View clinical trials related to Clinical Decision Support System.
Filter by:This study is to establish a preoperative respiratory imaging assessment database and develop a difficult intubation risk prediction model and further risk analysis. We attempt to construct it into a pre-anesthesia intubation risk assessment software as the clinical decision support system.
Standard formulas of PN have been developed and provided to patients. Only few randomized controlled studies compared standardized vs individualized PN. Individually tailored PN, only if standard PN solutions do not meet patient's nutritional needs. ASPEN society recommends the use computerized prescribing. Technology has enabled the incorporation of medical guidelines in CDSSs. New approach: Comparison of patient's calculated nutritional needs with commercial available solutions.
In neurocritical care, besides the standard intensive care monitoring, even more data are obtained from the very complex pathophysiological changes in brain disease. Medical staff for decision-making cannot integrate the huge amount of clinical data generated every second and visualized on different monitors, anymore. Lack of data integration and usability is a major reason that only few of the knowledge physicians use in this field is evidence based. Early warning systems, powered by predictive algorithms that detect critical states before they happen would allow the staff to intervene early and mitigate or even prevent such a critical state.
This study will estimate the impact of a suite of clinical decision-support tools on structural, process, and clinical outcomes related to HIV care. The "enhanced EMR" package under investigation will include EMR monitoring tools, data quality control procedures and support, patient reports, alerts, and reminders about patient care. This intervention will be delivered by the Ministry of Health and Rwanda Biomedical Centre and monitored by the study team led by University of Rwanda's School of Public Health and Brown University.
Acute coronary syndromes are among main complains for patients presenting to emergency department. Risk classification systems are used to classify patients to appropriate risks and help physicians manage diagnosis strategies and treatments. Purpose of this study is to develop a clinical decision support system for patients presenting to emergency department with the help of statistical machine learning.