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 Maximum-Entropy 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.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-4249 |
| Date | 01 January 2011 |
| Creators | Cure Vellojin, Laila Nadime |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
Page generated in 0.0017 seconds