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Health Informatics Master Thesis Presentation By: Chris Kiess Committee: 

  • Josette F. Jones – Thesis Advisor

  • Jason J. Saleem – Thesis Committee Member

  • Katherine Schilling – Thesis Committee Member 

Title: Errors And Adverse Consequences As A Result Of Information Technology Use In Healthcare: An Integrated Review Of The Literature

Purpose: Health Information Technology (HIT) has become an integral component of healthcare today. The HITECH Act and Meaningful Use objectives stand to bring wide-sweeping adoption and implementations of HIT in small, medium and large sized healthcare organizations across the country. Though recent literature has provided evidence for the benefits of HIT in the profession, there have also been a growing number of reports exploring the adverse effects of HIT. There has not, however, yet been a systematic account of the adverse effects of HIT in the healthcare system.

 Methods: This study uses an integrative literature review to identify and classify the adverse effects of HIT as reported in the literature. The purpose of this study is to perform an integrative review of the literature to 1) identify and classify the adverse effects of HIT; 2) determine the impact and prevalence of these effects; 3) identify the recommended actions and best practices to address the negative effects of HIT.

Results: Eighteen articles were analyzed for HIT-induced error and adverse consequences. In the process, 228 errors and/or adverse consequences were identified, classified and represented in a classification or taxonomic schema. The taxonomic representation consisted of 8 master categories and 30 subcategories. Additionally, the prevalence and impact of these errors were evaluated as well as recommendations and best practices in future systems design.

Conclusions: This study builds on previous work in the medical literature pertaining to HIT-induced errors and adverse consequences and offers a unique perspective in analyzing existing studies in the literature using the integrative review model of research. It is the first work in combining studies across healthcare technologies and analyzing the adverse consequences across 18 studies to form a cohesive classification of these events in healthcare technology.

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