Abstract
A ripple-down rules based clinical decision support system to detect drug-related problems (DRPs) has been previously designed and discussed. A commercial implementation of this system (MRM) was evaluated to determine how many additional DRPs would be identified by the reviewing pharmacist when supported by MRM, and whether these additional DRPs were clinically relevant. The DRPs identified by pharmacists were compared against those found by MRM on a dataset of 570 medication review cases, MRM found 2854 DRPs, pharmacists found 1974 DRPs, yet only 389 of the problems that MRM found were also found by the pharmacist. A sample of 20 of these cases were assessed by an expert panel to determine if the DRPs found by each source were clinically relevant. It was determined that DRPs found by both sources were clinically relevant. It is estimated that a pharmacist supported by MRM will find 2.25 times as many DRPs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Routledge, P.A., O’Mahony, M.S., Woodhouse, K.W.: Adverse drug reactions in elderly patients. Br. J. Clin. Pharmacol. 57(2), 121–126 (2004)
Karnon, J., et al.: Modelling the expected net benefits of interventions to reduce the burden of medication errors. J. Health Serv. Res. Policy 13(2), 85–91 (2008)
Bindoff, I., et al.: The potential for intelligent decision support systems to improve the quality and consistency of medication reviews. J. Clin. Pharm. Ther. 37(4), 452–458 (2012)
Peterson, G.M.: The future is now: the importance of medication review. Australian Pharmacist 21(4), 268–274 (2002)
Bindoff, I., Kang, B.-H., Ling, T., Tenni, P., Peterson, G.: Applying MCRDR to a multidisciplinary domain. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 519–528. Springer, Heidelberg (2007)
Bindoff, I., et al.: Development of an intelligent decision support system for medication review. Journal of Clinical Pharmacy and Therapeutics 32(1), 81–88 (2007)
Compton, P., et al.: Experience with ripple-down rules. Knowledge Based Systems 19(5), 356–362 (2006)
Deards, E.A.: MCRDR Applied to Email Classification. Department of Computer Science (2001)
Kang, B.: Validating knowledge acquisition: multiple classification ripple-down rules. In: Computer Science and Engineering. University of New South Wales, Sydney (1995)
Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. AIII-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems (1995)
Kang, B., et al.: Help desk system with intelligent interface. Applied Artificial Intelligence 11(7-8), 611–631 (1997)
Park, S., Kim, Y., Kang, B.H.: Personalized Web Document Classification using MCRDR. In: Pacific Knowledge Acquisition Workshop, Auckland (2004)
Stafford, A.C.: A clinical and economic evaluation of medication reviews conducted by pharmacists for community-dwelling Australians, University of Tasmania (2012)
Curtain, C., et al.: An investigation into drug-related problems identifiable by commercial medication review software. The Australasian Medical Journal 6(4), 183 (2013)
Zermansky, A.G., Freemantle, N.: Is medication review by pharmacists of any use? Pharmacoeconomics 25(2), 91–92 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bindoff, I., Curtain, C., Peterson, G., Westbury, J., Ling, T. (2014). Problems Detected by a Ripple-Down Rules Based Medication Review Decision Support System: Are They Relevant?. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-13332-4_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13331-7
Online ISBN: 978-3-319-13332-4
eBook Packages: Computer ScienceComputer Science (R0)