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

Monifa Vaughn-Cooke, Harriet Black Nembhard, Jan Ulbrecht, Robert Gabbay

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)124-130
Number of pages7
JournalEMJ - Engineering Management Journal
Issue number3
StatePublished - Sep 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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