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 article

4 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)124-130
Number of pages7
JournalEMJ - Engineering Management Journal
Volume27
Issue number3
DOIs
StatePublished - Sep 1 2015

Fingerprint

Glucose
Blood
Monitoring
Health risks
Medical problems
Decision making

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Vaughn-Cooke, Monifa ; Nembhard, Harriet Black ; Ulbrecht, Jan ; Gabbay, Robert. / Informing patient self-management technology design using a patient adherence error classification. In: EMJ - Engineering Management Journal. 2015 ; Vol. 27, No. 3. pp. 124-130.
@article{0386d119625245f0b19927a0c7473286,
title = "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.",
author = "Monifa Vaughn-Cooke and Nembhard, {Harriet Black} and Jan Ulbrecht and Robert Gabbay",
year = "2015",
month = "9",
day = "1",
doi = "10.1080/10429247.2015.1061889",
language = "English (US)",
volume = "27",
pages = "124--130",
journal = "EMJ - Engineering Management Journal",
issn = "1042-9247",
publisher = "American Society for Engineering Management",
number = "3",

}

Informing patient self-management technology design using a patient adherence error classification. / Vaughn-Cooke, Monifa; Nembhard, Harriet Black; Ulbrecht, Jan; Gabbay, Robert.

In: EMJ - Engineering Management Journal, Vol. 27, No. 3, 01.09.2015, p. 124-130.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Informing patient self-management technology design using a patient adherence error classification

AU - Vaughn-Cooke, Monifa

AU - Nembhard, Harriet Black

AU - Ulbrecht, Jan

AU - Gabbay, Robert

PY - 2015/9/1

Y1 - 2015/9/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84941274430&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941274430&partnerID=8YFLogxK

U2 - 10.1080/10429247.2015.1061889

DO - 10.1080/10429247.2015.1061889

M3 - Review article

AN - SCOPUS:84941274430

VL - 27

SP - 124

EP - 130

JO - EMJ - Engineering Management Journal

JF - EMJ - Engineering Management Journal

SN - 1042-9247

IS - 3

ER -