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

BACKGROUND: Web-based reviews of physicians have become exceedingly popular among health care consumers since the early 2010s. A factor that can potentially influence these reviews is the gender of the physician, because the physician's gender has been found to influence patient-physician communication. Our study is among the first to conduct a rigorous longitudinal analysis to study the effects of the gender of physicians on their reviews, after accounting for several important clinical factors, including patient risk, physician specialty, and temporal factors, using time fixed effects. In addition, this study is among the first to study the possible gender bias in web-based reviews using statewide data from Alabama, a predominantly rural state with high Medicaid and Medicare use. OBJECTIVE: This study conducts a longitudinal empirical investigation of the relationship between physician gender and their web-based reviews using data across the state of Alabama, after accounting for patient risk and temporal effects. METHODS: We created a unique data set by combining data from web-based physician reviews from the popular physician review website, RateMDs, and clinical data from the Center for Medicare and Medicaid Services for the state of Alabama. We used longitudinal econometric specifications to conduct an econometric analysis, while controlling for several important clinical and review characteristics across four rating dimensions (helpfulness, knowledge, staff, and punctuality). The overall rating and these four rating dimensions from RateMDs were used as the dependent variables, and physician gender was the key explanatory variable in our panel regression models. RESULTS: The panel used to conduct the main econometric analysis included 1093 physicians. After controlling for several clinical and review factors, the physician random effects specifications showed that male physicians receive better web-based ratings than female physicians. Coefficients and corresponding SEs and P values of the binary variable GenderFemale (1 for female physicians and 0 otherwise) with different rating variables as outcomes were as follows: OverallRating (coefficient -0.194, SE 0.060; P=.001), HelpfulnessRating (coefficient -0.221, SE 0.069; P=.001), KnowledgeRating (coefficient -0.230, SE 0.065; P<.001), StaffRating (coefficient -0.123, SE 0.062; P=.049), and PunctualityRating (coefficient -0.200, SE 0.067; P=.003). The negative coefficients indicate a bias toward male physicians versus female physicians for aforementioned rating variables. CONCLUSIONS: This study found that female physicians receive lower web-based ratings than male physicians even after accounting for several clinical characteristics associated with the physicians and temporal effects. Although the magnitude of the coefficients of GenderFemale was relatively small, they were statistically significant. This study provides support to the findings on gender bias in the existing health care literature. We contribute to the existing literature by conducting a study using data across the state of Alabama and using a longitudinal econometric analysis, along with incorporating important clinical and review controls associated with the physicians.

Pandemic-Triggered Adoption of Telehealth in Underserved Communities: Descriptive Study of Pre- and Postshutdown Trends.

BACKGROUND: The adoption of telehealth services has been a challenge in rural communities. The reasons for the slow adoption of such technology-driven services have been attributed to social norms, health care policies, and a lack of infrastructure to support the delivery of services. However, the COVID-19 pandemic-related shutdown of in-person health care services resulted in the usage of telehealth services as a necessity rather than a choice. The pandemic also fast-tracked some needed legislation to allow medical cost reimbursement for remote examination and health care services. As services return to normalcy, it is important to examine whether the usage of telehealth services during the period of a shutdown has changed any of the trends in the acceptance of telehealth as a reliable alternative to traditional in-person health care services. OBJECTIVE: Our aim was to explore whether the temporary shift to telehealth services has changed the attitudes toward the usage of technology-enabled health services in rural communities. METHODS: We examined the Medicaid reimbursement data for the state of Alabama from March 2019 through June 2021. Selecting the telehealth service codes, we explored the adoption rates in 3 phases of the COVID-19 shutdown: prepandemic, pandemic before the rollout of mass vaccination, and pandemic after the rollout of mass vaccination. RESULTS: The trend in telemedicine claims had an opposite pattern to that in nontelemedicine claims across the 3 periods. The distribution of various characteristics of patients who used telemedicine (age group, gender, race, level of rurality, and service provider type) was different across the 3 periods. Claims related to behavior and mental health had the highest rates of telemedicine usage after the onset of the pandemic. The rate of telemedicine usage remained at a high level after the rollout of mass vaccination. CONCLUSIONS: The current trends indicate that adoption of telehealth services is likely to increase postpandemic and that the consumers (patients), service providers, health care establishments, insurance companies, and state and local policies have changed their attitudes toward telehealth. An increase in the use of telehealth could help local and federal governments address the shortage of health care facilities and service providers in underserved communities, and patients can get the much-needed care in a timely and effective manner.

Growth in the development of health and fitness mobile apps amid COVID-19 pandemic.

Objectives: Technology in the form of mobile apps has played an essential role in facilitating, tracking, and maintaining health and fitness activities during the pandemic. When countries opted for partial or complete lockdowns to contain the spread of the coronavirus disease 2019 virus, it led to people working on their health and fitness-related activities from their homes, just as they continued working from home. This paper aims to quantify the impact of coronavirus disease 2019 on the development of health and fitness mobile apps. Specifically, we compute the effect of coronavirus disease 2019 on the growth of different sub-categories of health and fitness apps. Methods: We scraped data about a population of 78,890 health and fitness apps from the iOS App Store. First, categories of health and fitness apps are identified using text analysis on the descriptions of apps. Second, the rise in the development of new apps is analyzed. To quantify the impact of coronavirus disease 2019 on the growth of the health and fitness apps, multiple time-series forecasting models are created for different categories of health and fitness apps. Results: The text analysis identified twelve different types of health and fitness apps on the app market. Our models estimated that the number of health and fitness apps on the iOS app market exceeded the expected growth by 29.9% after the pandemic. The results of all categories of health and fitness are discussed in the paper. Conclusions: Our analysis found significant growth in the development of new health and fitness apps after the pandemic outbreak. The post hoc study of the population of health and fitness apps presented the current state of this particular area of the app market. In addition, it provided potential growth areas in app markets where there are fewer apps.

Text mining in healthcare. Applications and opportunities.

Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.