The Interplay Between Online Reviews and Physician Demand: An Empirical Investigation

2021 ◽  
Author(s):  
Yuqian Xu ◽  
Mor Armony ◽  
Anindya Ghose
Keyword(s):  
Service Quality ◽  
Operations Management ◽  
Choice Model ◽  
Healthcare Delivery ◽  
Online Reviews ◽  
The United States ◽  
Patient Choice ◽  
Data Set ◽  
Textual Information ◽  
Time Service

Social media platforms for healthcare services are changing how patients choose physicians. The digitization of healthcare reviews has been providing additional information to patients when choosing their physicians. On the other hand, the growing online information introduces more uncertainty among providers regarding the expected future demand and how different service features can affect patient decisions. In this paper, we derive various service-quality proxies from online reviews and show that leveraging textual information can derive useful operational measures to better understand patient choices. To do so, we study a unique data set from one of the leading appointment-booking websites in the United States. We derive from the text reviews the seven most frequently mentioned topics among patients, namely, bedside manner, diagnosis accuracy, waiting time, service time, insurance process, physician knowledge, and office environment, and then incorporate these service features into a random-coefficient choice model to quantify the economic values of these service-quality proxies. By introducing quality proxies from text reviews, we find the predictive power of patient choice increases significantly, for example, a 6%–12% improvement measured by mean squared error for both in-sample and out-of-sample tests. In addition, our estimation results indicate that contextual description may better characterize users’ perceived quality than numerical ratings on the same service feature. Broadly speaking, this paper shows how to incorporate textual information into an econometric model to understand patient choice in healthcare delivery. Our interdisciplinary approach provides a framework that combines machine learning and structural modeling techniques to advance the literature in empirical operations management, information systems, and marketing. This paper was accepted by David Simchi-Levi, operations management.

Service Quality Using Text Mining: Measurement and Consequences

2020 ◽  
Author(s):  
Jorge Mejia ◽  
Shawn Mankad ◽  
Anandasivam Gopal
Keyword(s):  
Service Quality ◽  
Service Industry ◽  
Service Providers ◽  
Nonnegative Matrix ◽  
Online Reviews ◽  
The United States ◽  
Large Set ◽  
External Data ◽  
Industry Standards ◽  
Managerial Implications

Problem description: Measuring quality in the service industry remains a challenge. Existing methodologies are often costly and unscalable. Furthermore, understanding how elements of service quality contribute to the performance of service providers continues to be a concern in the service industry. In this paper, we address these challenges in the restaurant sector, a vital component of the service industry. Academic/practical relevance: Our work provides a scalable methodology for measuring the quality of service providers using the vast amount of text in social media. The quality metrics proposed are associated with economic outcomes for restaurants and can help predict future restaurant performance. Methodology: We use text present in online reviews on Yelp.com to identify and extract service dimensions using nonnegative matrix factorization for a large set of restaurants located in a major city in the United States. We subsequently validate these service dimensions as proxies for service quality using external data sources and a series of laboratory experiments. Finally, we use econometrics to test the relationship between these dimensions and restaurant survival as additional validation. Results: We find that our proposed service quality dimensions are scalable, match industry standards, and are correctly identified by subjects in a controlled setting. Furthermore, we show that specific service dimensions are significantly correlated with the survival of merchants, even after controlling for competition and other factors. Managerial implications: This work has implications for the strategic use of text analytics in the context of service operations, where an increasingly large text corpus is available. We discuss the benefits of this work for service providers and platforms, such as Yelp and OpenTable.

scholarly journals Comparation of logistic regression methods and discrete choice model in the selection of habitats

2010 ◽  
Vol 67 (3) ◽  
pp. 327-333
Author(s):  
Sandra Vergara Cardozo ◽  
Bryan Frederick John Manly ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Logistic Regression ◽  
Discrete Choice ◽  
Resource Selection ◽  
Choice Model ◽  
The United States ◽  
Discrete Choice Models ◽  
Selection Function ◽  
Data Set ◽  
North West ◽  
Selection Of

Based on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States.

State Medical Boards' Perceptions of a Minimum Data Set and Current Practices for Collecting Physician Information

2013 ◽  
Vol 99 (4) ◽  
pp. 40-45 ◽  
Author(s):  
Aaron Young ◽  
Philip Davignon ◽  
Margaret B. Hansen ◽  
Mark A. Eggen
Keyword(s):  
Media Coverage ◽  
The United States ◽  
Minimum Data Set ◽  
Direct Patient Care ◽  
Physician Workforce ◽  
Data Set ◽  
Minimum Data ◽  
State Medical Boards ◽  
The Impact ◽  
Current Practices

ABSTRACT Recent media coverage has focused on the supply of physicians in the United States, especially with the impact of a growing physician shortage and the Affordable Care Act. State medical boards and other entities maintain data on physician licensure and discipline, as well as some biographical data describing their physician populations. However, there are gaps of workforce information in these sources. The Federation of State Medical Boards' (FSMB) Census of Licensed Physicians and the AMA Masterfile, for example, offer valuable information, but they provide a limited picture of the physician workforce. Furthermore, they are unable to shed light on some of the nuances in physician availability, such as how much time physicians spend providing direct patient care. In response to these gaps, policymakers and regulators have in recent years discussed the creation of a physician minimum data set (MDS), which would be gathered periodically and would provide key physician workforce information. While proponents of an MDS believe it would provide benefits to a variety of stakeholders, an effort has not been attempted to determine whether state medical boards think it is important to collect physician workforce data and if they currently collect workforce information from licensed physicians. To learn more, the FSMB sent surveys to the executive directors at state medical boards to determine their perceptions of collecting workforce data and current practices regarding their collection of such data. The purpose of this article is to convey results from this effort. Survey findings indicate that the vast majority of boards view physician workforce information as valuable in the determination of health care needs within their state, and that various boards are already collecting some data elements. Analysis of the data confirms the potential benefits of a physician minimum data set (MDS) and why state medical boards are in a unique position to collect MDS information from physicians.

How Research Will Adapt to HIPAA: A View from Within the Healthcare Delivery System

2002 ◽  
Vol 28 (4) ◽  
pp. 491-502
Author(s):  
Mary L. Durham
Keyword(s):  
Health Information ◽  
Healthcare Delivery ◽  
Healthcare Providers ◽  
The United States ◽  
Personal Health Information ◽  
Personal Health ◽  
Business Associations ◽  
Electronic Information ◽  
Healthcare Delivery System ◽  
Institutional Review

While the new Health Insurance Privacy and Accountability Act (HIPAA) research rules governing privacy, confidentiality and personal health information will challenge the research and medical communities, history teaches us that the difficulty of this challenge pales in comparison to the potential harms that such regulations are designed to avoid. Although revised following broad commentary from researchers and healthcare providers around the country, the HIPAA privacy requirements will dramatically change the way healthcare researchers do their jobs in the United States. Given our reluctance to change, we risk overlooking potentially valid reasons why access to personal health information is restricted and regulated. In an environment of electronic information, public concern, genetic information and decline of public trust, regulations are ever-changing. Six categories of HIPAA requirements stand out as transformative: disclosure accounting/tracking, business associations, institutional review board (IRB) changes, minimum necessary requirements, data de-identification, and criminal and civil penalties.

Legislative Diversity and the Rise of Women Lobbyists

2021 ◽  
pp. 106591292110093
Author(s):  
James M. Strickland ◽  
Katelyn E. Stauffer
Keyword(s):  
Gender Diversity ◽  
Personal Characteristics ◽  
The United States ◽  
Revolving Door ◽  
Women And Politics ◽  
Political Equality ◽  
Data Set ◽  
Women Legislators ◽  
Organized Interests ◽  
American States

Despite a growing body of literature examining the consequences of women’s inclusion among lobbyists, our understanding of the factors that lead to women’s initial emergence in the profession is limited. In this study, we propose that gender diversity among legislative targets incentivizes organized interests to hire women lobbyists, and thus helps to explain when and how women emerge as lobbyists. Using a comprehensive data set of registered lobbyist–client pairings from all American states in 1989 and 2011, we find that legislative diversity influences not only the number of lobby contracts held by women but also the number of former women legislators who become revolving-door lobbyists. This second finding further supports the argument that interests capitalize on the personal characteristics of lobbyists, specifically by hiring women to work in more diverse legislatures. Our findings have implications for women and politics, lobbying, and voice and political equality in the United States.

scholarly journals Understanding and Promoting Racial Diversity in Healthcare Settings to Address Disparities in Pandemic Crisis Management

2021 ◽  
Vol 12 ◽  
pp. 215013272110183
Author(s):  
Azza Sarfraz ◽  
Zouina Sarfraz ◽  
Alanna Barrios ◽  
Kuchalambal Agadi ◽  
Sindhu Thevuthasan ◽  
...  
Keyword(s):  
United States ◽  
Health Disparities ◽  
Racial Discrimination ◽  
Clinical Training ◽  
Minority Groups ◽  
Mental Health Problems ◽  
Minority Group ◽  
Healthcare Delivery ◽  
The United States ◽  
Majority Group

Background: Health disparities have become apparent since the beginning of the COVID-19 pandemic. When observing racial discrimination in healthcare, self-reported incidences, and perceptions among minority groups in the United States suggest that, the most socioeconomically underrepresented groups will suffer disproportionately in COVID-19 due to synergistic mechanisms. This study reports racially-stratified data regarding the experiences and impacts of different groups availing the healthcare system to identify disparities in outcomes of minority and majority groups in the United States. Methods: Studies were identified utilizing PubMed, Embase, CINAHL Plus, and PsycINFO search engines without date and language restrictions. The following keywords were used: Healthcare, raci*, ethnic*, discriminant, hosti*, harass*, insur*, education, income, psychiat*, COVID-19, incidence, mortality, mechanical ventilation. Statistical analysis was conducted in Review Manager (RevMan V.5.4). Unadjusted Odds Ratios, P-values, and 95% confidence intervals were presented. Results: Discrimination in the United States is evident among racial groups regarding medical care portraying mental risk behaviors as having serious outcomes in the health of minority groups. The perceived health inequity had a low association to the majority group as compared to the minority group (OR = 0.41; 95% CI = 0.22 to 0.78; P = .007), and the association of mental health problems to the Caucasian-American majority group was low (OR = 0.51; 95% CI = 0.45 to 0.58; P < .001). Conclusion: As the pandemic continues into its next stage, efforts should be taken to address the gaps in clinical training and education, and medical practice to avoid the recurring patterns of racial health disparities that become especially prominent in community health emergencies. A standardized tool to assess racial discrimination and inequity will potentially improve pandemic healthcare delivery.

scholarly journals Actors, Partisan Inclination, and Emotions: An Analysis of Government Shutdown News Stories Shared on Twitter

2021 ◽  
Vol 7 (2) ◽  
pp. 205630512110088
Author(s):  
Colin Agur ◽  
Lanhuizi Gan
Keyword(s):  
Emotional Responses ◽  
The United States ◽  
Online Information ◽  
News Frames ◽  
Data Set ◽  
Political Issues ◽  
News Stories ◽  
News Sharing ◽  
Government Shutdown

Scholars have recognized emotion as an increasingly important element in the reception and retransmission of online information. In the United States, because of existing differences in ideology, among both audiences and producers of news stories, political issues are prone to spark considerable emotional responses online. While much research has explored emotional responses during election campaigns, this study focuses on the role of online emotion in social media posts related to day-to-day governance in between election periods. Specifically, this study takes the 2018–2019 government shutdown as its subject of investigation. The data set shows the prominence of journalistic and political figures in leading the discussion of news stories, the nuance of emotions employed in the news frames, and the choice of pro-attitudinal news sharing.

STEM-Focused Career Courses and College Pipeline for Students with Learning Disabilities

2021 ◽  
pp. 089590482110199
Author(s):  
Jennifer A. Freeman ◽  
Michael A. Gottfried ◽  
Jay Stratte Plasman
Keyword(s):  
High School ◽  
Learning Disabilities ◽  
High School Students ◽  
The United States ◽  
Students With Learning Disabilities ◽  
School Students ◽  
Data Set ◽  
Stem Majors ◽  
Career And Technical ◽  
Nationally Representative

Recent educational policies in the United States have fostered the growth of science, technology, engineering, and mathematics (STEM) career-focused courses to support high school students’ persistence into these fields in college and beyond. As one key example, federal legislation has embedded new types of “applied STEM” (AS) courses into the career and technical education curriculum (CTE), which can help students persist in STEM through high school and college. Yet, little is known about the link between AS-CTE coursetaking and college STEM persistence for students with learning disabilities (LDs). Using a nationally representative data set, we found no evidence that earning more units of AS-CTE in high school influenced college enrollment patterns or major selection in non-AS STEM fields for students with LDs. That said, students with LDs who earned more units of AS-CTE in high school were more likely to seriously consider and ultimately declare AS-related STEM majors in college.

scholarly journals The COVID Connection: Pandemic Anxiety, COVID-19 Comprehension, and Digital Confidence

2021 ◽  
pp. 000276422110031
Author(s):  
Laura Robinson ◽  
Jeremy Schulz ◽  
Øyvind N. Wiborg ◽  
Elisha Johnston
Keyword(s):  
Logistic Models ◽  
The United States ◽  
Sociodemographic Factors ◽  
Future Research ◽  
Lower Probability ◽  
Data Set ◽  
Digital Inequality ◽  
Partner Status ◽  
General Anxiety ◽  
Nationally Representative

This article presents logistic models examining how pandemic anxiety and COVID-19 comprehension vary with digital confidence among adults in the United States during the first wave of the pandemic. As we demonstrate statistically with a nationally representative data set, the digitally confident have lower probability of experiencing physical manifestations of pandemic anxiety and higher probability of adequately comprehending critical information on COVID-19. The effects of digital confidence on both pandemic anxiety and COVID-19 comprehension persist, even after a broad range of potentially confounding factors are taken into account, including sociodemographic factors such as age, gender, race/ethnicity, metropolitan status, and partner status. They also remain discernable after the introduction of general anxiety, as well as income and education. These results offer evidence that the digitally disadvantaged experience greater vulnerability to the secondary effects of the pandemic in the form of increased somatized stress and decreased COVID-19 comprehension. Going forward, future research and policy must make an effort to address digital confidence and digital inequality writ large as crucial factors mediating individuals’ responses to the pandemic and future crises.

scholarly journals 910. A Comprehensive, One Year, Hospital-Wide Snapshot of All Serious Infectious Complications in People Who Inject Drugs

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S489-S490
Author(s):  
John T Henderson ◽  
Evelyn Villacorta Cari ◽  
Nicole Leedy ◽  
Alice Thornton ◽  
Donna R Burgess ◽  
...  
Keyword(s):  
Multivariable Analysis ◽  
Vertebral Osteomyelitis ◽  
The United States ◽  
Infectious Complications ◽  
Data Set ◽  
Cumulative Impact ◽  
Mortality And Morbidity ◽  
Icu Admission ◽  
Risk Of Death ◽  
The Impact

Abstract Background There has been a dramatic rise in IV drug use (IVDU) and its associated mortality and morbidity, however, the scope of this effect has not been described. Kentucky is at the epicenter of this epidemic and is an ideal place to better understand the health complications of IVDU in order to improve outcomes. Methods All adult in-patient admissions to University of Kentucky hospitals in 2018 with an Infectious Diseases (ID) consult and an ICD 9/10 code associated with IVDU underwent thorough retrospective chart review. Demographic, descriptive, and outcome data were collected and analyzed by standard statistical analysis. Results 390 patients (467 visits) met study criteria. The top illicit substances used were methamphetamine (37.2%), heroin (38.2%), and cocaine (10.3%). While only 4.1% of tested patients were HIV+, 74.2% were HCV antibody positive. Endocarditis (41.1%), vertebral osteomyelitis (20.8%), bacteremia without endocarditis (14.1%), abscess (12.4%), and septic arthritis (10.4%) were the most common infectious complications. The in-patient death rate was 3.0%, and 32.2% of patients were readmitted within the study period. The average length of stay was 26 days. In multivariable analysis, infectious endocarditis was associated with a statistically significant increase in risk of death, ICU admission, and hospital readmission. Although not statistically significant, trends toward mortality and ICU admission were identified for patients with prior endocarditis and methadone was correlated with decreased risk of readmission and ICU stay. FIGURE 1: Reported Substances Used FIGURE 2: Comorbidities FIGURE 3: Types of Severe Infectious Complications Conclusion We report on a novel, comprehensive perspective on the serious infectious complications of IVDU in an attempt to measure its cumulative impact in an unbiased way. This preliminary analysis of a much larger dataset (2008-2019) reveals some sobering statistics about the impact of IVDU in the United States. While it confirms the well accepted mortality and morbidity associated with infective endocarditis and bacteremia, there is a significant unrecognized impact of other infectious etiologies. Additional analysis of this data set will be aimed at identifying key predictive factors in poor outcomes in hopes of mitigating them. Disclosures All Authors: No reported disclosures

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