Volume 191 - Issue 6
Quality of drug interaction alerts in prescribing and dispensing software
Author: Bryan W Tan
Med J Aust 2009; 191 (6): 358-359. || doi: 10.5694/j.1326-5377.2009.tb02831.x
Published online: 21 September 2009
Published online: 21 September 2009
To the Editor: As the former Clinical Information Specialist Manager for the MIMS DrugAlert knowledgebase (from 2003 to 2005), I write in response to the study by Sweidan and colleagues examining the quality of drug interaction alerts in prescribing and dispensing software.1
The authors point out that the success of any knowledgebase in providing clear, correct and specific alerts at the point of care is subject to the quality of its technical integration into decision support software. I would like to add that the sensitivity of drug interaction decision support is determined largely by the knowledgebase, while the specificity of the system is subject to the intelligence of the software in which it is employed. I would be interested to know if the low specificity that Sweidan et al found for the MIMS DrugAlert database was due to lack of use of the severity or level of evidence settings, or having these set at inappropriate levels.
The MIMS DrugAlert knowledgebase was in some ways a unique decision support database, written by Australian staff for use in Australia and New Zealand. It soon became one of the largest commercially available databases of drug interaction information in the world, covering over 4600 drug-class and individual drug interactions. Its writing alone was a remarkable feat, being completed in a matter of months and further expanded over a subsequent 18-month period. There are significant variations in practical advice between American and European sources of drug interaction information. In writing the MIMS DrugAlert database, we sought to communicate “the right information, at the right time, in the right way” to local professionals.
The foundations of MIMS DrugAlert were based on a clear understanding that we would be representing relatively simple pharmacological principles through the structure and content of a relational database. This meant creating interacting drug classes reflective of the pharmacological properties of groups of drugs, rather than simply grouping drugs based on their chemical families alone.
Sweidan and colleagues should be congratulated on highlighting the need for comprehensive, accurate and useful information that can reduce medication error and save lives at the point of care. What is lacking is a clinical outcomes-based study focusing on the real-world benefits that can be achieved if the right system can be implemented in the right way, at the right price. Perhaps this type of research would then build on the excellent, basic foundational research carried out by Sweidan et al.
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_DHDIAFEH