Online Ratings of the Patient Experience: Emergency Departments Versus Urgent Care Centers.

STUDY OBJECTIVE: Individuals increasingly use online rating platforms to rate and review hospitals. We seek to describe and compare publicly available online review content and ratings of emergency departments (EDs) and urgent care centers. METHODS: We analyzed Yelp reviews of EDs and urgent care centers to identify topics most correlated with 1- and 5-star ratings. Latent Dirichlet Allocation, a method of identifying groups of co-occurring words in narrative text, was used to identify and label 25 topics across 1- and 5-star reviews of urgent care centers and EDs. Differential Language Analysis was then used to measure the correlation of these topics with 1- and 5-star reviews for urgent care centers and EDs. RESULTS: We analyzed 100,949 Yelp reviews, 16,447 from 1,566 EDs and 84,502 from 5,601 urgent care centers. There were significantly more 5-star urgent care center reviews (n=43,487; 51%) than 5-star ED reviews (n=4,437; 27%). Themes associated with 5-star reviews among EDs and urgent care centers were similar for comfort, professionalism, facilities, pediatric care, and staff interactions. Themes associated with 1-star reviews among EDs and urgent care centers were similar for communication, telephone experience, waiting, billing, pain management, and diagnostic testing. Themes unique to 5-star ED reviews included bedside manner, care for family members, and access. Themes unique to 5-star urgent care center reviews were based on recommendation and prescription refills. Themes unique to 1-star ED reviews were service and speed of care. Themes unique to 1-star urgent care center reviews were lack of confidence and reception experience. CONCLUSION: Understanding drivers for high and low online ratings and what patients value in their ED and urgent care center experiences offers insights for health systems and providers to improve acute care delivery. Patients' perspectives may become increasingly important as they seek care in the expanding urgent care markets.

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission.

Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets' novelty, utility, theme, and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and nonhealth organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

Opioid Discussion in the Twittersphere.

BACKGROUND: The rise in opioid use and overdose has increased the importance of improving data collection methods for the purpose of targeting resources to high-need populations and responding rapidly to emerging trends. OBJECTIVE: To determine whether Twitter data could be used to identify geographic differences in opioid-related discussion and whether opioid topics were significantly correlated with opioid overdose death rate. METHODS: We filtered approximately 10 billion tweets for keywords related to opioids between July 2009 and October 2015. The content of the messages was summarized into 50 topics generated using Latent Dirchlet Allocation, a machine learning analytic tool. The correlation between topic distribution and census region, census division, and opioid overdose death rate were quantified. RESULTS: We evaluated a tweet cohort of 84,023 tweets from 72,211 unique users across the US. Unique opioid-related topics were significantly correlated with different Census Bureau divisions and with opioid overdose death rates at the state and county level. Drug-related crime, language of use, and online drug purchasing emerged as themes in various Census Bureau divisions. Drug-related crime, opioid-related news, and pop culture themes were significantly correlated with county-level opioid overdose death rates, and online drug purchasing was significantly correlated with state-level opioid overdoses. CONCLUSIONS: Regional differences in opioid-related topics reflect geographic variation in the content of Twitter discussion about opioids. Analysis of Twitter data also produced topics significantly correlated with opioid overdose death rates. Ongoing analysis of Twitter data could provide a means of identifying emerging trends related to opioids.

Patient Experience and Satisfaction in Online Reviews of Obstetric Care: Observational Study.

BACKGROUND: The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. OBJECTIVE: The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. METHODS: We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. RESULTS: A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P<.001), describing staff as comforting (r=0.52, P<.001), the delivery experience (r=0.46, P<.001), modern and clean facilities (r=0.44, P<.001), and hospital food (r=0.38, P<.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (r=0.30, P<.001), a lack of agency among patients (r=0.47, P<.001), and issues with discharging from the hospital (r=0.32, P<.001). CONCLUSIONS: Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.

How Sweet Is This? A Review and Evaluation of Preoperative Carbohydrate Loading in the Enhanced Recovery After Surgery Model.

Preoperative carbohydrate loading is a contemporary element of the enhanced recovery after surgery (ERAS) paradigm. In addition to intraoperative surgical and anesthetic modifications and postoperative care practices, preoperative optimization is essential to good postsurgical outcomes. What was long held as dogma, a period of prolonged fasting prior to the administration of anesthesia, was later re-examined and challenged. Along with the proposed physiologic effects of decreasing the surgical stress response and insulin resistance, preoperative carbohydrate loading was also demonstrated to improve patient satisfaction and well-being, without an increase in perioperative complications. The benefits are most strongly observed in abdominal and cardiac surgery patients, but there has also been data which support its use in other specialties and surgeries. Barriers to the adoption of perioperative carbohydrate loading are few, but importantly include overcoming the inertia to modify older and more restrictive fasting guidelines and achieving the multidisciplinary consensus necessary to implement such changes. Despite these challenges, and with an existing body of evidence supporting its benefits, preoperative carbohydrate loading presents a significant contribution to the ERAS programs.

Characterizing Tweet Volume and Content About Common Health Conditions Across Pennsylvania: Retrospective Analysis.

BACKGROUND: Tweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about. OBJECTIVE: We sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content. METHODS: We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease. RESULTS: Common disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45%; 306,127/12,702,379 prevalence, 2.41%) and diabetes (40,217/174,381 messages, 23.06%; 2,189,890/12,702,379 prevalence, 17.24%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89%; 4,614,776/12,702,379 prevalence, 36.33%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95%; 1,083,627/12,702,379 prevalence, 8.53%), and heart disease (13,669/174,381 messages, 7.84%; 2,461,721/12,702,379 prevalence, 19.38%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88% (578/4487) of prostate cancer tweets and 24.17% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34%) than asthma (1040/16,742 messages, 6.21%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06%) than lymphoma (105/4927 messages, 2.13%). CONCLUSIONS: Twitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals' perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data.

Twitter as a Potential Data Source for Cardiovascular Disease Research.

IMPORTANCE: As society is increasingly becoming more networked, researchers are beginning to explore how social media can be used to study person-to-person communication about health and health care use. Twitter is an online messaging platform used by more than 300 million people who have generated several billion Tweets, yet little work has focused on the potential applications of these data for studying public attitudes and behaviors associated with cardiovascular health. OBJECTIVE: To describe the volume and content of Tweets associated with cardiovascular disease as well as the characteristics of Twitter users. DESIGN, SETTING, AND PARTICIPANTS: We used Twitter to access a random sample of approximately 10 billion English-language Tweets originating from US counties from July 23, 2009, to February 5, 2015, associated with cardiovascular disease. We characterized each Tweet relative to estimated user demographics. A random subset of 2500 Tweets was hand-coded for content and modifiers. MAIN OUTCOMES AND MEASURES: The volume of Tweets about cardiovascular disease and the content of these Tweets. RESULTS: Of 550 338 Tweets associated with cardiovascular disease, the terms diabetes (n = 239 989) and myocardial infarction (n = 269 907) were used more frequently than heart failure (n = 9414). Users who Tweeted about cardiovascular disease were more likely to be older than the general population of Twitter users (mean age, 28.7 vs 25.4 years; P < .01) and less likely to be male (59 082 of 124 896 [47.3%] vs 8433 of 17 270 [48.8%]; P < .01). Most Tweets (2338 of 2500 [93.5%]) were associated with a health topic; common themes of Tweets included risk factors (1048 of 2500 [41.9%]), awareness (585 of 2500 [23.4%]), and management (541 of 2500 [21.6%]) of cardiovascular disease. CONCLUSIONS AND RELEVANCE: Twitter offers promise for studying public communication about cardiovascular disease.