RESUMO
BACKGROUND: The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care. OBJECTIVE: This study aims to develop a machine learning (ML)-based automated trigger for outpatient health care settings in Brazil. METHODS: A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field. RESULTS: This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study. CONCLUSIONS: After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55466.
Assuntos
Assistência Ambulatorial , Aprendizado de Máquina , Humanos , Brasil , Segurança do PacienteRESUMO
OBJECTIVE: This study aimed to examine the relationship between patient safety climate, quality of care, and intention of nursing professionals to remain in their job. METHODS: A cross-sectional study was carried out in a teaching hospital in Brazil wherein nursing professionals were surveyed. The Brazilian version of the Patient Safety Climate in Healthcare Organizations tool was applied to measure the patient safety climate. Spearman correlation coefficient and multiple linear regression models were applied for the analysis. RESULTS: A high percentage of problematic response was observed for most dimensions, except for fear of shame. Quality of care resulted in a strong correlation with organizational resources for safety and with overall emphasis on patient safety, and the nurse-perceived staffing adequacy was strongly correlated with organizational resources for safety. The multiple linear regression model showed higher scores in quality of care in dimensions related to organizational, work unit, and interpersonal aspects as well as in the adequacy of the number of professionals. A higher score in intention to stay in one's job was also found in the dimensions of fear of blame and punishment, provision of safe care, and adequacy of the number of professionals. CONCLUSIONS: The organizational and work unit aspects can lead to a better perception of the quality of care. Improving interpersonal relationships and increasing the number of professionals on staff were found to increase nurses' intention to remain in their jobs. Assessing a hospital's patient safety climate will enable improvement in the provision of safe and harm-free health care assistance.