Browsing by Subject "Interoperability"

Now showing 1 - 3 of 3
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    Development Of Semi-Automated Tools To Map Cancer Research Common Data Elements To The Biomedical Research Integrated Domain Group Model
    (2020-03) Renner, Robinette
    While using data standards can facilitate research by making it easier to share data, manually mapping to data standards creates an obstacle to their adoption. Semi-automated mapping strategies can reduce the manual mapping burden. This research addresses the mapping dilemma by applying well-established and emerging techniques to a real-world use case. First, machine learning approaches were used and evaluated to map Common Data Elements (CDEs) from the National Cancer Institute’s (NCI) cancer Data Standards Registry and Repository to the Biomedical Research Integrated Domain Group (BRIDG) model. Second, a graph database that incorporates the CDEs, BRIDG Model, and the NCI Thesaurus was developed and evaluated. A shortest path algorithm was then used to predict mappings from CDEs to classes in the BRIDG model. Finally, analysis was conducted to: determine the strengths and weaknesses of each approach; highlight data quality issues; and determine when either approach or a combination of the approaches provides the optimal results. The results indicate that an artificial neural network-based mapping tool is able to predict CDE to BRIDG class mappings with between 34 - 94% accuracy but is limited by the availability of training data. The results also show that a graph database can be used to map CDEs to BRIDG classes but is limited by the subjective nature of the mapping process. An optimal mapping tool combines machine learning and graph database techniques with the knowledge and experience of a human subject matter expert.
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    Impact of task, structure, and environment on electronic health record adoption, use, and interoperability in hospitals.
    (2010-06) Park, Young-Taek, M.P.H.
    A paradigm in the field of Heath Informatics which has been taken for granted up until this point may be disappearing and a new paradigm may begin to take shape as paper-based medical record (PMR) systems are changing to the electronic health record (EHR) systems. Although the PMR has played a critical role in recording patient's clinical information, now many studies report that EHR systems improve quality of care beyond PMRs. For this reason, the governments across the world have initiated various approaches accelerating EHR adoption, use, and interoperability. However, there has been a paucity of studies explaining which factors affect EHR adoption, use, and interoperability in hospitals. The objective of this study is to predict and investigate those factors. This study used a non-experimental, retrospective, cross-sectional study design to measure hospitals' internal features. Specifically, this study conducted a nationwide EHR survey with the IT departments in South Korean hospitals by using online surveys from April 10 to August 3, 2009. It used Generalized Estimating Equations, an extension of the Generalized Linear Model, to interpret EHR system adoption and interoperability, and General Linear Mixed Model for the use of EHR systems. With respect to EHR system adoption, this study found that 1) the likelihood of EHR adoption increases as a hospital's task complexity - measured by the number of medical specialties - IT infrastructure, and organic structural characteristics, and environmental complexity - measured by the number of hospitals within the local area - increases and 2) there were significant interaction effects between task complexity and structural features. Assuming that a hospital adds additional medical specialties, the likelihood of adopting an EHR system of the hospital increases under the decentralized decision-making system, but decreases under the centralized decision-making system. The likelihood decreases under a high level of IT infrastructure, but increases under a lower level of IT infrastructure. For the hospitals' EHR use, there was not any relationship between EHR use and proposed hospital's internal features. Thus, alternative measures of EHR use and internal features were suggested. For EHR interoperability, this study found that 1) the likelihood of having EHR interoperability increases as task complexity and organic managerial features increases, and 2) two interaction effects were reported. Assuming that a hospital adds additional medical specialties, the likelihood of having EHR interoperability of the hospital increases at a high level of IT staff specialization, but decreases at a lower level of IT staff specialization. At a high level of environmental complexity with more than average number of hospitals within the local area, the likelihood of having EHR interoperability of the hospitals located in the area increases as IT staff specialization increases. However, the likelihood decreases as IT staff specialization increases at a lower level of environmental complexity with less than average number of hospitals within the local area. In conclusion, this study verified that hospitals' task, structure, and environmental features were critical factors affecting the EHR system adoption and interoperability. However, these factors did not affect EHR use. Different approaches measuring EHR use and hospitals' various internal features were recommended. This study's results can provide health informaticians, hospital IT managers, and health politicians with new information about EHR system adoption, use, and interoperability for their innovative decision-making.
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    Think Globally, Act Locally: The Importance of Elevating Data Repository Metadata to the Global Infrastructure
    (2022) Taylor, Shawna; Wright, Sarah; Narlock, Mikala R.; Habermann, Ted
    Inconsistent and incomplete applications of metadata standards and unsatisfactory approaches to connecting repository holdings across the global research infrastructure inhibit data discovery and reusability. The Realities of Academic Data Sharing (RADS) Initiative has found that institutions and researchers create and have access to the most complete metadata, but that valuable metadata found in these local institutional repositories (IRs) are not making their way into global data infrastructure such as DataCite or Crossref. This panel examines the local to global spectrum of metadata completeness, including the challenges of obtaining quality metadata at a local level, specifically at Cornell University, and the loss of metadata during the transfer processes from IRs into global data infrastructure. The metadata completeness increases over time, as users reuse data and contribute to the metadata. As metadata improves and grows, users find and develop connections within data not previously visible to them. By feeding local IR metadata into the global data infrastructure, the global infrastructure starts giving back in the form of these connections. We believe that this information will be helpful in coordinating metadata better and more effectively across data repositories and creating more robust interoperability and reusability between and among IRs.