Approach and Method for Generating Realistic Synthetic Electronic Healthcare Records for Secondary Use

This position paper presents research work involving the development of a publicly available

Realistic Synthetic Electronic Healthcare Record

(RS-EHR). The paper presents PADARSER, a novel approach in which the real

Electronic Healthcare Record

(EHR) and neither authorization nor anonymisation are required in generating the synthetic EHR data sets. The

GRiSER method

is presented for use in PADARSER to allow the RS-EHR to be synthesized for statistically significant localised synthetic patients with statistically prevalent

medical conditions

based upon information found from publicly available data sources. In treating the synthetic patient within the GRiSER method, clinical workflow or

careflows

(Cfs) are derived from

Clinical Practice Guidelines

(CPGs) and the standard local practices of clinicians. The Cfs generated are used together with health statistics, CPGs, medical coding and terminology systems to generate coded synthetic RS-EHR entries from statistically significant observations, treatments, tests, and procedures. The RS-EHR is thus populated with a complete medical history describing the resulting events from treating the medical conditions. The strength of the PADARSER approach is its use of publicly available information. The strengths of the GRiSER method are that (1) it does not require the use of the real EHR for generating the coded RS-EHR entries; and (2) the generic components for obtaining careflow from CPGs and for generating coded RS-EHR entries are applicable in other areas such as knowledge transfer and EHR user interfaces respectively.