Case report: evaluation of an open-source synthetic data platform for simulation studies.

Simulation is a mainstay of comparative- and cost-effectiveness research when empirical data are not available. The Synthea platform, originally designed for generating realistically coded longitudinal health records for software testing, implements data generation models specified in publicly contributed modules representing patients' life cycle and disease and treatment progression. We test the hypothesis that Synthea can be used for simulation studies that draw parameters from observational studies and randomized trials. We benchmarked the results and assessed the effort required to create a Synthea module that replicates a recently published cost-effectiveness simulation comparing levofloxacin prophylaxis to usual care for leukemia. A module was iteratively developed using published parameters from the original study; we replicated the initial conditions and simulation endpoints of demographics, health events, costs, and mortality. We compare Synthea's Generic Module Framework to platforms designed for simulation and show that Synthea can be used, with modifications, for some types of simulation studies.

Demonstrating "collect once, use many"--assimilating public health secondary data use requirements into an existing Domain Analysis Model.

The healthcare industry has an increasing need for clinical data content standards to support patient care and data re-use in areas such as research, quality and public health. The Diabetes Data Strategy (Diabe-DS) project was formed in 2009 by the HL7 EHR Working Group to demonstrate a repeatable process that identifies disease-specific Common Data Elements (CDEs) for clinical care and secondary use. The Diabe-DS project previously developed a set of important CDEs and supporting data models for clinical care, quality and research uses of diabetes data. This paper will describe the process for identifying the data elements and activities required for public health use of clinical data, and mapping them to Diabe-DS CDEs, use case and data models. The result is a model for consideration which provides data needed in the immediate clinical environment of care, and supports the use of data for multiple uses.