Download PDF
Appl Clin Inform 2018; 09(04): 849-855
DOI: 10.1055/s-0038-1676039
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation

S. M. M. Meslin
1   Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, University of New South Wales, Sydney, New South Wales, Australia
2   St Vincent's Clinical School, UNSW Medicine, University of New South Wales, Sydney, New South Wales, Australia
3   School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
,
W. Y. Zheng
4   Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
,
R. O. Day
1   Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, University of New South Wales, Sydney, New South Wales, Australia
2   St Vincent's Clinical School, UNSW Medicine, University of New South Wales, Sydney, New South Wales, Australia
,
E. M. Y. Tay
1   Department of Clinical Pharmacology and Toxicology, St Vincent's Hospital, University of New South Wales, Sydney, New South Wales, Australia
,
M. T. Baysari
2   St Vincent's Clinical School, UNSW Medicine, University of New South Wales, Sydney, New South Wales, Australia
4   Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
› Author Affiliations Funding This study was funded by National Health and Medical Research Council Program Grant APP1054146.
Further Information

Publication History

17 July 2018

12 October 2018

Publication Date:
28 November 2018 (online)

Also available at
   Permissions and Reprints

Abstract

Introduction Drug–drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review.

Methods After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE.

Results There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: –0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE.

Conclusion Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.

Keywords

medical order entry systems - drug interactions - alert fatigue - alert systems - clinical decision support

Protection of Human and Animal Subjects

Ethics approval was obtained by the local hospital's ethics board.


 

  • References

  • 1 Day RO, Snowden L, McLachlan AJ. Life-threatening drug interactions: what the physician needs to know. Intern Med J 2017; 47 (05) 501-512
    CrossrefPubMedSearch in Google Scholar
  • 2 Magro L, Moretti U, Leone R. Epidemiology and characteristics of adverse drug reactions caused by drug-drug interactions. Expert Opin Drug Saf 2012; 11 (01) 83-94
    CrossrefPubMedSearch in Google Scholar
  • 3 Schachter M. The epidemiology of medication errors: how many, how serious?. Br J Clin Pharmacol 2009; 67 (06) 621-623
    CrossrefPubMedSearch in Google Scholar
  • 4 Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly 2014; 144: w14073
    PubMedSearch in Google Scholar
  • 5 Zheng WY, Richardson LC, Li L, Day RO, Westbrook JI, Baysari MT. Drug-drug interactions and their harmful effects in hospitalised patients: a systematic review and meta-analysis. Eur J Clin Pharmacol 2018; 74 (01) 15-27
    CrossrefPubMedSearch in Google Scholar
  • 6 Peterson JF, Bates DW. Preventable medication errors: identifying and eliminating serious drug interactions. J Am Pharm Assoc (Wash) 2001; 41 (02) 159-160
    CrossrefPubMedSearch in Google Scholar
  • 7 Slight SP, Seger DL, Nanji KC. , et al. Are we heeding the warning signs? Examining providers' overrides of computerized drug-drug interaction alerts in primary care. PLoS One 2013; 8 (12) e85071
    CrossrefPubMedSearch in Google Scholar
  • 8 van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc 2006; 13 (02) 138-147
    CrossrefPubMedSearch in Google Scholar
  • 9 Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017; 24 (04) 806-812
    CrossrefPubMedSearch in Google Scholar
  • 10 Payne TH, Nichol WP, Hoey P. , et al. Characteristics and override rates of order checks in a practitioner order entry system. Proceedings of AMIA Symposium; 2002 :602–606
    PubMedSearch in Google Scholar
  • 11 van der Sijs H, Aarts J, van Gelder T, Berg M, Vulto A. Turning off frequently overridden drug alerts: limited opportunities for doing it safely. J Am Med Inform Assoc 2008; 15 (04) 439-448
    CrossrefPubMedSearch in Google Scholar
  • 12 Paterno MD, Maviglia SM, Gorman PN. , et al. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16 (01) 40-46
    CrossrefPubMedSearch in Google Scholar
  • 13 Beccaro MAD, Villanueva R, Knudson KM, Harvey EM, Langle JM, Paul W. Decision support alerts for medication ordering in a computerized provider order entry (CPOE) system: a systematic approach to decrease alerts. Appl Clin Inform 2010; 1 (03) 346-362
    Thieme ConnectPubMedSearch in Google Scholar
  • 14 HiMSS Analytics. Electronic Medical Record Adoption Model; 2017 . Available at: https://www.himssanalytics.org/emram . Accessed October 31, 2018
    PubMed
  • 15 Medicare and Medicaid Services. Electronic Health Record Incentive Program: Centers for Medicare and Medicaid Services; 2010 . Available at: https://www.federalregister.gov/documents/2010/07/28/2010-17207/medicare-and-medicaid-programs-electronic-health-record-incentive-program . Accessed October 31, 2018
    PubMed
  • 16 Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: no evidence of progress. Appl Clin Inform 2014; 5 (03) 802-813
    Thieme ConnectPubMedSearch in Google Scholar
  • 17 Baysari MT, Westbrook JI, Egan B, Day RO. Identification of strategies to reduce computerized alerts in an electronic prescribing system using a Delphi approach. Stud Health Technol Inform 2013; 192: 8-12
    PubMedSearch in Google Scholar
  • 18 Abarca J, Malone DC, Armstrong EP. , et al. Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc (2003) 2004; 44 (02) 136-141
    CrossrefPubMedSearch in Google Scholar
  • 19 van Leeuwen RW, Jansman FG, van den Bemt PM. , et al. Drug-drug interactions in patients treated for cancer: a prospective study on clinical interventions. Ann Oncol 2015; 26 (05) 992-997
    CrossrefPubMedSearch in Google Scholar
  • 20 Scheife RT, Hines LE, Boyce RD. , et al. Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support. Drug Saf 2015; 38 (02) 197-206
    CrossrefPubMedSearch in Google Scholar
  • 21 Phansalkar S, van der Sijs H, Tucker AD. , et al. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc 2013; 20 (03) 489-493
    CrossrefPubMedSearch in Google Scholar
  • 22 Strasberg HR, Chan A, Sklar SJ. Inter-Rater Agreement among Physicians on the Clinical Significance of Drug-Drug Interactions. AMIA Annual Symposium Proceedings. 2013 11/16; 2013 :1325–1328
    PubMedSearch in Google Scholar
  • 23 Smithburger PL, Kane-Gill SL, Benedict NJ, Falcione BA, Seybert AL. Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings. Ann Pharmacother 2010; 44 (11) 1718-1724
    CrossrefPubMedSearch in Google Scholar
  • 24 Seidling HM, Klein U, Schaier M. , et al. What, if all alerts were specific - estimating the potential impact on drug interaction alert burden. Int J Med Inform 2014; 83 (04) 285-291
    CrossrefPubMedSearch in Google Scholar
  • 25 MedChart. MedChart Electronic Medication Management: DXC.technology; 2017 . Available at: http://www.dxc.technology/providers/offerings/139499/140202-medchart_electronic_medication_management . Accessed October 31, 2018
    PubMed
  • 26 MIMS. Decision Support Overview. Available at: http://www.mims.com.au/index.php/products/emims2017 . Accessed June 5, 2017
    PubMed
  • 27 Vonbach P, Dubied A, Krähenbühl S, Beer JH. Evaluation of frequently used drug interaction screening programs. Pharm World Sci 2008; 30 (04) 367-374
    CrossrefPubMedSearch in Google Scholar
  • 28 Muhič N, Mrhar A, Brvar M. Comparative analysis of three drug-drug interaction screening systems against probable clinically relevant drug-drug interactions: a prospective cohort study. Eur J Clin Pharmacol 2017; 73 (07) 875-882
    CrossrefPubMedSearch in Google Scholar
  • 29 Reis AMM, Cassiani SHDB. Evaluation of three brands of drug interaction software for use in intensive care units. Pharm World Sci 2010; 32 (06) 822-828
    CrossrefPubMedSearch in Google Scholar
  • 30 Hayes AF, Krippendorff K. Answering the call for a standard reliability measure for coding data. Commun Methods Meas 2007; 1 (01) 77-89
    CrossrefPubMedSearch in Google Scholar
  • 31 Conde-Estévez D, Echeverría-Esnal D, Tusquets I, Albanell J. Potential clinical relevant drug-drug interactions: comparison between different compendia, do we have a validated method?. Ann Oncol 2015; 26 (06) 1272-1272
    PubMedSearch in Google Scholar
  • 32 Saverno KR, Hines LE, Warholak TL. , et al. Ability of pharmacy clinical decision-support software to alert users about clinically important drug-drug interactions. J Am Med Inform Assoc 2011; 18 (01) 32-37
    CrossrefPubMedSearch in Google Scholar