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

Perceptions of Hearing Health Care: A Qualitative Analysis of Satisfied and Dissatisfied Online Reviews

PURPOSE

The aim of this study was to examine the hearing health care experience of satisfied and dissatisfied consumers as reported on Google reviews.

METHOD

Using qualitative thematic analysis, open-text responses from Google regarding hearing health care clinics across 40 U.S. cities were examined. During the original search, 13,168 reviews were identified. Purposive sampling led to a total of 8,420 five-star reviews and 321 one-star reviews. The sample consisted of 500 five-star (satisfied) and 234 one-star (dissatisfied) reviews, describing experiences with audiology clinics, excluding reviews related to ear, nose, and throat services; other medical specialties; and those not relevant to hearing health care.

RESULTS

Satisfied and dissatisfied consumer reviews yielded nuanced dimensions of the hearing health care consumer experience, which were grouped into distinct domains, themes, and subthemes. Six and seven domains were identified from the satisfied and dissatisfied reviews, encompassing 23 and 26 themes, respectively. The overall experience domain revealed emotions ranging from contentment and gratitude to dissatisfaction and waning loyalty. The clinical outcomes domain highlights the pivotal contribution of well-being and hearing outcomes to the consumer experience, while the standard of care domain underscores shared expectations for punctuality, person-centered care, and efficient communication. Facility quality, professional competence, and inclusive care were also highlighted across positive and negative reviews.

CONCLUSIONS

Findings indicate dimensions of satisfied and dissatisfied hearing health care consumer experiences, identifying areas for potential service refinement. These consumer experiences inform person-centric service delivery in hearing health care.

A Taxonomy for Health Information Systems

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

A Bibliometric Review of Constituents, Themes, and Trends in Online Medical Consultation Research

As an emerging form of health care with accelerated growth in recent years, online medical consultation (OMC) has received extensive attention worldwide. Although the number of studies on OMC has increased substantially, few provide a comprehensive and up-to-date review of OMC's research constituents, themes, and trends. This study, therefore, extracted 1,801 OMC-related articles published in English from the Web of Science (WoS) Core Collection database during the past 30 years and employed a bibliometric analysis of WoS and CiteSpace to examine major constituents' distribution, collaboration relationships, themes, and trends. The results indicate that the United States, England, and China contributed the most to the proliferation of OMC studies. The United States had the greatest academic influence and the most collaborative connections, while China demonstrated the sharpest increase and most active development in recent years. However, there is a lack of substantial and close collaboration between researchers worldwide. The main themes of OMC research were Internet hospitals, COVID-19, mixed methods, online health community, and information technology. Researchers have recently shifted their attention to social media, management, efficacy, word of mouth, mental health, and anxiety. This review paper provides researchers and practitioners with a holistic and clear understanding of the features and trends of OMC research. It also identifies potential areas for future OMC research and sheds light on OMC practices.

Assessing reviews of academic oral and maxillofacial surgeons within the US on Healthgrades

PURPOSE

The purpose of the following study was to explore the patient feedback on academic oral and maxillofacial surgeons (OMSs) practicing in the United States (US) using the physician rating website (PRW) Healthgrades.com.

METHODS

We conducted a retrospective cohort study on academic OMSs in the US using data from Healthgrades.com. Predictor variables included OMS characteristics or rating characteristics. The primary outcome variable was overall rating. Linear regression was used to determine independent predictors of overall rating.

RESULTS

The final study sample consisted of 309 academic OMSs (mean age, 56.4 years; males, 86.4%). Age group was significantly associated with overall rating (p = 0.034). Dual-degree OMSs had a higher mean overall rating than single-degree OMSs (4.26 vs. 3.98, p = 0.012). The number of ratings was also significantly associated with overall rating (p = 0.019). Upon controlling for all other variables, merely age group was independently associated with overall rating. Specifically, OMSs aged 41-55 years were associated with a higher overall rating (+0.96, p = 0.022) relative to OMSs aged >70 years.

CONCLUSIONS

The OPRs on Healthgrades.com for academic OMSs within the US are generally positive. Age was the only independent predictor for overall rating - younger OMSs (aged 41-55 years) were independently associated with a higher overall rating relative to older OMSs (aged >70 years). The new generation of younger OMSs is likely to be aware of PRWs and their implications in the growing world of online exposure.

Assessment of laryngologists' ratings on physician review websites

Objective

To assess and characterize online ratings and comments on laryngologists and determine factors that correlate with higher ratings.

Methods

All the American Laryngological Association (ALA) members were queried across several online platforms. Ratings were normalized for comparison on a five-point Likert scale. Ratings were categorized based on context and for positive/negative aspects.

Results

Of the 331 ALA members, 256 (77%) were rated on at least one online platform. Across all platforms, the average overall rating was 4.39 ± 0.61 (range: 1.00-5.00). Specific positive ratings including "bedside manners," "diagnostic accuracy," "adequate time spent with patient," "appropriate follow-up," and "physician timeliness" had significant positive correlations to overall ratings, by Pearson's correlation (P < 0.001). Long wait times had significant negative correlations to overall ratings (P < 0.001).

Conclusion

Online ratings and comments for laryngologists are significantly influenced by patient perceptions of bedside manner, physician competence, and time spent with the patient.

Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews

Introduction: Use of both in-person and video visits have become a common norm in health care delivery, especially after the COVID-19 pandemic. It is imperative to understand how patients feel about their providers and their experiences during in-person and video visits. This study examines the important factors that patients use in their reviews and differences in the relative importance. Methods: We performed sentiment analysis and topic modeling on online physician reviews from April 2020 to April 2022. Our dataset comprised 34,824 reviews posted by patients after completing in-person or video visits. Results: Sentiment analysis yielded 27,507 (92.69%) positive and 2,168 (7.31%) negative reviews for in-person visits, and 4,610 (89.53%) positive and 539 (10.47%) negative reviews for video visits. Topic modeling identified seven factors patients used in their reviews: Bedside manners, Medical Expertise, Communication, Visit Environment, Scheduling and Follow-up, Wait times, and Costs and insurance. Patients who gave positive reviews after in-person consultations more frequently mentioned communication, office environment and staff, and bedside manners. Those who gave negative reviews after in-person visits mentioned longer wait times, providers' office and staff, medical expertise, and costs and insurance problems. Patients with positive reviews after video visits emphasized communication, bedside manners, and medical expertise. However, patients posting negative reviews after video visits frequently mentioned problems with appointment scheduling and follow-up, medical expertise, wait times, costs and insurance, and technical problems in video visits. Conclusions: This study identified key factors that influence patients' assessment of their providers in in-person and video visits. Paying attention to these factors can help improve the overall patient experience.

Doctors ranking through heterogeneous information: The new score functions considering patients' emotional intensity

With the popularity of the Internet and the growing complexity of COVID-19, more and more patients tend to consult doctors online. With the difficulty of doctor selection caused by a massive amount of information, this study proposes a hybrid multi-criteria decision-making framework, which can model patients' emotional intensity through heterogeneous information and rank doctors. Firstly, online reviews (ORs) are transformed into probabilistic linguistic term sets through sentiment analysis. Then, new score functions are proposed considering the nonlinear influence of doctors' information and the patients' negative bias toward ORs. Next, a method of weight determination combining the Term Frequency Inverse Document Frequency and the Decision-making Trial and Evaluation Laboratory method is proposed. Finally, the proposed score functions are applied to the Combined Compromise Solution (CoCoSo) method to aggregate information and rank doctors. The proposed method is verified in a case study on haodf.com. The results show that considering the emotional intensity of heterogeneous information will make the recommendations more realistic. Comparative analysis and sensitivity analysis are further performed to illustrate the availability and effectiveness of the proposed method.

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.

The Credibility of Physician Rating Websites: A Systematic Literature Review

OBJECTIVES

Increasingly, the credibility of online reviews is drawing critical attention due to the lack of control mechanisms, the constant debate about fake reviews and, last but not least, current developments in the field of artificial intelligence. For this reason, the aim of this study was to examine the extent to which assessments recorded on physician rating websites (PRWs) are credible, based on a comparison to other evaluation criteria.

METHODS

Referring to the PRISMA guidelines, a comprehensive literature search was conducted across different scientific databases. Data were synthesized by comparing individual statistical outcomes, objectives and conclusions.

RESULTS

The chosen search strategy led to a database of 36,755 studies of which 28 were ultimately included in the systematic review. The literature review yielded mixed results regarding the credibility of PRWs. While seven publications supported the credibility of PRWs, six publications found no correlation between PRWs and alternative datasets. 15 studies reported mixed results.

CONCLUSIONS

This study has shown that ratings on PRWs seem to be credible when relying primarily on patients' perception. However, these portals seem inadequate to represent alternative comparative values such as the medical quality of physicians. For health policy makers our results show that decisions based on patients' perceptions may be well supported by data from PRWs. For all other decisions, however, PRWs do not seem to contain sufficiently useful data.

Web-Based Public Ratings of General Practitioners in Norway: Validation Study

BACKGROUND

Understanding the complex relationships among multiple strategies for gathering users' perspectives in the evaluation of the performance of services is crucial for the interpretation of user-reported measures.

OBJECTIVE

The main objectives were to (1) evaluate the psychometric performance of an 11-item web-based questionnaire of ratings of general practitioners (GPs) currently used in Norway (Legelisten.no) and (2) assess the association between web-based and survey-based patient experience indicators.

METHODS

We included all published ratings on GPs and practices on Legelisten.no in the period of May 5, 2012, to December 15, 2021 (N=76,521). The questionnaire consists of 1 mandatory item and 10 voluntary items with 5 response categories (1 to 5 stars), alongside an open-ended review question and background variables. Questionnaire dimensionality and internal consistency were assessed with Cronbach α, exploratory factor, and item response theory analyses, and a priori hypotheses were developed for assessing construct validity (chi-square analysis). We calculated Spearman correlations between web-based ratings and reference patient experience indicators based on survey data using the patient experiences with the GP questionnaire (n=5623 respondents for a random sample of 50 GPs).

RESULTS

Web-based raters were predominantly women (n=32,074, 64.0%), in the age range of 20-50 years (n=35,113, 74.6%), and reporting 5 or fewer consultations with the GP each year (n=28,798, 64.5%). Ratings were missing for 18.9% (n=14,500) to 27.4% (n=20,960) of nonmandatory items. A total of 4 of 11 rating items showed a U-shaped distribution, with >60% reporting 5 stars. Factor analysis and internal consistency testing identified 2 rating scales: "GP" (5 items; α=.98) and "practice" (6 items; α=.85). Some associations were not consistent with a priori hypotheses and allowed only partial confirmation of the construct validity of ratings. Item response theory analysis results were adequate for the "practice" scale but not for the "GP" scale, with items with inflated discrimination (>5) distributed over a narrow interval of the scale. The correlations between the web-based ratings GP scale and GP reference indicators ranged from 0.34 (P=.021) to 0.44 (P=.002), while the correlation between the web-based ratings practice scale and reference indicators ranged from 0.17 (not significant) to 0.49 (P<.001). The strongest correlations between web-based and survey scores were found for items measuring practice-related experiences: phone availability (ρ=0.51), waiting time in the office (ρ=0.62), other staff (ρ=0.54-0.58; P<.001).

CONCLUSIONS

The practice scale of the web-based ratings has adequate psychometric performance, while the GP suffers from important limitations. The associations with survey-based patient experience indicators were accordingly mostly weak to modest. Our study underlines the importance of interpreting web-based ratings with caution and the need to further develop rating sites.

A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors

This paper proposes a method to assist patients in finding the most appropriate doctor for online medical consultation. To do that, it constructs an online doctor selection decision-making method that considers the correlation attributes, in which the measure of attribute correlation is derived from the history real decision data. To combine public and personal preference with correlated attributes, it proposes a Choquet integral based comprehensive online doctor ranking method. In detail, a two stage classification model based on BERT (Bidirectional Encoder Representations from Transformers) is used to extract service features from unstructured text reviews. Then, 2-additive fuzzy measure is adopted to represent the patient public group aggregated attribute preference. Next, a novel optimization model is proposed to combine the public preference and personal preference. Finally, a case study of dxy.com is carried out to illustrate the procedure of the method. The comparison result between proposed method and other traditional MADM (multi-attribute decision-making) methods prove its rationality.

Patient-Centered Insights and Biases Regarding Cardiologists Via Online Review Platform Analysis

Background Online cardiologist reviews, such as those on the Yelp website, are a frequently used method for patients to find a cardiologist. It remains unknown how bias may influence such reviews. Our objectives for this study were to (1) determine which cardiologist- or practice-related factors influence the overall rating of cardiologists and patient satisfaction and (2) discover any associations between sex and race with the overall rating of cardiologists or with cardiologist- or practice-related factors. Methods and Results Cardiologist Yelp reviews from practices in the United States from 2007 to 2020 were analyzed. A total of 563 reviews were coded for positive and negative themes. Binary logistic regression was used to determine whether certain factors increased the likelihood of high ratings. Chi-squared tests were used to determine associations between sex and race with certain factors and overall cardiologist ratings. Cardiologists were more likely to receive higher ratings when reviewers noted the characteristics of competency/knowledge base and thoroughness, positive interactions with staff, and when the cardiologist's name was mentioned in the review. Negative interactions with staff were associated with lower ratings. Female cardiologists received lower ratings and more negative mentions of cardiologist-patient communication than expected. White and Black cardiologists received lower ratings than expected compared with other racial groups. Conclusions Patient-perceived cardiologist competency, thoroughness, and positive staff interactions were associated with positive reviews in online assessments. Sex and racial differences were also found. Further research must be done to confirm these findings and to understand the association of online reviews with clinical care and patient outcomes.

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.

Findings From Implementing a Patient Experience Survey in a Quality Measurement System for Substance Abuse Disorder Treatment Facilities in 6 States

OBJECTIVES

Patient experience surveys (PESs) are an important component of determining the quality of health care. There is an absence of PES data available to people seeking to identify higher quality substance use disorder treatment providers. Our project aimed to correct this by implementing a PES for substance use disorder treatment providers and publicly disseminating PES information.

METHODS

We created a population frame of all addiction providers in 6 states. Providers were asked to disseminate a survey invitation letter directing patients to a survey Web site. No personally identifiable information was exchanged. We developed a 10-question survey, reflecting characteristics National Institute on Drug Abuse (NIDA), National Institute on Alcohol Abuse and Alcoholism (NIAAA), Substance Abuse and Mental Health Services Administration (SAMHSA) have identified as reflecting higher-quality addiction treatment.

RESULTS

Nineteen percent of facilities participated; among participating facilities, 9627 patients completed the survey. Patient experience varied significantly by facility with the percentage of a facility's patients who chose the most positive answer varying widely. We calculated that between-facility reliability will meet or exceed 0.80 for facilities with 20 or more responding patients. We searched for but did not find evidence of data falsification.

CONCLUSIONS

This cost-efficient survey protocol is low burden for providers and patients. Results suggest significant differences in quality of care among facilities, and facility-level results are important to provide to consumers when they evaluate the relative patient-reported quality of facilities. The data are not designed to provide population-based statistics. As more facilities and patients per facility participate, public-facing PES data will be increasingly useful to consumers seeking to compare and choose facilities.

Challenging encounters in clinical dentistry: a qualitative study investigating online reviews of patient satisfaction with Norwegian dentists

OBJECTIVE

Mapping key themes that characterize challenging and positive encounters in dental practice using online reviews of patient satisfaction.

MATERIALS AND METHODS

11,764 online patient reviews of dental encounters, consisting of an overall satisfaction rating (1-5 stars) and a free-text response, were collected from the web-site Legelisten.no. The reviews were split into two sets: reviews from patients with low satisfaction (1-2 stars) representing challenging encounters vs. patients with high satisfaction (4-5 stars) representing positive encounters. A qualitative thematic analysis was used to analyse the text materials in the datasets.

RESULTS

Five key themes to both challenging and positive patient encounters were identified: (1) Interpersonal factors, (2) Patient factors, (3) Dentist factors, (4) Situational factors, and (5) Consequences. These themes are discussed in light of their role in challenging and positive patient encounters, as well as previous studies of online reviews and patient satisfaction.

CONCLUSIONS

Based on the patients' experiences with dental encounters, challenging encounters seem to arise when dentists' personality traits and communication skills fail to match the patients' expectations or preferences. It appears central to patient satisfaction that dentists are able to shift between different communication styles in order to adapt to the personality and preferences of the patients.

Family Input for Quality and Safety (FIQS): Using mobile technology for in-hospital reporting from families and patients

OBJECTIVE

Despite three decades of effort, ensuring inpatient safety remains elusive. Patients and family members are a potential source of safety observations, but systems gathering these are limited. Our goal was to test a system to gather safety observations from hospitalized patients and their family members via a real-time mobile health tool.

METHODS

We developed a mobile-responsive website for reporting safety observations. We piloted the tool during June 2017-April 2018 on the medical-surgical unit of a children's hospital. Participants were English-speaking family members and patients ≥13 years. We sent a daily text with a website link. We assessed: (1) face validity by comparing observations to incident reporting (IR) criteria and to hospital IRs and (2) associations between the number of safety observations/100 patient-days and participant characteristics using Poisson regression.

RESULTS

We enrolled 235 patients (43.8% of 537 reviewed for eligibility), resulting in 8.15 safety reports/100 patient-days, most frequently regarding medications (29% of reports) and communication (20% of reports). Fifty-one (40% of 125) met IR criteria; only one (1.1%) had been reported via the IR system. Latinx participants submitted fewer observations than White participants (3.9 vs. 10.1, p = .002); participants with more prior hospitalizations submitted more observations (p < .001). In adjusted analyses, including measures of preference in decision making, and patient activation, the difference between Latinx and White participants diminished substantially (6.4 vs. 11.3, p = .16).

CONCLUSIONS

We demonstrated the feasibility of real-time patient and family-member technology-enabled safety observation reporting and elicited reports not otherwise identified. Variation in reporting may potentially exacerbate disparities in safety if not addressed.

Use of Online Patient Reviews to Assess Medical Oncologist Competency: Mixed-Method Sequential Explanatory Study (Preprint)

BACKGROUND

Patients increasingly use web-based evaluation tools to assess their physicians, health care teams, and overall medical experience.

OBJECTIVE

This study aimed to evaluate the extent to which the standardized physician competencies of the CanMEDS Framework are present in web-based patient reviews (WPRs) and to identify patients' perception of important physician qualities in the context of quality cancer care.

METHODS

The WPRs of all university-affiliated medical oncologists in midsized cities with medical schools in the province of Ontario (Canada) were collected. Two reviewers (1 communication studies researcher and 1 health care professional) independently assessed the WPRs according to the CanMEDS Framework and identified common themes. Comment scores were then evaluated to identify κ agreement rates between the reviewers, and a descriptive quantitative analysis of the cohort was completed. Following the quantitative analysis, an inductive thematic analysis was performed.

RESULTS

This study identified 49 actively practicing university-affiliated medical oncologists in midsized urban areas in Ontario. A total of 473 WPRs reviewing these 49 physicians were identified. Among the CanMEDS competencies, those defining the roles of medical experts, communicators, and professionals were the most prevalent (303/473, 64%; 182/473, 38%; and 129/473, 27%, respectively). Common themes in WPRs include medical skill and knowledge, interpersonal skills, and answering questions (from the patient to the physician). Detailed WPRs tend to include the following elements: experience and connection; discussion and evaluation of the physician's knowledge, professionalism, interpersonal skills, and punctuality; in positive reviews, the expression of feelings of gratitude and a recommendation; and in negative reviews, discouragement from seeking the physician's care. Patients' perception of medical skills is less specific than their perception of interpersonal qualities, although medical skills are the most commented-on element of care in WPRs. Patients' perception of interpersonal skills (listening, compassion, and overall caring demeanor) and other experiential phenomena, such as feeling rushed during appointments, is often specific and detailed. Details about a physician's interpersonal skills or "bedside manner" are highly perceived, valued, and shareable in an WPR context. A small number of WPRs reflected a distinction between the value of medical skills and that of interpersonal skills. The authors of these WPRs claimed that for them, a physician's medical skills and competence are more important than their interpersonal skills.

CONCLUSIONS

CanMEDS roles and competencies that are explicitly patient facing (ie, those directly experienced by patients in their interactions with physicians and through the care that physicians provide) are the most likely to be present and reported on in WPRs. The findings demonstrate the opportunity to learn from WPRs, not simply to discern physicians' popularity but to grasp what patients may expect from their physicians. In this context, WPRs can represent a method for the measurement and assessment of patient-facing physician competency.

Physician Gender, Patient Risk, and Web-Based Reviews: Longitudinal Study of the Relationship Between Physicians' Gender and Their Web-Based Reviews

Background

Web-based reviews of physicians have become exceedingly popular among health care consumers since the early 2010s. A factor that can potentially influence these reviews is the gender of the physician, because the physician’s gender has been found to influence patient-physician communication. Our study is among the first to conduct a rigorous longitudinal analysis to study the effects of the gender of physicians on their reviews, after accounting for several important clinical factors, including patient risk, physician specialty, and temporal factors, using time fixed effects. In addition, this study is among the first to study the possible gender bias in web-based reviews using statewide data from Alabama, a predominantly rural state with high Medicaid and Medicare use.

Objective

This study conducts a longitudinal empirical investigation of the relationship between physician gender and their web-based reviews using data across the state of Alabama, after accounting for patient risk and temporal effects.

Methods

We created a unique data set by combining data from web-based physician reviews from the popular physician review website, RateMDs, and clinical data from the Center for Medicare and Medicaid Services for the state of Alabama. We used longitudinal econometric specifications to conduct an econometric analysis, while controlling for several important clinical and review characteristics across four rating dimensions (helpfulness, knowledge, staff, and punctuality). The overall rating and these four rating dimensions from RateMDs were used as the dependent variables, and physician gender was the key explanatory variable in our panel regression models.

Results

The panel used to conduct the main econometric analysis included 1093 physicians. After controlling for several clinical and review factors, the physician random effects specifications showed that male physicians receive better web-based ratings than female physicians. Coefficients and corresponding SEs and P values of the binary variable GenderFemale (1 for female physicians and 0 otherwise) with different rating variables as outcomes were as follows: OverallRating (coefficient –0.194, SE 0.060; P=.001), HelpfulnessRating (coefficient –0.221, SE 0.069; P=.001), KnowledgeRating (coefficient –0.230, SE 0.065; P<.001), StaffRating (coefficient –0.123, SE 0.062; P=.049), and PunctualityRating (coefficient –0.200, SE 0.067; P=.003). The negative coefficients indicate a bias toward male physicians versus female physicians for aforementioned rating variables.

Conclusions

This study found that female physicians receive lower web-based ratings than male physicians even after accounting for several clinical characteristics associated with the physicians and temporal effects. Although the magnitude of the coefficients of GenderFemale was relatively small, they were statistically significant. This study provides support to the findings on gender bias in the existing health care literature. We contribute to the existing literature by conducting a study using data across the state of Alabama and using a longitudinal econometric analysis, along with incorporating important clinical and review controls associated with the physicians.

Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach

(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.

The Impact of Medical Comorbidities on Patient Satisfaction in Chronic Rhinosinusitis

BACKGROUND

Patient satisfaction has a significant bearing on medical therapy compliance and patient outcomes. The purpose of this study was to (1) describe patient satisfaction, as characterized by the Patient Satisfaction Questionnaire-18 (PSQ-18), in the care of patients with chronic rhinosinusitis (CRS) and (2) analyze the impact of comorbidities on satisfaction using the functional comorbidity index (FCI).

METHODS

Patient demographics, disease severity measures, and PSQ-18 scores for patients with CRS presenting to a tertiary rhinology clinic between November 2019 and April 2020 were collected and analyzed. FCI was calculated retrospectively using the electronic medical record; individual comorbidities were tabulated. Spearman's correlations followed by multivariate regression was used to assess the relationship between medical comorbidities and PSQ-18.

RESULTS

Sixty-nine patients met criteria for analysis. There were no significant differences in age, gender, and Sinonasal Outcomes Test-22 scores between CRS patients with (CRSwNP) and without (CRSsNP) nasal polyps. There was no significant difference in the mean FCI for patients with CRSwNP versus CRSsNP (5.1 and 4.3, respectively) (P = .843). Similarly, there was no significant difference in the mean sum PSQ-18 score (78/100 in both) between these cohorts (P = .148). The mean sum PSQ-18 score was not significantly associated with anxiety (P = .728), depression (P = .624), or FCI (P = .282), but was significantly associated with hearing impairment (P < .001).

CONCLUSION

Patient satisfaction in the care of CRS is generally high with a diagnosis of comorbid hearing impairment demonstrating a negative association with satisfaction in this cohort.

Patient Utilization of Online Information and its Influence on Orthopedic Surgeon Selection: Cross-sectional Survey of Patient Beliefs and Behaviors

Background

Patient attitudes and behavior are critical to understand owing to the increasing role of patient choice. There is a paucity of investigation into the perceived credibility of online information and whether such information impacts how patients choose their surgeons.

Objective

The purpose of this study was to explore the attitudes and behavior of patients regarding online information and orthopedic surgeon selection. Secondary purposes included gaining insight into the relative importance of provider selection factors, and their association with patient age and education level.

Methods

This was a cross-sectional study involving five multispecialty orthopedic surgery groups. A total of 329 patients who sought treatment by six different orthopedic surgeons were asked to anonymously answer a questionnaire consisting of 25 questions. Four questions regarded demographic information, 10 questions asked patients to rate the importance of specific criteria regarding the selection of their orthopedic surgeon (on a 4-point Likert scale), and 6 questions were designed to determine patient attitude and behaviors related to online information.

Results

Patient-reported referral sources included the emergency room (29/329, 8.8%), friend (42/329, 12.8%), insurance company (47/329, 14.3%), internet search/website (28/329, 8.5%), primary care physician (148/329, 45.0%), and other (34/329, 10.3%). Among the 329 patients, 130 (39.5%) reported that they searched the internet for information before their first visit. There was a trend of increased belief in online information to be accurate and complete in younger age groups (P=.02). There was an increased relative frequency in younger groups to perceive physician rating websites to be unbiased (P=.003), provide sufficient patient satisfaction information (P=.01), and information about physician education and training (P=.03). There was a significant trend for patients that found a surgeon’s website to be useful (P<.001), with the relative frequency increased in younger age groups.

Conclusions

This study shows that insurance network, physician referrals, appointment availability, and office location are important to patients, whereas advertising and internet reviews by other patients were considered to be not as helpful in choosing an orthopedic surgeon. Future studies may seek to identify obstacles to patients in integrating online resources for decision-making and strategies to improve health-seeking behaviors.

Choosing a Provider: What Factors Matter Most to Consumers and Patients?

Enhancing consumer and patient choice has been proposed as a means to improve care quality and reduce health-related costs. Choosing a medical provider is one of the most critical and often complex decisions patients make about their health care. We investigated the perceived importance of factors patients may consider when selecting a practitioner and if rated importance of the factors varies with their characteristics and prior experiences with different types of clinicians (physicians, physician assistants, and nurse practitioners). Participants most often identified medical license, certification, and whether the provider accepts the patients’ health insurance as important, while provider type, others’ recommendations, and online reviews were among the least important. We found wide-ranging differences based on patient characteristics. Prior experience with providers was also a strong determinant of patterns of factors patients considered valuable. Policy-makers, health systems, insurers, and providers need to take into account that patients rely on a range of factors that vary based on their distinct needs, backgrounds, and previous experiences—requiring tailored information to make more informed decisions.

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

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.

Marketing Your Practice: Setting Yourself Apart in a Competitive Market, Online Reputation Building, and Managing Patient Experience/Satisfaction

The days of making a first impression when you meet your patients in person are numbered. Rather, in today's digital age, your prospective patients have likely already formed opinions about you and your practice before they meet you. And these opinions are largely influenced by the information they discover about you online. While you cannot completely control your personal brand or reputation as a physician, you can certainly try by controlling your online narrative: communicate your expertise and your value by effectively using social media, by regularly updating your practice website, and by proactively managing patient satisfaction reflected on physician rating websites. Set yourself apart in a competitive market today by building a strong digital presence.

Exploring Online Health Reviews to Monitor COVID-19 Public Health Responses in Alabama State Department of Corrections: Case Example

Background

COVID-19, caused by SARS-CoV-2, has devastated incarcerated people throughout the United States.

Objective

The purpose of this study was to test the feasibility and acceptability of a COVID-19 Health Review for Correctional Facilities.

Methods

The COVID-19 Health Review survey for the Department of Corrections was developed in Qualtrics to assess the following: (1) COVID-19 testing, (2) providing personal protective equipment, (3) vaccination procedures, (4) quarantine procedures, (5) COVID-19 mortality rates for inmates, (6) COVID-19 mortality rates for correctional officers and prison staff, (7) COVID-19 infection rates for inmates, (8) COVID-19 infection rates for correctional officers and prison staff, and (9) uptake of COVID-19 vaccines. The estimated time to review the Alabama State Department of Corrections COVID-19 responses on their website and complete the survey items was 45 minutes to 1 hour.

Results

Of the 21 participants who completed the COVID-19 Health Review for Correctional Facilities survey, 48% (n=10) identified as female, 43% (n=9) identified as male, and 10% (n=2) identified as transgender. For race, 29% (n=6) self-identified as Black or African American, 24% (n=5) Asian, 24% (n=5) White, 5% (n=1) Pacific Islander or Native Hawaiian, and 19% (n=4) Other. In addition, 5 respondents self-identified as returning citizens. For COVID-19 review questions, the majority concluded that information on personal protective equipment was “poor” and “very poor,” information on COVID-19 testing was “fair” and above, information on COVID-19 death/infection rates between inmates and staff was “good” and “very good,” and information on vaccinations was “good” and “very good.” There was a significant difference observed (P=.03) between nonreturning citizens and returning citizens regarding the health grade review with respect to available information on COVID-19 infection rates.

Conclusions

COVID-19 health reviews may provide an opportunity for the public to review the COVID-19 responses in correctional settings.

Examining Different Factors in Web-Based Patients' Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System

(1) Background: The appearance of physician rating websites (PRWs) has raised researchers’ interest in the online healthcare field, particularly how users consume information available on PRWs in terms of online physician reviews and providers’ information in their decision-making process. The aim of this study is to consistently review the early scientific literature related to digital healthcare platforms, summarize key findings and study features, identify literature deficiencies, and suggest digital solutions for future research. (2) Methods: A systematic literature review using key databases was conducted to search published articles between 2010 and 2020 and identified 52 papers that focused on PRWs, different signals in the form of PRWs’ features, the findings of these studies, and peer-reviewed articles. The research features and main findings are reported in tables and figures. (3) Results: The review of 52 papers identified 22 articles for online reputation, 15 for service popularity, 16 for linguistic features, 15 for doctor–patient concordance, 7 for offline reputation, and 11 for trustworthiness signals. Out of 52 studies, 75% used quantitative techniques, 12% employed qualitative techniques, and 13% were mixed-methods investigations. The majority of studies retrieved larger datasets using machine learning techniques (44/52). These studies were mostly conducted in China (38), the United States (9), and Europe (3). The majority of signals were positively related to the clinical outcomes. Few studies used conventional surveys of patient treatment experience (5, 9.61%), and few used panel data (9, 17%). These studies found a high degree of correlation between these signals with clinical outcomes. (4) Conclusions: PRWs contain valuable signals that provide insights into the service quality and patient treatment choice, yet it has not been extensively used for evaluating the quality of care. This study offers implications for researchers to consider digital solutions such as advanced machine learning and data mining techniques to test hypotheses regarding a variety of signals on PRWs for clinical decision-making.

Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study's objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals' Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

Patient Experience Surveys Reveal Gender-Biased Descriptions of Their Care Providers

Patient experience surveys (PES) are collected by healthcare systems as a surrogate marker of quality and published unedited online for the purpose of transparency, but these surveys may reflect gender biases directed toward healthcare providers. This retrospective study evaluated PES at a single university hospital between July 2016 and June 2018. Surveys were stratified by overall provider rating and self-identified provider gender. Adjectives from free-text survey comments were extracted using natural language processing techniques and applied to a statistical machine learning model to identify descriptors predictive of provider gender. 109,994 surveys were collected, 17,395 contained free-text comments describing 687 unique providers. The mean overall rating between male (8.84, n = 8558) and female (8.80, n = 8837) providers did not differ (p = 0.149). However, highly-rated male providers were more often described for their agentic qualities using adjectives such as "informative," "forthright," "superior," and "utmost" (OR 1.48, p < 0.01)-whereas highly-rated female providers were more often described by their communal qualities through adjectives such as "empathetic," "sweet," "warm," "attentive," and "approachable" (OR 2.11, p < 0.0001). PES may contain gender stereotypes, raising questions about their impact on physicians and their validity as a quality metric which must be balanced with the need for unedited transparency. Future prospective studies are needed to further characterize this trend across geographically and racially diverse healthcare providers.

Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis

Online consultation based on Internet technology is gradually becoming the main way to seek health information and professional assistance. Online user reviews, such as content reviews and star ratings, are an important basis for reflecting users' views on the effectiveness of health services. Here, we used user reviews related to online psychological consultation services for content feature mining and usefulness analyses. We used a professional online psychological counseling service platform in China to collect user reviews that were liked by users as a data sample for a content analysis. An LDA topic model, dictionary-based sentiment analysis, and the NRC Word-Emotion Association Lexicon were used to extract the topic, sentiment, and context features of the content of 4254 useful reviews, and the influence of these features on the usefulness of the reviews was verified by a multiple linear regression analysis. Our results show that the content of online reviews by psychological counseling users presented a positive emotional attitude as a whole and expressed more views on the process, effects, and future expectations of counseling than on other topics. There was a significant correlation between the topic, sentiment, and context features of a user review and its usefulness: reviews giving high scores and containing topics such as "ease emotions" and "consulting expectations" received more user likes. However, the usefulness of a review was significantly reduced if it was in existence for too long. This research provides valuable suggestions for understanding the needs and emotional attitudes of users with mental health problems in terms of online psychological consultation; identifying the factors that affect the number of likes a review receives can help platform users write better consultation evaluations and thereby provide greater usefulness. In addition, the use of online reviews generated by users for content analysis effectively supplements the current research on online psychological counseling in terms of data and methods.

Building Veterinarians beyond Veterinary School: Challenges and Opportunities for Continuing Professional Development in Veterinary Medicine

Continuing professional development (CPD) is an important mechanism for veterinarians to acquire, maintain, and enhance their capability to perform competently in their chosen practice area over their career. Although most licensing bodies require veterinarians to complete a minimum number of CPD hours each registration cycle, there are known issues with verifying that these activities are having the desired effects of ensuring professional competence and improving outcomes for patients, owners, and veterinarians. In this review, we summarize the literature across different health care professions to highlight three key challenges for veterinary CPD programs. These are (a) defining what it means to be professionally competent across different career stages from graduation to retirement, (b) delivering CPD activities that are effective in promoting evidence-based medicine and behavioral change in practice, and (c) developing reliable and sustainable systems to formally assess the continued professional competence of veterinarians. A common theme across all challenges was the importance of interacting with colleagues as an opportunity to receive external feedback on their professional strengths and weaknesses and to develop stronger support networks for managing common stressors in clinical practice. There was also a recognized need to develop more transparent outlines of the available and acceptable options for managing different animal health concerns as a first step toward identifying new opportunities for the veterinary profession to elevate the level of care provided. Based on these findings, we propose a new framework for defining, delivering, and evaluating CPD that promotes stronger collaboration between veterinarians to improve professional and personal well-being.

A good dentist or not: understanding the role of storytelling in online dentist reviews

Purpose

Narrative comments about dentists on physician review sites have been documented to increasingly influence people's selection of their dentists. From a communication standpoint, these comments are a type of narrative communication that people share their experiences with dentists by telling stories. Based on the frameworks of rhetoric structure theory and extended elaborated likelihood model, this study aimed to examine the effects of such storytelling from two perspectives including narrative structure and narrative focus.

Design/methodology/approach

A 4 (narrative structure) × 2 (narrative focus) between-subjects experiment was conducted to examine the proposed hypotheses and research questions

Findings

The results showed that a one-sided comprehensive comment focusing on technical competence generated the strongest persuasion effects measured by attitude and behavioral intention. These effects were mediated by perceived narrative credibility and enjoyment.

Originality/value

This study contributes to the extant literature in two ways. First, it extends previous studies of online narrative comments by showing which narrative structure and focus are deemed to be more persuasive when selecting a dentist. Second, it offers a test of two routes of information processing (i.e. cognitive and experiential) to understand the mechanism underlying the effects of narrative comments.

Peer review

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0359

Facebook Reviews as a Supplemental Tool for Hospital Patient Satisfaction and Its Relationship with Hospital Accreditation in Malaysia

Patient satisfaction is one indicator used to assess the impact of accreditation on patient care. However, traditional patient satisfaction surveys have a few disadvantages, and some researchers have suggested that social media be used in their place. Social media usage is gaining popularity in healthcare organizations, but there is still a paucity of data to support it. The purpose of this study was to determine the association between online reviews and hospital patient satisfaction and the relationship between online reviews and hospital accreditation. We used a cross-sectional design with data acquired from the official Facebook pages of 48 Malaysian public hospitals, 25 of which are accredited. We collected all patient comments from Facebook reviews of those hospitals between 2018 and 2019. Spearman’s correlation and logistic regression were used to evaluate the data. There was a significant and moderate correlation between hospital patient satisfaction and online reviews. Patient satisfaction was closely connected to urban location, tertiary hospital, and previous Facebook ratings. However, hospital accreditation was not found to be significantly associated with online reports of patient satisfaction. This groundbreaking study demonstrates how Facebook reviews can assist hospital administrators in monitoring their institutions’ quality of care in real time.

The Impact of Signals Transmission on Patients' Choice through E-Consultation Websites: An Econometric Analysis of Secondary Datasets

(1) Background: The COVID-19 pandemic has dramatically and rapidly changed the overall picture of healthcare in the way how doctors care for their patients. Due to the significant strain on hospitals and medical facilities, the popularity of web-based medical consultation has drawn the focus of researchers during the deadly coronavirus disease (COVID-19) in the United States. Healthcare organizations are now reacting to COVID-19 by rapidly adopting new tools and innovations such as e-consultation platforms, which refer to the delivery of healthcare services digitally or remotely using digital technology to treat patients. However, patients' utilization of different signal transmission mechanisms to seek medical advice through e-consultation websites has not been discussed during the pandemic. This paper examines the impact of different online signals (online reputation and online effort), offline signals (offline reputation) and disease risk on patients' physician selection choice for e-consultation during the COVID-19 crisis. (2) Methods: Drawing on signaling theory, a theoretical model was developed to explore the antecedents of patients' e-consultation choice toward a specific physician. The model was tested using 3-times panel data sets, covering 4231 physicians on Healthgrades and Vitals websites during the pandemic months of January, March and May 2020. (3) Results: The findings suggested that online reputation, online effort and disease risk were positively related to patients' online physician selection. The disease risk has also affected patients' e-consultation choice. A high-risk disease positively moderates the relationship between online reputation and patients' e-consultation choice, which means market signals (online reputation) are more influential than seller signals (offline reputation and online effort). Hence, market signals strengthened the effect in the case of high-risk disease. (4) Conclusions: The findings of this study provide practical suggestions for physicians, platform developers and policymakers in online environments to improve their service quality during the crisis. This article offers a practical guide on using emerging technology to provide virtual care during the pandemic. This study also provides implications for government officials and doctors on the potentials of consolidating virtual care solutions in the near future in order to contribute to the integration of emerging technology into healthcare.

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

[Positive association between successful rehabilitation and patient hospital ratings: Experience reports of patients in rehabilitation on a hospital rating and review site]

INTRODUCTION

Feedback from patients about aspects of rehabilitation services is increasingly provided online, for example, on specialized hospital comparison websites. Therefore, we examined which kind of online statements from rehabilitation patients published on the leading hospital comparison website "Klinikbewertungen.de" (KB) is associated with a positive recommendation of a rehabilitation clinic and which negative aspects are associated with a non-recommendation.

METHODS

For eight indication groups stratified online statements of rehabilitants at KB were evaluated qualitatively using content analysis. The relationship between positive (negative) statements and the (non-) recommendation was examined.

RESULTS

Content analysis of 911 experience reports revealed 20 categories. Most often, it was the "rehabilitation success" perceived by rehabilitation patients that was significantly associated with a recommendation or a non-recommendation of a hospital, and in five quality assurance (QA) comparison groups the category "catering" was associated with a positive or negative recommendation. In all QS comparison groups, there was an association with at least one of the following process-oriented rehabilitation categories: "rehabilitation measures", "rehabilitation plan and rehabilitation goals" and / or "diagnosis to discharge".

DISCUSSION AND CONCLUSION

Patient experiences with the perceived "rehabilitation success" and with the central processes of rehabilitation are particularly important for the recommendation or non-recommendation of a hospital for patients in all eight indication groups. On the basis of these results, rehabilitation hospitals can specifically identify the aspects of care that are important when patients recommend a hospital for rehabilitation. Online narratives of patients provide additional insights into the reasons for patients' satisfaction or dissatisfaction with their rehabilitation. These narratives are available to potential rehabilitation patients as a low-threshold source of information and decision-making aid.

Online Communities as a Driver for Patient Empowerment: Systematic Review

BACKGROUND

The use of online resources has changed how people manage health care processes. Patients seek information about health conditions, guidance in treatment, and support from peers online, complementary to traditional health care trajectories. Online communities have the potential to contribute to the quality of care by increasing patient empowerment; however, there is a gap in research regarding in what way online communities contribute to patient empowerment.

OBJECTIVE

We synthesized research regarding how online communities contribute to patient empowerment to address the research question "In what ways can participation in online communities support patient empowerment?" by studying how patient empowerment is operationalized in different studies. The definition of patient empowerment used in this paper is enablement for people to develop mastery over actions and control over decisions that influence their lives. The mastery is both through processes and outcomes of the development.

METHODS

A systematic review was conducted by searching in the following databases: Scopus, ACM Digital Library, EBSCO (CINAHL and MEDLINE), PubMed, and Web of Science. In total, there were 1187 papers after excluding duplicates, and through selection processes using an analytical framework with definitions of patient empowerment and related concepts, 33 peer-reviewed papers were included.

RESULTS

Findings indicated that online communities support patient empowerment both as a process and as outcomes of these processes. Additionally, it was seen as a complement to traditional health care and encouragement for health care professionals to have a more positive attitude toward patients' usage. There was a mix between deductive (19/33, 58%), inductive (11/33, 33%), and a mixed approach (3/33, 9%) of studying patient empowerment in various forms. The online communities in most papers (21/33, 64%) were well-established and represented patients' initiatives.

CONCLUSIONS

There is a need to include professionals' perspectives regarding how health care can embrace patient empowerment through online communities. This systematic review's main contribution is the proposal of a new framework and conceptualization of how patient empowerment in online communities can be understood from different hierarchical levels.

Analysis of Online Urologist Ratings: Are Rating Differences Associated With Subspecialty?

Patients are increasingly using online rating websites to obtain information about physicians and to provide feedback. We performed an analysis of urologist online ratings, with specific focus on the relationship between overall rating and urologist subspecialty. We conducted an analysis of urologist ratings on Healthgrades.com. Ratings were sampled across 4 US geographical regions, with focus across 3 practice types (large and small private practice, academic) and 7 urologic subspecialties. Statistical analysis was performed to assess for differences among subgroup ratings. Data were analyzed for 954 urologists with a mean age of 53 (±10) years. The median overall urologist rating was 4.0 [3.4-4.7]. Providers in an academic practice type or robotics/oncology subspecialty had statistically significantly higher ratings when compared to other practice settings or subspecialties (P < 0.001). All other comparisons between practice types, specialties, regions, and sexes failed to demonstrate statistically significant differences. In our study of online urologist ratings, robotics/oncology subspecialty and academic practice setting were associated with higher overall ratings. Further study is needed to assess reasons underlying this difference.

Big data simulations for capacity improvement in a general ophthalmology clinic

Purpose

Long total waiting times (TWT) experienced by patients during a clinic visit have a significant adverse effect on patient’s satisfaction. Our aim was to use big data simulations of a patient scheduling calendar and its effect on TWT in a general ophthalmology clinic. Based on the simulation, we implemented changes to the calendar and verified their effect on TWT in clinical practice.

Design and methods

For this retrospective simulation study, we generated a discrete event simulation (DES) model based on clinical timepoints of 4.401 visits to our clinic. All data points were exported from our clinical warehouse for further processing. If not available from the electronic health record, manual time measurements of the process were used. Various patient scheduling models were simulated and evaluated based on their reduction of TWT. The most promising model was implemented into clinical practice in 2017.

Results

During validation of our simulation model, we achieved a high agreement of mean TWT between the real data (229 ± 100 min) and the corresponding simulated data (225 ± 112 min). This indicates a high quality of the simulation model. Following the simulations, a patient scheduling calendar was introduced, which, compared with the old calendar, provided block intervals and extended time windows for patients. The simulated TWT of this model was 153 min. After implementation in clinical practice, TWT per patient in our general ophthalmology clinic has been reduced from 229 ± 100 to 183 ± 89 min.

Conclusion

By implementing a big data simulation model, we have achieved a cost-neutral reduction of the mean TWT by 21%. Big data simulation enables users to evaluate variations to an existing system before implementation into clinical practice. Various models for improving patient flow or reducing capacity loads can be evaluated cost-effectively.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00417-020-05040-9.

Correlation of Online Physician Rating Subscores and Association With Overall Satisfaction: Observational Study of 212,933 Providers

BACKGROUND

Online physician rating websites commonly ask consumers to rate providers across multiple physician-based (eg, spending sufficient time, listening) and office-based (eg, appointment scheduling, friendliness) subdimensions of care in addition to overall satisfaction. However, it is unclear if consumers can differentiate between the various rated subdimensions of physicians. It is also unclear how each subdimension is related to overall satisfaction.

OBJECTIVE

The objectives of our study were to determine the correlation of physician-based and office-based subdimensions of care and the association of each with overall satisfaction.

METHODS

We sampled 212,933 providers from the Healthgrades website and calculated average provider metrics for overall satisfaction (likelihood to recommend doctor), physician-based subdimensions (trust in physician, ability to explain, ability to listen and answer questions, and spending adequate time), and office-based subdimensions (ease of scheduling, office environment, staff friendliness, and wait time). We used Spearman rank correlation to assess correlation between subdimension ratings. Factor analysis was used to identify potential latent factors predicting overall satisfaction. Univariate and multivariable linear regression were performed to assess the effect of physician and office-based factors on overall satisfaction.

RESULTS

Physician-based metrics were highly correlated with each other (r=.95 to .98, P<.001), as were office-based metrics (r=.84 to .88, P<.001). Correlations between physician-based and office-based ratings were less robust (r=.79 to .81, P<.001). Factor analysis identified two factors, clearly distinguishing between physician-based metrics (factor loading = 0.84 to 0.88) and office-based metrics (factor loading = 0.76 to 0.84). In multivariable linear regression analysis, the composite factor representing physician-based metrics (0.65, 95% CI 0.65 to 0.65) was more strongly associated with overall satisfaction than the factor representing office-based metrics (0.42, 95% CI 0.42 to 0.42). These factors eclipsed other demographic variables in predicting overall satisfaction.

CONCLUSIONS

Consumers do not differentiate between commonly assessed subdimensions of physician-based care or subdimensions of office-based care, but composite factors representing these broader categories are associated with overall satisfaction. These findings argue for a simpler ratings system based on two metrics: one addressing physician-based aspects of care and another addressing office-based aspects of care.

The Association Between Physician Race/Ethnicity and Patient Satisfaction: an Exploration in Direct to Consumer Telemedicine

BACKGROUND

Patient satisfaction measures have important implications for physicians. Patient bias against non-White physicians may impact physician satisfaction ratings, but this has not been widely studied.

OBJECTIVE

To assess differences in patient satisfaction by physician race/ethnicity.

DESIGN

A cross-sectional observational study.

PARTICIPANTS

Patients seeking care on a large nationwide direct to consumer telemedicine platform between July 2016 and July 2018 and their physicians.

MAIN MEASURES

Patient satisfaction was ascertained immediately following the encounter on scales of 1 to 5 stars and scored two ways: (1) top-box satisfaction (5 stars versus fewer) and (2) dissatisfaction (2 or fewer stars versus 3 or more). To approximate the information patients would use to make assumptions about physician race/ethnicity, four reviewers classified physicians into categories based on physician name and photo. These included White American, Black American, South Asian, Middle Eastern, Hispanic, and East Asian. Mixed effects logistic regression was used to assess differences in patient top-box satisfaction and patient dissatisfaction by physician race/ethnicity, controlling for patient characteristics, prescription receipt, physician specialty, and whether the physician trained in the USA versus internationally.

KEY RESULTS

The sample included 119,016 encounters with 390 physicians. Sixty percent were White American, 14% South Asian, 7% Black American, 7% Hispanic, 6% Middle Eastern, and 6% East Asian. Encounters with South Asian physicians (aOR 0.70; 95% CI 0.54-0.91) and East Asian physicians (aOR 0.72; 95% CI 0.53-0.99) were significantly less likely than those with White American physicians to result in top-box satisfaction. Compared to encounters with White American physicians, those with Black American physicians (aOR 1.72; 95% CI 1.12-2.64), South Asian physicians (aOR 1.77; 95% CI 1.23-2.56), and East Asian physicians (aOR 2.10; 95% CI 1.38-3.20) were more likely to result in patient dissatisfaction.

CONCLUSIONS

In our study, patients reported lower satisfaction with some groups of non-White American physicians, which may have implications for their compensation, professional reputation, and job satisfaction.

Gender, Soft Skills, and Patient Experience in Online Physician Reviews: A Large-Scale Text Analysis

Background

Online physician reviews are an important source of information for prospective patients. In addition, they represent an untapped resource for studying the effects of gender on the doctor-patient relationship. Understanding gender differences in online reviews is important because it may impact the value of those reviews to patients. Documenting gender differences in patient experience may also help to improve the doctor-patient relationship. This is the first large-scale study of physician reviews to extensively investigate gender bias in online reviews or offer recommendations for improvements to online review systems to correct for gender bias and aid patients in selecting a physician.

Objective

This study examines 154,305 reviews from across the United States for all medical specialties. Our analysis includes a qualitative and quantitative examination of review content and physician rating with regard to doctor and reviewer gender.

Methods

A total of 154,305 reviews were sampled from Google Place reviews. Reviewer and doctor gender were inferred from names. Reviews were coded for overall patient experience (negative or positive) by collapsing a 5-star scale and coded for general categories (process, positive/negative soft skills), which were further subdivided into themes. Computational text processing methods were employed to apply this codebook to the entire data set, rendering it tractable to quantitative methods. Specifically, we estimated binary regression models to examine relationships between physician rating, patient experience themes, physician gender, and reviewer gender).

Results

Female reviewers wrote 60% more reviews than men. Male reviewers were more likely to give negative reviews (odds ratio [OR] 1.15, 95% CI 1.10-1.19; P<.001). Reviews of female physicians were considerably more negative than those of male physicians (OR 1.99, 95% CI 1.94-2.14; P<.001). Soft skills were more likely to be mentioned in the reviews written by female reviewers and about female physicians. Negative reviews of female doctors were more likely to mention candor (OR 1.61, 95% CI 1.42-1.82; P<.001) and amicability (OR 1.63, 95% CI 1.47-1.90; P<.001). Disrespect was associated with both female physicians (OR 1.42, 95% CI 1.35-1.51; P<.001) and female reviewers (OR 1.27, 95% CI 1.19-1.35; P<.001). Female patients were less likely to report disrespect from female doctors than expected from the base ORs (OR 1.19, 95% CI 1.04-1.32; P=.008), but this effect overrode only the effect for female reviewers.

Conclusions

This work reinforces findings in the extensive literature on gender differences and gender bias in patient-physician interaction. Its novel contribution lies in highlighting gender differences in online reviews. These reviews inform patients’ choice of doctor and thus affect both patients and physicians. The evidence of gender bias documented here suggests review sites may be improved by providing information about gender differences, controlling for gender when presenting composite ratings for physicians, and helping users write less biased reviews.

Assessing Patient Experience and Healthcare Quality of Dental Care Using Patient Online Reviews in the United States: Mixed Methods Study

Background

Over the last two decades, patient review websites have emerged as an essential online platform for doctor ratings and reviews. Recent studies suggested the significance of such websites as a data source for patients to choose doctors for healthcare providers to learn and improve from patient feedback and to foster a culture of trust and transparency between patients and healthcare providers. However, as compared to other medical specialties, studies of online patient reviews that focus on dentists in the United States remain absent.

Objective

This study sought to understand to what extent online patient reviews can provide performance feedbacks that reflect dental care quality and patient experience.

Methods

Using mixed informatics methods incorporating statistics, natural language processing, and domain expert evaluation, we analyzed the online patient reviews of 204,751 dentists extracted from HealthGrades with two specific aims. First, we examined the associations between patient ratings and a variety of dentist characteristics. Second, we identified topics from patient reviews that can be mapped to the national assessment of dental patient experience measured by the Patient Experience Measures from the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Dental Plan Survey.

Results

Higher ratings were associated with female dentists (t₇₁₈₈₁=2.45, P<.01, g=0.01), dentists at a younger age (F_{7, 107128}=246.97, P<.001, g=0.11), and those whose patients experienced a short wait time (F_{4, 150055}=10417.77, P<0.001, g=0.18). We also identified several topics that corresponded to CAHPS measures, including discomfort (eg, painful/painless root canal or deep cleaning), and ethics (eg, high-pressure sales, and unnecessary dental work).

Conclusions

These findings suggest that online patient reviews could be used as a data source for understanding the patient experience and healthcare quality in dentistry.

Factors Associated With the Actual Behavior and Intention of Rating Physicians on Physician Rating Websites: Cross-Sectional Study

BACKGROUND

Although online physician rating information is popular among Chinese health consumers, the limited number of reviews greatly hampers the effective usage of this information. To date, little has been discussed on the variables that influence online physician rating from the users' perspective.

OBJECTIVE

This study aims to investigate the factors associated with the actual behavior and intention of generating online physician rating information in urban China.

METHODS

A web-based cross-sectional survey was conducted, and the valid responses of 1371 Chinese health consumers were recorded. Using a pilot interview, we analyzed the effects of demographics, health variables, cognitive variables, and technology-related variables on online physician rating information generation. Binary multivariate logistic regression, multiple linear regression, one-way analysis of variance analyses, and independent samples t test were performed to analyze the rating behavior and the intentions of the health consumers. The survey instrument was designed based on the existing literature and the pilot interview.

RESULTS

In this survey, 56.7% (778/1371) of the responders used online physician rating information, and 20.9% (287/1371) of the responders rated the physicians on the physician rating website at least once (posters). The actual physician rating behavior was mainly predicted by health-related factors and was significantly associated with seeking web-based physician information (odds ratio [OR] 5.548, 95% CI 3.072-10.017; P<.001), usage of web-based physician service (OR 2.771, 95% CI 1.979-3.879; P<.001), health information-seeking ability (OR 1.138, 95% CI 0.993-1.304; P=.04), serious disease development (OR 2.699, 95% CI 1.889-3.856; P<.001), good medical experience (OR 2.149, 95% CI 1.473-3.135; P<.001), altruism (OR 0.612, 95% CI 0.483-0.774; P<.001), self-efficacy (OR 1.453, 95% CI 1.182-1.787; P<.001), and trust in online physician rating information (OR 1.315, 95% CI 1.089-1.586; P=.004). Some factors influencing the intentions of the posters and nonposters rating the physicians were different, and the rating intention was mainly determined by cognitive and health-related factors. For posters, seeking web-based physician information (β=.486; P=.007), using web-based medical service (β=.420; P=.002), ability to seek health information (β=.193; P=.002), rating habits (β=.105; P=.02), altruism (β=.414; P<.001), self-efficacy (β=.102; P=.06), trust (β=.351; P<.001), and perceived ease of use (β=.275; P<.001) served as significant predictors of the rating intention. For nonposters, ability to seek health information (β=.077; P=.003), chronic disease development (β=.092; P=.06), bad medical experience (β=.047; P=.02), rating habits (β=.085; P<.001), altruism (β=.411; P<.001), self-efficacy (β=.171; P<.001), trust (β=.252; P<.001), and perceived usefulness of rating physicians (β=.109; P<.001) were significantly associated with the rating intention.

CONCLUSIONS

We showed that different factors affected the physician rating behavior and rating intention. Health-related variables influenced the physician rating behavior, while cognitive variables were critical in the rating intentions. We have proposed some practical implications for physician rating websites and physicians to promote online physician rating information generation.

The Communicative Ecology of Chinese Patients' Experiences with Health Care

Patient satisfaction and trust are important intermediate outcomes along pathways linking clinician-patient communication to improve well-being, but they are difficult to achieve in Chinese health care. Problematic physician-patient interactions, questionable health-care organizational practices, and media coverage of medical scandals may have contributed to this problem. Nevertheless, there isscant literature documenting reasons underlying dissatisfaction with Chinese health care. Using Street's ecological model of communication in medical encounters as a conceptual framework, this study explores how media and organizational factors affect Chinese patients' satisfaction and trust both directly and as mediated by the quality of patients' past communication experiences with clinicians. A survey was conducted among 257 Ob-gyn patients in a top-tier hospital in Sichuan, China. The results show that several organizational and media factors, along with patients' experiences with physician communication, predict patient satisfaction and trust. Perceptions of physician communication mediated some of the relationships between organizational and media factors with outcomes. Theoretical and practical implications are discussed, particularly with respect to improving health-care services in China.

Online Ratings of Urologists: Comprehensive Analysis

Background

Physician-rating websites are being increasingly used by patients to help guide physician choice. As such, an understanding of these websites and factors that influence ratings is valuable to physicians.

Objective

We sought to perform a comprehensive analysis of online urology ratings information, with a specific focus on the relationship between number of ratings or comments and overall physician rating.

Methods

We analyzed urologist ratings on the Healthgrades website. The data retrieval focused on physician and staff ratings information. Our analysis included descriptive statistics of physician and staff ratings and correlation analysis between physician or staff performance and overall physician rating. Finally, we performed a best-fit analysis to assess for an association between number of physician ratings and overall rating.

Results

From a total of 9921 urology profiles analyzed, there were 99,959 ratings and 23,492 comments. Most ratings were either 5 (“excellent”) (67.53%, 67,505/99,959) or 1 (“poor”) (24.22%, 24,218/99,959). All physician and staff performance ratings demonstrated a positive and statistically significant correlation with overall physician rating (P<.001 for all analyses). Best-fit analysis demonstrated a negative relationship between number of ratings or comments and overall rating until physicians achieved 21 ratings or 6 comments. Thereafter, a positive relationship was seen.

Conclusions

In our study, a dichotomous rating distribution was seen with more than 90% of ratings being either excellent or poor. A negative relationship between number of ratings or comments and overall rating was initially seen, after which a positive relationship was demonstrated. Combined, these data suggest that physicians can benefit from understanding online ratings and that proactive steps to encourage patient rating submissions may help optimize overall rating.