OBJECTIVE: To investigate the quality of drug interaction decision support in selected prescribing and dispensing software systems, and to compare this information with that found in a range of reference sources. DESIGN AND SETTING: A comparative study, conducted between June 2006 and February 2007, of the support provided for making decisions about 20 major and 20 minor drug interactions in six prescribing and three dispensing software systems used in primary care in Australia. Five electronic reference sources were evaluated for comparison. MAIN OUTCOME MEASURES: Sensitivity, specificity and quality of information; for major interactions: whether information on clinical effects, timeframe and pharmacological mechanism was included, whether management advice was helpful, and succinctness. RESULTS: Six of the nine software systems had a sensitivity rate > or = 90%, detecting most of the major interactions. Only 3/9 systems had a specificity rate of > or = 80%, with other systems providing inappropriate or unhelpful alerts for many minor interactions. Only 2/9 systems provided adequate information about clinical effects for more than half the major drug interactions, and 1/9 provided useful management advice for more than half of these. The reference sources had high sensitivity and in general provided more comprehensive clinical information than the software systems. CONCLUSIONS: Drug interaction decision support in commonly used prescribing and dispensing software has significant shortcomings.
OBJECTIVE: To investigate the quality of drug interaction decision support in selected prescribing and dispensing software systems, and to compare this information with that found in a range of reference sources. DESIGN AND SETTING: A comparative study, conducted between June 2006 and February 2007, of the support provided for making decisions about 20 major and 20 minor drug interactions in six prescribing and three dispensing software systems used in primary care in Australia. Five electronic reference sources were evaluated for comparison. MAIN OUTCOME MEASURES: Sensitivity, specificity and quality of information; for major interactions: whether information on clinical effects, timeframe and pharmacological mechanism was included, whether management advice was helpful, and succinctness. RESULTS: Six of the nine software systems had a sensitivity rate > or = 90%, detecting most of the major interactions. Only 3/9 systems had a specificity rate of > or = 80%, with other systems providing inappropriate or unhelpful alerts for many minor interactions. Only 2/9 systems provided adequate information about clinical effects for more than half the major drug interactions, and 1/9 provided useful management advice for more than half of these. The reference sources had high sensitivity and in general provided more comprehensive clinical information than the software systems. CONCLUSIONS: Drug interaction decision support in commonly used prescribing and dispensing software has significant shortcomings.
Authors: Annemieke Floor-Schreudering; Arjen F J Geerts; Jeffrey K Aronson; Marcel L Bouvy; Robin E Ferner; Peter A G M De Smet Journal: Eur J Clin Pharmacol Date: 2013-12-05 Impact factor: 2.953
Authors: Hanna M Seidling; Shobha Phansalkar; Diane L Seger; Marilyn D Paterno; Shimon Shaykevich; Walter E Haefeli; David W Bates Journal: J Am Med Inform Assoc Date: 2011-05-12 Impact factor: 4.497
Authors: Jane Robertson; Annette J Moxey; David A Newby; Malcolm B Gillies; Margaret Williamson; Sallie-Anne Pearson Journal: Fam Pract Date: 2010-11-25 Impact factor: 2.267
Authors: Robyn Tamblyn; Tewodros Eguale; David L Buckeridge; Allen Huang; James Hanley; Kristen Reidel; Sherry Shi; Nancy Winslade Journal: J Am Med Inform Assoc Date: 2012-01-12 Impact factor: 4.497