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Key information and important choices are constantly being presented in health care. Yet often the frames or default options used are selected without attention to strategic goals. Creating a nudge unit in a health care system can lead to consistently better decisions.

Depression, the most prevalent mental illness, is underdiagnosed and undertreated, highlighting the need to extend the scope of current screening methods. Here, we use language from Facebook posts of consenting individuals to predict depression recorded in electronic medical records. We accessed the history of Facebook statuses posted by 683 patients visiting a large urban academic emergency department, 114 of whom had a diagnosis of depression in their medical records. Using only the language preceding their first documentation of a diagnosis of depression, we could identify depressed patients with fair accuracy [area under the curve (AUC) = 0.69], approximately matching the accuracy of screening surveys benchmarked against medical records. Restricting Facebook data to only the 6 months immediately preceding the first documented diagnosis of depression yielded a higher prediction accuracy (AUC = 0.72) for those users who had sufficient Facebook data. Significant prediction of future depression status was possible as far as 3 months before its first documentation. We found that language predictors of depression include emotional (sadness), interpersonal (loneliness, hostility), and cognitive (preoccupation with the self, rumination) processes. Unobtrusive depression assessment through social media of consenting individuals may become feasible as a scalable complement to existing screening and monitoring procedures.

We studied whether medical conditions across 21 broad categories were predictable from social media content across approximately 20 million words written by 999 consenting patients. Facebook language significantly improved upon the prediction accuracy of demographic variables for 18 of the 21 disease categories; it was particularly effective at predicting diabetes and mental health conditions including anxiety, depression and psychoses. Social media data are a quantifiable link into the otherwise elusive daily lives of patients, providing an avenue for study and assessment of behavioral and environmental disease risk factors. Analogous to the genome, social media data linked to medical diagnoses can be banked with patients' consent, and an encoding of social media language can be used as markers of disease risk, serve as a screening tool, and elucidate disease epidemiology. In what we believe to be the first report linking electronic medical record data with social media data from consenting patients, we identified that patients' Facebook status updates can predict many health conditions, suggesting opportunities to use social media data to determine disease onset or exacerbation and to conduct social media-based health interventions.

In this randomized trial of financial incentives in smokers, both reward-based and deposit-based incentive programs were more effective than usual care in achieving smoking cessation. Reward programs were much more commonly accepted than deposit-based programs. Financial incentives have been shown to promote a variety of health behaviors. 1 – 8 For example, in a randomized, clinical trial involving 878 General Electric employees, a bundle of incentives worth $750 for smoking cessation nearly tripled quit rates, from 5.0% to 14.7%, 8 and led to a program adapted by General Electric for its U.S. employees. 9 Although incentive programs are increasingly used by governments, employers, and insurers to motivate changes in health behavior, 10 , 11 their design is usually based on the traditional economic assumption that the size of the incentive determines its effectiveness. In contrast, behavioral economic theory suggests that incentives . . .

The medical community is often reminded how far we are from consensus regarding how to address today’s most important clinical problems. For generations, two apparently opposing models of disease — the biologic model and the social model — have dominated our approach.

In this randomized, controlled trial of smokers employed by a large company, financial incentives for participation in a smoking-cessation program and for smoking cessation confirmed by biochemical testing increased cessation rates at 9 or 12 months (15% for the incentive group vs. 5% for the control group). In this trial of smokers employed by a large company, financial incentives for participation in a smoking-cessation program and for smoking cessation confirmed by biochemical testing increased cessation rates at 9 or 12 months (15% for the incentive group vs. 5% for the control group). Smoking remains the leading preventable cause of premature death in the United States, accounting for approximately 438,000 deaths each year. 1 Seventy percent of smokers report that they want to quit, 2 but annually only 2 to 3% of smokers succeed. 3 , 4 Although smoking-cessation programs and pharmacologic therapies have been associated with higher rates of cessation, rates of participation in such programs and use of such therapies are low. 5 , 6 Work sites offer a promising venue for encouraging smoking cessation because employers are likely to bear many of the excess health care costs and productivity losses that are due to missed work . . .

Use of social media has become widespread across the United States. Although businesses have invested in social media to engage consumers and promote products, less is known about the extent to which hospitals are using social media to interact with patients and promote health. The aim was to investigate the relationship between hospital social media extent of adoption and utilization relative to hospital characteristics. We conducted a cross-sectional review of hospital-related activity on 4 social media platforms: Facebook, Twitter, Yelp, and Foursquare. All US hospitals were included that reported complete data for the Centers for Medicare and Medicaid Services Hospital Consumer Assessment of Healthcare Providers and Systems survey and the American Hospital Association Annual Survey. We reviewed hospital social media webpages to determine the extent of adoption relative to hospital characteristics, including geographic region, urban designation, bed size, ownership type, and teaching status. Social media utilization was estimated from user activity specific to each social media platform, including number of Facebook likes, Twitter followers, Foursquare check-ins, and Yelp reviews. Adoption of social media varied across hospitals with 94.41% (3351/3371) having a Facebook page and 50.82% (1713/3371) having a Twitter account. A majority of hospitals had a Yelp page (99.14%, 3342/3371) and almost all hospitals had check-ins on Foursquare (99.41%, 3351/3371). Large, urban, private nonprofit, and teaching hospitals were more likely to have higher utilization of these accounts. Although most hospitals adopted at least one social media platform, utilization of social media varied according to several hospital characteristics. This preliminary investigation of social media adoption and utilization among US hospitals provides the framework for future studies investigating the effect of social media on patient outcomes, including links between social media use and the quality of hospital care and services.

New rules for residents that go into effect in July extend permissible work shifts for first-year residents from 16 hours to 24. The policy seems to reverse the direction we have been moving in, on the basis of a new approach to developing and using evidence to inform education policy. On March 10, 2017, the Accreditation Council for Graduate Medical Education (ACGME) issued revised common program requirements for residents that go into effect this July. The revisions emphasize the importance of teamwork, flexibility, and physician welfare during training, but all the attention has been (and will no doubt remain) focused on the changes in duty hours. The new rules maintain an 80-hour-per-week cap on residents’ work, averaged over 4 weeks, but extend the permissible work shifts for first-year residents from 16 hours to 24 — limits already in place for residents in year 2 and beyond — and permit more . . .

Background Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients’ perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use. Methods Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types’ health relatedness. Factor analysis was used to identify the structure in patients’ perceptions of willingness to share different digital data, and their health relatedness. Results Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know. Conclusion Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

Severe adverse events (AEs) after COVID-19 vaccination are not well studied in randomized controlled trials (RCTs) due to rarity and short follow-up. To monitor the safety of COVID-19 vaccines (“Pfizer” vaccine dose 1 and 2, “Moderna” vaccine dose 1 and 2, and “Janssen” vaccine single dose) in the U.S., especially regarding severe AEs, we compare the relative rankings of these vaccines using both RCT and the Vaccine Adverse Event Reporting System (VAERS) data. The risks of local and systemic AEs were assessed from the three pivotal COVID-19 vaccine trials and also calculated in the VAERS cohort consisting of 559,717 reports between December 14, 2020 and September 17, 2021. AE rankings of the five vaccine groups calculated separately by RCT and VAERS were consistent, especially for systemic AEs. For severe AEs reported in VAERS, the reported risks of thrombosis and GBS after Janssen vaccine were highest. The reported risk of shingles after the first dose of Moderna vaccine was highest, followed by the second dose of the Moderna vaccine. The reported risk of myocarditis was higher after the second dose of Pfizer and Moderna vaccines. The reported risk of anaphylaxis was higher after the first dose of Pfizer vaccine. Limitations of this study are the inherent biases of the spontaneous reporting system data, and only including three pivotal RCTs and no comparison with other active vaccine safety surveillance systems.