With the introduction of electronic medical records (EMRs), it has become possible to accumulate massive amounts of qualitative medical data. As such, EMRs have become increasingly used in clinical decision support systems (CDSSs). While CDSSs aim to reduce medical errors normally occurring in the process of treating patients by physicians, technical maturity and the completeness of CDSSs do not meet standards for medical use yet. As data further accumulates, CDSS algorithms must be continuously updated to allow CDSSs to perform their core functions. Doing so, however, requires extensive time and manpower investments. In current practice, computational systems already perform a wide variety of functions in medical settings to allow medical staff to focus on other tasks. However, no prior research has evaluated the potential effectiveness of future CDSSs nor analyzed possibilities for their further development. In this article, we evaluate CDSS technology with the consideration that medical staff also understand the core functions of such systems.

Background: Spontaneous adverse drug reaction (ADR) reporting data has been used for safety of post-market drug surveillance. A system has been required that is able to detect signals associated with drugs by analyzing the collected ADR data. Methods: We developed the web-based automated analysis system (ADR-detector). We used the data which reported ADR spontaneously between March 2009 and December 2010 to Korean Food and Drug Administration. We used 3 statistical indicators for evaluating ADR signals: proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The ADR reports which were detected as significant signals based on the indicators have been reviewed. Results: Among 153,774 reports, 9,955 cases were related to 4 analgesics which were most frequently reported analgesic drugs during the study period. The numbers of ADR reports associated with each drug are as follow: 5,623 reports in tramadol (56.5 %), 1,720 reports in fentanyl (17.3 %), 1,463 reports in tramadol-combination (14.7 %), and 1,149 reports in ketorolac (11.5 %). Top 5 ADR were nausea (3,351 reports -33.7 %), vomiting (1,755 reports -17.6 %), dizziness (1,130 -11.4 %), rash (412 reports -4.1 %), and pruritus (354 reports -3.6 %). 6,674 ADR reports were significant based on PRR and ROR, and 336 reports were significant based on IC. Conclusion: By using the automated analysis system, not only statisticians but also general researchers are able to analyze ADR signals in real-time. Also ADR-detector would provide rapid review and cross-check of ADR.

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