Browsing by Subject "Pipeline"

Now showing 1 - 3 of 3
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    From Hometown to Practice: Mapping and Analyzing the Medical Student Pipeline at the Indiana University School of Medicine
    (2019-10) Fancher, Laurie Michelle; Wilson, Jeffrey; Kochhar, Komal; Lulla, Vijay
    Indiana University School of Medicine (IUSM) teaches approximately 350 medical students each year. These students come from varied backgrounds and eventually end up practicing in a vast array of clinical specialties and settings. It is extremely important to monitor specialties and practice locations to understand exactly how IUSM is fulfilling physician workforce needs. This knowledge can help policymakers and school administrators shape programs and policies to better fulfill physician workforce needs. Geographic information technologies provide a framework to organize, analyze and visualize medical student data. Maps are a convenient and easily understandable method of conveying information with a location-based component. This project represents a step towards creating a coherent student database visualized with maps. Using data about the graduating classes from 2011-2018, a database was created that linked together geographic information of students from the various segments of their medical education such as residency, fellowship, and practice location. ArcGIS 10.5 was used to produce maps visualizing segments of this database. These maps also served to answer questions about the medical student graduates at IUSM, such as how many came from an in-state location and how many practice in-state. SPSS 25 was also used to compare results of various segments of the medical education pipeline. The database proves to be an incredibly necessary tool for keeping track of all IUSM graduates. Coherent, clean, and complete data is necessary for researchers at all levels as well as administrators. Keeping data up to date and centralized is essential and this project provides an easily updateable and useable format. The maps created from this database are also useful in showing trends across the graduates of IUSM, such as the Indiana counties that the graduates are most likely to practice in or the likelihood of practicing in specific shortage areas.
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    Just in Time Radiology Decision Support Using Real-time Data Feeds
    (SpringerLink, 2020-02) Burns, John L.; Hasting, Dan; Gichoya, Judy W.; McKibben, Ben, III.; Shea, Lindsey; Frank, Mark; Radiology and Imaging Sciences, School of Medicine
    Ready access to relevant real-time information in medical imaging offers several potential benefits. Knowing both when important information will be available and that important information is available can facilitate optimization of workflow and management of time. Unexpected findings, as well as deficiencies in reporting and documentation, can be immediately managed. Herein, we present our experience developing and implementing a real-time web-centric dashboard system for radiologists, clinicians, and support staff. The dashboards are driven by multi-sourced HL7 message streams that are monitored, analyzed, aggregated, and transformed into multiple real-time displays to improve operations within our department. We call this framework Pipeline. Ruby on Rails, JavaScript, HTML, and SQL serve as the foundations of the Pipeline application. HL7 messages are processed in real-time by a Mirth interface engine which posts exam data into SQL. Users utilize web browsers to visit the Ruby on Rails-based dashboards on any device connected to our hospital network. The dashboards will automatically refresh every 30 seconds using JavaScript. The Pipeline application has been well received by clinicians and radiologists.
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    Odyssey: a semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data
    (Biomed Central, 2019-06-28) Eller, Ryan J.; Janga, Sarath C.; Walsh, Susan; Biology, School of Science
    BACKGROUND: Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses. RESULTS: In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS). CONCLUSION: Odyssey facilitates efficient and fast genome-wide association analysis automation and can go from raw genetic data to genome: phenome association visualization and analyses results in 3-8 h on average, depending on the input data, choice of programs within the pipeline and available computer resources. Odyssey was built to be flexible, portable, compatible, scalable, and easy to setup. Biologists less familiar with programing can now work hands on with their own big data using this easy-to-use pipeline.