Volume 191 - Issue 6
Quality of drug interaction alerts in prescribing and dispensing software
Author: Ian R Cheong
Med J Aust 2009; 191 (6): 358-359. || doi: 10.5694/j.1326-5377.2009.tb02830.x
Published online: 21 September 2009
Published online: 21 September 2009
To the Editor: I was interested to read the results of Sweidan and colleagues’ study of drug interaction alerts in prescribing and dispensing software.1 I believe their use of the terms “sensitivity” and “specificity” differ from the standard definitions, which are usually:
Sensitivity = true positives ÷ (true positives + false negatives)
Specificity = true negatives ÷ (true negatives + false positives)2
To test sensitivity and specificity, one requires a dataset that includes positives and negatives. I do not view “minor interactions” as a complete set of negatives, because a complete set of negatives should include a statistically valid number of randomly chosen drug sets without interactions. Minor interactions do not meet my criteria for “negatives” because, to me, a minor interaction is still an interaction that may sometimes be clinically significant. A true negative should meet the test of “never clinically significant”. Some reported minor interactions would meet that test and some would not. I believe sensitivity and specificity data should be reported for both major and minor interaction alerts.
I also believe there should be some alignment of definitions between “drug interaction” research and “adverse drug event” research.3 Bates and colleagues talked about “adverse drug events” and “preventable adverse drug events” in 1995.4 Of most clinical interest are the preventable adverse drug events, which could be minimised by the use of appropriate decision support.5,6
Certainly, there is a need for independent assessment of the quality of electronic prescribing decision support systems. A robust assessment methodology is required to permit potential government regulation of such resources, which are of national and community importance.
References
- Sweidan M, Reeve JF, Brien JE, et al. Quality of drug interaction alerts in prescribing and dispensing software. Med J Aust 2009; 190: 251-254. 0_CBBDCAFJ
- Riegelman RK. Studying a study and testing a test: how to read the medical evidence. 5th ed. Philadelphia: Lippincott Williams & Wilkins, 2004: 158. 0_pgfId-1861662
- Morimoto T, Gandhi TK, Seger AC, et al. Adverse drug events and medication errors: detection and classification methods. Qual Saf Health Care 2004; 13: 306-314. 0_CBBJJIBC
- Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA 1995; 274: 29-34. 0_i1091857
- Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003; 163: 1409-1416. 0_CBBGEJBC
- Kuperman GJ, Bobb A, Payne TH, et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007; 14: 29-40. 0_CBBJFBIE