Informing patient self-management technology design using a patient adherence error classification

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.