Collecting and Analyzing Patient Experiences of Health Care From Social Media

BackgroundSocial Media, such as Yelp, provides rich information of consumer experience. Previous studies suggest that Yelp can serve as a new source to study patient experience. However, the lack of a corpus of patient reviews causes a major bottleneck for applying computatio...

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Main Authors: Rastegar-Mojarad, Majid, Ye, Zhan, Wall, Daniel, Murali, Narayana, Lin, Simon
Format: Article
Language:English
Published: JMIR Publications 2015-07-01
Series:JMIR Research Protocols
Online Access:http://www.researchprotocols.org/2015/3/e78/
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spelling doaj-08d7dab9784d40baa54774a05446ce402021-05-02T19:28:38ZengJMIR PublicationsJMIR Research Protocols1929-07482015-07-0143e7810.2196/resprot.3433Collecting and Analyzing Patient Experiences of Health Care From Social MediaRastegar-Mojarad, MajidYe, ZhanWall, DanielMurali, NarayanaLin, Simon BackgroundSocial Media, such as Yelp, provides rich information of consumer experience. Previous studies suggest that Yelp can serve as a new source to study patient experience. However, the lack of a corpus of patient reviews causes a major bottleneck for applying computational techniques. ObjectiveThe objective of this study is to create a corpus of patient experience (COPE) and report descriptive statistics to characterize COPE. MethodsYelp reviews about health care-related businesses were extracted from the Yelp Academic Dataset. Natural language processing (NLP) tools were used to split reviews into sentences, extract noun phrases and adjectives from each sentence, and generate parse trees and dependency trees for each sentence. Sentiment analysis techniques and Hadoop were used to calculate a sentiment score of each sentence and for parallel processing, respectively. ResultsCOPE contains 79,173 sentences from 6914 patient reviews of 985 health care facilities near 30 universities in the United States. We found that patients wrote longer reviews when they rated the facility poorly (1 or 2 stars). We demonstrated that the computed sentiment scores correlated well with consumer-generated ratings. A consumer vocabulary to describe their health care experience was constructed by a statistical analysis of word counts and co-occurrences in COPE. ConclusionsA corpus called COPE was built as an initial step to utilize social media to understand patient experiences at health care facilities. The corpus is available to download and COPE can be used in future studies to extract knowledge of patients’ experiences from their perspectives. Such information can subsequently inform and provide opportunity to improve the quality of health care.http://www.researchprotocols.org/2015/3/e78/
collection DOAJ
language English
format Article
sources DOAJ
author Rastegar-Mojarad, Majid
Ye, Zhan
Wall, Daniel
Murali, Narayana
Lin, Simon
spellingShingle Rastegar-Mojarad, Majid
Ye, Zhan
Wall, Daniel
Murali, Narayana
Lin, Simon
Collecting and Analyzing Patient Experiences of Health Care From Social Media
JMIR Research Protocols
author_facet Rastegar-Mojarad, Majid
Ye, Zhan
Wall, Daniel
Murali, Narayana
Lin, Simon
author_sort Rastegar-Mojarad, Majid
title Collecting and Analyzing Patient Experiences of Health Care From Social Media
title_short Collecting and Analyzing Patient Experiences of Health Care From Social Media
title_full Collecting and Analyzing Patient Experiences of Health Care From Social Media
title_fullStr Collecting and Analyzing Patient Experiences of Health Care From Social Media
title_full_unstemmed Collecting and Analyzing Patient Experiences of Health Care From Social Media
title_sort collecting and analyzing patient experiences of health care from social media
publisher JMIR Publications
series JMIR Research Protocols
issn 1929-0748
publishDate 2015-07-01
description BackgroundSocial Media, such as Yelp, provides rich information of consumer experience. Previous studies suggest that Yelp can serve as a new source to study patient experience. However, the lack of a corpus of patient reviews causes a major bottleneck for applying computational techniques. ObjectiveThe objective of this study is to create a corpus of patient experience (COPE) and report descriptive statistics to characterize COPE. MethodsYelp reviews about health care-related businesses were extracted from the Yelp Academic Dataset. Natural language processing (NLP) tools were used to split reviews into sentences, extract noun phrases and adjectives from each sentence, and generate parse trees and dependency trees for each sentence. Sentiment analysis techniques and Hadoop were used to calculate a sentiment score of each sentence and for parallel processing, respectively. ResultsCOPE contains 79,173 sentences from 6914 patient reviews of 985 health care facilities near 30 universities in the United States. We found that patients wrote longer reviews when they rated the facility poorly (1 or 2 stars). We demonstrated that the computed sentiment scores correlated well with consumer-generated ratings. A consumer vocabulary to describe their health care experience was constructed by a statistical analysis of word counts and co-occurrences in COPE. ConclusionsA corpus called COPE was built as an initial step to utilize social media to understand patient experiences at health care facilities. The corpus is available to download and COPE can be used in future studies to extract knowledge of patients’ experiences from their perspectives. Such information can subsequently inform and provide opportunity to improve the quality of health care.
url http://www.researchprotocols.org/2015/3/e78/
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