We help you transform and load very large healthcare databases, such as administrative claims or EHR. We provide encryption, versioning, incremental or total refreshes, as well as historical views ("what did the data look like in December 2005?"). We are database-technology agnostic and have experience with Oracle, Sybase, MySQL, SQL Server, Netezza and others.

Large healthcare data contains artifacts of human error and inconsistancies created during collection and entry. Therefore the data is often "dirty" and requires identification of problems and subsequent cleaning as needed. We have experience with major large US-based and international databases to provide a systematic approach to data characterization (OMOP OSCAR), code transformations (e.g. ICD-9 codes with and without periods), field type changes over time, missing data, etc.

We have extensive experience with the transformation of large-scale healthcare data to prepare them for specialized analytical applications. We have transformed major US and international observational healthcare databases to OMOP Common Data Model [ref to OMOP] and Mini-Sentinel Common Data Model. We have optimized these transformations to accommodate data as large as 107 Million lives.

Data anomalies and issues can arise during the primary data collection and during data transformation to an analytical data model. A standard systematic QA helps to identify these and to ensure high quality data and analysis results. We have run XXX different large-scale observational databases through OMOP GROUCH [Rev to OMOP], issuing extensive reports of potential quality issues with the data.

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To identify associations between healthcare intervention (such as treatments or healthcare delivery changes), and outcomes (benefit and risk) complex algorithms are required that can control for the significant amount of confounding the data contain. These SAS, R, SQL and C++ based algorithms are very computationally intensive. We have extensive experience with the development, testing and running these algorithms in a parallel cloud environment.

Large-scale complex data require effective visualization techniques to spot trends, identify outliers and capture the insights that are hidden in the "ocean" of data. Our R and Spotfire-based tools are effectively utilizing interactive visualization techniques such as trellis plots, tree plots and many other types of graphs and plots.

We built a suite of user-friendly web apps for our customers that help with the management, manipulation, analysis and visualization of large-scale data. These applications are optimized for speed to deal with complex queries of large data and high-volume interactive visualizations.