BACKGROUND: Doctor review websites have become increasingly popular as a source of information for patients looking to select a primary care provider. Zocdoc is one such platform that allows patients to not only rate and review their experiences with doctors but also directly schedule appointments. This study examines how several physician characteristics including gender, age, race, languages spoken in a physician\'s office, education, and facial attractiveness impact the average numerical rating of primary care doctors on Zocdoc.
OBJECTIVE: The aim of this study was to investigate the association between physician characteristics and patient satisfaction ratings on Zocdoc.
METHODS: A data set of 1455 primary care doctor profiles across 30 cities was scraped from Zocdoc. The profiles contained information on the physician\'s gender, education, and languages spoken in their office. Age, facial attractiveness, and race were imputed from profile pictures using commercial facial analysis software. Each doctor profile listed an average overall satisfaction rating, bedside manner rating, and wait time rating from verified patients. Descriptive statistics, the Wilcoxon rank sum test, and multivariate logistic regression were used to analyze the data.
RESULTS: The average overall rating on Zocdoc was highly positive, with older age, lower facial attractiveness, foreign degrees, allopathic degrees, and speaking more languages negatively associated with the average rating. However, the effect sizes of these factors were relatively small. For example, graduates of Latin American medical schools had a mean overall rating of 4.63 compared to a 4.77 rating for US graduates (P<.001), a difference roughly equivalent to a 2.8% decrease in appointments. On multivariate analysis, being Asian and having a doctor of osteopathic medicine degree were positively associated with higher overall ratings, while attending a South Asian medical school and speaking more European and Middle Eastern languages in the office were negatively associated with higher overall ratings.
CONCLUSIONS: Overall, the findings suggest that age, facial attractiveness, education, and multilingualism do have some impact on web-based doctor reviews, but the numerical effect is small. Notably, bias may play out in many forms. For example, a physician\'s appearance or accent may impact a patient\'s trust, confidence, or satisfaction with their physician, which could in turn influence their take-up of preventative services and lead to either better or worse health outcomes. The study highlights the need for further research in how physician characteristics influence patient ratings of care.

BACKGROUND: Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding substance use disorder (SUD) treatment, and many patients do not have access to evidence-based treatment options. Patients and their families seeking SUD treatment may begin their search for an SUD treatment facility online, where they can find information about individual facilities, as well as a summary of patient-generated web-based reviews via popular platforms such as Google or Yelp. Web-based reviews of health care facilities may reflect information about factors associated with positive or negative patient satisfaction. The association between patient satisfaction with SUD treatment and drug-induced mortality is not well understood.
OBJECTIVE: The objective of this study was to examine the association between online review content of SUD treatment facilities and drug-induced state mortality.
METHODS: A cross-sectional analysis of online reviews and ratings of Substance Abuse and Mental Health Services Administration (SAMHSA)-designated SUD treatment facilities listed between September 2005 and October 2021 was conducted. The primary outcomes were (1) mean online rating of SUD treatment facilities from 1 star (worst) to 5 stars (best) and (2) average drug-induced mortality rates from the Centers for Disease Control and Prevention (CDC) WONDER Database (2006-2019). Clusters of words with differential frequencies within reviews were identified. A 3-level linear model was used to estimate the association between online review ratings and drug-induced mortality.
RESULTS: A total of 589 SAMHSA-designated facilities (n=9597 reviews) were included in this study. Drug-induced mortality was compared with the average. Approximately half (24/47, 51%) of states had below average (\"low\") mortality rates (mean 13.40, SD 2.45 deaths per 100,000 people), and half (23/47, 49%) had above average (\"high\") drug-induced mortality rates (mean 21.92, SD 3.69 deaths per 100,000 people). The top 5 themes associated with low drug-induced mortality included detoxification and addiction rehabilitation services (r=0.26), gratitude for recovery (r=-0.25), thankful for treatment (r=-0.32), caring staff and amazing experience (r=-0.23), and individualized recovery programs (r=-0.20). The top 5 themes associated with high mortality were care from doctors or providers (r=0.24), rude and insensitive care (r=0.23), medication and prescriptions (r=0.22), front desk and reception experience (r=0.22), and dissatisfaction with communication (r=0.21). In the multilevel linear model, a state with a 10 deaths per 100,000 people increase in mortality was associated with a 0.30 lower average Yelp rating (P=.005).
CONCLUSIONS: Lower online ratings of SUD treatment facilities were associated with higher drug-induced mortality at the state level. Elements of patient experience may be associated with state-level mortality. Identified themes from online, organically derived patient content can inform efforts to improve high-quality and patient-centered SUD care.

OBJECTIVE: To demonstrate a framework for calculating daily dose distributions for proton therapy in a timeframe amenable to online evaluation using CT-on-Rails.
METHODS: Tasks associated with calculation of daily dose are fully automated. A rigid registration between daily and planning images is used to propagate beams and targets for calculation of daily dose; additionally, risk structures are propagated using deformable registration to facilitate online evaluation. An end-to-end constancy test was carried out using a pelvis phantom containing a simulated target and bladder contour. 97 Daily fan-beam CT data sets associated with 10 clinical patients were processed to demonstrate feasibility and utility of online evaluation. Computing times and dosimetric differences are reported.
RESULTS: The phantom constancy test took 62 s to complete with no notable discrepancies in the registrations or calculated dose. Max doses were identical for target and bladder contours on initial and repeat scans (359 and 310 cGy (RBE) respectively). Total processing time for 97 daily patient images averaged 154.6 s (73.0 - 222.0 s; SD = 31.8 s). On average, dose calculation accounted for 35 % of total processing time. Average differences in D95 for target contours was 1.5 % (SD = 1.6 %) with a max decrease of 5.9 % on a single daily image.
CONCLUSIONS: Daily dose can be automatically calculated in a timeframe amenable to online evaluation using scanner utilities in conjunction with the scripting API of a commercial treatment planning system. Online evaluation of dose in proton therapy is useful to detect clinically relevant changes, guide setup, and facilitate treatment or replanning decisions.

The tourism and hospitality industry, particularly the restaurant business, has been greatly affected by the COVID-19 pandemic. To comprehend customer behavior and preferences during this unprecedented time, it is crucial to analyze online restaurant customer reviews. Thus, this study utilized the valence aware dictionary for sentiment reasoning (VADER) model to examine TripAdvisor reviews of restaurants in Pattaya City, Chon Buri, Thailand, covering the period 2017-2022, which encompasses both pre-pandemic and pandemic years. The findings reveal a significant decrease in the number of reviews and a notable increase in negative sentiments during the COVID-19 pandemic compared to normal circumstances. We noticed two concern areas, i.e., service and staff, and food and taste, that should be addressed urgently. The findings of this study offer valuable insights into customer behavior and requirements, thereby empowering restaurant businesses to enhance service quality, satisfy customer requirements, and strategically plan for a post-COVID-19 future.

The Great Resignation has brought significant challenges to the recovery of the hospitality industry from the depression caused by the coronavirus pandemic (COVID-19). Prior studies have revealed that the leading cause of the Great Resignation is negative employee experience. However, few empirical studies have been conducted to obtain deep insights into the negative experiences of hospitality employees. Hotel managers still lack the knowledge to help them resolve the workforce problem and maintain competitiveness during the pandemic. This study proposes a novel framework, named HENEX, that uses data-mining technologies and employees\' online reviews about hotels to identify the factors that lead to hospitality employees\' negative experiences and changes in these factors caused by COVID-19. We demonstrate the effectiveness of HENEX through a case study that involves major hotels in Australia. The findings could help hotel managers develop strategies to resolve the workforce problem and maintain competitiveness during the Great Resignation period.

Booking decision is a typical decision-making behavior in hospitality, while the neural processing of it is still unclear. To address this issue, with the help of event-related potential (ERP), this work uncovered the neural mechanism of the influence of two extrinsic cues, namely, brand familiarity (familiar vs. unfamiliar) and online reviews (positive vs. negative) on online hotel booking decisions. Behavioral results indicated that the booking rate under the condition of positive reviews was higher than that of negative reviews. In addition, the response time in the case of familiar brands was longer than that of unfamiliar brands. ERP results showed that the P200 amplitude of familiar brands was smaller than that of unfamiliar brands, while for the late positive potential amplitude, the opposite was the case. It is suggested that in the early stage of cognitive processing, unfamiliar brands evoke more automatic and unconscious attention while in the later stage, familiar brands attract more conscious attention. This study also found that the N400 amplitude of negative online reviews was larger than that of positive online reviews, indicating that negative stimuli can result in a larger emotional conflicts than that of positive stimuli. This study provides new insights into the neural mechanism of online booking decisions in the hospitality.

UNASSIGNED: Online review system contains multiple components, such as ratings, review text, product pictures, and video uploads, that could affect consumer loyalty. However, how the affordance of such components influences perceptions and behaviors of consumers remains unclear. We extend stimulus-organism-response (S-O-R) theory to the online review system. Specifically, we combine affordance theory and the technology acceptance model (TAM) to investigate the relations among the affordance of review systems, consumers\' perceived beliefs, and their loyalty.
UNASSIGNED: We surveyed 320 customers on their online shopping experiences in China. We tested our hypotheses using the partial least squares path structural equation modeling (PLS-SEM) method. We report the direct effect of affordances of review components on consumer loyalty and its indirect effects on consumer loyalty through perceived beliefs.
UNASSIGNED: Our results show that integrity and social interaction affordance of review components have significant relations with perceived ease of use, perceived usefulness, and perceived enjoyment. Intelligent topic mining reveals a positive relation on perceived enjoyment. Operability has a positive relation with perceived ease of use and perceived usefulness. These three consumer-perceived beliefs can mediate, to different degrees, the relationship between affordance of review components and consumer loyalty.
UNASSIGNED: This research takes an innovative approach to offer insights into the relationships between IT affordances and consumer perceptions. We examine S-O-R theory through the lens of information technology and extend S-O-R theory by integrating IT affordances. Our research findings pave the way for businesses to design and implement more effective online review systems.

The application of robots in service industry is increasing. Compared with related studies in other countries, the research on users\' acceptance of mid-range and high-range hotel service robots in China is preliminary. Based on the interaction between Chinese consumers and hotel service robots, this study explored the factors that influence consumers\' willingness to accept robots in human-robot interaction. According to the service robot integration willingness scale (performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating conditions, and emotion), this study conducted content analysis and sentiment analysis on 4,107 online reviews from 68 mid-range and high-range hotels in Qunar. The results showed that users\' overall evaluation of robot service in mid-range and high-range hotels is positive. The most frequently mentioned dimension by users is performance efficacy, followed by intrinsic motivation, anthropomorphism, and emotion, finally, the facilitating conditions, the five dimensions have positive impact on users\' evaluation of service robots; the influence of social influence on human-robot interaction evaluation has not been found. This study supplements the research on service robot and provides a reference for hotel managers to make decisions.

Since the outbreak of the COVID-19 pandemic in 2020, China has adopted a zero-clearing policy under closed control. It is rather common for residents who are quarantined at home to buy fresh agricultural products online, when COVID-19 spread in big cities. Many e-commerce platforms are trying to develop online shopping channels for fresh agricultural products. However, negative comments and news about those platforms have been increasing because of several reasons, such as the difference in the quality of fresh products, inadequate categories of commodity and inefficient delivery caused by the shortage of personnel and so on. The smooth daily supply of online fresh agricultural products is conducive to soothing the pessimistic emotions and to encouraging their active obedience to epidemic prevention and control policy. Therefore, it is of great importance to explore the preference characteristics of consumers\' online purchase of fresh agricultural products under this critical situation. In this paper, firstly, Pycharm software is used to collect online comment texts of fresh agricultural products on the online platforms with a total of 34,546 pieces of evaluation data. Secondly, the collected data is preformed into the text preprocessing. To be specific, the obtained online comments are processed by Python, including the process of text duplication between sentences, text duplication within sentences and short sentence filtering. After that, processed texts are subjected to Jieba Text Segmentation to form the final word frequency ranking, involving two procedures, part-of-speech tagging and stop-words removal. Lastly, the results of the LDA model indicate the factors that influence consumers\' preferences when they purchase fresh agricultural products online. This study could not only identify the typical features of residents\' online shopping preference in the context of the spread of COVID-19, but also provide pragmatic suggestions for the local government to appease the residents\' negative emotions for the prevention of widespread complaints at the social level.

Big data (BD) research articles are on new issues, this study sought to fill the knowledge gap of linkage the relationships between big data and marketing strategy with comprehensive viewpoints across different research fields in tourism and hospitality literatures. Content analysis was conducted to gather materials from the particular studies. For each study, the content analysis included the title, abstract, journal, type of sample, exploration design, statistical and analytical techniques, data collection process and keywords was also conducted to confirm the main results of the criteria. The research shows that big data adds value to marketing strategies by using social media to collect information from consumers, which is complemented with appropriate evidence relevant to predicting their needs and behaviors.