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Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study

BACKGROUND: Clinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digita...

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Autores principales: Nwe, Khine, Larsen, Mark Erik, Nelissen, Natalie, Wong, David Chi-Wai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305556/
https://www.ncbi.nlm.nih.gov/pubmed/32501271
http://dx.doi.org/10.2196/17457
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author Nwe, Khine
Larsen, Mark Erik
Nelissen, Natalie
Wong, David Chi-Wai
author_facet Nwe, Khine
Larsen, Mark Erik
Nelissen, Natalie
Wong, David Chi-Wai
author_sort Nwe, Khine
collection PubMed
description BACKGROUND: Clinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digital health technologies (DHTs), as determined by their clinical function. The framework can potentially be used to assess mobile apps, which are a subset of DHTs. To be used reliably in this context, the framework must allow unambiguous classification of an app’s clinical function. OBJECTIVE: The objective of this study was to determine whether mobile health apps could be reliably classified using the NICE evidence standards framework for DHTs. METHODS: We manually extracted app titles, screenshots, and content descriptions for all apps listed on the National Health Service (NHS) Apps Library website on July 12, 2019; none of the apps were downloaded. Using this information, 2 mobile health (mHealth) researchers independently classified each app to one of the 4 functional tiers (ie, 1, 2, 3a, and 3b) described in the NICE digital technologies evaluation framework. Coders also answered contextual questions from the framework to identify whether apps were deemed to be higher risk. Agreement between coders was assessed using Cohen κ statistic. RESULTS: In total, we assessed 76 apps from the NHS Apps Library. There was classification agreement for 42 apps. Of these, 0 apps were unanimously classified into Tier 1; 24, into Tier 2; 15, into Tier 3a; and 3, into Tier 3b. There was disagreement between coders in 34/76 cases (45%); interrater agreement was poor (Cohen κ=0.32, 95% CI 0.16-0.47). Further investigation of disagreements highlighted 5 main explanatory themes: apps that did not correspond to any tier, apps that corresponded to multiple tiers, ambiguous tier descriptions, ambiguous app descriptions, and coder error. CONCLUSIONS: The current iteration of the NICE evidence standards framework for DHTs did not allow mHealth researchers to consistently and unambiguously classify digital health mobile apps listed on the NHS app library according to their functional tier.
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spelling pubmed-73055562020-06-24 Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study Nwe, Khine Larsen, Mark Erik Nelissen, Natalie Wong, David Chi-Wai J Med Internet Res Original Paper BACKGROUND: Clinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digital health technologies (DHTs), as determined by their clinical function. The framework can potentially be used to assess mobile apps, which are a subset of DHTs. To be used reliably in this context, the framework must allow unambiguous classification of an app’s clinical function. OBJECTIVE: The objective of this study was to determine whether mobile health apps could be reliably classified using the NICE evidence standards framework for DHTs. METHODS: We manually extracted app titles, screenshots, and content descriptions for all apps listed on the National Health Service (NHS) Apps Library website on July 12, 2019; none of the apps were downloaded. Using this information, 2 mobile health (mHealth) researchers independently classified each app to one of the 4 functional tiers (ie, 1, 2, 3a, and 3b) described in the NICE digital technologies evaluation framework. Coders also answered contextual questions from the framework to identify whether apps were deemed to be higher risk. Agreement between coders was assessed using Cohen κ statistic. RESULTS: In total, we assessed 76 apps from the NHS Apps Library. There was classification agreement for 42 apps. Of these, 0 apps were unanimously classified into Tier 1; 24, into Tier 2; 15, into Tier 3a; and 3, into Tier 3b. There was disagreement between coders in 34/76 cases (45%); interrater agreement was poor (Cohen κ=0.32, 95% CI 0.16-0.47). Further investigation of disagreements highlighted 5 main explanatory themes: apps that did not correspond to any tier, apps that corresponded to multiple tiers, ambiguous tier descriptions, ambiguous app descriptions, and coder error. CONCLUSIONS: The current iteration of the NICE evidence standards framework for DHTs did not allow mHealth researchers to consistently and unambiguously classify digital health mobile apps listed on the NHS app library according to their functional tier. JMIR Publications 2020-06-05 /pmc/articles/PMC7305556/ /pubmed/32501271 http://dx.doi.org/10.2196/17457 Text en ©Khine Nwe, Mark Erik Larsen, Natalie Nelissen, David Chi-Wai Wong. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.06.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), 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
Nwe, Khine
Larsen, Mark Erik
Nelissen, Natalie
Wong, David Chi-Wai
Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title_full Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title_fullStr Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title_full_unstemmed Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title_short Medical Mobile App Classification Using the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies: Interrater Reliability Study
title_sort medical mobile app classification using the national institute for health and care excellence evidence standards framework for digital health technologies: interrater reliability study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305556/
https://www.ncbi.nlm.nih.gov/pubmed/32501271
http://dx.doi.org/10.2196/17457
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