Experimental evidence for structured information-sharing networks reducing medical errors.

Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information-sharing networks among clinicians to improve clinicians' diagnostic accuracy and treatment decisions. We use a pool of 2,941 practicing clinicians recruited from around the United States to conduct 84 independent group-level trials, ranging across seven different clinical vignettes for topics known to exhibit high rates of diagnostic or treatment error (e.g., acute cardiac events, geriatric care, low back pain, and diabetes-related cardiovascular illness prevention). We compare collective performance in structured information-sharing networks to collective performance in independent control groups, and find that networks significantly reduce clinical errors, and improve treatment recommendations, as compared to control groups of independent clinicians engaged in isolated reflection. Our results show that these improvements are not a result of simple regression to the group mean. Instead, we find that within structured information-sharing networks, the worst clinicians improved significantly while the best clinicians did not decrease in quality. These findings offer implications for the use of social network technologies to reduce errors among clinicians.

An Ultrasound Screening Exam During Medicare Wellness Visits May Be Beneficial.

OBJECTIVES: The physical exam component of a periodic health visit in the elderly has not been considered useful. Standard Medicare Wellness visits require no physical exam beyond blood pressure and most physicians perform limited exams during these visits. The objective of this study was to test the feasibility, potential benefit, and costs of performing a screening ultrasound (US) exam during Medicare Wellness visits. METHODS: A physician examiner at an academic internal medicine primary care clinic performed a screening US exam targeting important abnormalities of patients 65-85 years old during a Medicare Wellness visit. The primary care physician (PCP) recorded the follow-up items for each abnormality identified by the US examiner and assessed the benefit of each abnormality for the participant. Abnormality benefit, net exam benefit per participant, follow-up items and costs, participant survey results, and exam duration were assessed. RESULTS: Participants numbered 108. Total abnormalities numbered 283 and new diagnoses were 172. Positive benefit scores were assigned to 38.8%, neutral (zero) scores to 59.4%, and negative benefit scores to 1.8% of abnormalities. Net benefit scores per participant were positive in 63.9%, 0 in 34.3%, and negative in 1.8%. Follow-up items were infrequent resulting in 76% of participants without follow-up cost. Participant survey showed excellent acceptance of the exam. CONCLUSIONS: The US screening exam identified frequent abnormalities in Medicare Wellness patients. The assessed benefits were rarely negative and often mild to moderately positive, with important new chronic conditions identified. Follow-up costs were low when the PCPs were also US experts.

Empirica: a virtual lab for high-throughput macro-level experiments.

Virtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability-functionality trade-off by employing a "flexible defaults" design strategy. This strategy enables us to maintain complete "build anything" flexibility while offering a development platform that is accessible to novice programmers. Empirica's architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.

Probabilistic social learning improves the public's judgments of news veracity.

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influence suggests that framing decisions in binary terms can amplify judgment errors and limit social learning, whereas framing decisions in probabilistic terms can reliably improve judgments. In this preregistered experiment, we compare online peer networks that collaboratively evaluated the veracity of news by communicating either binary or probabilistic judgments. Exchanging probabilistic estimates of news veracity substantially improved individual and group judgments, with the effect of eliminating polarization in news evaluation. By contrast, exchanging binary classifications reduced social learning and maintained polarization. The benefits of probabilistic social learning are robust to participants' education, gender, race, income, religion, and partisanship.

The wisdom of partisan crowds.

Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme-and less accurate-when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains.

Social learning and partisan bias in the interpretation of climate trends.

Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon's Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects' interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.

Point-of-Care Sinus Ultrasound: Impact Within a Large Internal Medicine Clinic and Review of Technique.

Outpatient diagnosis of acute bacterial sinusitis, using only traditional physical examination and clinical criteria, results in the overuse of antibiotics in patients with upper respiratory complaints. Point-of-care maxillary sinus ultrasound is easy to learn and quick to perform in a primary care clinic. The technique can reduce antibiotic prescribing by reassuring both patients and providers of the absence of fluid in the sinus, the hallmark of maxillary sinusitis. A review of the literature, description of technique, and results of sinus ultrasound implementation in a large internal medicine clinic are included.

Experimental evidence for tipping points in social convention.

Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions. When minority groups reached the critical mass-that is, the critical group size for initiating social change-they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.

Network dynamics of social influence in the wisdom of crowds.

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.

Support or competition? How online social networks increase physical activity: A randomized controlled trial.

To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.

Glomerular filtration rate in evaluation of the effect of iodinated contrast media on renal function.

OBJECTIVE: The purpose of this study was to use measured glomerular filtration rate (GFR), the reference standard of renal function, to assess the deleterious effect of iodinated contrast media on renal function. Such an effect has been traditionally defined as a greater than 0.5-mg/dL increase in serum creatinine concentration or a 25% or greater increase 24-72 hours after the injection of iodinated contrast medium. SUBJECTS AND METHODS: This pilot investigation was focused on the consequences of clinically indicated IV injection of iodinated contrast media; intraarterial injection was excluded. One hundred thirteen patients with normal serum creatinine concentrations were enrolled in an approved protocol. At random, as chosen by one of the investigators, patients underwent imaging with one of three monomeric agents (iopamidol 300, iopromide 300, iohexol 300) and one dimeric agent (iodixanol 320). Measured GFR was determined immediately before CT and approximately 3 and 72 hours after the contrast injection for the CT examination. Iodinated contrast medium, a glomerular filtrate with no tubular excretion or reabsorption, was the GFR marker. Measured GFR was determined by x-ray fluorescence analysis with nonisotopic iodinated contrast media. RESULTS: Monomeric and dimeric contrast agents in diagnostic CT volumes (based on bodyweight and imaging protocol) did not induce a significant change in measured GFR (95% confidence by Wilcoxon test), suggesting that use of the evaluated contrast media will not lead to more than a 12% variation. CONCLUSION: The three monomeric agents studied and the one dimeric agent were equivalent in terms of lack of a significant effect on measured GFR when administered to patients with a normal GFR.

Renal function status.

OBJECTIVE: The objective of this article is to review and evaluate the various parameters used in determining renal status. CONCLUSION: The physiologic determination of renal status is the measured glomerular filtration rate (mGFR). Serum creatinine, blood urea nitrogen, cystatin C, and estimated GFR (eGFR), based on serum creatinine have failed to replace mGFR. All physicians should be aware of limitations of substituted mGFR.