Online Reviews as Health Data: Examining the Association Between Availability of Health Care Services and Patient Star Ratings Exemplified by the Yelp Academic Dataset

The Relationship between Internet Patient Satisfaction Ratings and COVID-19 Outcomes

Our prior research showed that patient experience-as reported by Google, Yelp, and the Hospital Consumer Assessment of Healthcare Providers and Systems survey-is associated with health outcomes. Upon learning that COVID-19 mortality rates differed among U.S. geographic areas, we sought to determine if COVID-19 outcomes were associated with patient experience. We reviewed daily, U.S.-county-level-accrued COVID-19 infections and deaths during the first year of the pandemic using each locality's mean online patient review rating, correcting for county-level demographic factors. We found doctor star ratings were significantly associated with COVID-19 outcomes. We estimated the absolute risk reduction (ARR) and relative risk reduction (RRR) for each outcome by comparing the real-world-observed outcomes, observed with the mean star rating, to the outcomes predicted by our model with a 0.3 unit higher average star rating. Geographic areas with higher patient satisfaction online review ratings in our models had substantially better COVID-19 outcomes. Our models predict that, had medical practices nationwide maintained a 4-star average online review rating-a 0.3-star increase above the current national average-the U.S may have experienced a nearly 11% lower COVID-19 infection rate and a nearly 17% lower death rate among those infected.

How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers

To reach large groups of vaccine recipients, several high-income countries introduced mass vaccination centers for COVID-19. Understanding user experiences of these novel structures can help optimize their design and increase patient satisfaction and vaccine uptake. This study drew on user online reviews of vaccination centers to assess user experience and identify its key determinants over time, by sentiment, and by interaction. Machine learning methods were used to analyze Google reviews of six COVID-19 mass vaccination centers in Berlin from December 2020 to December 2021. 3647 user online reviews were included in the analysis. Of these, 89% (3261/3647) were positive according to user rating (four to five of five stars). A total of 85% (2740/3647) of all reviews contained text. Topic modeling of the reviews containing text identified five optimally latent topics, and keyword extraction identified 47 salient keywords. The most important themes were organization, friendliness/responsiveness, and patient flow/wait time. Key interactions for users of vaccination centers included waiting, scheduling, transit, and the vaccination itself. Keywords connected to scheduling and efficiency, such as "appointment" and "wait", were most prominent in negative reviews. Over time, the average rating score decreased from 4.7 to 4.1, and waiting and duration became more salient keywords. Overall, mass vaccination centers appear to be positively perceived, yet users became more critical over the one-year period of the pandemic vaccination campaign observed. The study shows that online reviews can provide real-time insights into newly set-up infrastructures, and policymakers should consider their use to monitor the population's response over time.

Patient experiences at dental school clinics: A comparative analysis of online reviews

PURPOSE/OBJECTIVES

Online review sites have become popular platforms for health care consumers to rate their experiences. An analysis of online reviews can help dental school clinics learn from patient feedback that is not accessible through formal patient surveys. This study's objectives were to characterize patient-generated reviews posted on the Yelp and Google review sites and assess any associations between dental schools' ratings and their respective characteristics.

METHODS

This cross-sectional study analyzed patient-generated Yelp and Google reviews regarding their experiences at 65 dental school clinics between June and August 2019. Quantitative analysis of selected dental school characteristics and patient ratings was performed using analysis of variance, with effect size measured by the omega-squared (ω² ) statistic. Statistical significance was set at p = 0.05. Qualitative analysis of review content was conducted using NVivo software.

RESULTS

Among 65 dental schools in the Unite States, 63 schools (96.9%) had reviews available on Google, and 55 schools (84.6%) had reviews available on Yelp. A total of 4702 patient reviews were included in the analysis (2516 [53.5%] from Google [mean rating = 3.6 ± 0.6 stars] and 2186 [46.5%] from Yelp [mean rating = 3.1 ± 0.9 stars). A school location in the South/Midwest was significantly associated with a higher Yelp rating compared with a school location in the Northeast/West (ω²  = 0.140, 95% confidence interval = 0.002-0.310, p = 0.003). Qualitative analysis found that review content was predominantly negative; while subthemes in care delivery generated positive responses to care quality, those regarding the pre-care and post-care categories garnered negative responses. This finding was particularly true for comments addressing staff helpfulness, billing and payment, and clinic functioning.

CONCLUSIONS

This study's findings indicated that dental programs' online reviews were predominantly negative and covered categories that were not addressed in existing satisfaction surveys.

Patient hospital experience and satisfaction on social media

Purpose

The purpose of this paper is to show the value of open-ended narrative patient reviews on social media for elucidating aspects of hospital patient satisfaction.

Design/methodology/approach

Mixed methods analyses using qualitative (manual content analyses using grounded theory and algorithmic analyses using the Natural Language Toolkit) followed by quantitative analyses (negative binomial regression).

Findings

Health-care team communication, health-care team action orientation and patient hospital room environment are positively related to patient hospital satisfaction. Patients form their hospital satisfaction perceptions based on the three facets of their hospital stay experience.

Research limitations/implications

In the spirit of continuous quality improvement, periodically analyzing patient social media comments could help health-care teams understand the patient satisfaction inhibitors that they need to avoid to offer patient-centric care.

Practical implications

By periodically analyzing patient social media comments hospital leaders can quickly identify the gaps in their health service delivery and plug them, which could ultimately give the hospital a competitive advantage.

Originality/value

To the best of the authors’ knowledge, this is one of the first studies to apply mixed methods to patient hospital review comments given freely on social media to critically understand what drives patient hospital satisfaction ratings.

What Do Patients Say About Doctors Online? A Systematic Review of Studies on Patient Online Reviews

Background

The number of patient online reviews (PORs) has grown significantly, and PORs have played an increasingly important role in patients’ choice of health care providers.

Objective

The objective of our study was to systematically review studies on PORs, summarize the major findings and study characteristics, identify literature gaps, and make recommendations for future research.

Methods

A major database search was completed in January 2019. Studies were included if they (1) focused on PORs of physicians and hospitals, (2) reported qualitative or quantitative results from analysis of PORs, and (3) peer-reviewed empirical studies. Study characteristics and major findings were synthesized using predesigned tables.

Results

A total of 63 studies (69 articles) that met the above criteria were included in the review. Most studies (n=48) were conducted in the United States, including Puerto Rico, and the remaining were from Europe, Australia, and China. Earlier studies (published before 2010) used content analysis with small sample sizes; more recent studies retrieved and analyzed larger datasets using machine learning technologies. The number of PORs ranged from fewer than 200 to over 700,000. About 90% of the studies were focused on clinicians, typically specialists such as surgeons; 27% covered health care organizations, typically hospitals; and some studied both. A majority of PORs were positive and patients’ comments on their providers were favorable. Although most studies were descriptive, some compared PORs with traditional surveys of patient experience and found a high degree of correlation and some compared PORs with clinical outcomes but found a low level of correlation.

Conclusions

PORs contain valuable information that can generate insights into quality of care and patient-provider relationship, but it has not been systematically used for studies of health care quality. With the advancement of machine learning and data analysis tools, we anticipate more research on PORs based on testable hypotheses and rigorous analytic methods.

Trial Registration

International Prospective Register of Systematic Reviews (PROSPERO) CRD42018085057; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=85057 (Archived by WebCite at http://www.webcitation.org/76ddvTZ1C)