A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis

Anthony J Avery, Sarah Rodgers, Judith A Cantrill, Sarah Armstrong, Kathrin Cresswell, Martin Eden, Rachel A Elliott, Rachel Howard, Denise Kendrick, Caroline J Morris, Robin J Prescott, Glen Swanwick, Matthew Franklin, Koen Putman, Matthew Boyd, Aziz Sheikh, Anthony J Avery, Sarah Rodgers, Judith A Cantrill, Sarah Armstrong, Kathrin Cresswell, Martin Eden, Rachel A Elliott, Rachel Howard, Denise Kendrick, Caroline J Morris, Robin J Prescott, Glen Swanwick, Matthew Franklin, Koen Putman, Matthew Boyd, Aziz Sheikh

Abstract

Background: Medication errors are common in primary care and are associated with considerable risk of patient harm. We tested whether a pharmacist-led, information technology-based intervention was more effective than simple feedback in reducing the number of patients at risk of measures related to hazardous prescribing and inadequate blood-test monitoring of medicines 6 months after the intervention.

Methods: In this pragmatic, cluster randomised trial general practices in the UK were stratified by research site and list size, and randomly assigned by a web-based randomisation service in block sizes of two or four to one of two groups. The practices were allocated to either computer-generated simple feedback for at-risk patients (control) or a pharmacist-led information technology intervention (PINCER), composed of feedback, educational outreach, and dedicated support. The allocation was masked to researchers and statisticians involved in processing and analysing the data. The allocation was not masked to general practices, pharmacists, patients, or researchers who visited practices to extract data. [corrected]. Primary outcomes were the proportions of patients at 6 months after the intervention who had had any of three clinically important errors: non-selective non-steroidal anti-inflammatory drugs (NSAIDs) prescribed to those with a history of peptic ulcer without co-prescription of a proton-pump inhibitor; β blockers prescribed to those with a history of asthma; long-term prescription of angiotensin converting enzyme (ACE) inhibitor or loop diuretics to those 75 years or older without assessment of urea and electrolytes in the preceding 15 months. The cost per error avoided was estimated by incremental cost-effectiveness analysis. This study is registered with Controlled-Trials.com, number ISRCTN21785299.

Findings: 72 general practices with a combined list size of 480,942 patients were randomised. At 6 months' follow-up, patients in the PINCER group were significantly less likely to have been prescribed a non-selective NSAID if they had a history of peptic ulcer without gastroprotection (OR 0·58, 95% CI 0·38-0·89); a β blocker if they had asthma (0·73, 0·58-0·91); or an ACE inhibitor or loop diuretic without appropriate monitoring (0·51, 0·34-0·78). PINCER has a 95% probability of being cost effective if the decision-maker's ceiling willingness to pay reaches £75 per error avoided at 6 months.

Interpretation: The PINCER intervention is an effective method for reducing a range of medication errors in general practices with computerised clinical records.

Funding: Patient Safety Research Portfolio, Department of Health, England.

Copyright © 2012 Elsevier Ltd. All rights reserved.

Figures

Figure 1
Figure 1
Trial profile *Repeated cross-sectional design accounts for no loss to follow-up of patients. PINCER=pharmacist-led information technology-based intervention.
Figure 2
Figure 2
Cost-effectiveness acceptability curves at 6 and 12 months PINCER=pharmacist-led information technology-based intervention.

References

    1. Gandhi TK, Weingart SN, Borus J. Adverse drug events in ambulatory care. N Engl J Med. 2003;348:1556–1564.
    1. Gurwitz JH, Field TS, Harrold LR. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003;289:1107–1116.
    1. Bates DW, Cullen DJ, Laird N. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;274:29–34.
    1. Department of Health . An organisation with a memory: report of an expert group on learning from adverse events in the NHS. Department of Health; London: 2000.
    1. Kohn L, Corrigan J, Donaldson M. To err is human—building a safer health system. Institute of Medicine; Washington, DC: 1999.
    1. Smith J. Building a safer NHS for patients: improving medication safety. Department of Health; London: 2004.
    1. Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med. 2003;348:2526–2534.
    1. Schedlbauer A, Prasad V, Mulvaney C. What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior? J Am Med Inform Assoc. 2009;16:531–538.
    1. Garg AX, Adhikari NK, McDonald H. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223–1238.
    1. Royal S, Smeaton L, Avery AJ, Hurwitz B, Sheikh A. Interventions in primary care to reduce medication related adverse events and hospital admissions: systematic review and meta-analysis. Qual Saf Health Care. 2006;15:23–31.
    1. Howard RL, Avery AJ, Slavenburg S. Which drugs cause preventable admissions to hospital? A systematic review. Br J Clin Pharmacol. 2007;63:136–147.
    1. Howard RL, Avery AJ, Howard PD, Partridge M. Investigation into the reasons for preventable drug related admissions to a medical admissions unit: observational study. Qual Saf Health Care. 2003;12:280–285.
    1. Chen YF, Avery AJ, Neil KE, Johnson C, Dewey ME, Stockley IH. Incidence and possible causes of prescribing potentially hazardous/contraindicated drug combinations in general practice. Drug Saf. 2005;28:67–80.
    1. Thomson O'Brien MA, Oxman AD, Davis DA, Haynes RB, Freemantle N, Harvey EL. Educational outreach visits: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. 2000;2:CD000409.
    1. Catwell L, Sheikh A. Evaluating eHealth interventions: the need for continuous systemic evaluation. PLoS Med. 2009;6:e1000126.
    1. Richmond S, Morton V, Cross B, RESPECT Trial Team Effectiveness of shared pharmaceutical care for older patients: RESPECT trial findings. Br J Gen Pract. 2010:e10–e19.
    1. Holland R, Lenaghan E, Harvey I. Does home based medication review keep older people out of hospital? The HOMER randomised controlled trial. BMJ. 2005;330:293.
    1. MRC . A framework for the development and evaluation of RCT's for complex interventions. Medical Research Council; London: 2000.
    1. Avery AJ, Rodgers S, Cantrill JA. Protocol for the PINCER trial: a cluster randomised trial comparing the effectiveness of a pharmacist-led IT-based intervention with simple feedback in reducing rates of clinically important errors in medicines management in general practices. Trials. 2009;10:28.
    1. Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ. 1999;319:670–674.
    1. Shih WJ. Problems in dealing with missing data and informative censoring in clinical trials. Curr Control Trials Cardiovasc Med. 2002;3:4.
    1. Department for Communities and Local Government Indices of Deprivation 2004. (accessed Jan 20, 2012).
    1. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet. 2000;355:1064–1069.
    1. StataCorp LP. Stata data analysis and statistical software. Special Edition Release. 10.1 ed. Stata; College Station: 2008.
    1. Elashoff JD. nQuery Advisor®. Version 6.0 software. Los Angeles; 2005.
    1. Kerry SM, Bland JM. Statistics notes: the intracluster correlation coefficient in cluster randomisation. BMJ. 1998;316:1455–1460.
    1. Hilbe JM. Negative binomial regression. Cambridge University Press; New York: 2007.
    1. Lilford RJ, Brown CA, Nicholl J. Use of process measures to monitor the quality of clinical practice. BMJ. 2007;335:648–650.
    1. Karnon J, Campbell F, Czoski-Murray C. Model-based cost-effectiveness analysis of interventions aimed at preventing medication error at hospital admission (medicines reconciliation) J Eval Clin Pract. 2009;15:299–306.
    1. Schnipper JL, Roumie CL, Cawthon C. Rationale and design of the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL–CVD) study. Circ Cardiovasc Qual Outcomes. 2010;3:212–219.
    1. Winterstein AG, Sauer BC, Hepler CD, Poole C. The incidence of preventable drug-related hospital admissions. Pharmacoepidemiol Drug Saf. 2000;9:S118–S147.
    1. Garfield S, Barber N, Walley P, Willson A, Eliasson L. Quality of medication use in primary care—mapping the problem, working to a solution: a systematic review of the literature. BMC Med. 2009;7:50.
    1. Black AD, Car J, Pagliari C. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8:e1000387.
    1. Grimshaw JM, Shirran L, Thomas R. Changing provider behavior: an overview of systematic reviews of interventions. Med Care. 2001;39:II2–I45.

Source: PubMed

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