Graduation Year

2011

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

José L. Zayas-Castro

Keywords

Clustering Near-Miss Reports, Maximum Entropy, Patient Safety, Patient Safety Interventions, Risk Sources

Abstract

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.