Synthetic health data
Synthetic health data are realistic but simulated health records for artificial patients. These include manufactured medication, laboratory, procedure, observation, allergy, and diagnosis records for fictitious patients. Semedy’s Synthetic Health Data Solution is for creating and managing these valuable knowledge assets and utilizing them to test software applications, clinical decision support, and patient cohort definitions; as well as to support interoperability and data analytics initiatives. This solution allows an organization to:
Create, maintain, import, and export synthetic health records
Perform testing and quality assurance on knowledge content and software applications that depend on realistic patient data without resorting to using and potentially compromising or corrupting real patient data
Robustly test clinical information system functionality such as clinical decision support, population health management registries, and electronic clinical quality measures
Robustly test research information system functionality such as patient cohort specification, generation, and reporting
Share synthetic health data across disparate systems within and amongst different
Semedy’s Synthetic Health Data Solution includes preconfigured and extensible models, extract/transform/load (ETL) pipelines for input and/or export, convenient authoring templates, configurable views, queries, and reports. Source content can be loaded from an external source, such as Synthea™ or SyntheticMass; cloned and modified from existing content; or created new.
The demonstration includes how simulated patient records can be represented using FHIR-based models; imported from and exported to external repositories or applications; searched, queried, and validated using the platform’s built-in semantic reasoner; and leveraged within other CKMS solutions, such as the CDS, eMeasures, and Patient Cohorts.