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Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study

BACKGROUND: Establishing a validated scale of patient engagement through use of information technology (ie, digital patient engagement) is the first step to understanding its role in health and health care quality, outcomes, and efficient implementation by health care providers and systems. OBJECTIV...

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Detalles Bibliográficos
Autores principales: Garvin, Lynn A, Simon, Steven R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466699/
https://www.ncbi.nlm.nih.gov/pubmed/28550008
http://dx.doi.org/10.2196/jmir.4778
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author Garvin, Lynn A
Simon, Steven R
author_facet Garvin, Lynn A
Simon, Steven R
author_sort Garvin, Lynn A
collection PubMed
description BACKGROUND: Establishing a validated scale of patient engagement through use of information technology (ie, digital patient engagement) is the first step to understanding its role in health and health care quality, outcomes, and efficient implementation by health care providers and systems. OBJECTIVE: The aim of this study was to develop and prioritize measures of digital patient engagement based on patients’ use of the US Department of Veterans Affairs (VA)’s MyHealtheVet (MHV) portal, focusing on the MHV/Blue Button and Secure Messaging functions. METHODS: We aligned two models from the information systems and organizational behavior literatures to create a theory-based model of digital patient engagement. On the basis of this model, we conducted ten key informant interviews to identify potential measures from existing VA studies and consolidated the measures. We then conducted three rounds of modified Delphi rating by 12 national eHealth experts via Web-based surveys to prioritize the measures. RESULTS: All 12 experts completed the study’s three rounds of modified Delphi ratings, resulting in two sets of final candidate measures representing digital patient engagement for Secure Messaging (58 measures) and MHV/Blue Button (71 measures). These measure sets map to Donabedian’s three types of quality measures: (1) antecedents (eg, patient demographics); (2) processes (eg, a novel measure of Web-based care quality); and (3) outcomes (eg, patient engagement). CONCLUSIONS: This national expert panel study using a modified Delphi technique prioritized candidate measures to assess digital patient engagement through patients’ use of VA’s My HealtheVet portal. The process yielded two robust measures sets prepared for future piloting and validation in surveys among Veterans.
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spelling pubmed-54666992017-06-19 Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study Garvin, Lynn A Simon, Steven R J Med Internet Res Original Paper BACKGROUND: Establishing a validated scale of patient engagement through use of information technology (ie, digital patient engagement) is the first step to understanding its role in health and health care quality, outcomes, and efficient implementation by health care providers and systems. OBJECTIVE: The aim of this study was to develop and prioritize measures of digital patient engagement based on patients’ use of the US Department of Veterans Affairs (VA)’s MyHealtheVet (MHV) portal, focusing on the MHV/Blue Button and Secure Messaging functions. METHODS: We aligned two models from the information systems and organizational behavior literatures to create a theory-based model of digital patient engagement. On the basis of this model, we conducted ten key informant interviews to identify potential measures from existing VA studies and consolidated the measures. We then conducted three rounds of modified Delphi rating by 12 national eHealth experts via Web-based surveys to prioritize the measures. RESULTS: All 12 experts completed the study’s three rounds of modified Delphi ratings, resulting in two sets of final candidate measures representing digital patient engagement for Secure Messaging (58 measures) and MHV/Blue Button (71 measures). These measure sets map to Donabedian’s three types of quality measures: (1) antecedents (eg, patient demographics); (2) processes (eg, a novel measure of Web-based care quality); and (3) outcomes (eg, patient engagement). CONCLUSIONS: This national expert panel study using a modified Delphi technique prioritized candidate measures to assess digital patient engagement through patients’ use of VA’s My HealtheVet portal. The process yielded two robust measures sets prepared for future piloting and validation in surveys among Veterans. JMIR Publications 2017-05-26 /pmc/articles/PMC5466699/ /pubmed/28550008 http://dx.doi.org/10.2196/jmir.4778 Text en ©Lynn A Garvin, Steven R Simon. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.05.2017. http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Garvin, Lynn A
Simon, Steven R
Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title_full Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title_fullStr Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title_full_unstemmed Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title_short Prioritizing Measures of Digital Patient Engagement: A Delphi Expert Panel Study
title_sort prioritizing measures of digital patient engagement: a delphi expert panel study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466699/
https://www.ncbi.nlm.nih.gov/pubmed/28550008
http://dx.doi.org/10.2196/jmir.4778
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