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Importance: Value in health care is quality per unit cost (V = Q/C), and an emergency department-based intensive care unit (ED-ICU) model has been associated with improved quality. To assess the value of this care delivery model, it is essential to determine the incremental direct cost of care. Objective: To determine the association of an ED-ICU with inflation-adjusted change in mean direct cost of care, net revenue, and direct margin per ED patient encounter. Design, Setting, and Participants: This retrospective economic analysis evaluated the cost of care delivery to patients in the ED before and after deployment of the Joyce and Don Massey Family Foundation Emergency Critical Care Center, an ED-ICU, on February 16, 2015, at a large academic medical center in the US with approximately 75 000 adult ED visits per year. The pre-ED-ICU cohort was defined as all documented ED visits by patients 18 years or older with a complete financial record from September 8, 2012, through June 30, 2014 (660 days); the post-ED-ICU cohort, all visits from July 1, 2015, through April 21, 2017 (660 days). Fiscal year 2015 was excluded from analysis to phase in the new care model. Statistical analysis was performed March 1 through December 30, 2021. Exposures: Implementation of an ED-ICU. Main Outcomes and Measures: Inflation-adjusted direct cost of care, net revenue, and direct margin per patient encounter in the ED. Results: A total of 234 884 ED visits during the study period were analyzed, with 115 052 patients (54.7% women) in the pre-ED-ICU cohort and 119 832 patients (54.5% women) in the post-ED-ICU cohort. The post-ED-ICU cohort was older (mean [SD] age, 49.1 [19.9] vs 47.8 [19.6] years; P < .001), required more intensive respiratory support (2.2% vs 1.1%; P < .001) and more vasopressor use (0.5% vs 0.2%; P < .001), and had a higher overall case mix index (mean [SD], 1.7 [2.0] vs 1.5 [1.7]; P < .001). Implementation of the ED-ICU was associated with similar inflation-adjusted total direct cost per ED encounter (pre-ED-ICU, mean [SD], $4875 [$15 175]; post-ED-ICU, $4877 [$17 400]; P = .98). Inflation-adjusted net revenue per encounter increased by 7.0% (95% CI, 3.4%-10.6%; P < .001), and inflation-adjusted direct margin per encounter increased by 46.6% (95% CI, 32.1%-61.2%; P < .001). Conclusions and Relevance: Implementation of an ED-ICU was associated with no significant change in inflation-adjusted total direct cost per ED encounter. Holding delivery costs constant while improving quality demonstrates improved value via the ED-ICU model of care.

Additional file 1. Supplementary materials include detailed methods and supplementary results as referenced inthe article.

Political activities and interactions between different global entities are becoming growing field for data-intensive computing with a wide scope of research opportunities for both social science and computer science researchers. This research needs to be carried out at a local (limited to a particular region) and global scale, often divided in temporal manner. It is also useful to have the most recently updated dataset for relevant analysis. For these purposes, we need timestamped, geolocaated structured information about political interactions. Keeping this in mind, we develop a datatset that complies with Conflict and Mediation Event Observation (CAMEO) ontology inspired by the ”who-did-what-to- whom” format. We use a distributed framework for data collection and processing that works in real-time with Apache Kafka and SPARK in order to process a global collection of news data in different languages (i.e., Spanish, Arabic) and generate those structured event data in real-time. We also provide an API for easy access to the data. In this paper, we describe how the data is represented, collected, and processed, how we generate the most up-to-date dataset with dynamic ontology extension, and how to access the data and possible analytical problems that can be addressed by building a model on the dataset.

Conference presentation from the inaugural NCCRED symposium, held in Hobart, Australia on November 10th 2019.

The main goal of this project is to integrate and expand our end-to-end cyberinfrastructure for robust creation, validation, access, and analysis of political event data. Event data in this context refers to a machine-coded description of someone doing something to someone else as extracted from news reports. We focus on political and social events about conflict and cooperation between governments, individuals, non-governmental organizations, rebel groups, and others. We rely on natural language processing tools to code event data by annotating the kinds of political events that are of interest to political scientists, international relations scholars, sociologists, and the national security community. Our system scrapes contemporaneous news reports in English and Spanish, automatically encodes relevant political events for data analysts, and serves our data along with other open event data through our API hosted in XSEDE’s JetStream cloud.

What factors led to the surprise defeat of the Colombian peace referendum? Descriptive analyses suggested a link between support for peace and the experience of violence, but economic conditions and political support for incumbent parties also affect electoral outcomes. Here we use Bayesian hierarchical models to test links between referendum result and previous violence victimization, economic conditions, and support for Centro Democrático (the main party opposed to the peace agreement). There was lower support for peace in the Andean region, and departments with lower support had higher unemployment and growth in GDP. Support for the opposition was the dominant covariate of decreasing support for the peace accords, while previous violence victimization increased the percent vote for peace. Regional variation in baseline support for the agreements will be critical during implementation of the newly revised accords, and the mobilization of the opposition shows partisan engagement determined the referendum outcome., ¿Qué factores llevaron a la derrota sorpresiva del plebiscito por la paz en Colombia? Aunque los análisis iniciales encontraron conexiones entre el apoyo por la paz y la experiencia de violencia, las condiciones económicas y el apoyo a los partidos en el poder también afectaron tales resultados electorales. En este artículo utilizamos modelos jerárquicos bayesianos para evaluar las conexiones entre los resultados del plebiscito y la experiencia previa de victimización, las condiciones económicas, y el apoyo al Centro Democrático (el partido político opositor al acuerdo de paz principal). Hubo menos apoyo por la paz en la zona andina comparada con otras regiones, y los departamentos con menos apoyo tuvieron mayor desempleo y crecimiento del PIB. A pesar que la experiencia previa de victimización aumentó la proporción de votos por la paz, el apoyo a la oposición fue la covariante dominante en el descenso en el apoyo por los acuerdos de paz. Teniendo en cuenta estos resultados, la fuerte variación regional en la línea base de apoyo a los acuerdos - una variable compleja gobernada por el grado de compromiso político, pero también influenciada por factores económicos estructurales - será crítica en la implementación de los nuevos acuerdos.