Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

Metro Richmond Latino Health Services & Resource Guide – 2006

This Guide was produced by the VCU Institute for Women’s Health, VCU Center on Health Disparities, and CLAS Act Virginia as a resource for the fall 2006 Latino Health Summit: Latino Cultures and Beliefs in Health Care. The purpose of this Guide is to provide a practical tool for community health care professionals to use in their work by assisting in cataloguing key provider and patient resources and services. The guide will be posted on the VCU Institute for Women’s Health and VCU Center on Health Disparities websites, which will be updated on a regular basis

Efficacy and Safety of Pediatric Critical Care Physician Telemedicine Involvement in Rapid Response Team and Code Response in a Satellite Facility

OBJECTIVES: Satellite inpatient facilities of larger children's hospitals often do not have on-site intensivist support. In-house rapid response teams and code teams may be difficult to operationalize in such facilities. We developed a system using telemedicine to provide pediatric intensivist involvement in rapid response team and code teams at the satellite facility of our children's hospital. Herein, we compare this model with our in-person model at our main campus. DESIGN: Cross-sectional. SETTING: A tertiary pediatric center and its satellite facility. PATIENTS: Patients admitted to the satellite facility. INTERVENTIONS: Implementation of a rapid response team and code team model at a satellite facility using telemedicine to provide intensivist support. MEASUREMENTS AND MAIN RESULTS: We evaluated the success of the telemedicine model through three a priori outcomes: 1) reliability: involvement of intensivist on telemedicine rapid response teams and codes, 2) efficiency: time from rapid response team and code call until intensivist response, and 3) outcomes: disposition of telemedicine rapid response team or code calls. We compared each metric from our telemedicine model with our established main campus model. MAIN RESULTS: Critical care was involved in satellite campus rapid response team activations reliably (94.6% of the time). The process was efficient (median response time 7 min; mean 8.44 min) and effective (54.5 % patients transferred to PICU, similar to the 45-55% monthly rate at main campus). For code activations, the critical care telemedicine response rate was 100% (6/6), with a fast response time (median 1.5 min). We found no additional risk to patients, with no patients transferred from the satellite campus requiring a rapid escalation of care defined as initiation of vasoactive support, greater than 60 mL/kg in fluid resuscitation, or endotracheal intubation. CONCLUSIONS: Telemedicine can provide reliable, timely, and effective critical care involvement in rapid response team and Code Teams at satellite facilities

Spatial Concentration of Opioid Overdose Deaths in Indianapolis: An Application of the Law of Crime Concentration at Place to a Public Health Epidemic

The law of crime concentration at place has become a criminological axiom and the foundation for one of the strongest evidence-based policing strategies to date. Using longitudinal data from three sources, emergency medical service calls, death toxicology reports from the Marion County (Indiana) Coroner’s Office, and police crime data, we provide four unique contributions to this literature. First, this study provides the first spatial concentration estimation of opioid-related deaths. Second, our findings support the spatial concentration of opioid deaths and the feasibility of this approach for public health incidents often outside the purview of traditional policing. Third, we find that opioid overdose death hot spots spatially overlap with areas of concentrated violence. Finally, we apply a recent method, corrected Gini coefficient, to best specify low-N incident concentrations and propose a novel method for improving upon a shortcoming of this approach. Implications for research and interventions are discussed

Committed to Safety: Ten Case Studies on Reducing Harm to Patients

Presents case studies of healthcare organizations, clinical teams, and learning collaborations to illustrate successful innovations for improving patient safety nationwide. Includes actions taken, results achieved, lessons learned, and recommendations