Entity

Time filter

Source Type

San Francisco, CA, United States

Yu K.,Kaiser Permanente | Rho J.,Kp Corporation Inc. | Morcos M.,Los Angeles Medical Center | Nomura J.,Los Angeles Medical Center | And 5 more authors.
American Journal of Health-System Pharmacy | Year: 2014

Purpose. Patient care improvements and cost savings achieved by a large integrated health system through the implementation of antimicrobial stewardship programs (ASPs) at two hospitals are reported. Methods. A pre-post analysis was conducted to evaluate cost and quality outcomes at the two ASP sites and three similar sites within the same health system not included in the ASP initiative. The utilization of 15 targeted antimicrobials and associated costs at the five sites during designated preimplementation and postimplementation periods were compared; changes in Hospital Standardized Mortality Ratio (HSMR) values for specific infections among Medicare patients were also assessed. Results. In the year after ASP implementation, aggregate direct antimicrobial acquisition costs at the two study sites decreased 17.3% from prior-year levels and increased by 9.1% at the three comparator sites. Significant decreases in the consumption of targeted antimicrobial classes (antipseudomonals, quinolones, and agents active against methicillin-resistant Staphylococcus aureus) were observed at the ASP sites. Among the 2446 ASP interventions recorded, 72% involved discontinuing or narrowing the use of broad-spectrum antimicrobials. Although rates of health care-associated Clostridium difficile infection were little changed at both study sites after ASP implementation, HSMR data indicated substantial gains in combating sepsis and C. difficile and respiratory infections. Conclusion. After implementation of ASPs at two study sites, the utilization of all classes of antibiotics decreased and antimicrobial costs per 1000 patient-days decreased. While HSMR values for sepsis (including C. difficile-associated cases) and respiratory infections improved, the rate of C. difficile infections stayed the same. Copyright © 2014, American Society of Health-System Pharmacists, Inc. All rights reserved. Source


Bonser C.D.,Support Intelligence | Fawcett J.R.,Headquarters 1st United Kingdom Armoured Division formerly SO2 Medical Force Protection
Journal of the Royal Army Medical Corps | Year: 2013

Disease and non-battle injury have historically caused greater morbidity and mortality than battle trauma during military operations, and continue to do so. As a countermeasure, medical force protection (Med FP) measures will assist in the maintenance of combat efficiency, reducing manpower wastage and the inherent consumption of medical, infrastructural and logistical resources at the tactical, operational and strategic levels. This paper considers recent improvements in provision and delivery of essential Med FP measures and outlines the effect and confounding factors associated with pragmatic Med FP delivery across the Task Force Helmand area of responsibility during Op HERRICK 11B-14A (January 2010-July 2011) in Afghanistan, with a particular focus on military environmental health assets. Source


Maher J.,Mount Vernon Cancer Center | McConnell H.,Support Intelligence
British Journal of Cancer | Year: 2011

Background:Two million people in the UK had a cancer diagnosis at the end of 2008. Understanding the number of people diagnosed with cancer with and without health needs is valuable information that can be used to inform service planning, treatment provision and support for people at the right time in the right place as demand grows over time.Methods:Using available data and clinically led assumptions about patient need and outcomes, we make indicative estimates. We quantify, for three common cancers, the number of people in each of the five main identified phases of the cancer care pathway.Results:Estimates are provided for each phase of the pathway for breast, colorectal and lung cancers. We estimate that there are nearly 575 000 women a year with breast cancer in the care pathway at some point in the year, 8% are in the rehabilitation phase and 4% in the progressive illness phase. This compares to nearly 270 000 with colorectal and around 95 000 with lung cancer.Conclusion: Using readily available data, we estimate the numbers of patients with different health needs. These numbers could inform the targeting of resources for service providers. © 2011 Cancer Research UK All rights reserved. Source


Moreira-Matias L.,University of Porto | Mendes-Moreira J.,University of Porto | Mendes-Moreira J.,Support Intelligence | De Sousa J.F.,INESC Porto | And 2 more authors.
IEEE Transactions on Intelligent Transportation Systems | Year: 2015

Intelligent transportation systems based on automated data collection frameworks are widely used by the major transit companies around the globe. This paper describes the current state of the art on improving both planning and control on public road transportation companies using automatic vehicle location (AVL) data. By surveying this topic, the expectation is to help develop a better understanding of the nature, approaches, challenges, and opportunities with regard to these problems. This paper starts by presenting a brief review on improving the network definition based on historical location-based data. Second, it presents a comprehensive review on AVL-based evaluation techniques of the schedule plan (SP) reliability, discussing the existing metrics. Then, the different dimensions on improving the SP reliability are presented in detail, as well as the works addressing such problem. Finally, the automatic control strategies are also revised, along with the research employed over the location-based data. A comprehensive discussion on the techniques employed is provided to encourage those who are starting research on this topic. It is important to highlight that there are still gaps in AVL-based literature, such as the following: 1) long-term travel time prediction; 2) finding optimal slack time; or 3) choosing the best control strategy to apply in each situation in the event of schedule instability. Hence, this paper includes introductory model formulations, reference surveys, formal definitions, and an overview of a promising area, which is of interest to any researcher, regardless of the level of expertise. © 2000-2011 IEEE. Source


Haug P.J.,Intermountain Healthcare | Ferraro J.P.,Intermountain Healthcare | Holmen J.,Intermountain Healthcare | Wu X.,Intermountain Healthcare | And 4 more authors.
Journal of the American Medical Informatics Association | Year: 2013

Objectives: To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. Materials and methods: We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. Results: The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. Discussion: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. Conclusions: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible. Source

Discover hidden collaborations