View clinical trials related to Medical Informatics.
Filter by:The goal of this clinical trial is to test giving all medical/non-medical information in the pediatric emergency room(ER). Main questions it aims to answer are: - Does providing medical/non-medical information to parents of patients visiting the emergency room raise the satisfaction with the ER visit? - Does providing medical/non-medical information to parents of patients visiting the emergency room lower the workload of medical staff? 60 participants will be randomly assigned to treatment group and control group. Both groups will communicate freely with the researchers through mobile chat service. Treatment group will get information of medical/non-medical information in emergency room and control group will get information if they need. Before leaving the emergency room, both group will fill out a questionnaire related to satisfaction with the emergency room visits. 5 out of 30 participants of each group will be interviewed about their satisfaction with service. 10 nurses in charge of patients participating in the study record the number of questions directly received and 5 out of 10 nurse will be interviewed about their nursing experience for participants using mobile chatbot service. Researchers will compare treatment group and control group to see if providing medical/non-medical information raise the satisfaction with emergency room visits.
a cross-sectional survey of nurses' perception of patient monitoring in the ICUs
Purpose: The study aims to examine the effect of web-based multi-source training on the prevention of urinary tract infections in adult kidney transplant recipients on clinical outcomes. Design: The study is a single-center, parallel-group, single-blind, pretest, and posttest randomized controlled experimental study. Methods: A total of 90 kidney transplant recipients, 45 in the control group and 45 in the intervention group will be included in the study. Kidney transplant recipients will be randomized on the day of discharge. Before discharge, the Patient Socio-Demographical/Descriptive Characteristics Form and the Discharge Readiness Scale will be applied to the control and intervention groups. Routine training and aimed at preventing urinary tract infections web-based multi-source training will be provided to the intervention group. The developed educational material was evaluated by experts in terms of literacy, reliability, and information quality. As multiple resources on the web: there will be written and visual texts of the educational material, as well as podcasts and animation videos. Web page usability will be evaluated with the System Usability Scale. Individuals will be able to benefit from each of these multiple educational resources according to their preferences. The control group will be directed to the organ transplantation handbook on the website of the routine education and organ transplant center. After discharge, the follow-ups of the intervention and control group were carried out during the first 6-month post-transplant standard follow-up process of the center (2. day; once a week for the first month; every ten days for up to 1-3 months; every three weeks for the next 3-6 months) will be performed. When patients come to their controls, the results of routine examinations (complete urinalysis, urine culture taken when necessary, hospitalization, emergency application, and other data) will be taken from the Hospital Information Management System. In addition, the recipients' opinions in the intervention group on the Web-Based Multi-Resource Training Program will also be received at the end of the 6th month. The research adhered to the Standard Protocol Items: Recommendations for Interventional Trials-SPIRIT (2013) and Consolidated Standards of Reporting Trials-CONSORT (2018) checklists.
To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.