Information Systems and Biopower: Evaluating the Exchange of Health Information through Foucault’s Philosophy

Policy makers in the U.S. government laud the electronic exchange of health information as critical to providing more affordable, better quality health care and are investing significant resources to support this initiative. Some believe that the exchange of detailed health information is critical to gaining new knowledge in medical care and should be considered a public good. The philosophical work of Michel Foucault provides an effective lens to critically examine the implementation of health exchanges and new information technology. Foucault‘s discussion of concepts like knowledge, power, and surveillance are used to argue that a health record is a full representation of the physical body and is a means of controlling populations through information. Foucault‘s insights help us understand how storing and exchanging complete health data undermines bodily autonomy, leads to greater marginalization of minority groups, extends biopolitical control, and spurs forced conformity to physical norms

The New IT Product/Project Lifecyle

Recent studies have found that Information Technology (IT) project success hovers around 40%, despite the increased adoption of project management methods by the IT community. This paper explores the possibility that the project lifecycle, with distinct beginning and ending points, may not be the best model to understand the implementation of an IT product. Using project data from two organizations, and incorporating ideas from the product management literature, this paper presents an enhanced project lifecycle that incorporates the need for ongoing support that is unique to IT products. This analysis discusses the need to incorporate product management thinking and lifetime support into a project management construct, and identifies the deficiencies in trying to apply a pure project management lifecycle structure to IT implementations

How Can Universities Best Encourage Women to Major in Information Systems?

Despite both government and industry initiatives, the under-representation of women in information systems (IS) continues. Can academia help right this imbalance by helping fill the pipeline for technically qualified female employees? We analyze the results of four experimental interventions based on empirical studies and prior surveys designed to address this issue. We conducted these interventions as projects in an introductory undergraduate IS class in a public university in the western US. Sadly, none were effective in encouraging more female students to consider majoring in IS

Applying business intelligence concepts to medicaid claim fraud detection

Abstract U.S. governmental agencies are striving to do more with less. Controlling the costs of delivering healthcare services such as Medicaid is especially critical at a time of increasing program enrollment and decreasing state budgets. Fraud is estimated to steal up to ten percent of the taxpayer dollars used to fund governmentally supported healthcare, making it critical for government authorities to find cost effective methods to detect fraudulent transactions. This paper explores the use of a business intelligence system relying on statistical methods to detect fraud in one state's existing Medicaid claim payment data. This study shows that Medicaid claim transactions that have been collected for payment purposes can be reformatted and analyzed to detect fraud and provide input for decision makers charged with making the best use of available funding. The results illustrate the efficacy of using unsupervised statistical methods to detect fraud in healthcare-related data

Type 1 Diabetes Risk in African-Ancestry Participants and Utility of an Ancestry-Specific Genetic Risk Score

ObjectiveGenetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS.Research design and methodsWe generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (P < 5.0 × 10-8) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (P < 2.79 × 10-5).ResultsAfrican-specific (HLA-DQA1*03:01-HLA-DQB1*02:01) and known European-ancestry HLA haplotypes (HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01, HLA-DRB1*04:01-HLA-DQA1*03:01-HLA-DQB1*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts.ConclusionsGenetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10-8) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4+ effector T cells. Using chromatin-accessibility profiling of CD4+ T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D