A Focus Area Maturity Model for a Statewide Master Person Index

Objective: The sharing of personally identifiable information across organizational boundaries to facilitate patient identification in Utah presents significant policy challenges. Our objective was to create a focus area maturity model to describe and evaluate our progress in developing a policy framework to support a statewide master person index (sMPI) for healthcare and public health operations and research in Utah. Materials and Methods: We used various artifacts, including minutes from policy guidance committee meetings over a span of 18 months, a report from Utah’s Digital Health Services Commission, and a draft technical requirements document to retrospectively analyze our work and create a focus area maturity model describing the domain of policy needed to support the sMPI. We then used our model to assess our progress and future goals. Conclusions: The focus area maturity model provides an orderly path that can guide the complex process of developing a functional statewide master person index among diverse, autonomous partners. While this paper focuses on our experience in Utah, we believe that the arguments for using a focus area maturity model to guide the development of state or regional MPIs is of general interest

Prenatal and Perinatal Factors Associated with Intellectual Disability

AbstractPrenatal and perinatal risk factors associated with intellectual disability (ID) were studied in 8-year-old Utah children from a 1994 birth cohort (N  =  26,108) using broad ascertainment methods and birth records following the most current recording guidelines. Risk factor analyses were performed inclusive and exclusive of children with a known or suspected underlying genetic disorder. Risk factors identified were poly/oligohydramnios, advanced paternal/maternal age, prematurity, fetal distress, premature rupture of membranes, primary/repeat cesarean sections, low birth weight, assisted ventilation greater than 30 min, small-for-gestational age, low Apgar scores, and congenital infection. Although several risk factors lost significance once children with underlying genetic disorders were excluded, socioeconomic variables were among those that maintained a prominent association with increased ID risk.</jats:p

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

<p>Abstract</p> <p>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.</p