The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

BACKGROUND: Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. OBJECTIVE: The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. METHODS: We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. RESULTS: On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical skills and bedside manner, general appreciation from patients, and description of various symptoms. CONCLUSIONS: To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China's health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.

The development of online doctor reviews in China: an analysis of the largest online doctor review website in China.

BACKGROUND: Since the time of Web 2.0, more and more consumers have used online doctor reviews to rate their doctors or to look for a doctor. This phenomenon has received health care researchers' attention worldwide, and many studies have been conducted on online doctor reviews in the United States and Europe. But no study has yet been done in China. Also, in China, without a mature primary care physician recommendation system, more and more Chinese consumers seek online doctor reviews to look for a good doctor for their health care concerns. OBJECTIVE: This study sought to examine the online doctor review practice in China, including addressing the following questions: (1) How many doctors and specialty areas are available for online review? (2) How many online reviews are there on those doctors? (3) What specialty area doctors are more likely to be reviewed or receive more reviews? (4) Are those reviews positive or negative? METHODS: This study explores an empirical dataset from Good Doctor website, haodf.com­the earliest and largest online doctor review and online health care community website in China­from 2006 to 2014, to examine the stated research questions by using descriptive statistics, binary logistic regression, and multivariate linear regression. RESULTS: The dataset from the Good Doctor website contained 314,624 doctors across China and among them, 112,873 doctors received 731,543 quantitative reviews and 772,979 qualitative reviews as of April 11, 2014. On average, 37% of the doctors had been reviewed on the Good Doctor website. Gynecology-obstetrics-pediatrics doctors were most likely to be reviewed, with an odds ratio (OR) of 1.497 (95% CI 1.461-1.535), and internal medicine doctors were less likely to be reviewed, with an OR of 0.94 (95% CI 0.921-0.960), relative to the combined small specialty areas. Both traditional Chinese medicine doctors and surgeons were more likely to be reviewed than the combined small specialty areas, with an OR of 1.483 (95% CI 1.442-1.525) and an OR of 1.366 (95% CI 1.337-1.395), respectively. Quantitatively, traditional Chinese medicine doctors (P<.001) and gynecology-obstetrics-pediatrics doctors (P<.001) received more reviews than the combined small specialty areas. But internal medicine doctors received fewer reviews than the combined small specialty areas (P<.001). Also, the majority of quantitative reviews were positive-about 88% were positive for the doctors' treatment effect measure and 91% were positive for the bedside manner measure. This was the case for the four major specialty areas, which had the most number of doctors­internal medicine, gynecology-obstetrics-pediatrics, surgery, and traditional Chinese medicine. CONCLUSIONS: Like consumers in the United States and Europe, Chinese consumers have started to use online doctor reviews. Similar to previous research on other countries' online doctor reviews, the online reviews in China covered almost every medical specialty, and most of the reviews were positive even though all of the reviewing procedures and the final available information were anonymous. The average number of reviews per rated doctor received in this dataset was 6, which was higher than that for doctors in the United States or Germany, probably because this dataset covered a longer time period than did the US or German dataset. But this number is still very small compared to any doctor's real patient population, and it cannot represent the reality of that population. Also, since all the data used for analysis were from one single website, the data might be biased and might not be a representative national sample of China.

Synthesis, acaricidal activity, and structure-activity relationships of novel phenyl trifluoroethyl thioether derivatives containing substituted benzyl groups.

BACKGROUND: To discover and develop novel acaricidal compounds, a series of 2-fluoro-4-methyl/chlorine-5-((2,2,2-trifluoroethyl)thio)aniline/phenol compounds containing N/O-benzyl moieties were synthesized based on lead compound LZ-1. RESULTS: The activity of these compounds against carmine spider mites (Tetranychus cinnabarinus) was determined using the leaf-spray method. Bioassays indicated that most of the designed target compounds possessed moderate to excellent acaricidal activity against adult T. cinnabarinus. The median lethal concentrations of 25b and 26b were 0.683 and 2.448 mg L-1 against adult mites, respectively; exceeding those of bifenazate (7.519 mg L-1 ) and lead compound LZ-1(3.658 mg L-1 ). Compound 25b exhibited 100% mortality in T. cinnabarinus larvae at 10 mg L-1 . CONCLUSION: Continuing the study of these compounds in field trials, we compared the efficacy of mite killing by compound 25b with the commercial pesticide spirodiclofen and showed that mite control achieved 95.9% and 83.0% lethality at 10 and 22 days post-treatment. In comparison, spirodiclofen showed 92.7% lethality at 10 days and 77.2% lethality at 22 days post-treatment at a concentration of 100 mg L-1 . Results showed that 25b produced more facile and long-lasting control against T. cinnabarinus than the commercial acaricide spirodiclofen. Density functional theory analysis and electrostatic potential calculations of various molecular substitutions suggested some useful models to achieve other highly active miticidal compounds. © 2023 Society of Chemical Industry.

Effects of online support and social media communities on gestational diabetes: A systematic review.

BACKGROUND: Gestational diabetes mellitus (GDM) is a common complication in pregnancy that can lead to negative maternal and fetal outcomes. Online support interventions have been suggested as a potential tool to improve the management of GDM. OBJECTIVE: This systematic review aimed to summarize the effectiveness of social media and online support interventions for the management of GDM. METHODS: We conducted a thorough systematic search across Web of Science, Scopus, and PubMed, following PRISMA guidelines, and supplemented it with a manual search. Our results included both qualitative and quantitative research. We rigorously assessed quantitative studies for bias using ROBINS-I and RoB 2 tools, ensuring the reliability of our findings. RESULTS: We incorporated a total of 22 studies, which were comprised of ten qualitative and twelve quantitative studies. Online support interventions were found to have a positive impact on promoting self-care and improving healthcare outcomes for women with GDM. Individualized diet and exercise interventions resulted in lower odds of weight gain and GDM diagnosis, while online prenatal education increased breastfeeding rates. In addition, telemedicine options reduced the need for in-person clinical visits and improved patient satisfaction. CONCLUSIONS: Online support interventions show potential to improve outcomes in patients with GDM in this small literature review. Future research is also necessary to determine the effectiveness of different types of online interventions and identify strategies to improve engagement and the quality of the information provided through online resources.

Which factors affect the performance of technology business incubators in China? An entrepreneurial ecosystem perspective.

To examine which factors affect the performance of technology business incubators in China, the present study proposes an entrepreneurial ecosystem framework with four key areas, i.e., people, technology, capital, and infrastructure. We then assess this framework using a three-year panel data set of 857 national-level technology business incubators in 33 major cities from 28 provinces in China, from 2015 to 2017. We utilize factor analysis to downsize dozens of characteristics of these technology business incubators into seven factors related to the four proposed areas. Panel regression model results show that four of the seven factors related to three areas of the entrepreneurial ecosystem, namely people, technology, and capital areas, have statistically significant associations with an incubator's performance when applied to the overall national data set. Further, seven factors related to all four areas have various statistically significant associations with an incubator's performance in five major regional data set. In particular, a technology related factor has a consistently statistically significant association with the performance of the incubator in both national model and the five regional models, as we expected.

Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.

BACKGROUND: Order sets are a clinical decision support (CDS) tool in computerized provider order entry systems. Order set use has been associated with improved quality of care. Particularly related to opioids and pain management, order sets have been shown to standardize and reduce the prescription of opioids. However, clinician-level barriers often limit the uptake of this CDS modality. OBJECTIVE: To identify the barriers to order sets adoption, we surveyed clinicians on their training, knowledge, and perceptions related to order sets for pain management. METHODS: We distributed a cross-sectional survey between October 2020 and April 2021 to clinicians eligible to place orders at two campuses of a major academic medical center. Survey questions were adapted from the widely used framework of Unified Theory of Acceptance and Use of Technology. We hypothesize that performance expectancy (PE) and facilitating conditions (FC) are associated with order set use. Survey responses were analyzed using logistic regression. RESULTS: The intention to use order sets for pain management was associated with PE to existing order sets, social influence (SI) by leadership and peers, and FC for electronic health record (EHR) training and function integration. Intention to use did not significantly differ by gender or clinician role. Moderate differences were observed in the perception of the effort of, and FC for, order set use across gender and roles of clinicians, particularly emergency medicine and internal medicine departments. CONCLUSION: This study attempts to identify barriers to the adoption of order sets for pain management and suggests future directions in designing and implementing CDS systems that can improve order sets adoption by clinicians. Study findings imply the importance of order set effectiveness, peer influence, and EHR integration in determining the acceptability of the order sets.

The Determinants of Length of Homeless Shelter Stays: Evidence-Based Regression Analyses.

Objective: To identify determinants that contribute to the length of homeless shelter stay. Methods: We utilized a unique dataset from the Homeless Management Information Systems from Boston, Massachusetts, United States, which contains 44,197 shelter stays for 17,070 adults between Jan. 2014 and May 2018. Results: Our statistical analyses and regression model analyses show that factors that contribute to the length of a homeless shelter stay include being female, senior, disability, being Hispanic, or being Asian or Black African. A significant fraction of homeless shelter stays (76%) are experienced by individuals with at least one of three disabilities: physical disability, mental health issues, or substance use disorder. Recidivism also contributes to longer homeless shelter stays. Conclusion: The results suggest possible program and policy implications. Several factors that contribute to longer homeless shelter stay, such as gender, age, disability, race, and ethnicity, may have funding implications. Age may point to the need for early interventions. Disability is developmental and may benefit from treatment and intervention. Finally, we find that length of stay and recidivism are not independent, and may form a vicious cycle that requires additional investigation.

An empirical study of opinion leader effects on mobile technology implementation by physicians in an American community health system.

This study empirically examines the opinion leader effects on a mobile clinical information technology implementation by physicians in an American community health system using a fixed effect regression model. The model result suggests that the opinion leader effects are statistically significant during this information technology implementation process. Quantitatively, if opinion leaders increase their technology usage by 10 percent, the physicians who work closely with those opinion leaders would increase their technology usage by 3.5 percent, after controlling for physician individual-level fixed effects, time effects, working environment, and workload. This empirical result of opinion leader effects provides policy implications such as, if a healthcare system wants to promote a new information technology or a new mobile information technology implementation within their organization, they should leverage this opinion leader effects.

Facile construction of a hyperbranched poly(acrylamide) bearing tetraphenylethene units: a novel fluorescence probe with a highly selective and sensitive response to Zn2.

Thermo-responsive hyperbranched copoly(bis(N,N-ethyl acrylamide)/(N,N-methylene bisacrylamide)) (HPEAM-MBA) was synthesized by using reversible addition-fragmentation chain-transfer polymerization (RAFT). Interestingly, the zinc ion (Zn2+) was found to have a crucial influence on the lowest critical solution temperature (LCST) of the thermo-responsive polymer. The tetraphenylethylene (TPE) unit was then introduced onto the backbone of the as-prepared thermo-responsive polymer, which endows a Zn2+-responsive "turn-off" effect on the fluorescence properties. The TPE-bearing polymer shows a highly specific response over other metal ions and the "turn-off" response can even be tracked as the concentration of Zn2+ reduces to 2 × 10-5 M. The decrement of fluorescence intensity was linearly dependent on the concentration of Zn2+ in the range of 4-18 µmol L-1. The flexible, versatile and feasible approach, as well as the excellent detection performance, may generate a new type of Zn2+ probe without the tedious synthesis of the moiety bearing Zn2+ recognition units.

A tale of two countries: International comparison of online doctor reviews between China and the United States.

BACKGROUND: Worldwide, patients have posted millions of online reviews for their doctors. The rich textual information in the online reviews holds the potential to generate insights into how patients' experience with their doctors differ across nations and how should we use them to improve our health service. OBJECTIVE: We apply customized text mining techniques to compare online doctor reviews from China and the United States, in order to measure the systematic differences in patient reviews between the two countries, and assess the potential insights that can be derived from this large volume of online text data. METHODS: We compare the textual reviews of obstetrics and gynecology (OBGYN) doctors from the two most popular online doctor rating websites in the U.S. and China, respectively: RateMDs.com and Haodf.com. We apply a customized text mining technique, Latent Dirichlet Allocation (LDA) topic modeling to identify the major topics in positive and negative reviews of those two countries. We then compare their similarities and differences. RESULTS: Among the positive reviews, both Chinese and American patients talked about medical treatment, bedside manner, and appreciation/recommendation, but Chinese patients commented more about medical treatment while American patients focused more on recommendation. Also, reviews about bedside manner from Chinese patients were more related to doctors while on the American side, they were more about staff. This reflects the difference between the two countries' health systems. Further, among the negative reviews, both countries' patients talked about medical treatment, bedside manner, and logistics. However, Chinese patients focus more on the registration process, while American patients are more related to the staff, wait time, and insurance, which further shows the differences between the two nations' health systems. CONCLUSIONS: Online doctor reviews contain valuable information that can generate insights on the similarities and differences of patient experience across nations. They are useful assets to assist healthcare consumers, providers, and administrators in moving toward a patient-centered care. In this age of big data, online doctor reviews can be a valuable source for international perspectives on healthcare systems.

An empirical study of opinion leader effects on mobile information technology adoption in healthcare.

Given the increasing number of applications but slow adoption of IT, including mobile IT, in healthcare, it is important to develop a better understanding of the contextual factors that motivate IT adoption by physicians. Although studies have shown that age or gender may affect physicians' IT adoption, those factors cannot be controlled when deploying a new IT. Therefore, the current research examines empirical evidence of a contextual factor, opinion leader effects, on IT adoption in healthcare that can be influenced by organizational policies. Using a unique panel dataset of physicians' usage of a mobile clinical IT from a community hospital, we observe a significant result that physicians under the influence of opinion leaders are three times more likely to adopt the IT than otherwise. This finding suggests that incentivizing a small proportion of opinion leaders to adopt a new IT has the potential to motivate wider adoption across the organization.