Author: Peyman Zabihinoury, BS (CG)
Primary Advisor: Yang Gong, M.D., PhD
Committee Members: Juliana Brixey, PhD, MPH, RN
Masters thesis, The University of Texas Health Science Center School of Health Information Sciences at Houston.
In 1999, the Institute of Medicine (IOM) estimated that up to 98,000 Americans die each year from medical errors (Hendee et al., 2001). Today, there are approximately 210,000 - 400,000 deaths per year associated with preventable harm in hospitals (James, 2013). In turn, this increase in deaths in healthcare settings is due to a combination of factors, including errors attributed to a variety of health information technologies (HIT) (i.e. electronic medical records, CPOE, etc.) (James, 2013). It has been suggested that one in six incidents is related to HIT (Cheung et al., 2014). Of all adverse events, on the average, approximately 55.3% are related to human–machine interaction (Cheung et al., 2014). In fact, as recent as of February 2014, the US Food and Drug Administration (FDA) reported receiving information on 260 incidents with potential for patient harm, including 44 injuries and six deaths related to HIT errors (Health IT. HHS. Gov, 2014). Even in other nations around the world, it is evident that HIT errors are a significant problem. For example, in New South Wale the Advanced Incident Management System (AIMS) receives approximately 140,000 and South Australia and Western Australia each receive about 20, 000 reports of errors from HIT usage per year (Runciman, et al., 2006). Thus, it is evident that the causes of this pervasive occurrence of HIT errors contributing to patient harm must be addressed.
Fundamentally, the problem in understanding HIT errors is a lack of a usability analysis classification system to classify errors upon their occurrence in terms of their origin. Strategies to minimize errors and improve designs of HIT need to be identified and applied in a comprehensive manner in order to develop a proper understanding of the nature of problems encountered, their contributing factors, and their safety implications (Bloomrosen, 2011; Goodman, et.al, 2011).
The aim of this project is to develop a usability analysis classification system to analyze, classify, and evaluate, HIT error reports submitted to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. Based on the HIT errors reported to MAUDE, a classification system will be developed to identify, classify, and subsequently prioritize errors. As a result, a trend or pattern will be identified to aid in reducing the errors present in HIT. By doing so, this will shed light on the necessity for further research on HIT and on the importance of finding the cause of such errors that can have a direct impact on patient safety.