Appl Clin Inform 2010; 01(03): 346-362
DOI: 10.4338/ACI-2009-11-RA-0014
Research Article
Schattauer GmbH

Decision Support Alerts for Medication Ordering in a Computerized Provider Order Entry (CPOE) System

A systematic approach to decrease alerts
M. A. Del Beccaro
1   Seattle Children’s Hospital, Seattle Washington
2   Department of Pediatrics, University of Washington School of Medicine, Seattle Washington
,
R. Villanueva
1   Seattle Children’s Hospital, Seattle Washington
,
K. M. Knudson
1   Seattle Children’s Hospital, Seattle Washington
,
E. M. Harvey
1   Seattle Children’s Hospital, Seattle Washington
,
J. M. Langle
1   Seattle Children’s Hospital, Seattle Washington
,
W. Paul
1   Seattle Children’s Hospital, Seattle Washington
› Author Affiliations
Further Information

Correspondence to:

Mark A. Del Beccaro, MD
4800 Sandpoint Way NE
Seattle Children’s Hospital
Mail Stop T-0111
Seattle, WA 98105
Phone: 206-987-2012   
Fax: 206-987-3830   

Publication History

received: 27 November 2009

accepted: 07 September 2010

Publication Date:
16 December 2017 (online)

 

Summary

Objective: We sought to determine the frequency and type of decision support alerts by location and ordering provider role during Computerized Provider Order Entry (CPOE) medication ordering. Using these data we adjusted the decision support tools to reduce the number of alerts.

Design: Retrospective analyses were performed of dose range checks (DRC), drug-drug interaction and drug-allergy alerts from our electronic medical record. During seven sampling periods (each two weeks long) between April 2006 and October 2008 all alerts in these categories were analyzed. Another audit was performed of all DRC alerts by ordering provider role from November 2008 through January 2009. Medication ordering error counts were obtained from a voluntary error reporting system.

Measurement/Results: Between April 2006 and October 2008 the percent of medication orders that triggered a dose range alert decreased from 23.9% to 7.4%. The relative risk (RR) for getting an alert was higher at the start of the interventions versus later (RR= 2.40, 95% CI 2.28-2.52; p< 0.0001). The percentage of medication orders that triggered alerts for drug-drug interactions also decreased from 13.5% to 4.8%. The RR for getting a drug interaction alert at the start was 1.63, 95% CI 1.60-1.66; p< 0.0001. Alerts decreased in all clinical areas without an increase in reported medication errors.

Conclusion: We reduced the quantity of decision support alerts in CPOE using a systematic approach without an increase in reported medication errors


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Conflicts of interest

None of the authors has any conflict of interest to report

  • References

  • 1 Kohn LT, Corrigan JM, Donaldson MS. eds. To Err Is Human: Building a Safer Health System. Institute of Medicine Committee on Quality Health Care in America. Washington DC: National Academy Press; 1999
  • 2 Leapfrog Group.. Leapfrog hospital survey results. Available at: www.leapfroggroup.org/for_hospitals/leapfrog_safety_practices/cpoe .
  • 3 Fortescue EB. et al. Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients. Pediatrics 2003; 111: 722-729.
  • 4 King WJ. et al. The effect of computerized physician order entry on medication errors and adverse drug events in pediatric inpatients. Pediatrics 2003; 112: 506-509.
  • 5 Potts AL. et al. Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 2004; 113: 59-63.
  • 6 Holdsworth MT. et al. The effect of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 2007; 120: 1058-1066.
  • 7 Galanter WL, Polikiatis A, Didomenico RJ. A trial of automated safety alerts for inpatient digoxin use with computerized physician order entry?. JAMIA 2004; 11: 270-277.
  • 8 Galanter WL, Didomenico RJ, Polikiatis A. A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. JAMIA 2005; 12: 269-274.
  • 9 Steele AW. et al. The effect of automated alerts on provider ordering behavior in an outpatient setting. PLoS Med. 2005; 2 (09) e255. Epub 2005 Sep 6. online at www.plosmedicine.org .
  • 10 Evans RS. et al. Improving empiric antibiotic selection using computer decision support. Arch Intern Med 1994; 154 (08) 878-884.
  • 11 Smith DH. et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record. Arch Intern Med 2006; 166: 1098-1104.
  • 12 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: 1409-1416.
  • 13 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. JAMIA 2006; 13: 138-147.
  • 14 Killelea BK, Kaushal R, Cooper M, Kuperman GJ. To what extent do pediatricians accept computer-based dosing suggestions?. Pediatrics 2007; 119: e69-e75.
  • 15 Judge J. et. al. Prescribers’ responses to alerts during medication ordering in the long term care setting. JAMIA 2006; 13: 285-390.
  • 16 Lin CP. et al. Evaluating clinical decision support systems: Monitoring CPOE order check override rates in the Department of Veterans Affairs’ computerized patient record system. JAMIA 2008; 15: 620-626.
  • 17 Isaac T. et al. Overrides of medication alerts in ambulatory care. Arch Int Med 2009; 169 (03) 305-311.
  • 18 Ash JS. et al. The extent and importance of unintended consequences related computerized provider order entry. JAMIA 2007; 14: 415-423.
  • 19 Glassman PA. et al. Exposure to automated drug alerts over time: effects on clinicians’ knowledge and perceptions. Med Care 2006; 44 (03) 250-256.
  • 20 Reichley RM. et al. Implementing a commercial rule base as a medication order safety net. JAMIA 2005; 12: 383-389.
  • 21 Zwart-van Rijkom JEF. et al. Frequency and nature of drug–drug interactions in a Dutch university hospital. Br J Clin Pharmacol 2009; 68 (Suppl. 02) 187-193.
  • 22 Paterno MD. et. al. Tiering drug-drug interaction alerts by severity increases compliance rates. JAMIA 2009; 16: 40-46.
  • 23 van der Sijs H. et al. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. JAMIA 2008; 15: 439-448.
  • 24 Kim GR, Lehmann C. and the Council on Clinical Information Technology. Pediatric aspects of inpatient health information technology systems. Pediatrics 2008; 122: e1287-296.

Correspondence to:

Mark A. Del Beccaro, MD
4800 Sandpoint Way NE
Seattle Children’s Hospital
Mail Stop T-0111
Seattle, WA 98105
Phone: 206-987-2012   
Fax: 206-987-3830   

  • References

  • 1 Kohn LT, Corrigan JM, Donaldson MS. eds. To Err Is Human: Building a Safer Health System. Institute of Medicine Committee on Quality Health Care in America. Washington DC: National Academy Press; 1999
  • 2 Leapfrog Group.. Leapfrog hospital survey results. Available at: www.leapfroggroup.org/for_hospitals/leapfrog_safety_practices/cpoe .
  • 3 Fortescue EB. et al. Prioritizing strategies for preventing medication errors and adverse drug events in pediatric inpatients. Pediatrics 2003; 111: 722-729.
  • 4 King WJ. et al. The effect of computerized physician order entry on medication errors and adverse drug events in pediatric inpatients. Pediatrics 2003; 112: 506-509.
  • 5 Potts AL. et al. Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 2004; 113: 59-63.
  • 6 Holdsworth MT. et al. The effect of computerized prescriber order entry on the incidence of adverse drug events in pediatric inpatients. Pediatrics 2007; 120: 1058-1066.
  • 7 Galanter WL, Polikiatis A, Didomenico RJ. A trial of automated safety alerts for inpatient digoxin use with computerized physician order entry?. JAMIA 2004; 11: 270-277.
  • 8 Galanter WL, Didomenico RJ, Polikiatis A. A trial of automated decision support alerts for contraindicated medications using computerized physician order entry. JAMIA 2005; 12: 269-274.
  • 9 Steele AW. et al. The effect of automated alerts on provider ordering behavior in an outpatient setting. PLoS Med. 2005; 2 (09) e255. Epub 2005 Sep 6. online at www.plosmedicine.org .
  • 10 Evans RS. et al. Improving empiric antibiotic selection using computer decision support. Arch Intern Med 1994; 154 (08) 878-884.
  • 11 Smith DH. et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record. Arch Intern Med 2006; 166: 1098-1104.
  • 12 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: 1409-1416.
  • 13 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. JAMIA 2006; 13: 138-147.
  • 14 Killelea BK, Kaushal R, Cooper M, Kuperman GJ. To what extent do pediatricians accept computer-based dosing suggestions?. Pediatrics 2007; 119: e69-e75.
  • 15 Judge J. et. al. Prescribers’ responses to alerts during medication ordering in the long term care setting. JAMIA 2006; 13: 285-390.
  • 16 Lin CP. et al. Evaluating clinical decision support systems: Monitoring CPOE order check override rates in the Department of Veterans Affairs’ computerized patient record system. JAMIA 2008; 15: 620-626.
  • 17 Isaac T. et al. Overrides of medication alerts in ambulatory care. Arch Int Med 2009; 169 (03) 305-311.
  • 18 Ash JS. et al. The extent and importance of unintended consequences related computerized provider order entry. JAMIA 2007; 14: 415-423.
  • 19 Glassman PA. et al. Exposure to automated drug alerts over time: effects on clinicians’ knowledge and perceptions. Med Care 2006; 44 (03) 250-256.
  • 20 Reichley RM. et al. Implementing a commercial rule base as a medication order safety net. JAMIA 2005; 12: 383-389.
  • 21 Zwart-van Rijkom JEF. et al. Frequency and nature of drug–drug interactions in a Dutch university hospital. Br J Clin Pharmacol 2009; 68 (Suppl. 02) 187-193.
  • 22 Paterno MD. et. al. Tiering drug-drug interaction alerts by severity increases compliance rates. JAMIA 2009; 16: 40-46.
  • 23 van der Sijs H. et al. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. JAMIA 2008; 15: 439-448.
  • 24 Kim GR, Lehmann C. and the Council on Clinical Information Technology. Pediatric aspects of inpatient health information technology systems. Pediatrics 2008; 122: e1287-296.