A survey of practices for the use of electronic health records to support research recruitment

Electronic health records (EHRs) provide great promise for identifying cohorts and enhancing research recruitment. Such approaches are sorely needed, but there are few descriptions in the literature of prevailing practices to guide their use. A multidisciplinary workgroup was formed to examine current practices in the use of EHRs in recruitment and to propose future directions. The group surveyed consortium members regarding current practices. Over 98% of the Clinical and Translational Science Award Consortium responded to the survey. Brokered and self-service data warehouse access are in early or full operation at 94% and 92% of institutions, respectively, whereas, EHR alerts to providers and to research teams are at 45% and 48%, respectively, and use of patient portals for research is at 20%. However, these percentages increase significantly to 88% and above if planning and exploratory work were considered cumulatively. For most approaches, implementation reflected perceived demand. Regulatory and workflow processes were similarly varied, and many respondents described substantive restrictions arising from logistical constraints and limitations on collaboration and data sharing. Survey results reflect wide variation in implementation and approach, and point to strong need for comparative research and development of best practices to protect patients and facilitate interinstitutional collaboration and multisite research

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Streamlining Study Design and Statistical Analysis

Introduction: Key factors causing irreproducibility of research include those related to inappropriate study design methodologies and statistical analysis; In modern statistical practice irreproducibility could arise due to; Statistical: False discoveries, p-Hacking, Overuse/misuse of p-values, Low power, Poor experimental design; Computational: Data, Code & Software management issues; These require understanding the processes and workflows practiced by an organization, and the development and use of metrics to quantify reproducibility. Conclusion: Automated platforms for statistical workflows; Data-intensive process use process-workflow management platforms: Activiti, Pegasus and Taverna; These strategies for sharing and publishing study protocols, data, code and results across the spectrum, active collaboration with the research team, automation of key steps, along with decision support will ensure quality of statistical methods and reproducibility of research

A Conceptual Architecture for Reproducible On-demand Data Integration for Complex Diseases

Eosinophilic Esophagitis, which is a complex and emerging condition characterized by poorly defined phenotypes, and associated with both genetic and environmental conditions. Understanding such diseases requires researchers to seamlessly navigate across multiple scales (e.g., metabolome, proteome, genome, phenome, exposome) and models (sources using different stores, formats, and semantics), interrogate existing knowledge bases, and obtain results in formats of choice to answer different types of research questions. All of these would need to be performed to support reproducibility and sharability of methods used for selecting data sources, designing research queries, as well as query execution, understanding results and their quality. We present a higher level of formalizations for building multi-source data platforms on-demand based on the principles of meta-process modeling and provide reproducible and shareable data query and interrogation workflows and artifacts. A framework based on these formalizations consists of a layered abstraction of processes to support administrative and research end users: Top layer (meta-process): An extendable library of computable generic process concepts (PC) stored in a metadata repository1 (MDR) and describe steps/phases in the translational research life cycle; Middle layer (process): Methods to generate on-demand queries by assembling instantiated PC into query processes and rules. Researchers design query processes using PC, and evaluate their feasibility and validity by leveraging metadata content in the MDR; Bottom layer (execution): Interaction with a hyper-generalized federation platform (e.g. OpenFurther1) that performs complex interrogation and integration queries that require consideration of interdependencies and precedence across the selected sources. This framework can be implemented using process exchange formats (e.g., DAX, BPMN); and scientific workflow systems (e.g., Pegasus2, Apache Taverna3). All content (PC, rules, and workflows), assembling, and executing mechanism are sharable. The content, design, and development of the framework is informed by user-centered design methodology and consists of researcher and integration-centric components to provide robust and reproducible workflows. References: 1. Gouripeddi R, Facelli JC, et al. FURTHeR: An Infrastructure for Clinical, Translational and Comparative Effectiveness Research. AMIA Annual Fall Symposium. 2013; Wash, DC. 2. Pegasus. The Pegasus Project. 2016; https://pegasus.isi.edu/. 3. Apache Software Foundation. Apache Taverna. 2016; https://taverna.incubator.apache.org/.

Making the Case for Informatics in Global Health

Health Informatics: the scientific field that deals with the storage, retrieval, sharing, and optimal use of biomedical information, data, and knowledge for problem solving and decision making. Its application for improving global health and achieving health equity for all people worldwide can be called Global Health Informatics. Informatics has the potential to improve healthcare quality and enable the next generation of biomedical and translational research through the use of technology and complex analytics. We present example informatics methods, infrastructure, and projects undertaken by the Department of Biomedical Informatics, College of Nursing, and Informatics faculty in the Center for Clinical Translational Sciences. Generalized methods and infrastructure developed at the University are applicable to Global Health and under-resourced settings

The Utah PRISMS Infrastructure for Generating Air Quality Exposomes

Understanding the effects of the modern environment on pediatric asthma requires generation of a complete picture of the contributing environmental exposures and socio-economic factors. Such an exposome requires integration of data from wearable and stationary sensors, environmental monitors, physiology, medication use and other clinical data. In addition, such an integration would need to have a high spatial-temporal resolution for correlating times and location of exposures to occurrences of conditions and their severities. This would require filling any gaps in the measured data with modeled data along with characterization of any uncertainties. The Utah PRISMS Federated Integration Architecture is a comprehensive, standards-based, open-source informatics platform that allows sensor data and biomedical data to be integrated in a meaningful manner. We are developing the architecture, data models, processes, hardware and software to acquire, manage, process, and communicate high-resolution clinically relevant exposome information from environmental, physiological, and behavioral sensors and computational models. We envision this infrastructure to support different types of environmental biomedical studies of pediatric asthma and other chronic conditions, and potentially other research. In this presentation, we discuss two main components of the Utah PRISMS infrastructure: Sensor Common Data Model: Federating or integrating increasingly large, complex and multi-dimensional sensor data requires a thorough human understanding of them. These metadata specifications are designed to support the conduct of research utilizing personalized and environmental sensors. These includes sensors ranging from nano-sensors up to satellites. Sensor measurements may include physical, chemical, and biological species. In addition, these sensors include those that instantaneously (or with a transient storage) measure these species or those that collect physical samples with material transfer for later analysis. Sensors may be deployed in various environments, including personal (i.e. implanted & mobile), immediate (i.e. indoor), and general environment (i.e. external environmental protection agency monitors). The purpose of the data model is to establish a library of instruments; describe and document deployments of sensors; assess quality of data collected by different instruments within its deployment environments; support harmonization and integration of data collected from various sensors; and guide for structuring and storing sensor output data. Central Big Data Federation/Integration Platform: This is a standards-based, open-access infrastructure that integrates sensor data and mathematically modeled data with biomedical information along with characterizing uncertainties associated with using these data. The platform consists of components that: Discover, characterize, store and version metadata of different sensor data sources Standardize semantics across different sensor data sources using ontology services Store data in a temporal event based model to support different research uses-cases Methods to transform and present data for different use-cases of exposomic studies. In this symposium, we present initial lessons from the Utah PRISMS Platform that summarizes the interconnected work by diverse expertise including electrical, computer, chemical and industrial engineers, atmospheric and computer scientists, informaticists and pediatric researchers in developing an infrastructure for generating exposomes

Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations

Human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. A significant empirical data source, like the American Time Use Survey (ATUS), can be leveraged to model human activity. However, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. Using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the ATUS data. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors

A Low-cost, Low-barrier Clinical Trials Registry to Support Effective Recruitment

<p>Cummins, M.R., Gouripeddi, R., & Facelli, J. (2016). A Low-cost, Low-barrier Clinical Trials Registry to Support Effective Recruitment (poster). Research Reproducibility 2016. Salt Lake City, UT, USA</p

Incidence of Idiopathic Intracranial Hypertension (IIH) Among Users of Tetracycline Antibiotics

"Previous studies have shown an association between the use of tetracycline antibiotics and the development of IIH. We sought to calculate the incidence of tetracycline-associated IIH in the University of Utah patient population.