Proactive Identification of False Alert for Drug-Drug Interaction
Researchers of drug-drug interaction alert systems
have often suggested that there were high overridden rate for alerts and
also too false alerts. However, research about decreasing false alerts is
scant. Therefore, the aim of this article attempts to proactive
identification of false alert for drug-drug interaction and provide
solution to decrease false alerts. This research involved retrospective
analysis prescribing database and calculated false alert rate by using
MYSQL and JAVA. Results of this study showed 17% of false alerts
and the false alert rate in the hospitals (37%) was more than in the
clinics. To conclude, this study described the importance that
drug-drug interaction alert system should not only detect drug name
but also detect frequency or route, as well as in providing solution to
decrease false alerts.
[1] Drug Interaction Facts.(2009)
[2] Kuhlmann, J. and W. Muck, Clinical-pharmacological strategies to assess
drug interaction potential during drug development. Drug safety, 2001.
24(10): p. 715-725.
[3] Jankel, C. and L. Fitterman, Epidemiology of drug-drug interactions as a
cause of hospital admissions. Drug safety: an international journal of
medical toxicology and drug experience, 1993. 9(1): p. 51.
[4] Lepori, V., A. Perren, and C. Marone, Adverse internal medicine drug
effects at hospital admission. Schweizerische medizinische
Wochenschrift, 1999. 129(24): p. 915.
[5] Pirmohamed, M., et al., Adverse drug reactions as cause of admission to
hospital: prospective analysis of 18 820 patients. British Medical Journal,
2004. 329(7456): p. 15.
[6] Kaushal, R., K. Shojania, and D. Bates, Effects of computerized physician
order entry and clinical decision support systems on medication safety: a
systematic review. Archives of internal medicine, 2003. 163(12): p. 1409.
[7] Grizzle, A., et al., Reasons provided by prescribers when overriding
drug-drug interaction alerts. The American journal of managed care,
2007. 13(10): p. 573.
[8] Payne, T., et al. Characteristics and override rates of order checks in a
practitioner order entry system. 2002: American Medical Informatics
Association.
[9] Weingart, S., et al., Physicians' decisions to override computerized drug
alerts in primary care. Archives of internal medicine, 2003. 163(21): p.
2625.
[10] van der Sijs, H., et al., Turning off frequently overridden drug alerts:
limited opportunities for doing it safely. Journal of the American Medical
Informatics Association, 2008. 15(4): p. 439-448.
[11] Buurma, H., P. De Smet, and A. Egberts, Clinical risk management in
Dutch community pharmacies: the case of drug-drug interactions. Drug
safety, 2006. 29(8): p. 723-732.
[12] http://w3.nhri.org.tw/nhird//en/Background.html
[1] Drug Interaction Facts.(2009)
[2] Kuhlmann, J. and W. Muck, Clinical-pharmacological strategies to assess
drug interaction potential during drug development. Drug safety, 2001.
24(10): p. 715-725.
[3] Jankel, C. and L. Fitterman, Epidemiology of drug-drug interactions as a
cause of hospital admissions. Drug safety: an international journal of
medical toxicology and drug experience, 1993. 9(1): p. 51.
[4] Lepori, V., A. Perren, and C. Marone, Adverse internal medicine drug
effects at hospital admission. Schweizerische medizinische
Wochenschrift, 1999. 129(24): p. 915.
[5] Pirmohamed, M., et al., Adverse drug reactions as cause of admission to
hospital: prospective analysis of 18 820 patients. British Medical Journal,
2004. 329(7456): p. 15.
[6] Kaushal, R., K. Shojania, and D. Bates, Effects of computerized physician
order entry and clinical decision support systems on medication safety: a
systematic review. Archives of internal medicine, 2003. 163(12): p. 1409.
[7] Grizzle, A., et al., Reasons provided by prescribers when overriding
drug-drug interaction alerts. The American journal of managed care,
2007. 13(10): p. 573.
[8] Payne, T., et al. Characteristics and override rates of order checks in a
practitioner order entry system. 2002: American Medical Informatics
Association.
[9] Weingart, S., et al., Physicians' decisions to override computerized drug
alerts in primary care. Archives of internal medicine, 2003. 163(21): p.
2625.
[10] van der Sijs, H., et al., Turning off frequently overridden drug alerts:
limited opportunities for doing it safely. Journal of the American Medical
Informatics Association, 2008. 15(4): p. 439-448.
[11] Buurma, H., P. De Smet, and A. Egberts, Clinical risk management in
Dutch community pharmacies: the case of drug-drug interactions. Drug
safety, 2006. 29(8): p. 723-732.
[12] http://w3.nhri.org.tw/nhird//en/Background.html
@article{"International Journal of Medical, Medicine and Health Sciences:49517", author = "Hsuan-Chia Yang and Yan-Jhih Haung and Yu-Chuan Li", title = "Proactive Identification of False Alert for Drug-Drug Interaction", abstract = "Researchers of drug-drug interaction alert systems
have often suggested that there were high overridden rate for alerts and
also too false alerts. However, research about decreasing false alerts is
scant. Therefore, the aim of this article attempts to proactive
identification of false alert for drug-drug interaction and provide
solution to decrease false alerts. This research involved retrospective
analysis prescribing database and calculated false alert rate by using
MYSQL and JAVA. Results of this study showed 17% of false alerts
and the false alert rate in the hospitals (37%) was more than in the
clinics. To conclude, this study described the importance that
drug-drug interaction alert system should not only detect drug name
but also detect frequency or route, as well as in providing solution to
decrease false alerts.", keywords = "drug-drug interaction, proactive identification,false alert", volume = "4", number = "8", pages = "309-4", }