Physician Rating Websites in the United States: A systematic review (Preprint)

BACKGROUND Physician rating websites are gaining popularity, however, data on their usability and influence on healthcare quality is limited. OBJECTIVE to provide an overview of physician rating websites in the US and find answers for the following questions: 1. What are the most commonly studied/rated physician rating websites in the US? 2. Which specialty of physicians/providers are most commonly studied/rated? 3. How many physicians were rated on the studied PRWs? 4. What is the average number of ratings on these websites and are they positive or negative? 5. How does the profile of providers influence their rating? 6. How are PRWs associated with healthcare quality? 7. How PRWs are associated with patient-physician relationship? METHODS A systematic literature search was conducted through Medline for peer-reviewed articles in the English language on studies conducted in the US. RESULTS 33 articles published in peer-reviewed journals were included in the final review. Most of the studies were conducted on surgeons. A significant number of studies observed no correlation of online ratings with gender, geographic location, and years of experience. Additionally, no significant correlation was found between PRWs and healthcare quality. CONCLUSIONS It has been observed that with the current structure of these websites, the reliability of information available on them is rather questionable, and hence more research is required to assess the credibility of these websites along with their cost-effectiveness, effect on the patient-physician relationship, and quality of healthcare delivery.

Exploring Important Aspects of Service Quality While Choosing a Good Doctor

Online reviews generated by patients on physician rating Websites (PRWs) have recently received much attention from physicians and their patients. In these reviews, patients exchange opinions as a diverse set of topics regarding different aspects of healthcare quality. This study aimed to propose a novel service quality-based text analytics (SQTA) model with other qualitative methods to mine different aspects of physicians and their clinical relevance in choosing a good doctor. Data included 45,560 online reviews that the authors scraped from a U.S.-based PRW (Healthgrades.com). The resulting topics demonstrate excellent classification results across different disease ranks, with overall accuracy and recall of 98%. The proposed classifier’s performance was 3% better than the existing topic classification methods applied in previous studies. The resulting clinically informative topics could help patients and physicians to maximize the usefulness of online reviews for efficient clinical decisions and improving the quality of care.

Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly mediated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.

Survey of the Use of Physician Review Websites in Patients Under Orthopaedic Care (Preprint)

BACKGROUND Physician-rating websites (PRWs) are rising in popularity as the healthcare sector focuses more on patient-centered value-based care delivery. Recent studies have reported that the vast majority of orthopaedic surgeons across subspecialties already have at least one rating or review on at least one PRW. However, there are few studies identifying patient-facing factors related to the knowledge and use of PRWs and the level of influence PRWs may have on patient decision-making. OBJECTIVE The objectives of this study are: 1) to determine the prevalence of PRW use by patients seeking orthopaedic care, 2) to identify the influence PRWs have on patient decision-making, and 3) to identify factors associated with knowledge and use of PRWs. METHODS Survey administration was performed at three outpatient orthopaedic multi-specialty. Patients were asked about demographic characteristics including age, sex, occupation, and education, as well as their familiarity with PRWs, use of PRWs, and how PRWs have influenced their decision to see their surgeon. RESULTS A total of 350 patients completed the survey. The majority were women (59%) and Caucasian (77%), and the mean age of the population was 58 years old (range 15-91). 155 patients (44%) reported being somewhat (31%) or very (13%) familiar with PRWs, and 195 patients (56%) reported no familiarity with PRWs. Patients most familiar with PRWs were between 45-64 years old (p = 0.02), women (p < 0.01), had graduate/professional degrees (p < 0.01), and seen by adult reconstruction surgeons (p = 0.02). Seventy-two percent of patients utilizing PRWs reported that PRWs had an impact on their decision to see their chosen provider, with 93% noting ratings and reviews as the most valuable information. CONCLUSIONS Many patients in this study were not familiar with PRWs and did not utilize PRWs prior to their visit. However, most patients who did consult PRWs reported that the ratings and reviews did have an impact in their decision to choose their orthopaedic specialist. As PRW use increases, factors that influence patient decision-making are important to understand for surgeons and administrators.

Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

Emerging voices of patients in the form of opinions and expectations about the quality of care can improve healthcare service quality. A large volume of patients’ opinions as online doctor reviews (ODRs) are available online to access, analyze, and improve patients’ perceptions. This paper aims to explore COVID-19-related conversations, complaints, and sentiments using ODRs posted by users of the physician rating website. We analyzed 96,234 ODRs of 5,621 physicians from a prominent health rating website in the United Kingdom (Iwantgreatcare.org) in threetime slices (i.e., from February 01 to October 31, 2020). We employed machine learning approach, dynamic topic modeling, to identify prominent bigrams, salient topics and labels, sentiments embedded in reviews and topics, and patient-perceived root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to examine SWOT for healthcare organizations. This method finds a total of 30 latent topics with 10 topics across each time slice. The current study identified new discussion topics about COVID-19 occurring from time slice 1 to time slice 3, such as news about the COVID-19 pandemic, violence against the lockdown, quarantine process and quarantine centers at different locations, and vaccine development/treatment to stop virus spread. Sentiment analysis reveals that fear for novel pathogen prevails across all topics. Based on the SWOT analysis, our findings provide a clue for doctors, hospitals, and government officials to enhance patients’ satisfaction and minimize dissatisfaction by satisfying their needs and improve the quality of care during the COVID-19 crisis.

One Decade of Online Patient Feedback: Longitudinal Analysis of Data From a German Physician Rating Website

Background Feedback from patients is an essential element of a patient-oriented health care system. Physician rating websites (PRWs) are a key way patients can provide feedback online. This study analyzes an entire decade of online ratings for all medical specialties on a German PRW. Objective The aim of this study was to examine how ratings posted on a German PRW have developed over the past decade. In particular, it aimed to explore (1) the distribution of ratings according to time-related aspects (year, month, day of the week, and hour of the day) between 2010 and 2019, (2) the number of physicians with ratings, (3) the average number of ratings per physician, (4) the average rating, (5) whether differences exist between medical specialties, and (6) the characteristics of the patients rating physicians. Methods All scaled-survey online ratings that were posted on the German PRW jameda between 2010 and 2019 were obtained. Results In total, 1,906,146 ratings were posted on jameda between 2010 and 2019 for 127,921 physicians. The number of rated physicians increased constantly from 19,305 in 2010 to 82,511 in 2018. The average number of ratings per rated physicians increased from 1.65 (SD 1.56) in 2010 to 3.19 (SD 4.69) in 2019. Overall, 75.2% (1,432,624/1,906,146) of all ratings were in the best rating category of “very good,” and 5.7% (107,912/1,906,146) of the ratings were in the lowest category of “insufficient.” However, the mean of all ratings was 1.76 (SD 1.53) on the German school grade 6-point rating scale (1 being the best) with a relatively constant distribution over time. General practitioners, internists, and gynecologists received the highest number of ratings (343,242, 266,899, and 232,914, respectively). Male patients, those of higher age, and those covered by private health insurance gave significantly (P<.001) more favorable evaluations compared to their counterparts. Physicians with a lower number of ratings tended to receive ratings across the rating scale, while physicians with a higher number of ratings tended to have better ratings. Physicians with between 21 and 50 online ratings received the lowest ratings (mean 1.95, SD 0.84), while physicians with >100 ratings received the best ratings (mean 1.34, SD 0.47). Conclusions This study is one of the most comprehensive analyses of PRW ratings to date. More than half of all German physicians have been rated on jameda each year since 2016, and the overall average number of ratings per rated physicians nearly doubled over the decade. Nevertheless, we could also observe a decline in the number of ratings over the last 2 years. Future studies should investigate the most recent development in the number of ratings on both other German and international PRWs as well as reasons for the heterogeneity in online ratings by medical specialty.

Physician ranking optimization based on patients' browse behaviors and resource capacities

PurposeDigital healthcare has become one of the most important Internet applications in the recent years, and digital platforms have been acting as interfaces between the patients and physicians. Although these technologies enhance patient convenience, they create new challenges in platform management. For instance, on physician rating websites, information overload negatively influences patients' decision-making in relation to selecting a physician. This scenario calls for an automated mechanism to provide real-time rankings of physicians. Motivated by an online healthcare platform, this study develops a method to deliver physician ranking on platforms by considering patients' browse behaviors and the capacities of service resources.Design/methodology/approachThe authors use a probabilistic model for explicitly capturing the browse behaviors of patients. Since the large volume of information in digital systems makes it intractable to solve the dynamic ranking problem, we design a ranking with value approximation algorithm that combines a greedy ranking policy and the value function approximation methods.FindingsThe authors found that the approximation methods are quite effective in dealing with the ranking optimization on the digital healthcare system, and it is mainly because the authors incorporate the patient behaviors and patient availability in the model.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies to present solutions to the dynamic physician ranking problem. The ranking algorithms can also help platforms improve system and operational performance.