ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Optimizing the migration of a data warehouse to the cloud using network analysis.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
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Abstract
Passage of the HITECH Act in 2009 led to an increase in the adoption of electronic health record (EHR) and health information technology (HIT) systems by hospitals. These systems require hospitals to maintain complex data warehouses supporting clinical and operational activities. Periodically modernizing this data infrastructure is a critical task for IT departments, but this is often managed heuristically. Our team at Children's Hospital of Philadelphia (CHOP) sought an efficient, data-driven method to assist in this modernization activity by prioritizing data warehouse assets for migration into a cloud environment. We created a network graph to capture the dependencies between assets and used graph theoretic methods to score the relative influence of each asset within the overall network. The influence score, based on centrality measures, is proportional to the number of downstream dependencies of an asset. Using this score, we proposed a data-driven and rational strategy for efficiently migrating assets.
Indexed as
Identifiers
40417564PMC12099367What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.