Rating Websites Research Articles

Considering the potential unintended consequences of RateMDs: an exploratory study in one specialty

Background: Websites that facilitate communication between patients regarding their experiences with individual physicians are now relatively commonplace. Given patient-generated ratings are publicly available, physicians could use these to access rarely available patient feedback. We explored the content of reviews associated with low physician ratings and consider the potential benefits and consequences of relying on this form of freely available data to support individual life-long learning. Methods: We conducted an exploratory qualitative descriptive study. We collected narrative comments associated with low numerical ratings on one physician-rating website (RateMDs) drawn from one specialty in Canada. Written reviews associated with low numerical ratings (≤2/5) for Canadian otolaryngologists were collected yielding a total of 878 comment sets that were analyzed deductively and iteratively. Results: We found that patient comments described poor performance in areas that aligned, for the most part, with the CanMEDS roles including Professional, Communicator, and Leader; specifically referring to management of the clinical environment, administrative staff, and trainees. Conclusion: While not intended for physician feedback, physicians could access patient-to-patient ratings and associated written reviews as a means to identify areas of practice improvement. However, this represents an unintended use of these websites. While speculative, access to patient-to-patient rating websites could negatively impact physician confidence or self-worth – representing a negative consequence of their use. The utilization of these data for potential self-improvement represents an unintended use of patient-to-patient ratings and so may be accompanied by unintended consequences for physicians who use these data as potential feedback, and patients who contribute to physician rating sites.

Categorizing Extremely Positive Five-Star Online Reviews for Orthopedic Foot and Ankle Surgeons: A Retrospective Study.

Background Multiple studies have emphasized the increased use of physician rating websites by patients when searching for surgeons to perform elective procedures. This study aimed to analyze the comments associated with online five-star patient reviews for orthopedic foot and ankle surgeons. Methods A retrospective analysis of five-star online reviews and corresponding comments using Vitals.com in 2024 was completed. Surgeons were included if they could be found on Vitals.com, were within a 10-mile radius of one of the top 10 largest cities in the United States, and if they had at least one review with one corresponding comment. Comments were further stratified into the following categories: good outcomes, well-controlled pain, correct diagnosis, clear plan, bedside manner/patient experience, staff compliment, wait time, nice facility, and offering a nonsurgical option. Results In a sampling of 2,425 orthopedic foot and ankle surgeons, 148 physicians (6.1%) had at least one review with one comment. Ultimately, 1,833 five-star reviews comprising 3,215 comments were included in the final analysis. Comments stratified by category revealed the most common comments being related to good outcomes (n = 940; 29.2%) and bedside manner/patient experience (n = 921; 28.6%). From the comments related to bedside manner/patient experience (p < 0.001) and presence of a clear plan (p < 0.001), a significantly higher proportion of comments in the nonoperative group was found relative to the operative group. Conversely, from the comments related to well-controlled pain (p < 0.001), a significantly higher proportion of comments in the operative group was found relative to the nonoperative group. Conclusions The most common reasons behind five-star patient comments for orthopedic foot and ankle surgeons were related to good outcomes and bedside manner/patient experience. Comments from surgical patients were most likely to include mention of well-controlled pain, whereas comments from patients who underwent nonoperative care were more likely to center on bedside manner/patient experience and presence of a clear plan.

Social media use and patient ratings in shoulder and elbow surgeons: how many “likes” for five stars?

Social media plays an important role in healthcare and physician selection by facilitating direct communication with patients and impacting physician ratings. A concern however is the increased online scrutiny and negative impact on patient satisfaction with these connections. This study aimed to investigate whether social media activity by fellowship-trained shoulder and elbow surgeons impacts patient's perceptions and ratings on physician review websites (PRWs). The American Shoulder and Elbow Surgeons physician directory was used to identify currently practicing shoulder and elbow surgeons in the United States. Physician ratings were collected from Healthgrades, Google reviews, and Vitals. The surgeons were divided into two groups: social media users (SMU) and non- SMU (NSMU). The association of social media use with online physician ratings was evaluated using simple and multilinear regressions. A total of 385 American Shoulder and Elbow Surgeons surgeons were included and 21.3% were SMU. SMU were younger (mean age, 48years) compared to NSMU (mean age, 51years) (P=.01), all other demographics were comparable including sex (P=.797), medical degree (P=.114), and geographic location within the United States (P=.49). SMU had significantly higher ratings on Healthgrades (P<.001) and Vitals (P<.001). However, social media use did not impact the total number of ratings on PRWs. Additionally, surgeons who utilized Facebook had higher physician ratings and number of website reviews on Healthgrades (P=.028 and P=.014, respectively). In addition, surgeons who used Twitter had higher ratings on Healthgrades (P<.001) and Vitals (P=.001). Surgeons with a greater average number of likes per post on Twitter had significantly higher average ratings across all three sites (P=.004). Surgeons with a greater number of Twitter followers and greater average number of likes per post had significantly higher average ratings on Healthgrades (P=.052 and P=.005, respectively) while surgeons with a greater average number of likes per post had significantly higher average ratings on Vitals (P=.006). Finally, surgeons with a greater average posting frequency on Instagram had significantly higher average ratings across all websites (P=.029). Shoulder and elbow surgeons who use Twitter and Facebook had significantly higher online ratings. However, the increased use of these platforms in terms of postcontent, postfrequency, comments, and number of followers was not as influential on PRWs. This suggests that social media is an important marketing and outreach method for orthopedic surgeons to improve their ratings and patient reviews.

Application of sentiment and word frequency analysis of physician review sites to evaluate refractive surgery care

Online physician reviews increase transparency in health care, helping patients make informed decisions about their provider. Language processing techniques can quantify this data and allow providers to better understand patients' experiences, perspectives, and priorities. The objective of this study was to assess patient satisfaction and understand the aspects of care that are valued by patients seeking refractive care using sentiment and word frequency analysis. Written reviews and Star ratings for members of the Refractive Surgery Alliance Society practicing in the United States were collected from Healthgrades, a popular physician rating website. Surgeons with at least one written review were included in the study. Reviews were scored from -1 (most negative) to +1 (most positive) using Valence Aware Dictionary sEntiment Reasoner (VADER). Reviews were stratified by demographic characteristics, namely gender, region, and years in practice. Word frequency analysis was applied to find the most common words and phrases. A total of 254 specialists and 3104 reviews were analyzed, with an average of 4.4/5 stars and mean 48 ratings each. Most physicians had positive reviews (96%, average VADER​=​0.69). Younger physicians (<20 years since residency) had significantly higher Stars rating than senior peers (>20 years) (P​<​0.001). A similar trend was observed in VADER score (0.71 vs 0.69), although not statistically significant (P​=​0.06). No statistical differences were observed between Stars rating and VADER score by gender (P​=​0.66, P​=​0.83) or by geographical region (P​=​0.74, P​=​0.07). "Staff" (n​=​1269), "professional" (n​=​631), "office" (n​=​523), "questions" (n​=​424), and "friendly" (n​=​386) were frequently used in reviews, along with phrases such as "the staff" (n​=​273) and "my questions" (n​=​174). "Surgery" (n​=​719), "staff" (n​=​576), "procedure" (n​=​251), "experience" (n​=​243), and "professional" (n​=​240) were the most common words in positive reviews, while "surgery" (n​=​147), "office" (n​=​86), "staff" (n​=​54), "time" (n​=​47), and "insurance" (n​=​28) were the most commonly used in negative reviews. Both the average Stars and VADER sentiment score suggest a high satisfaction among refractive patients. Word frequency analysis revealed that patients value non-clinical aspects of care, including interactions with staff, insurance coverage, and wait-times, suggesting that improving non-clinical factors could enhance patient satisfaction with refractive surgery.

Artificial Intelligence Versus Human Focus Group Rating of Facial Attractiveness.

Background: Many open-access artificial intelligence (AI)-based websites that rate facial attractiveness are available, but none have been compared with human focus group outcomes. Objective: To compare human and AI-based websites scoring of facial attractiveness of adult female white faces. Methods: A 40-photograph database of AI-generated adult, white, female, expressionless, and frontal-view facial images were scored by otolaryngology residents and five AI-based facial rating websites: prettyscale.com, attractivenesstest.com, face-score.com/en, hotchat3000.com, and beautyscoretest.com. Sample t-test and bivariate correlation were performed for statistical analyses. Results: The focus group of 24 otolaryngology residents consisted of 62.5% males and 58.3% white participants. There was a strong positive correlation between average human score and average AI score for each photo (Pearson's correlation 0.84, p < 0.01). The average human raters' scores were significantly lower than the average AI scores (5.0 ± 1.8 vs. 6.9 ± 0.9, p < 0.01). Thirty images (75.0%) had statistically higher scores from the AI websites versus the focus group. On correlation analysis, all AI-based websites individually had scores that positively correlate with the human scores (all p < 0.05). Conclusion: AI-based websites and human focus-group scoring of facial attractiveness of adult white female faces were significantly correlated with the AI ratings biased toward higher values, encouraging their cautious utilization in future research.

Comparison of hospital rating websites among each other and with data from hospital quality reports and quality assurance based on routine data

In addition to the objectifiable treatment quality, patients' perspectives are gaining relevance. This study aimed to characterize available hospital rating websites (HRW) with regards to patient ratings and to compare them with data from hospital quality reports and quality assurance based on routine data (QSR) for urological departments. After astructured online search for HRWs, websites were compared based on patient ratings from the 10urologic departments with the largest intervention rates in 2021 using generalized estimated equations. For radical prostatectomy (RPE), quantitative comparison of patient ratings (klinikbewertungen.de) and QSR-based ratings was performed using Spearman's rank correlation. Of 1845hits, 25portals were analyzed. The department-wise comparison of HRWs resulted in significantly different patient ratings (p < 0.001). Patient ratings (klinikbewertungen.de) and QSR data (AOK-Gesundheitsnavigator) showed no significant correlation. An internal comparison of QSR data and patient ratings from the AOK-Gesundheitsnavigator on RPE showed asignificant negative correlation between the overall rating and unplanned reoperations (r = -0.81) or other complications (r = -0.91). There was no significant correlation with the recommendation rate by patients. Hospital rating websites show considerable heterogeneity regarding patient ratings of the same urology department in different portals. Furthermore, based on the selected examples, there seems to be no correlation between subjective and objective evaluations between different websites or within one website.

Predicting IMDb Rating of Movies by Machine Learning Techniques

Film Industry is not only a industry or a centre of entertainment, rather it is now a centre of global business. All over the world is now excited about a movie’s box office success, popularity etc. A huge data is available online about these movies success or popularity. We have used Hollywood movie list from Wikipedia and their rating from IMDb movie rating website to create our data set. Then machine learning classification algorithms are applied of the data set. Lastly an efficient model is developed to predict a movie’s IMDb rating. The model gives good classification measures with the data set. Index Terms-Movie Rating, Machine Learning, Prediction, Box Office, IMDb.

The impact of 1-star physician ratings on new patient volume.

To quantify the impact of 1-star reviews across multiple physician rating websites (PRWs) on new patient volume. Retrospective analysis of 1.12 million new patient appointments and 12,882 physician reviews from a proprietary data set from a large single-specialty practice in the New York and New Jersey area. We compiled new patient appointments scheduled and kept between January 1, 2015, and April 25, 2018, and the reviews of the practice's affiliated physicians from 10 PRWs. Assuming that reviews are read prior to appointment creation, an ordinary least squares regression model was run with a time series analysis to compare patient volume in the period immediately prior to the posting of a 1-star review with patient volume in the period immediately after a 1-star review was posted. An additional sensitivity analysis was performed at 4, 6, 8, 10, 12, 14, and 16 weeks to validate a robust effect. The majority of reviews on PRWs were overwhelmingly positive, with only 6.7% of reviews (n = 733) rating a physician with 1 star. A mean of 6.2 new patient appointments were made per half-day session. The mean new patient volume decreased 2.3% to 2.6% following a 1-star review, with effects of the 1-star review affecting patient volume for at least 16 weeks. Given the limited yet longitudinal negative impact of 1-star reviews and the growing influence of PRWs, physicians should consider the magnitude of the effect as they consider responding to bad reviews.

What Makes a 5-Star Orthopaedic Sports Medicine Surgeon? An Analysis of Positive Online Patient Reviews.

The increased focus on patient satisfaction has led to growth in the use of physician rating websites. To analyze the factors associated with online 5-star patient reviews for orthopaedic sports medicine surgeons. Cross-sectional study. A total of 70 orthopaedic sports medicine surgeons were randomly selected from the AOSSM website. A search was performed for these surgeons on Yelp.com. All reviews other than 5 stars (of a possible 5 stars) were excluded from the study. Each review was categorized as referring to a surgical or nonsurgical aspect of care, and each comment within the review was categorized as being clinically or nonclinically related. Comments were further subcategorized by specific features such as bedside manner, clinical outcomes, and patient education. Categorical variables were analyzed using the chi-square test. Overall, 400 five-star reviews were included in the study, comprising 1225 total positive comments. Of the 400 five-star reviews, 200 (50%) were from surgically treated patients, and 200 (50%) were from nonsurgically treated patients. Of the 1225 positive comments, 505 (41%) were clinically related, and 720 (59%) were nonclinical. The most common positive clinical comments were for clear treatment plans (191 reviews [48%]), good outcomes (173 reviews [43%]), and providing alternative treatment plans (55 reviews [14%]). The most common positive nonclinical comments were for good physician bedside manner (287 reviews [72%]), friendly/professional staff (194 reviews [49%]), and ease of scheduling (68 reviews [17%]). The majority of 5-star patient reviews left positive comments regarding nonclinical aspects of care such as physician bedside manner and friendly staff. The most common positive comments regarding clinical aspects concerned good outcomes and clear treatment plans. The overall most common positive comment, in both surgically and nonsurgically treated patients, referred to good bedside manner.

Classification of Patients' Judgments of Their Physicians in Web-Based Written Reviews Using Natural Language Processing: Algorithm Development and Validation.

Patients increasingly rely on web-based physician reviews to choose a physician and share their experiences. However, the unstructured text of these written reviews presents a challenge for researchers seeking to make inferences about patients' judgments. Methods previously used to identify patient judgments within reviews, such as hand-coding and dictionary-based approaches, have posed limitations to sample size and classification accuracy. Advanced natural language processing methods can help overcome these limitations and promote further analysis of physician reviews on these popular platforms. This study aims to train, test, and validate an advanced natural language processing algorithm for classifying the presence and valence of 2 dimensions of patient judgments in web-based physician reviews: interpersonal manner and technical competence. We sampled 345,053 reviews for 167,150 physicians across the United States from Healthgrades.com, a commercial web-based physician rating and review website. We hand-coded 2000 written reviews and used those reviews to train and test a transformer classification algorithm called the Robustly Optimized BERT (Bidirectional Encoder Representations from Transformers) Pretraining Approach (RoBERTa). The 2 fine-tuned models coded the reviews for the presence and positive or negative valence of patients' interpersonal manner or technical competence judgments of their physicians. We evaluated the performance of the 2 models against 200 hand-coded reviews and validated the models using the full sample of 345,053 RoBERTa-coded reviews. The interpersonal manner model was 90% accurate with precision of 0.89, recall of 0.90, and weighted F1-score of 0.89. The technical competence model was 90% accurate with precision of 0.91, recall of 0.90, and weighted F1-score of 0.90. Positive-valence judgments were associated with higher review star ratings whereas negative-valence judgments were associated with lower star ratings. Analysis of the data by review rating and physician gender corresponded with findings in prior literature. Our 2 classification models coded interpersonal manner and technical competence judgments with high precision, recall, and accuracy. These models were validated using review star ratings and results from previous research. RoBERTa can accurately classify unstructured, web-based review text at scale. Future work could explore the use of this algorithm with other textual data, such as social media posts and electronic health records.

A sentiment analysis on online psychiatrist reviews to identify clinical attributes of psychiatrists that shape the therapeutic alliance.

While online reviews from physician rating websites are increasingly utilized by healthcare providers to better understand patient needs, it remains difficult to objectively identify areas for improvement in providing psychiatric care. To quantitatively characterize the sentiment of online written reviews of psychiatrists to determine clinical attributes that can be strengthened to improve psychiatrists' therapeutic alliance with their patients. Sentiment scores of 6,400 written reviews of 400 US-based psychiatrists on a US-based online physician rating website were obtained through a natural-language-processing-based sentiment analysis. Relationships among sentiment scores, average star ratings, and demographics were examined. Linguistic analyses determined words and bigrams that were highly associated with reviews with the most positive and negative sentiment. Sentiment scores were significantly correlated with average star ratings of the psychiatrists (R = 0.737, p < 0.001). Psychiatrists who were younger (< 56 years old) and/or practiced in the Northeast had significantly higher average star ratings than those older and/or practicing in the Southwest. Frequency analysis showed that positive reviews most frequently contained "time" (N = 1,138) and "caring" (N = 784) while negative reviews most frequently contained "medication" (N = 495) and "time" (N = 379). Logistic regression analysis revealed that reviews were more likely to be considered positive when they included "great listener" (OR = 16.89) and "comfortable" (OR = 10.72) and more likely to be negative when they included "meds" (OR = 0.55) and "side effect" (OR = 0.59). Psychiatrists who are younger and located in the Northeast receive more positive reviews; there may be potential for demographic bias among patient reviewers. Patients positively rate psychiatrists who make them feel heard and comfortable but negatively rate encounters centered around medications and their side effects. Our study lends quantitative evidence to support the importance of thorough and empathetic communication of psychiatrists in building a strong therapeutic alliance.

0934 An Analysis of 5,212 Sleep Medicine Providers’ Online Patient Reviews

Abstract Introduction Online provider rating websites are an increasingly popular medium for patients to provide reviews &amp; feedback and obtain information about varying healthcare providers. There have yet to be studies on what factors significantly impact sleep medicine provider ratings. Healthgrades is a popular healthcare provider rating database with over 11 million patient reviews. This study analyzes various dimensions to determine what factors correlate with average sleep medicine provider ratings to improve patient satisfaction. Methods Data were collected on 5,212 sleep medicine providers’ online profiles using web data extraction software. Factors such as medical degree, gender, age, and ethnicity were compared to online ratings using Pearson correlation and ANOVA analysis via statistics software. Results Variables with positive correlation in provider rating include the presence of a headshot (pc=0.106, p&amp;lt; 0.01), designation of trustworthiness (pc=0.152, p&amp;lt; 0.01), and presence of care philosophy statement (pc=0.105, p&amp;lt; 0.01). The only variable with a negative correlation in provider rating was increasing age (p=-0.109, p&amp;lt; 0.01). A one-way ANOVA revealed that there was a statistically significant difference in mean rating between different healthcare degrees (F(122.271, 5062.570)=[17.955], p=&amp;lt;.001). Tukey’s HSD Test for multiple comparisons found that the mean value of ratings was significantly different between allopathic physicians and sleep medicine dentists (p=0.000, 95% C.I.=[-0.943,0.070]), physician assistants (p=0.000, 95% C.I.=[-1.268,-0.239]), and nurse practitioners (p=0.000, 95% C.I.=[-0.941,-0.188]). There was no statistically significant difference in mean online rating between allopathic physicians and osteopathic physicians (p=0.505), sleep psychologists (p=0.344), or foreign medical graduates (p=0.998), respectively. Conclusion This study suggests patients perceive sleep medicine providers’ online profiles with headshots, care philosophy statements, and designation of trustworthiness as contributing factors when rating. Additionally, degree type and age matter when rating online. Patients do not value factors such as ethnicity or gender strongly when rating their sleep medicine provider. Further study is needed to determine factors influencing non-physician providers’ higher ratings when compared to U.S. medical graduates. This study demonstrates the importance of building an online presence for sleep medicine practices and identifying characteristics patients value. The expectation is that this information can be used to improve the quality of care delivered in the field of sleep medicine. Support (if any)

SARS-CoV-2-Related Adaptation Mechanisms of Rehabilitation Clinics Affecting Patient-Centered Care: Qualitative Study of Online Patient Reports.

The SARS-CoV-2 pandemic impacted access to inpatient rehabilitation services. At the current state of research, it is unclear to what extent the adaptation of rehabilitation services to infection-protective standards affected patient-centered care in Germany. The aim of this study was to determine the most relevant aspects of patient-centered care for patients in inpatient rehabilitation clinics under early phase pandemic conditions. A deductive-inductive framework analysis of online patient reports posted on a leading German hospital rating website, Klinikbewertungen (Clinic Reviews), was performed. This website is a third-party, patient-centered commercial platform that operates independently of governmental entities. Following a theoretical sampling approach, online reports of rehabilitation stays in two federal states of Germany (Brandenburg and Saarland) uploaded between March 2020 and September 2021 were included. Independent of medical specialty groups, all reports were included. Keywords addressing framework domains were analyzed descriptively. In total, 649 online reports reflecting inpatient rehabilitation services of 31 clinics (Brandenburg, n=23; Saarland, n=8) were analyzed. Keywords addressing the care environment were most frequently reported (59.9%), followed by staff prerequisites (33.0%), patient-centered processes (4.5%), and expected outcomes (2.6%). Qualitative in-depth analysis revealed SARS-CoV-2-related reports to be associated with domains of patient-centered processes and staff prerequisites. Discontinuous communication of infection protection standards was perceived to threaten patient autonomy. This was amplified by a tangible gratification crisis of medical staff. Established and emotional supportive relationships to clinicians and peer groups offered the potential to mitigate the adverse effects of infection protection standards. Patients predominantly reported feedback associated with the care environment. SARS-CoV-2-related reports were strongly affected by increased staff workloads as well as patient-centered processes addressing discontinuous communication and organizationally demanding implementation of infection protection standards, which were perceived to threaten patient autonomy. Peer relationships formed during inpatient rehabilitation had the potential to mitigate these mechanisms.

The Influence of Paid Memberships on Physician Rating Websites With the Example of the German Portal Jameda: Descriptive Cross-sectional Study.

The majority of Germans see a deficit in information availability for choosing a physician. An increasing number of people use physician rating websites and decide upon the information provided. In Germany, the most popular physician rating website is Jameda.de, which offers monthly paid membership plans. The platform operator states that paid memberships have no influence on the rating indicators or list placement. The goal of this study was to investigate whether a physician's membership status might be related to his or her quantitative evaluation factors and to possibly quantify these effects. Physician profiles were retrieved through the search mask on Jameda.de website. Physicians from 8 disciplines in Germany's 12 most populous cities were specified as search criteria. Data Analysis and visualization were done with Matlab. Significance testing was conducted using a single factor ANOVA test followed by a multiple comparison test (Tukey Test). For analysis, the profiles were grouped according to member status (nonpaying, Gold, and Platinum) and analyzed according to the target variables-physician rating score, individual patient's ratings, number of evaluations, recommendation quota, number of colleague recommendations, and profile views. A total of 21,837 nonpaying profiles, 2904 Gold, and 808 Platinum member profiles were acquired. Statistically significant differences were found between paying (Gold and Platinum) and nonpaying profiles in all parameters we examined. The distribution of patient reviews differed also by membership status. Paying profiles had more ratings, a better overall physician rating, a higher recommendation quota, and more colleague recommendations, and they were visited more frequently than nonpaying physicians' profiles. Statistically significant differences were found in most evaluation parameters within the paid membership packages in the sample analyzed. Paid physician profiles could be interpreted to be optimized for decision-making criteria of potential patients. With our data, it is not possible to draw any conclusions of mechanisms that alter physicians' ratings. Further research is needed to investigate the causes for the observed effects.

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

Credit ratings of Chinese online loan platforms based on factor scores and K-means clustering algorithm

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

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. 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. 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. 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.

Effective Community Search on Large Attributed Bipartite Graphs

Community search over bipartite graphs has attracted significant interest recently. In many applications such as the user–item bipartite graph in e-commerce and customer–movie bipartite graph in movie rating website, nodes tend to have attributes. However, the previous community search algorithms on bipartite graphs ignore attributes, thus making them to return results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex [Formula: see text], we aim to find the attributed [Formula: see text]-communities of [Formula: see text], where the structure cohesiveness of the community is described by the [Formula: see text]-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonicity property of attributed bipartite graphs, then we adopt different generating methods and verify the order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

Hospital rating websites play a minor role for uro-oncologic patients when choosing a hospital for major surgery: results of the German multicenter NAVIGATOR-study

PurposeHospital rating websites (HRW) offer decision support in hospital choice for patients. To investigate the impact of HRWs of uro-oncological patients undergoing elective surgery in Germany.MethodsFrom 01/2020 to 04/2021, patients admitted for radical prostatectomy, radical cystectomy, or renal tumor surgery received a questionnaire on decision-making in hospital choice and the use of HRWs at 10 German urologic clinics.ResultsOur study includes n = 812 completed questionnaires (response rate 81.2%). The mean age was 65.2 ± 10.2 years; 16.5% were women. Patients were scheduled for prostatectomy in 49.1%, renal tumor surgery in 20.3%, and cystectomy in 13.5% (other 17.1%). Following sources of information influenced the decision process of hospital choice: urologists’ recommendation (52.6%), previous experience in the hospital (20.3%), recommendations from social environment (17.6%), the hospital's website (10.8%) and 8.2% used other sources. Only 4.3% (n = 35) used a HRW for decision making. However, 29% changed their hospital choice due to the information provided HRW. The most frequently used platforms were Weisse-Liste.de (32%), the AOK-Krankenhausnavigator (13%) and Qualitaetskliniken.de (8%). On average, patients rated positively concerning satisfaction with the respective HRW on the Acceptability E-Scale (mean values of the individual items: 1.8–2.1).ConclusionIn Germany, HRWs play a minor role for uro-oncologic patients undergoing elective surgery. Instead, personal consultation of the treating urologist seems to be far more important. Although patients predominantly rated the provided information of the HRW as positive, only a quarter of users changed the initial choice of hospital.

Awareness of and interaction with physician rating websites: A cross-sectional study in Austria.

To date, the digital assessment of service experiences represents a decisive process step of a feedback culture in numerous economic areas. In view of this digitalization of service assessments, the importance of Physician Rating Websites (PRWs) has also increased steadily in recent years. Even though these websites could be perceived as a powerful communication tool for the exchange of health specific information, the knowledge about whether and how different population segments use these portals has been limited so far. For this reason, our aim was to investigate the level of awareness regarding PRWs among the study population and to discover how users interact with this specific type of online portals. We performed an online survey including 558 participants. To ensure the attention and integrity of participants, attention checks were included in the questionnaire. Study participants who did not exceed the mentioned security levels were excluded from the study. Statistical analyses were carried out, using IBM SPSS Statistics 27. To illustrate the relationship between demographic variables and dependent variables, two tailed chi square tests were performed. Comparison of means and t-testing was used to investigate the relationship between psychographic variables and the dependent variables. In addition to that, the awareness levels regarding different rating portals were evaluated using descriptive methods. Our results suggest that the general awareness regarding PRWs is relatively high (75.6%, 423/558), especially among female (x21 = 9.880, P = .002), middle-aged (x29 = 26.810, P = .002), more highly educated (x24 = 19.038, P = .001), urban (x21 = 6.274, P = .012), digitally literate (t203 = 2.63, P = .009) individuals and particularly among respondents with a higher eHealth literacy (t203 = 2.37, P = .019). Even though more than three quarters of the respondents know that PRWs exist, compared to other rating platforms, they are only in the lower midfield. The upper ranks are taken by websites on which restaurant visits (98.9%, 552/558), hotel stays (97.7%, 545/558) or movies (95.5%, 533/558) can be rated. The most popular PRWs in Austria include Docfinder.at (31.3%, 175/558; 77.8%, 434/558) followed by the evaluation tools provided by Google.at (8.24%, 46/558; 70.3%, 392/558) and Herold.at (1.61%, 9/558; 44.8%, 250/558). In Austria, PRWs seem to be characterized by a high degree of interaction (89.2%, 498/558) with a wide variety of different types of interactions. While many respondents use PRWs to retrieve general information (83.2%, 464/558), there are significantly fewer who read physicians' reviews (60.9%, 340/558) and use this portal to select a physician (60.6%, 338/558). Respondents who have already rated a doctor themselves belong to the smallest group accounting for just 14.7% (82/558). Significant effects regarding the interaction with PRWs exist between different genders, ages, education levels, marital statuses, occupations and areas of living. In addition to that, respondents with better feelings towards the internet, greater digital literacy as well as a higher eHealth literacy were also characterized with a higher interaction rate regarding PRWs. According to the high level of awareness of and interaction with PRWs within our study population, PRWs appear to be a successful medium for health-related communication. Especially for female, middle-aged, more highly educated, urban and more technology savvy population segments, PRWs seem to represent an effective tool to support the health-specific decision-making process.

Effects of Industrial Protection Policies on Firms’ Incentives and Consumer Welfare

Industrial protection policies have been widely implemented to protect domestic firms from foreign competition. This project specifically focused on examining the effects of the Domestic Movie Protection Month Policy that started in China in 2014. First, I demonstrated the existence of this policy by measuring the obvious delay of foreign movies during the summer (July and August) after 2014. Second, I compared the quality of domestic movies released during the summer (treatment group) with movies released at other times of the year (control group) to evaluate the effect of the policy. Two proxies for movie quality and consumer welfare were constructed for robustness checks: (1) Douban rating, which is the general rating of the entire population of the audience on Douban website, the biggest movie rating website in China; (2) Welfare Index which is based on the sentiment analysis constructed by the NLP (Natural Language Processing) of around 70,000 consumers’ comments. The final result showed that the Douban rating and Welfare Index were both significantly higher for domestic movies released during the summer after 2014 than before 2014, while the quality of movies in the control group remained unchanged. This finding supports the hypothesis that domestic firms will have more incentives to invest in quality upgrading if the industrial protection policy is implemented. This conclusion also adds to the recent heated discussions on whether policymakers shall continue to implement industrial protection policy not only in the movie industry but also in other industries that face severe foreign competition.