Gaurav Tuli

Measuring patient-perceived quality of care in US hospitals using Twitter

Background Patients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches. Objective To assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures. Design 404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients. Key results Roughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a non-profit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003). Conclusions Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.

Blocking Simple and Complex Contagion by Edge Removal

Eliminating interactions among individuals is an important means of blocking contagion spread, e.g., closing schools during an epidemic or shutting down electronic communication channels during social unrest. We study contagion blocking in networked populations by identifying edges to remove from a network, thus blocking contagion transmission pathways. We formulate various problems to minimize contagion spread and show that some are efficiently solvable while others are formally hard. We also compare our hardness results to those from node blocking problems and show interesting differences between the two. Our main problem is not only hard, but also has no approximation guarantee, unless P=NP. Therefore, we devise a heuristic for the problem and compare its performance to state-of-the-art heuristics from the literature. We show, through results of 12 (network, heuristic) combinations on three real social networks, that our method offers considerable improvement in the ability to block contagions in weighted and unweighted networks. We also conduct a parametric study to understand the limitations of our approach.

A general-purpose graph dynamical system modeling framework

We describe InterSim, a general purpose flexible framework for simulating graph dynamical systems (GDS) and their generalizations. GDS provide a powerful formalism to model and analyze agent-based systems (ABS) because there is a direct mapping between nodes and edges (which denote interactions) in a GDS and agents and interactions in an ABS, thereby providing InterSim with great expressive power. We describe the design, implementation, capabilities, and features of InterSim; e.g., it enables users to quickly produce simulations of ABS in many application domains. We present illustrative case studies that focus on the simulation of social phenomena. InterSim has been used to simulate networks with 4 million agents and to execute large parametric simulation studies.

Using Twitter to Identify and Respond to Food Poisoning: The Food Safety Stl Project

Context: Foodborne illness affects 1 in 4 US residents each year. Few of those sickened seek medical care or report the illness to public health authorities, complicating prevention efforts. Citizens who report illness identify food establishments with more serious and critical violations than found by regular inspections. New media sources, including online restaurant reviews and social media postings, have the potential to improve reporting. Objective: We implemented a Web-based Dashboard (HealthMap Foodborne Dashboard) to identify and respond to tweets about food poisoning from St Louis City residents. Design and Setting: This report examines the performance of the Dashboard in its first 7 months after implementation in the City of St Louis Department of Health. Main Outcome Measures: We examined the number of relevant tweets captured and replied to, the number of foodborne illness reports received as a result of the new process, and the results of restaurant inspections following each report. Results: In its first 7 months (October 2015-May 2016), the Dashboard captured 193 relevant tweets. Our replies to relevant tweets resulted in more filed reports than several previously existing foodborne illness reporting mechanisms in St Louis during the same time frame. The proportion of restaurants with food safety violations was not statistically different (P = .60) in restaurants inspected after reports from the Dashboard compared with those inspected following reports through other mechanisms. Conclusion: The Dashboard differs from other citizen engagement mechanisms in its use of current data, allowing direct interaction with constituents on issues when relevant to the constituent to provide time-sensitive education and mobilizing information. In doing so, the Dashboard technology has potential for improving foodborne illness reporting and can be implemented in other areas to improve response to public health issues such as suicidality, spread of Zika virus infection, and hospital quality.

Addiction dynamics may explain the slow decline of smoking prevalence

The prevalence of cigarette smoking in the United States has declined very slowly over the last four decades, despite much effort by multiple governmental and non-governmental institutions. Peer influence has been shown to be the largest contributing factor to the spread of smoking behavior, which suggests the use of epidemic models for understanding this phenomenon. Here we develop a structured resistance model, which is an SIS model extended to include multiple S and I states corresponding to different levels of addiction. This model exhibits a backward bifurcation, which means that once the behavior is endemic, it can be very difficult to remove entirely from the population. We do numerical experiments with the Framingham Heart Study social network to show that the resulting epicurve closely matches empirical data on the overall decline in smoking behavior.

Investigating inequities in hospital care among lesbian, gay, bisexual, and transgender (LGBT) individuals using social media

RATIONALE Persons who identify as lesbian, gay, bisexual, and transgender (LGBT) face health inequities due to unwarranted discrimination against their sexual orientation or identity. An important contributor to LGBT health disparities is the inequitable or substandard care that LGBT individuals receive from hospitals. OBJECTIVE To investigate inequities in hospital care among LGBT patients using the popular social media platform Twitter. METHOD This study examined a dataset of Twitter communications (tweets) collected from February 2015 to May 2017. The tweets mentioned Twitter handles for hospitals (i.e., usernames for hospitals) and LGBT related terms. The topics discussed were explored to develop an LGBT position index referring to whether the hospital appears supportive or not supportive of LGBT rights. Results for each hospital were then compared to the Healthcare Equality Index (HEI), an established index to evaluate equity of hospital care towards LGBT patients. RESULTS In total, 1856 tweets mentioned LGBT terms representing 653 unique hospitals. Of these hospitals, 189 (28.9%) were identified as HEI leaders. Hospitals in the Northeast showed significantly greater support towards LGBT issues compared to hospitals in the Midwest. Hospitals deemed as HEI leaders had higher LGBT position scores compared to non-HEI leaders (p = 0.042), when controlling for hospital size and location. CONCLUSIONS This exploratory study describes a novel approach to monitoring LGBT hospital care. While these initial findings should be interpreted cautiously, they can potentially inform practices to improve equity of care and efforts to address health disparities among gender minority groups.

Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study

Background There are documented differences in access to health care across the United States. Previous research indicates that Web-based data regarding patient experiences and opinions of health care are available from Twitter. Sentiment analyses of Twitter data can be used to examine differences in patient views of health care across the United States. Objective The objective of our study was to provide a characterization of patient experience sentiments across the United States on Twitter over a 4-year period. Methods Using data from Twitter, we developed a set of 4 software components to automatically label and examine a database of tweets discussing patient experience. The set includes a classifier to determine patient experience tweets, a geolocation inference engine for social data, a modified sentiment classifier, and an engine to determine if the tweet is from a metropolitan or nonmetropolitan area in the United States. Using the information retrieved, we conducted spatial and temporal examinations of tweet sentiments at national and regional levels. We examined trends in the time of the day and that of the week when tweets were posted. Statistical analyses were conducted to determine if any differences existed between the discussions of patient experience in metropolitan and nonmetropolitan areas. Results We collected 27.3 million tweets between February 1, 2013 and February 28, 2017, using a set of patient experience-related keywords; the classifier was able to identify 2,759,257 tweets labeled as patient experience. We identified the approximate location of 31.76% (876,384/2,759,257) patient experience tweets using a geolocation classifier to conduct spatial analyses. At the national level, we observed 27.83% (243,903/876,384) positive patient experience tweets, 36.22% (317,445/876,384) neutral patient experience tweets, and 35.95% (315,036/876,384) negative patient experience tweets. There were slight differences in tweet sentiments across all regions of the United States during the 4-year study period. We found the average sentiment polarity shifted toward less negative over the study period across all the regions of the United States. We observed the sentiment of tweets to have a lower negative fraction during daytime hours, whereas the sentiment of tweets posted between 8 pm and 10 am had a higher negative fraction. Nationally, sentiment scores for tweets in metropolitan areas were found to be more extremely negative and mildly positive compared with tweets in nonmetropolitan areas. This result is statistically significant (P<.001). Tweets with extremely negative sentiments had a medium effect size (d=0.34) at the national level. Conclusions This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States.

Disparities in digital reporting of illness: A demographic and socioeconomic assessment

Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors. The results show that reviews of foodservice businesses and digital reports of foodborne illness follow a clear seasonal pattern with higher reporting observed in the summer, when most foodborne outbreaks are reported and to a lesser extent in the winter months. Additionally, factors typically associated with affluence (such as, higher median income and fraction of the population with a bachelors degrees) were positively correlated with foodborne illness reports. However, restaurants per capita and education were the most significant predictors of illness reporting at the US county level. These results suggest that well-known health disparities might also be reflected in the online environment. Although this is an observational study, it is an important step in understanding disparities in the online public health environment.