Main Article Content
Drug interactions, computerized decision support systems, adverse drug event, systematic review
Adverse drug events (ADEs) represent an important problem for hospital and primary care. Software that detects potential adverse drug interactions has been widely implemented in an effort to reduce the rate of ADEs. However, the impact of drug interaction detection software (DIS) on patient safety outcomes remains unknown.
To systematically review the literature on DIS in preventing adverse drug events and determine the effectiveness and cost-effectiveness of DIS.
A literature search of MEDLINE, EMBASE, CINAHL, IPA and Healthstar, using terms “Computer, Software or Decision Support” combined with “Drug Interactions, Drug Errors or Drug Monitoring” sought English language, post-1990 prospective studies that examined drug interaction (drug-drug) software as an intervention and adverse drug interactions as an outcome. Relevant studies were analyzed using a Bayesian meta-analysis approach.
Of 5848 citations, only four studies met our inclusion criteria. Most of the excluded studies were not prospective or measured only prescriber attitudes, implementation success or changes in workflow. No study examined the impact of drug interaction software exclusively, rather as a component of decision support software. A Bayesian meta-analysis of these studies showed no significant difference in event rate between intervention and control groups (relative risk 0.66, 95% CI 0.33 to 1.18). The posterior median Isquared was 52%.
No good quality studies address the specific benefits and harms or cost-effectiveness of drug interaction software on medication safety or clinical outcomes. The evidence at present does not support a benefit for these systems or support any policy to widely disseminate their use.
2. Mollon B, Chong J, Jr., Holbrook AM, Sung M, Thabane L, Foster G. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials. BMC Med Inform Decis Mak 2009;9:11.
3. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10): 1223-38.
4. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330(7494):765.
5. 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(12): 1409-16.
6. Health Canada. Electronic Health Record 2003. (2006) http://www.hc-sc.gc.ca/hcs-sss/ehealthesante/ ehr-dse/index_e.html (September 1, 2006).
7. Teich JM, Osheroff JA, Pifer EA, Sittig DF, Jenders RA. Clinical Decision Support in Electronic Prescribing: Recommendations and an Action Plan. (March 1, 2005) http://www.amia.org/inside/initiatives/cds/cdswh itepaperforhhs-final2005-03-08.pdf (September 15, 2006)
8. Schade CP, Sullivan FM, de LS, Madeley J. e-Prescribing, efficiency, quality: lessons from the computerization of UK family practice. J Am Med Inform Assoc 2006;13(5):470-5.
9. National Health Service. Electronic Prescription Service.(2007) http://www.connectingforhealth.nhs.uk/systemsa ndservices/eps. (September 9, 2008)
10. Holbrook A. COMPETE (2006). http://www.compete-study.com (September 1, 2006)
11. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA 2005;293(10):1197-203.
12. Nieuwstraten C, Labiris NR, Holbrook A. Systematic overview of drug interactions with antidepressant medications. Can J Psychiatry 2006;51(5):300-16.
13. Holbrook AM, Pereira JA, Labiris R, et al. Systematic overview of warfarin and its drug and food interactions. Arch Intern Med 2005;165(10):1095-106.
14. Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in pharmacovigilance. Br J Clin Pharmacol 2004;57(2):127-34.
15. The Cochrane Collaboration. Cochrane Handbook Systematic Reviews Interventions 4.2.5. (2005) www.cochrane.org (March 24, 2006).
16. Potts AL, Barr FE, Gregory DF,Wright L, Patel NR. Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 2004;113(1:Pt 1):t-63.
17. Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998;280(15):1311-6.
18. Anonymous. Computerized drug-laboratory interaction alerts change doctors' prescriptions. Res Activities 2006;(305):2.
19. Bates DW. Frequency, consequences and prevention of adverse drug events. J Qual Clin Practice 1999;19(1):13-7.
20. Cavuto NJ, Woosley RL, Sale M. Pharmacies and prevention of potentially fatal drug interactions. JAMA;275(10):1086.
21. Colon L, Armstrong EP, Malone DC, Murphy JE. Evaluation of drug interaction screening software programs in community pharmacies in Tucson, Arizona (Abst). ASHP Midyear Clinical Meeting 39(DEC):P420E.
22. Degnan D, Merryfield D, Hultgren S. Reaching out to clinicians: implementation of a computerized alert system. J Healthcare Qual 2004;26(6):26-30.
23. Dormann H, Muth-Selbach U, Krebs S, Criegee- Rieck M, Geisslinger G. Incidence and costs of adverse drug reactions during hospitalization: computerized monitoring versus stimulated spontaneous reporting. Drug Safety 2000;22(Feb):161-8.
24. Evans RS, Pestotnik SL, Classen DC, Horn SD, Bass SB, Burke JP. Preventing adverse drug events in hospitalized patients. Ann Pharmacotherapy 1994;(4):523-7.
25. George D, Austin-Bishop N. Error rates for computerized order entry by physicians versus nonphysicians. Am J Health Syst Pharm. 2003;60(21):2250-2.
26. Haumschild MJ, Ward ES, Bishop JM, Haumschild MS. Pharmacy-based computer system for monitoring and reporting drug interactions. Am J Hosp Pharm 1987;44(2):345-8.
27. Hazlet TK, Lee TA, Hansten PD, Horn JR. Performance of community pharmacy drug interaction software. J Am Pharm Assoc 2001;41(2):200-4.
28. Hsieh TC, Kuperman GJ, Jaggi T et al. Characteristics and consequences of drug allergy alert overrides in a computerized physician order entry system. J Am Med Inform Assoc 2004;11(6): 482-91.
29. Jankel CA, Speedie SM, McMillan JA. Effectiveness of computerized drug interaction screening programs in hospitals. Drug Information Journal 1990;24(3):513-29.
30. McMullin ST, Reichley RM, Watson LA, Steib SA, Frisse ME, Bailey TC. Impact of a webbased clinical information system on cisapride drug interactions and patient safety. Arch Intern Med 1918; 1999 Sep 27;159(17):2077-82.
31. Mirco A, Campos L, Falcao F, Nunes JS, Aleixo A. Medication errors in an internal medicine department. Evaluation of a computerized prescription system. Pharmacy World & Science 2005;27 (4):351-2.
32. Murphy JE, Wang VS, Malone DC, Armstrong EP. Performance of drug interaction software in Tucson hospital pharmacies (Abst). ASHP Midyear Clinical Meeting 39(DEC):P89E.
33. Nebeker JR, Hoffman JM, Weir CR, Bennett CL, Hurdle JF. High rates of adverse drug events in a highly computerized hospital. Arch Intern Med 2005;165(10):1111-6.
34. Papshev D, Peterson AM. Electronic prescribing in ambulatory practice: promises, pitfalls, and potential solutions. Am J Manage Care 2001;7(7):725-36.
35. Rothschild JM, Keohane CA, Cook EF, et al. A controlled trial of smart infusion pumps to improve medication safety in critically ill patients. Crit Care Med 2005;33(3):533-40.
36. Smith DH, Perrin N, Feldstein A, et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record: An interrupted time series evaluation. Arch Intern Med 2006;(10): 1098-104.
37. Spencer DC, Leininger A, Daniels R, Granko RP, Coeytaux RR. Effect of a computerized prescriber-order-entry system on reported medication errors. Am J Health Syst Pharm 2005;62(4):416-9.
38. Spina JR, Glassman PA, Belperio P, Cader R, Asch S, Primary Care Investigative Group of the VA Los Angeles Healthcare System. Clinical relevance of automated drug alerts from the perspective of medical providers. Am J Med Qual 2005;20(1):7-14.
39. Steele AW, Eisert S, Witter J, et al. The effect of automated alerts on provider ordering behavior in an outpatient setting. Plos Medicine 2005;(9):864-70.
40. Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999;6(4):313-21.
41. Tamblyn R, Huang A, Perreault R, et al. The medical office of the 21st century (MOXXI): effectiveness of computerized decision-making support in reducing inappropriate prescribing in primary care. CMAJ 2003;169(6):549-56.
42. Oliven A, Michalake I, Zalman D, Dorman E, Yeshurun D, Odeh M. Prevention of prescription errors by computerized, on-line surveillance of drug order entry. Int J Med Inform 2005;(5):377- 86.