GBF1: A novel Golgi-associated BFA-resistant guanine nucleotide exchange factor that displays specificity for ADP-ribosylation factor 5.

Expression cloning from a cDNA library prepared from a mutant CHO cell line with Golgi-specific resistance to Brefeldin A (BFA) identified a novel 206-kD protein with a Sec7 domain termed GBF1 for Golgi BFA resistance factor 1. Overexpression of GBF1 allowed transfected cells to maintain normal Golgi morphology and grow in the presence of BFA. Golgi- enriched membrane fractions from such transfected cells displayed normal levels of ADP ribosylation factors (ARFs) activation and coat protein recruitment that were, however, BFA resistant. Hexahistidine-tagged-GBF1 exhibited BFA-resistant guanine nucleotide exchange activity that appears specific towards ARF5 at physiological Mg2+concentration. Characterization of cDNAs recovered from the mutant and wild-type parental lines established that transcripts in these cells had identical sequence and, therefore, that GBF1 was naturally BFA resistant. GBF1 was primarily cytosolic but a significant pool colocalized to a perinuclear structure with the beta-subunit of COPI. Immunogold labeling showed highest density of GBF1 over Golgi cisternae and significant labeling over pleiomorphic smooth vesiculotubular structures. The BFA-resistant nature of GBF1 suggests involvement in retrograde traffic.

Understanding Unintended Consequences and Health Information Technology:. Contribution from the IMIA Organizational and Social Issues Working Group.

OBJECTIVE: No framework exists to identify and study unintended consequences (UICs) with a focus on organizational and social issues (OSIs). To address this shortcoming, we conducted a literature review to develop a framework for considering UICs and health information technology (HIT) from the perspective of OSIs. METHODS: A literature review was conducted for the period 2000- 2015 using the search terms "unintended consequences" and "health information technology". 67 papers were screened, of which 18 met inclusion criteria. Data extraction was focused on the types of technologies studied, types of UICs identified, and methods of data collection and analysis used. A thematic analysis was used to identify themes related to UICs. RESULTS: We identified two overarching themes. One was the definition and terminology of how people classify and discuss UICs. Second was OSIs and UICs. For the OSI theme, we also identified four sub-themes: process change and evolution, individual-collaborative interchange, context of use, and approaches to model, study, and understand UICs. CONCLUSIONS: While there is a wide body of research on UICs, there is a lack of overall consensus on how they should be classified and reported, limiting our ability to understand the implications of UICs and how to manage them. More mixed-methods research and better proactive identification of UICs remain priorities. Our findings and framework of OSI considerations for studying UICs and HIT extend existing work on HIT and UICs by focusing on organizational and social issues.

Big Data in Healthcare - Defining the Digital Persona through User Contexts from the Micro to the Macro. Contribution of the IMIA Organizational and Social Issues WG.

OBJECTIVES: While big data offers enormous potential for improving healthcare delivery, many of the existing claims concerning big data in healthcare are based on anecdotal reports and theoretical vision papers, rather than scientific evidence based on empirical research. Historically, the implementation of health information technology has resulted in unintended consequences at the individual, organizational and social levels, but these unintended consequences of collecting data have remained unaddressed in the literature on big data. The objective of this paper is to provide insights into big data from the perspective of people, social and organizational considerations. METHOD: We draw upon the concept of persona to define the digital persona as the intersection of data, tasks and context for different user groups. We then describe how the digital persona can serve as a framework to understanding sociotechnical considerations of big data implementation. We then discuss the digital persona in the context of micro, meso and macro user groups across the 3 Vs of big data. RESULTS: We provide insights into the potential benefits and challenges of applying big data approaches to healthcare as well as how to position these approaches to achieve health system objectives such as patient safety or patient-engaged care delivery. We also provide a framework for defining the digital persona at a micro, meso and macro level to help understand the user contexts of big data solutions. CONCLUSION: While big data provides great potential for improving healthcare delivery, it is essential that we consider the individual, social and organizational contexts of data use when implementing big data solutions.

Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks. Contribution of the IMIA Primary Healthcare Working Group.

BACKGROUND: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. OBJECTIVE: To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. METHOD: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. RESULTS: We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowdsourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the "internet of things", and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. CONCLUSIONS: Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.