Preemptive Interventions to Increase Patient Safety by Using Behavior-based Feedback
Open Access
- Author:
- Kim, Inki
- Graduate Program:
- Industrial Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 15, 2015
- Committee Members:
- Scarlett Rae Miller, Dissertation Advisor/Co-Advisor
Andris Freivalds, Dissertation Advisor/Co-Advisor
David Arthur Nembhard, Committee Member
Jason Zachary Moore, Committee Member - Keywords:
- Surgical Training
Simulation-based Training
Skill Acquisition
Text Mining
Motion Analysis - Abstract:
- For the past decade, patient safety has become a focus for both governments and researchers alike due to the 44,000 deaths and 29 billion dollars spent annually on preventable medical errors. In fact, up to 70 percent of hospitalization that end in extended stays or permanent disabilities have been attributable to preventable human errors. These human errors are especially problematic in technology-condensed medical specialties such as vascular-, cardiac-, and neurosurgeries. Despite over-a-decade of efforts to reduce these human errors, however, patient safety has not been significantly improved and there has even been backward progress in medical workforce training. While many people still argue that the best way to prevent human errors is to monitor performance and better train healthcare providers, there is currently limited understanding of how to appropriately train these individuals or how to objectively assess their performance in these areas. Thus, the goal of this dissertation is to reduce medical errors and instill safe and effective behaviors in healthcare through a three-pronged approached. First, it aims to develop and test a method for identifying risk-prone medical devices through the creation of a data-mining algorithm that mines the Food and Drug Administration’s adverse event reports and identifies problematic medical instrumentation. Next, it aims to develop an objective method for assessing clinical skills with the identified risk-prone medical devices. Finally, it aims to develop an effective, objective, training methodology based on behavioral feedback to minimize adverse medical events. In order to achieve this, a series of observational and experimental investigations were completed with cooperation from the Simulation Center at the Pennsylvania State University Hershey Medical Center. The results of this dissertation contribute new insights into how to identify preventable medical errors and improve them through a data-centric, objective interventions.