Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Placona, Andrew; Rathert, Cheryl

doi:10.1177/10775587211014534

Cited by 14 publications

(20 citation statements)

References 65 publications

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“…Furthermore, the consumer perspective on healthcare is becoming more accepted as a valid measure of quality, complementing traditional clinical measures [ 27 ]. Specifically, in healthcare, the greater importance and popularity of online review platforms means that consumers can easily inform other potential consumers about their experiences so as to form a consumer-oriented perspective [ 5 , 28 ]. As a consequence, we focussed our analysis on consumer-determined quality.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies have analysed NH quality from a medical perspective [ 2 ], but the necessary assessment from the point of view of consumers (residents and families) is scarce. Online patient reviews (OPRs) are a good mechanism for capturing the consumer perspective of NH quality, and consumers use them in their selection process [ 5 ]. The previous research is focused on the USA, probably due to the existence of databases about NH quality [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Nursing Homes: Affiliation to Large Chains, Quality and Public–Private Collaboration

2022

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The objective of this paper was to estimate the influence of being affiliated with an NH chain on perceived consumer quality, and whether this relationship is affected by maintaining a collaboration agreement with public administrations. We used a combination of theoretical foundations: (1) From the consumer perspective, we focussed on online reviews of the quality of nursing homes (NHs); (2) from the industrial organisation literature, we proposed arguments regarding the advantages and disadvantages of belonging to a chain; (3) the theory of transaction costs was used to explain public–private collaboration. The study was carried out on a sample of 642 chain-affiliated Spanish NHs, with data from quality scores downloaded from the website topMayores.es. We distinguished between the six largest chains and the rest. We applied linear regression models. The results show that NHs affiliated with one of the largest NH chains obtained worse quality scores in the assessment made by users, although quality scores improved for the largest chains of NHs involved in an agreement with the public administration.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nursing Homes: Affiliation to Large Chains, Quality and Public–Private Collaboration

2022

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“…Our analysis adds to the stream of prior studies analyzing the associations between social media ratings and HCSR or Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings and other selected measures of clinical quality [ 3 ]. A recent systematic literature review identified 32 peer reviewed studies (25 using US data) published before 2020, which quantitatively examined relations between web-based ratings and health care outcomes [ 18 ]. These include studies by Bardach et al [ 14 ] and Ranard et al [ 15 ], which find a significant correlation between Yelp hospital ratings and HCAHPS survey scores of patient experience, and studies by Campbell and Li [ 16 ] and Huppetz and Otto [ 23 ], which find a positive association between Facebook star ratings and HCAHPS scores.…”

Section: Discussionmentioning

confidence: 99%

Wisdom of the Experts Versus Opinions of the Crowd in Hospital Quality Ratings: Analysis of Hospital Compare Star Ratings and Google Star Ratings

2022

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Background Popular web-based portals provide free and convenient access to user-generated hospital quality reviews. The Centers for Medicare & Medicaid Services (CMS) also publishes Hospital Compare Star Ratings (HCSR), a comprehensive expert rating of US hospital quality that aggregates multiple measures of quality. CMS revised the HCSR methods in 2021. It is important to analyze the degree to which web-based ratings reflect expert measures of hospital quality because easily accessible, crowdsourced hospital ratings influence consumers’ hospital choices. Objective This study aims to assess the association between web-based, Google hospital quality ratings that reflect the opinions of the crowd and HCSR representing the wisdom of the experts, as well as the changes in these associations following the 2021 revision of the CMS rating system. Methods We extracted Google star ratings using the Application Programming Interface in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from the CMS Hospital Compare website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by Hospital Compare. Fractional response models were used to estimate the association between Google star ratings and HCSR as well as individual components of quality (n=2619). Results The Google star ratings are statistically associated with HCSR (P<.001) after controlling for hospital-level effects; however, they are not associated with clinical components of HCSR that require medical expertise for evaluation such as safety of care (P=.30) or readmission (P=.52). The revised CMS rating system ameliorates previous partial inconsistencies in the association between Google star ratings and quality component scores of HCSR. Conclusions Crowdsourced Google star hospital ratings are informative regarding expert CMS overall hospital quality ratings and individual quality components that are easier for patients to evaluate. Improvements in hospital quality metrics that require expertise to assess, such as safety of care and readmission, may not lead to improved Google star ratings. Hospitals can benefit from using crowdsourced ratings as timely and easily available indicators of their quality performance while recognizing their limitations and biases.

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“…Meanwhile, topic or text analysis is a technique for analysing vast amounts of unstructured data in order to elucidate the text’s primary subjects [ 43 ]. Social media data had the same enormous potential for researching health quality issues or themes as a validated and established traditional survey [ 33 , 44 ].…”

Section: Related Workmentioning

confidence: 99%

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

et al. 2021

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While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital’s Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.

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Are Online Patient Reviews Associated With Health Care Outcomes? A Systematic Review of the Literature

Cited by 14 publications

References 65 publications

Nursing Homes: Affiliation to Large Chains, Quality and Public–Private Collaboration

Nursing Homes: Affiliation to Large Chains, Quality and Public–Private Collaboration

Wisdom of the Experts Versus Opinions of the Crowd in Hospital Quality Ratings: Analysis of Hospital Compare Star Ratings and Google Star Ratings

Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier

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