A Framework for Dynamic Composition and Management of Emergency Response Processes.

An emergency response process outlines the workflow of different activities that need to be performed in response to an emergency. Effective emergency response requires communication and coordination with the operational systems belonging to different collaborating organizations. Therefore, it is necessary to establish information sharing and system-level interoperability among the diverse operational systems. Unlike typical e-government processes that are well structured and have a well-defined outcome, emergency response processes are knowledge-centric and their workflow structure and execution may evolve as the incident unfolds. It is impractical to define static plans and response process workflows for every possible situation. Instead, a dynamic response should be adaptable to the changing situation. We present an integrated approach that facilitates the dynamic composition of an executable response process. The proposed approach employs ontology-based reasoning to determine the default actions and resource requirements for the given incident and to identify relevant response organizations based on their jurisdictional and mutual aid agreement rules. The Web service APIs of the identified response organizations are then used to generate an executable response process that evolves dynamically. The proposed approach is implemented and experimentally validated using an example scenario derived from the FEMA Hazardous Materials Tabletop Exercises Manual.

Data science, learning, and applications to biomedical and health sciences.

The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.

Improving cancer patient emergency room utilization: A New Jersey state assessment.

INTRODUCTION: Due to its increasing incidence and its major contribution to healthcare costs, cancer is a major public health problem in the United States. The impact across different services is not well documented and utilization of emergency departments (ED) by cancer patients is not well characterized. The aim of our study was to identify factors that can be addressed to improve the appropriate delivery of quality cancer care thereby reducing ED utilization, decreasing hospitalizations and reducing the related healthcare costs. METHODS: The New Jersey State Inpatient and Emergency Department Databases were used to identify the primary outcome variables; patient disposition and readmission rates. The independent variables were demographics, payer and clinical characteristics. Multivariable unconditional logistic regression models using clinical and demographic data were used to predict hospital admission or emergency department return. RESULTS: A total of 37,080 emergency department visits were cancer related with the most common diagnosis attributed to lung cancer (30.0%) and the most common presentation was pain. The disposition of patients who visit the ED due to cancer related issues is significantly affected by the factors of race (African American OR=0.6, p value=0.02 and Hispanic OR=0.5, p value=0.02, respectively), age aged 65 to 75years (SNF/ICF OR 2.35, p value=0.00 and Home Healthcare Service OR 5.15, p value=0.01, respectively), number of diagnoses (OR 1.26, p value=0.00), insurance payer (SNF/ICF OR 2.2, p value=0.02 and Home Healthcare Services OR 2.85, p value=0.07, respectively) and type of cancer (breast OR 0.54, p value=0.01, prostate OR 0.56, p value=0.01, uterine OR 0.37, p value=0.02, and other OR 0.62, p value=0.05, respectively). In addition, comorbidities increased the likelihood of death, being transferred to SNF/ICF, or utilization of home healthcare services (OR 1.6, p value=0.00, OR 1.18, p value=0.00, and OR 1.16, p value=0.04, respectively). Readmission is significantly affected by race (American Americans OR 0.41, standard error 0.08, p value=0.001 and Hispanics OR 0.29, standard error 0.11, p value=0.01, respectively), income (Quartile 2 OR 0.98, standard error 0.14, p value 0.01, Quartile 3 OR 1.07, standard error 0.13, p value 0.01, and Quartile 4 OR 0.88, standard error 0.12, p value 0.01, respectively), and type of cancer (prostate OR 0.25, standard error 0.09, p value=0.001). CONCLUSION: Web based symptom questionnaires, patient navigators, end of life nursing and clinical cancer pathways can identify, guide and prompt early initiation of treat before progression of symptoms in cancer patients most likely to visit the ED. Thus, improving cancer patient satisfaction, outcomes and reduce health care costs.