Abstract
Objective: Computerised monitors can detect and, with clinical intervention, often prevent or ameliorate adverse drug events (ADEs). We evaluated whether a computer-based alerting system was useful in a community hospital setting.
Methods: We evaluated 6 months of retrospectively collected medication and laboratory data from a 140-bed community hospital, and applied the rules from a computerised knowledge base to determine if the resulting alerts might have allowed a clinician to prevent or lessen harm related to medication toxicity. We randomly selected 11% (n = 58, of which 56 were available) of charts deemed to be high- or critical-priority alerts, based on the likelihood of the alerts being associated with injury, to determine the frequencies of ADEs and preventable ADEs.
Results: In 6 months, there were 8829 activations of the rule set, generating a total of 3547 alerts. Of these, 528 were of high or critical priority, 664 were of medium priority and 2355 were of low priority. Chart review among the sample (56 charts) of high- or critical-priority alerts found five non-preventable and two preventable ADEs, suggesting that among the total high- or critical-priority alerts alone, there would be approximately 94 non-preventable ADEs and 37 preventable ADEs annually in this hospital that could be detected using this method.
Conclusions: Computer-based rules engines have the potential to identify and, with clinical intervention, mitigate preventable ADEs, and implementation is feasible in community hospitals without an advanced information technology application.
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References
Brennan TA, Leape LL, Laird N, et al. Incidence of adverse events and negligence in hospitalized patients. N Engl J Med 1991; 324(6): 370–6
Field TS, Gilman BH, Subramanian S, et al. The costs associated with adverse drug events among older adults in the ambulatory setting. Med Care 2005 Dec; 43(12): 1171–6
Senst BL, Achusim LE, Genest RP, et al. Practical approach to determining costs and frequency of adverse drug events in a health care network. Am J Health Sys Pharm 2001; 58(12): 1126–32
White TJ, Arakelian A, Rho JP. Counting the costs of drug-related adverse events. Pharmacoeconomics 1999; 15(5): 445–58
Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA 1995; 324: 377–84
Jha AK, Kuperman GJ, Teich JM, et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Informat 1998; 5(3): 305–14
Classen DC, Pestotnik SL, Evans RS, et al. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991; 266(20): 2847–51
Bates DW, Evans RS, Murff H, et al. Detecting adverse events using information technology. J Am Med Informat 2003; 10(2): 115–28
Miller JE, Reichley RM, McNamee LA, et al. Notification of real-time clinical alerts generated by pharmacy expert systems. Proc AMIA Symp 1999: 325–9
Levy M, Azaz-Livshits T, Sadan B, et al. Computerized surveillance of adverse drug reactions in hospital: implementation. Eur J Clin Pharmacol 1999 Jan; 54(11): 887–92
Silverman JB, Stapinski CD, Huber C, et al. Computer-based system for preventing adverse drug events. Am J Health Syst Pharm 2004; 61(15): 1599–603
Kane-Gill SL, Dasta JF, Schneider PJ, et al. Monitoring abnormal laboratory values as antecedents to drug-induced injury. J Trauma 2005; 59(6): 1457–62
Grasela TH, Walawander CA, Kennedy DL, et al. Capability of hospital computer systems in performing drug-use evaluations and adverse drug event monitoring. Am J Hosp Pharm 1993; 50(9): 1889–95
Raschke RA, Gollihare B, Wunderlich TA, et al. A computer alert system to prevent injury from adverse events: development and evaluation in a community teaching hospital. JAMA 2006; 280(15): 1317–20
Bates DW, Spell N, Cullen DJ, et al. The costs of adverse drug events in hospitalized patients. JAMA 1997; 277: 307–11 824
Bates DW, Teich JM, Lee J, et al. The impact of computerized physican order entry on medication error prevention. JAMA 1999; 6: 313–21
Nebeker JR, Hoffman JM, Weir CR, et al. High rates of adverse drug events in a highly computerized hospital. Arch Intern Med 2005; 165: 1111–6
Poon EG, Cina JL, Churchill W, et al. Effect of barcode technology on the incidence of medication dispensing errors and potential adverse drug events in a hospital pharmacy. Proc AMIA Annu Fall Symp 2005; Suppl. 1: 199
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
Van Den Berghe G, Wouters P, Weekers F, et al. Intensive insulin therapy in critically ill patients. N Engl J Med 2001; 345: 1359–67
Malmberg K, Ryden L, Wedel H, et al. Intense metabolic control by means of insulin in patients with diabetes mellitus and acute myocardial infarction (DIGAMI 2): effects on mortality and morbidity. Eur Heart J 2005; 26: 650–61
Malmberg K. Prospective randomized study of intensive insulin treatment on long term survival after acute myocardial infarction in patients with diabetes mellitus. DIGAMI (Diabetes Mellitus, Insulin Glucose Infusion in Acute Myocardial Infarction) Study Group. BMJ 1997; 314: 1512–5
Kuperman GJ, Teich JM, Tanasijevic MJ, et al. Improving response to critical laboratory results with automation: results of a randomized controlled trial. JAMA 1999; 6: 512–22
Chertow GM, Burdick, Honour M, et al. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol 2005; 16: 3365–70
High-alert medications and patient safety. Sentinel Event Alert 1999; (11): 1–3
Winterstein AG, Hatton RC, Gonzalez-Rothi R, et al. Identifying clinically significant preventable adverse drug events through a hospital’s database of adverse drug reaction reports. Am J Health Syst Pharm 2002; 59: 1742–9
Acknowledgements
No sources of funding were used to assist in the preparation of this study. Vigilanz Corporation provided the software for evaluation free of charge. Dr Bates has received honoraria from Vigilanz for speaking about drug-laboratory checking. Drs Seger and Jha have no conflicts of interest that are directly relevant to the content of this study.
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Seger, A.C., Jha, A.K. & Bates, D.W. Adverse Drug Event Detection in a Community Hospital Utilising Computerised Medication and Laboratory Data. Drug-Safety 30, 817–824 (2007). https://doi.org/10.2165/00002018-200730090-00007
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DOI: https://doi.org/10.2165/00002018-200730090-00007