MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.

We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four diseases (diabetes, dyslipidemia, hyperthyroidism, and acute renal failure) and the validity of the outcome definitions was evaluated by calculating positive predictive values (PPV). The maximum positive predictive value (PPV) for diabetes based on medical claim data was 40.7% and that based on prescription data from SS-MIX2 Standardized Storage was 44.7%. The PPV for dyslipidemia was 50% or higher under either of the conditions. The PPV for hyperthyroidism based on disease name data alone was 20-30%, but exceeded 60% when prescription data was included in the evaluation. Acute renal failure was evaluated using information from medical records in addition to the data. The PPV for acute renal failure based on the data of disease names and laboratory examination results was slightly higher at 53.7% and increased to 80-90% when patients who previously had a high serum creatinine (Cre) level were excluded. When defining a disease, it is important to include the condition specific to the disease; furthermore, it is very useful if laboratory examination results are also included. Therefore, the inclusion of laboratory examination results in the definitions, as in the present study, was considered very useful for the analysis of multi-center SS-MIX2 standardized storage data.

Development of a new seamless data stream from EMR to EDC system using SS-MIX2 standards applied for observational research in diabetes mellitus.

Over the last decade, redundant entry of data in electronic medical records (EMR) for health care and electronic data capture (EDC) systems for research has been the typical medical research methodology. The corresponding data transcription this methodology requires not only increases the burden for clinician investigators and clinical research coordinators (CRCs), but it also decreases the quality of data. We designed and developed a new standards-based and platform-independent system to use data in the EMR to directly populate clinical data management systems in the EDC to eliminate the need for data transcription, streamline the clinical research process, and reduce clinician burden. Standardized structured medical information eXchange2 (SS-MIX2) was implemented along with the Integrating the Healthcare Enterprise (IHE) Retrieve Form for Data Capture (RFD) Integration Profile. Standards from Clinical Data Interchange Standards Consortium (CDISC) were used to define metadata for research data collection forms and as a means to standardize data exchange semantics. These standards and the associated methodology were applied to observational research in patients with diabetes mellitus. The system we developed complies with global requirements for regulated research. It provides a standard-based and platform-independent method that can serve to accelerate the cycle of a learning health system.