was collected through the analysis of the health team's characteristics, nursing errors, and strategies for error reduction. Results: The results of the study led to the development of five strategies and 28 interventions aimed at reducing nursing errors. The study revealed that two-thirds of the participants had a high perception of strategies for reducing nursing errors, while one-third had a low perception. Conclusion: The study highlights the importance of addressing nursing errors in the healthcare system and provides evidence-based strategies and interventions to reduce them. The findings emphasize the need for ongoing efforts to enhance the perception and understanding of healthcare professionals in reducing errors and improving patient care.
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BACKGROUND: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. OBJECTIVE: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. METHOD: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. RESULTS: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. CONCLUSION: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.
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Aim: Innovate a standard for the future model of nursing care at medical-surgical units in Najran University through a training program for the standard of the future model evaluation on studied nurses' knowledge, attitude about innovation standards, and innovative behavior among nurses. Methods: A quasi-experimental research was used to achieve the study's goal; the research was carried out at Najran University Hospital at Najran, in the medical and surgical units, as well as outpatient clinics. The sample is a convenience type; 100 nurses were used. Tool. A structured questionnaire sheet was used for data collection that includes nurses' knowledge, attitude, and individual innovative scale. Results: This reveals the studied nurses related to their individual innovative scale pre- and postintervention. Concerning resistance to change, the mean of them preintervention is x ® SD 9.08 ± 2.60. Concerning opinion leadership, the mean of them postintervention is x ® SD 14.32 ± 3.16. There is a highly significant difference (p < 0.01∗∗) preintervention as regards all domains listed. Conclusion: The educational program significantly enhances nurses' knowledge and attitude, according to our present study. Nurses' innovative skills are also improved by enhancing their knowledge and attitude. Before and after the educational program was implemented, there was a highly positive linear association between the nurses' knowledge, attitude, and innovative skills at p < 0.01.
