Human Collective Intelligence under Dual Exploration-Exploitation Dilemmas

The exploration-exploitation dilemma is a recurrent adaptive problem for humans as well as non-human animals. Given a fixed time/energy budget, every individual faces a fundamental trade-off between exploring for better resources and exploiting known resources to optimize overall performance under uncertainty. Colonies of eusocial insects are known to solve this dilemma successfully via evolved coordination mechanisms that function at the collective level. For humans and other non-eusocial species, however, this dilemma operates within individuals as well as between individuals, because group members may be motivated to take excessive advantage of others'' exploratory findings through social learning. Thus, even though social learning can reduce collective exploration costs, the emergence of disproportionate “information scroungers” may severely undermine its potential benefits. We investigated experimentally whether social learning opportunities might improve the performance of human participants working on a “multi-armed bandit” problem in groups, where they could learn about each other''s past choice behaviors. Results showed that, even though information scroungers emerged frequently in groups, social learning opportunities reduced total group exploration time while increasing harvesting from better options, and consequentially improved collective performance. Surprisingly, enriching social information by allowing participants to observe others'' evaluations of chosen options (e.g., Amazon''s 5-star rating system) in addition to choice-frequency information had a detrimental impact on performance compared to the simpler situation with only the choice-frequency information. These results indicate that humans groups can handle the fundamental “dual exploration-exploitation dilemmas” successfully, and that social learning about simple choice-frequencies can help produce collective intelligence.     

Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face

Recent research with face-to-face groups found that a measure of general group effectiveness (called “collective intelligence”) predicted a group’s performance on a wide range of different tasks. The same research also found that collective intelligence was correlated with the individual group members’ ability to reason about the mental states of others (an ability called “Theory of Mind” or “ToM”). Since ToM was measured in this work by a test that requires participants to “read” the mental states of others from looking at their eyes (the “Reading the Mind in the Eyes” test), it is uncertain whether the same results would emerge in online groups where these visual cues are not available. Here we find that: (1) a collective intelligence factor characterizes group performance approximately as well for online groups as for face-to-face groups; and (2) surprisingly, the ToM measure is equally predictive of collective intelligence in both face-to-face and online groups, even though the online groups communicate only via text and never see each other at all. This provides strong evidence that ToM abilities are just as important to group performance in online environments with limited nonverbal cues as they are face-to-face. It also suggests that the Reading the Mind in the Eyes test measures a deeper, domain-independent aspect of social reasoning, not merely the ability to recognize facial expressions of mental states.     

Quantifying Collective Attention from Tweet Stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.     

The Health System and Population Health Implications of Large-Scale Diabetes Screening in India: A Microsimulation Model of Alternative Approaches

Background

Like a growing number of rapidly developing countries, India has begun to develop a system for large-scale community-based screening for diabetes. We sought to identify the implications of using alternative screening instruments to detect people with undiagnosed type 2 diabetes among diverse populations across India.

Methods and Findings

We developed and validated a microsimulation model that incorporated data from 58 studies from across the country into a nationally representative sample of Indians aged 25–65 y old. We estimated the diagnostic and health system implications of three major survey-based screening instruments and random glucometer-based screening. Of the 567 million Indians eligible for screening, depending on which of four screening approaches is utilized, between 158 and 306 million would be expected to screen as “high risk” for type 2 diabetes, and be referred for confirmatory testing. Between 26 million and 37 million of these people would be expected to meet international diagnostic criteria for diabetes, but between 126 million and 273 million would be “false positives.” The ratio of false positives to true positives varied from 3.9 (when using random glucose screening) to 8.2 (when using a survey-based screening instrument) in our model. The cost per case found would be expected to be from US$5.28 (when using random glucose screening) to US$17.06 (when using a survey-based screening instrument), presenting a total cost of between US$169 and US$567 million. The major limitation of our analysis is its dependence on published cohort studies that are unlikely fully to capture the poorest and most rural areas of the country. Because these areas are thought to have the lowest diabetes prevalence, this may result in overestimation of the efficacy and health benefits of screening.

Conclusions

Large-scale community-based screening is anticipated to produce a large number of false-positive results, particularly if using currently available survey-based screening instruments. Resource allocators should consider the health system burden of screening and confirmatory testing when instituting large-scale community-based screening for diabetes.     

Short and Long Term Investor Synchronization Caused by Decoupling

The dynamics of collective decision making is not yet well understood. Its practical relevance however can be of utmost importance, as experienced by people who lost their fortunes in turbulent moments of financial markets. In this paper we show how spontaneous collective “moods” or “biases” emerge dynamically among human participants playing a trading game in a simple model of the stock market. Applying theory and computer simulations to the experimental data generated by humans, we are able to predict the onset of such moments before they actually happen.     

Collective predator evasion: Putting the criticality hypothesis to the test

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the “criticality hypothesis”, appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the “criticality hypothesis”, but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.     

A Geometric Representation of Collective Attention Flows

With the fast development of Internet and WWW, “information overload” has become an overwhelming problem, and collective attention of users will play a more important role nowadays. As a result, knowing how collective attention distributes and flows among different websites is the first step to understand the underlying dynamics of attention on WWW. In this paper, we propose a method to embed a large number of web sites into a high dimensional Euclidean space according to the novel concept of flow distance, which both considers connection topology between sites and collective click behaviors of users. With this geometric representation, we visualize the attention flow in the data set of Indiana university clickstream over one day. It turns out that all the websites can be embedded into a 20 dimensional ball, in which, close sites are always visited by users sequentially. The distributions of websites, attention flows, and dissipations can be divided into three spherical crowns (core, interim, and periphery). 20% popular sites (Google.com, Myspace.com, Facebook.com, etc.) attracting 75% attention flows with only 55% dissipations (log off users) locate in the central layer with the radius 4.1. While 60% sites attracting only about 22% traffics with almost 38% dissipations locate in the middle area with radius between 4.1 and 6.3. Other 20% sites are far from the central area. All the cumulative distributions of variables can be well fitted by “S”-shaped curves. And the patterns are stable across different periods. Thus, the overall distribution and the dynamics of collective attention on websites can be well exhibited by this geometric representation.     

A Digital PCR-Based Method for Efficient and Highly Specific Screening of Genome Edited Cells

The rapid adoption of gene editing tools such as CRISPRs and TALENs for research and eventually therapeutics necessitates assays that can rapidly detect and quantitate the desired alterations. Currently, the most commonly used assay employs “mismatch nucleases” T7E1 or “Surveyor” that recognize and cleave heteroduplexed DNA amplicons containing mismatched base-pairs. However, this assay is prone to false positives due to cancer-associated mutations and/or SNPs and requires large amounts of starting material. Here we describe a powerful alternative wherein droplet digital PCR (ddPCR) can be used to decipher homozygous from heterozygous mutations with superior levels of both precision and sensitivity. We use this assay to detect knockout inducing alterations to stem cell associated proteins, NODAL and SFRP1, generated using either TALENs or an “all-in-one” CRISPR/Cas plasmid that we have modified for one-step cloning and blue/white screening of transformants. Moreover, we highlight how ddPCR can be used to assess the efficiency of varying TALEN-based strategies. Collectively, this work highlights how ddPCR-based screening can be paired with CRISPR and TALEN technologies to enable sensitive, specific, and streamlined approaches to gene editing and validation.     

Plant intelligence: Why,why not or where?

The concept of plant intelligence, as proposed by Anthony Trewavas, has raised considerable discussion. However, plant intelligence remains loosely defined; often it is either perceived as practically synonymous to Darwinian fitness, or reduced to a mere decorative metaphor. A more strict view can be taken, emphasizing necessary prerequisites such as memory and learning, which requires clarifying the definition of memory itself. To qualify as memories, traces of past events have to be not only stored, but also actively accessed. We propose a criterion for eliminating false candidates of possible plant intelligence phenomena in this stricter sense: an “intelligent” behavior must involve a component that can be approximated by a plausible algorithmic model involving recourse to stored information about past states of the individual or its environment. Re-evaluation of previously presented examples of plant intelligence shows that only some of them pass our test.

“You were hurt?” Kumiko said, looking at the scar.Sally looked down. “Yeah.”“Why didn''t you have it removed?”“Sometimes it''s good to remember.”“Being hurt?”“Being stupid.”—(W. Gibson: Mona Lisa Overdrive)

Key words: intelligence, memory, learning, plant development, mathematical models, plant neurobiology, definition of terms     

Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis

The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.     

Withholding or Withdrawing Extraordinary Life Support—Optimizing Rights and Limiting Liability

For many years physicians, ethicists and members of the legal community have attempted to minimize ambiguity and unpredictability in making decisions to withhold or withdraw extraordinary life support. Recent developments in national and California law now afford medical care providers unparalleled protection from criminal and civil liability in surrogate decision-making situations. They also reinforce the concept of patient''s rights by providing medical care consumers with new and effective mechanisms for enforcing their “right to decide,” even after they have lost decision-making capacity. A case in point is California''s new Durable Power of Attorney for Health Care, which serves as a model for other jurisdictions that do not have such legislation. Thus, the medical and legal professions, working together, can contribute immeasurably to respectful medical decision making by educating the public about these developments and by adopting policies that reinforce these rights.     

Higher Landing Accuracy in Expert Pilots is Associated with Lower Activity in the Caudate Nucleus

The most common lethal accidents in General Aviation are caused by improperly executed landing approaches in which a pilot descends below the minimum safe altitude without proper visual references. To understand how expertise might reduce such erroneous decision-making, we examined relevant neural processes in pilots performing a simulated landing approach inside a functional MRI scanner. Pilots (aged 20–66) were asked to “fly” a series of simulated “cockpit view” instrument landing scenarios in an MRI scanner. The scenarios were either high risk (heavy fog–legally unsafe to land) or low risk (medium fog–legally safe to land). Pilots with one of two levels of expertise participated: Moderate Expertise (Instrument Flight Rules pilots, n = 8) or High Expertise (Certified Instrument Flight Instructors or Air-Transport Pilots, n = 12). High Expertise pilots were more accurate than Moderate Expertise pilots in making a “land” versus “do not land” decision (CFII: d′ = 3.62±2.52; IFR: d′ = 0.98±1.04; p<.01). Brain activity in bilateral caudate nucleus was examined for main effects of expertise during a “land” versus “do not land” decision with the no-decision control condition modeled as baseline. In making landing decisions, High Expertise pilots showed lower activation in the bilateral caudate nucleus (0.97±0.80) compared to Moderate Expertise pilots (1.91±1.16) (p<.05). These findings provide evidence for increased “neural efficiency” in High Expertise pilots relative to Moderate Expertise pilots. During an instrument approach the pilot is engaged in detailed examination of flight instruments while monitoring certain visual references for making landing decisions. The caudate nucleus regulates saccade eye control of gaze, the brain area where the “expertise” effect was observed. These data provide evidence that performing “real world” aviation tasks in an fMRI provide objective data regarding the relative expertise of pilots and brain regions involved in it.     

Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed

Cell fate decision remarkably generates specific cell differentiation path among the multiple possibilities that can arise through the complex interplay of high-dimensional genome activities. The coordinated action of thousands of genes to switch cell fate decision has indicated the existence of stable attractors guiding the process. However, origins of the intracellular mechanisms that create “cellular attractor” still remain unknown. Here, we examined the collective behavior of genome-wide expressions for neutrophil differentiation through two different stimuli, dimethyl sulfoxide (DMSO) and all-trans-retinoic acid (atRA). To overcome the difficulties of dealing with single gene expression noises, we grouped genes into ensembles and analyzed their expression dynamics in correlation space defined by Pearson correlation and mutual information. The standard deviation of correlation distributions of gene ensembles reduces when the ensemble size is increased following the inverse square root law, for both ensembles chosen randomly from whole genome and ranked according to expression variances across time. Choosing the ensemble size of 200 genes, we show the two probability distributions of correlations of randomly selected genes for atRA and DMSO responses overlapped after 48 hours, defining the neutrophil attractor. Next, tracking the ranked ensembles'' trajectories, we noticed that only certain, not all, fall into the attractor in a fractal-like manner. The removal of these genome elements from the whole genomes, for both atRA and DMSO responses, destroys the attractor providing evidence for the existence of specific genome elements (named “genome vehicle”) responsible for the neutrophil attractor. Notably, within the genome vehicles, genes with low or moderate expression changes, which are often considered noisy and insignificant, are essential components for the creation of the neutrophil attractor. Further investigations along with our findings might provide a comprehensive mechanistic view of cell fate decision.     

Validity of Antibodies in Lymphocyte Supernatant in Diagnosing Tuberculosis in Severely Malnourished Children Presenting with Pneumonia

BackgroundThe diagnosis of tuberculosis (TB) in young children can be challenging, especially in severely malnourished children. There is a critical need for improved diagnostics for children. Thus, we sought to evaluate the performance of a technique that measures antibodies in lymphocyte supernatant (ALS) for the diagnosis of TB in severely malnourished children presenting with suspected pneumonia.MethodsChildren less than 5 years with severe acute malnutrition and radiological features of pneumonia admitted to the Dhaka Hospital of International Centre for Diarrhoeal Disease Research, Bangladesh, were enrolled consecutively following informed written consent. In addition to clinical and radiological assessment, samples taken for TB diagnosis included gastric lavage fluid and induced sputum for microbiological confirmation. ALS was measured from venous blood, and results were evaluated in children classified as “confirmed”, “non-confirmed TB” or “not TB”.ResultsAmong 224 children who had ALS analysis, 12 (5.4%) children had microbiologically “confirmed TB”, a further 41 (18%) had clinically diagnosed “non-confirmed TB” and the remaining 168 (75%) were considered not to have TB. ALS was positive in 89 (40%) and negative in 85 (39%) of children, with a large number (47 or 21%) reported as “borderline”. These proportions were similar between the three diagnostic groups. The sensitivity and specificity of ALS when comparing “Confirmed TB” to “Not TB” was only 67% (95% CI: 31–91%) and 51% (95% CI: 42–60%), respectively.

Conclusions and Significance

Our data suggest that ALS is not sufficiently accurate to improve the diagnosis of TB in children with severe malnutrition.     

Decision-Making in Multiple Sclerosis Consultations in Italy: Third Observer and Patient Assessments

Objective

To assess decision-making in multiple sclerosis (MS) from third observer and patient perspectives.

Method

Audio recordings of first-ever consultations with a participating physician (88 outpatients, 10 physicians) at four tertiary MS care clinics in Italy, were rated by a third observer using the Observing Patient Involvement in Shared Decision Making (OPTION) and by patients using the Perceived Involvement in Care Scale (PICS).

Results

Mean patient age was 37.5, 66% were women, 72% had MS, and 28% had possible MS or other disease. Mean PICS subscale scores (range 0 poor, 100 best possible) were 71.9 (SD 24.3) for "physician facilitation" (PICS-F); 74.6 (SD 22.9) for "patient information exchange" (PICS-I); and only 22.5 (SD 16.2) for "patient decision making" (PICS-DM). Mean OPTION total score (0 poor, 100 best possible) was 29.6 (SD 10.3). Poorest OPTION scores were found for items assessing “preferred patient approach to receiving information” and “preferred patient level of involvement.” Highest scores were for “clinician drawing attention to identified problem”, “indicating need for decision making,” and “need to review the decision.” Consultation time, woman physician, patient-physician gender concordance and PICS-F were associated with higher OPTION total score; older physician and second opinion consultation were associated with lower OPTION score.

Conclusions

In line with findings in other settings, our third observer findings indicated limited patient involvement abilities of MS physicians during first consultations. Patient perceptions of physician skills were better than third observers’, although they correlated. Consultations with women physicians, and younger physicians, were associated with higher third observer and patient-based scores. Our findings reveal a need to empower Italian MS physicians with better communication and shared decision-making skills, and show in particular that attention to MS patient preferences for reception of information and involvement in health decisions, need to be improved.     

Seeds of Locally Aligned Motion and Stress Coordinate a Collective Cell Migration

We find how collective migration emerges from mechanical information transfer between cells. Local alignment of cell velocity and mechanical stress orientation—a phenomenon dubbed “plithotaxis”—plays a crucial role in inducing coordinated migration. Leader cells at the monolayer edge better align velocity and stress to migrate faster toward the open space. Local seeds of enhanced motion then generate stress on neighboring cells to guide their migration. Stress-induced motion propagates into the monolayer as well as along the monolayer boundary to generate increasingly larger clusters of coordinately migrating cells that move faster with enhanced alignment of velocity and stress. Together, our analysis provides a model of long-range mechanical communication between cells, in which plithotaxis translates local mechanical fluctuations into globally collective migration of entire tissues.     

Chorioamnionitis and Patent Ductus Arteriosus: A Systematic Review and Meta-Analysis

Background

Chorioamnionitis has recently been reported as a risk factor for various neonatal diseases, including cerebral palsy, bronchopulmonary dysplasia, and necrotizing enterocolitis, but its effect on patent ductus arteriosus (PDA) is unclear. We performed a systematic review and meta-analysis to evaluate the effect of chorioamnionitis on PDA.

Methods

We searched PubMed, EMBASE, Cochrane Library, and KoreaMed databases using the terms: “intrauterine infection” or “maternal infection” or “antenatal infection” or “chorioamnionitis” or “placenta inflammation” or “placenta pathology” or “neonatal outcome” or “neonatal morbidity” or “PDA or patent ductus arteriosus” or “ductus arteriosus,” and “prematurity” or “very low birth weight infant.” Studies were included if they were randomized controlled trials, case–control studies, or cohort studies that included information relating to chorioamnionitis and PDA.

Results

Among 1,571 studies, a total of 23 studies (17,708 cases) were included in the meta-analysis to analyze the relationship between chorioamnionitis and PDA, except one study that only included PDA requiring surgical ligation. The association between chorioamnionitis and PDA was statistically significant (odds ratio [OR] 1.43; 95% confidence interval [CI] 1.19, 1.72; P < 0.0001). In subgroup analysis, clinical chorioamnionitis was not associated with PDA (OR 1.28; 95% CI 1.00, 1.64, 1.790; P = 0.05), whereas histologic chorioamnionitis (OR 1.54; 95% CI 1.10, 2.15; P = 0.01) and chorioamnionitis diagnosed from both clinical and histologic findings (OR 1.75; 95% CI 1.07, 2.86; P = 0.03) showed significant associations with PDA. Chorioamnionitis did not increase the risk of PDA requiring surgical ligation (OR 1.23; 95% CI 0.69, 2.17; P = 0.48), and antenatal steroid use reduced the risk of PDA (OR 0.62; 95% CI 0.42, 0.90; P = 0.01) after chorioamnionitis.

Conclusions

The results from this meta-analysis support an association between maternal chorioamnionitis and PDA in offspring.     

Linkage Rules for Plant–Pollinator Networks: Trait Complementarity or Exploitation Barriers?

Recent attempts to examine the biological processes responsible for the general characteristics of mutualistic networks focus on two types of explanations: nonmatching biological attributes of species that prevent the occurrence of certain interactions (“forbidden links”), arising from trait complementarity in mutualist networks (as compared to barriers to exploitation in antagonistic ones), and random interactions among individuals that are proportional to their abundances in the observed community (“neutrality hypothesis”). We explored the consequences that simple linkage rules based on the first two hypotheses (complementarity of traits versus barriers to exploitation) had on the topology of plant–pollination networks. Independent of the linkage rules used, the inclusion of a small set of traits (two to four) sufficed to account for the complex topological patterns observed in real-world networks. Optimal performance was achieved by a “mixed model” that combined rules that link plants and pollinators whose trait ranges overlap (“complementarity models”) and rules that link pollinators to flowers whose traits are below a pollinator-specific barrier value (“barrier models”). Deterrence of floral parasites (barrier model) is therefore at least as important as increasing pollination efficiency (complementarity model) in the evolutionary shaping of plant–pollinator networks.     

Estimates of Electronic Medical Records in U.S. Emergency Departments

Background

Policymakers advocate universal electronic medical records (EMRs) and propose incentives for “meaningful use” of EMRs. Though emergency departments (EDs) are particularly sensitive to the benefits and unintended consequences of EMR adoption, surveillance has been limited. We analyze data from a nationally representative sample of US EDs to ascertain the adoption of various EMR functionalities.

Methodology/Principal Findings

We analyzed data from the National Hospital Ambulatory Medical Care Survey, after pooling data from 2005 and 2006, reporting proportions with 95% confidence intervals (95% CI). In addition to reporting adoption of various EMR functionalities, we used logistic regression to ascertain patient and hospital characteristics predicting “meaningful use,” defined as a “basic” system (managing demographic information, computerized provider order entry, and lab and imaging results). We found that 46% (95% CI 39–53%) of US EDs reported having adopted EMRs. Computerized provider order entry was present in 21% (95% CI 16–27%), and only 15% (95% CI 10–20%) had warnings for drug interactions or contraindications. The “basic” definition of “meaningful use” was met by 17% (95% CI 13–21%) of EDs. Rural EDs were substantially less likely to have a “basic” EMR system than urban EDs (odds ratio 0.19, 95% CI 0.06–0.57, p = 0.003), and Midwestern (odds ratio 0.37, 95% CI 0.16–0.84, p = 0.018) and Southern (odds ratio 0.47, 95% CI 0.26–0.84, p = 0.011) EDs were substantially less likely than Northeastern EDs to have a “basic” system.

Conclusions/Significance

EMRs are becoming more prevalent in US EDs, though only a minority use EMRs in a “meaningful” way, no matter how “meaningful” is defined. Rural EDs are less likely to have an EMR than metropolitan EDs, and Midwestern and Southern EDs are less likely to have an EMR than Northeastern EDs. We discuss the nuances of how to define “meaningful use,” and the importance of considering not only adoption, but also full implementation and consequences.