Degree Granting Department
Industrial and Management Systems Engineering
José Zayas-Castro, Ph.D.
Tapas Das, Ph.D.
Alex Savachkin, Ph.D.
Peter Fabri, Ph.D.
Joseph Pekny, Ph.D.
Patient Safety, Clustering Near-Miss Reports, Patient Safety Interventions, Risk Sources, Maximum Entropy
Healthcare systems require continuous monitoring of risk to prevent adverse events. Risk analysis is a time consuming activity that depends on the background of analysts and available data. Patient safety data is often incomplete and biased. This research proposes systematic approaches to monitor risk in healthcare using available patient safety data. The methodologies combine traditional healthcare risk analysis methods with safety theory concepts, in an innovative manner, to allocate available evidence to potential risk sources throughout the system. We propose the use of data mining to analyze near-miss reports and guide the identification of risk sources. In addition, we propose a MaximumEntropy based approach to monitor risk sources and prioritize investigation efforts accordingly. The products of this research are intended to facilitate risk analysis and allow for timely identification of risks to prevent harm to patients
Scholar Commons Citation
Cure Vellojin, Laila Nadime, "Analytical Methods to Support Risk Identification and Analysis in Healthcare Systems" (2011). USF Tampa Graduate Theses and Dissertations.