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Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications

OBJECTIVES: Qualitative research aimed at identifying patient acceptance of active surveillance (AS) has been identified as a public health research priority. The primary objective of this study was to determine if analysis of a large-sample of anonymous internet conversations (ICs) could be utilize...

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Autores principales: Mishra, Mark V., Bennett, Michele, Vincent, Armon, Lee, Olivia T., Lallas, Costas D., Trabulsi, Edouard J., Gomella, Leonard G., Dicker, Adam P., Showalter, Timothy N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770634/
https://www.ncbi.nlm.nih.gov/pubmed/24039699
http://dx.doi.org/10.1371/journal.pone.0068563
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author Mishra, Mark V.
Bennett, Michele
Vincent, Armon
Lee, Olivia T.
Lallas, Costas D.
Trabulsi, Edouard J.
Gomella, Leonard G.
Dicker, Adam P.
Showalter, Timothy N.
author_facet Mishra, Mark V.
Bennett, Michele
Vincent, Armon
Lee, Olivia T.
Lallas, Costas D.
Trabulsi, Edouard J.
Gomella, Leonard G.
Dicker, Adam P.
Showalter, Timothy N.
author_sort Mishra, Mark V.
collection PubMed
description OBJECTIVES: Qualitative research aimed at identifying patient acceptance of active surveillance (AS) has been identified as a public health research priority. The primary objective of this study was to determine if analysis of a large-sample of anonymous internet conversations (ICs) could be utilized to identify unmet public needs regarding AS. METHODS: English-language ICs regarding prostate cancer (PC) treatment with AS from 2002–12 were identified using a novel internet search methodology. Web spiders were developed to mine, aggregate, and analyze content from the world-wide-web for ICs centered on AS. Collection of ICs was not restricted to any specific geographic region of origin. NLP was used to evaluate content and perform a sentiment analysis. Conversations were scored as positive, negative, or neutral. A sentiment index (SI) was subsequently calculated according to the following formula to compare temporal trends in public sentiment towards AS: [(# Positive IC/#Total IC) - (#Negative IC/#Total IC) x 100]. RESULTS: A total of 464 ICs were identified. Sentiment increased from -13 to +2 over the study period. The increase sentiment has been driven by increased patient emphasis on quality-of-life factors and endorsement of AS by national medical organizations. Unmet needs identified in these ICs include: a gap between quantitative data regarding long-term outcomes with AS vs. conventional treatments, desire for treatment information from an unbiased specialist, and absence of public role models managed with AS. CONCLUSIONS: This study demonstrates the potential utility of online patient communications to provide insight into patient preferences and decision-making. Based on our findings, we recommend that multidisciplinary clinics consider including an unbiased specialist to present treatment options and that future decision tools for AS include quantitative data regarding outcomes after AS.
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spelling pubmed-37706342013-09-13 Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications Mishra, Mark V. Bennett, Michele Vincent, Armon Lee, Olivia T. Lallas, Costas D. Trabulsi, Edouard J. Gomella, Leonard G. Dicker, Adam P. Showalter, Timothy N. PLoS One Research Article OBJECTIVES: Qualitative research aimed at identifying patient acceptance of active surveillance (AS) has been identified as a public health research priority. The primary objective of this study was to determine if analysis of a large-sample of anonymous internet conversations (ICs) could be utilized to identify unmet public needs regarding AS. METHODS: English-language ICs regarding prostate cancer (PC) treatment with AS from 2002–12 were identified using a novel internet search methodology. Web spiders were developed to mine, aggregate, and analyze content from the world-wide-web for ICs centered on AS. Collection of ICs was not restricted to any specific geographic region of origin. NLP was used to evaluate content and perform a sentiment analysis. Conversations were scored as positive, negative, or neutral. A sentiment index (SI) was subsequently calculated according to the following formula to compare temporal trends in public sentiment towards AS: [(# Positive IC/#Total IC) - (#Negative IC/#Total IC) x 100]. RESULTS: A total of 464 ICs were identified. Sentiment increased from -13 to +2 over the study period. The increase sentiment has been driven by increased patient emphasis on quality-of-life factors and endorsement of AS by national medical organizations. Unmet needs identified in these ICs include: a gap between quantitative data regarding long-term outcomes with AS vs. conventional treatments, desire for treatment information from an unbiased specialist, and absence of public role models managed with AS. CONCLUSIONS: This study demonstrates the potential utility of online patient communications to provide insight into patient preferences and decision-making. Based on our findings, we recommend that multidisciplinary clinics consider including an unbiased specialist to present treatment options and that future decision tools for AS include quantitative data regarding outcomes after AS. Public Library of Science 2013-09-11 /pmc/articles/PMC3770634/ /pubmed/24039699 http://dx.doi.org/10.1371/journal.pone.0068563 Text en © 2013 Mishra et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mishra, Mark V.
Bennett, Michele
Vincent, Armon
Lee, Olivia T.
Lallas, Costas D.
Trabulsi, Edouard J.
Gomella, Leonard G.
Dicker, Adam P.
Showalter, Timothy N.
Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title_full Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title_fullStr Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title_full_unstemmed Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title_short Identifying Barriers to Patient Acceptance of Active Surveillance: Content Analysis of Online Patient Communications
title_sort identifying barriers to patient acceptance of active surveillance: content analysis of online patient communications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3770634/
https://www.ncbi.nlm.nih.gov/pubmed/24039699
http://dx.doi.org/10.1371/journal.pone.0068563
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