A case for using grid architecture for state public health informatics: the Utah perspective

Abstract

This paper presents the rationale for designing and implementing the next-generation of public health information systems using grid computing concepts and tools. Our attempt is to evaluate all grid types including data grids for sharing information and computational grids for accessing computational resources on demand. Public health is a broad domain that requires coordinated uses of disparate and heterogeneous information systems. System interoperability in public health is limited. The next-generation public health information systems must overcome barriers to integration and interoperability, leverage advances in information technology, address emerging requirements, and meet the needs of all stakeholders. Grid-based architecture provides one potential technical solution that deserves serious consideration. Within this context, we describe three discrete public health information system problems and the process by which the Utah Department of Health (UDOH) and the Department of Biomedical Informatics at the University of Utah in the United States has approached the exploration for eventual deployment of a Utah Public Health Informatics Grid. These three problems are: i) integration of internal and external data sources with analytic tools and computational resources; ii) provide external stakeholders with access to public health data and services; and, iii) access, integrate, and analyze internal data for the timely monitoring of population health status and health services. After one year of experience, we have successfully implemented federated queries across disparate administrative domains, and have identified challenges and potential solutions concerning the selection of candidate analytic grid services, data sharing concerns, security models, and strategies for reducing expertise required at a public health agency to implement a public health grid.

Participatory Design of an Integrated Information System Design to Support Public Health Nurses and Nurse Managers

“…Other efforts specifically aim to support the work of public health nurses (PHNs) through the implementation of data standards and information infrastructure (Issel, Bekemeier, & Kneipp, ; Monsen, Bekemeier, P. Newhouse, & Scutchfield, ). Despite these efforts and numerous studies of the information needs of the public health workforce (Revere et al., ; Turner, Stavri, Revere, & Altamore, ), public health workers must interact with multiple disparate systems that do not interoperate or exchange data (Staes et al., ). As the largest professional group of the public health workforce (University of Michigan Center of Excellence in Public Health Workforce Studies & University of Kentucky Center of Excellence in Public Health Workforce Research and Policy, ), PHNs are strongly affected by information system designs that do not fit their work.…”

SaTScan on a Cloud: On-Demand Large Scale Spatial Analysis of Epidemics

“…While the work presented here could be implemented using SOAP, the WSRF implementation is a better approach because it allows the integration of the cloud version of SaTScan into emerging public health grid infrastructures [10, 11]. At the time of this implementation the only WSRF (grid) solution for cloud computing accessible to the authors was the Nimbus cloud deployed at Argonne National Lab.…”

Human Genome Project, Personalised Medicine and Future Health Care

“…Also, the genome project has stimulated the development of advanced technology to characterize DNA and study genes. Several initiatives for the development of infrastructures for genomic data sharing have been born since, to allow exchange of empirical data but also facilitate analysis of clinical situations (Merelli, Perez-Sanchez, Gesing, & D'Agostino, 2014;Ray, 2015;Staes et al, 2009;Wang & Krishnan, 2014). A crucial spinoff of these activities has been the development and routinization of specific and reliable diagnostic tests, which in a (relatively limited) number of cases have considerably shortened the process it takes to come to a reliable diagnosis (Eisenstein, 2014;Keating & Cambrosio, 2013;Khoury, Evans, & Burke, 2010).…”

“…The importance of GDB in this process exemplifies how the availability of well-curated, comprehensive phenotype-oriented mapping and annotation databases, providing not only gene maps but mapping and application data for clinical research, is crucial to the fruitful translation of genome knowledge into practical applications. Indeed, data infrastructure initiatives have flourished over the last decade, encompassing a wide spectrum of genomic data-based services, from general repositories to support for the analysis of individual cases (Bin Han Ong, 2015;Merelli et al, 2014;Staes et al, 2009;Wang & Krishnan, 2014). At the same time, the demise of GDB in 2008 highlights how databases and related infrastructures have unclear F o r R e v i e w O n l y sustainability and longevity, as their operational costs are not shared across the community in a proportioned way and their adoption is not unanimous (Ribes & Bowker, 2009;Ure et al, 2009;Bastow & Leonelli, 2010).…”