Abstract
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.
Highlights
A number of studies have attempted to utilize secondary data directly from electronic medical records (EMR) for clinical research, etc
We investigated whether known adverse events could be detected from the data of several institutions that use Standardized Structured Medical Information eXchange2 (SS-MIX2) standardized storage and compared the results with those from paper-based medical records and electronic data capture (EDC) systems
With respect to the outcome definition for newly developed diabetes, the number of cases and positive predictive values (PPV) based on medical claim data and SS-MIX2 standardized storage data are shown in Table 5 (Number of cases and PPV according to the outcome definition for diabetes)
Summary
A number of studies have attempted to utilize secondary data directly from EMR for clinical research, etc. Validation study of the signal detection of adverse events of drugs using export data from EMR. ASTER enables adverse event reporting through current electronic health records (EHR) systems. After about 20 minutes, a MedWatch report, derived from the EHR, is delivered to the Food and Drug Administration (FDA) to report on the adverse event. When ASTER was tested previously, 20% of these reports were deemed as "serious," 100% had height/weight and lab data, and 91% of participating physicians had not submitted any adverse drug event (ADE) reports the prior year. ASTER’s ease of use, and the opportunity to report adverse events at the point of care within 60 seconds (vs 34 minutes for a fax report), makes this innovative approach an essential step in enabling safer, more effective drugs
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