Journal article
Informing Patient Self-Management Technology Design Using a Patient Adherence Error Classification
Engineering management journal, Vol.27(3), pp.124-130
07/03/2015
DOI: 10.1080/10429247.2015.1061889
Abstract
Patient non-adherence with self-management increases patient health risks and financial burdens on the healthcare system. Human error classifications can potentially elucidate and quantify the behavioral manifestations of patient non-adherence and inform design decision making. We present the results of a study of the error classification approach focusing on self-monitoring of blood glucose (SMBG) adherence in diabetes patients. In these patients, the significant error types are: (1) skill-based errors and (2) intentional violations. We also discuss risk mitigation strategies for SMBG patient adherence and the use of an error classification approach to inform formative device evaluations.
Details
- Title: Subtitle
- Informing Patient Self-Management Technology Design Using a Patient Adherence Error Classification
- Creators
- Monifa Vaughn-Cooke - The University of Maryland, College ParkHarriet Black Nembhard - The Pennsylvania State UniversityJan Ulbrecht - The Pennsylvania State UniversityRobert Gabbay - Joslin Diabetes Center
- Resource Type
- Journal article
- Publication Details
- Engineering management journal, Vol.27(3), pp.124-130
- Publisher
- Taylor & Francis
- DOI
- 10.1080/10429247.2015.1061889
- ISSN
- 1042-9247
- eISSN
- 2377-0643
- Language
- English
- Date published
- 07/03/2015
- Academic Unit
- Industrial and Systems Engineering; Engineering Administration
- Record Identifier
- 9984121963202771
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