Appl Clin Inform 2020; 11(01): 046-058
DOI: 10.1055/s-0039-3402757
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Reducing Interruptive Alert Burden Using Quality Improvement Methodology

Juan D. Chaparro
1   Division of Clinical Informatics, Nationwide Children's Hospital, Columbus, Ohio, United States
2   Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
Cory Hussain
3   Department of Family Medicine, The Ohio State University College of Medicine, Columbus, Ohio, United States
Jennifer A. Lee
3   Department of Family Medicine, The Ohio State University College of Medicine, Columbus, Ohio, United States
Jessica Hehmeyer
4   Department of Information Services, Nationwide Children's Hospital, Columbus, Ohio, United States
Manjusri Nguyen
4   Department of Information Services, Nationwide Children's Hospital, Columbus, Ohio, United States
Jeffrey Hoffman
1   Division of Clinical Informatics, Nationwide Children's Hospital, Columbus, Ohio, United States
2   Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
› Author Affiliations
Further Information

Publication History

21 August 2019

04 December 2019

Publication Date:
15 January 2020 (online)


Background Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The “pop-up” or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing requests for new interruptive alerts.

Objectives Using Institute for Healthcare Improvement (IHI) quality improvement (QI) methodology, the primary objective was to reduce the total volume of interruptive alerts received by providers.

Methods We created an interactive dashboard for baseline alert data and to monitor frequency and outcomes of alerts as well as to prioritize interventions. A key driver diagram was developed with a specific aim to decrease the number of interruptive alerts from a baseline of 7,250 to 4,700 per week (35%) over 6 months. Interventions focused on the following key drivers: appropriate alert display within workflow, clear alert content, alert governance and standardization, user feedback regarding overrides, and respect for user knowledge.

Results A total of 25 unique alerts accounted for 90% of the total interruptive alert volume. By focusing on these 25 alerts, we reduced interruptive alerts from 7,250 to 4,400 per week.

Conclusion Systematic and structured improvements to interruptive alerts can lead to overall reduced interruptive alert burden. Using QI methods to prioritize our interventions allowed us to maximize our impact. Further evaluation should be done on the effects of reduced interruptive alerts on patient care outcomes, usability heuristics on cognitive burden, and direct feedback mechanisms on alert utility.

Protection of Human and Animal Subjects

Activities in this project were designed solely for evaluation of process and QI and did not require Institutional Review Board approval.

  • References

  • 1 Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospital: 2008–2015. Washington, DC: The Office of the National Coordinator for Health Information Technology; Available at: 2016
  • 2 Ash JS, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some unintended consequences of clinical decision support systems. AMIA Annu Symp. Proc 2007; 26-30 . PMCID: PMC2813668
  • 3 Billings CE. Aviation automation: the search for a human-centered approach. In: Mahwah, NJ: Lawrence Erlbaum Associates; 1997: 103 105
  • 4 Walker GH, Waterfield S, Thompson P. All at sea: An ergonomic analysis of oil production platform control rooms. Int J Ind Ergon 2014; 44 (05) 723-731
  • 5 Instrumentation AftAoM. A siren call to action: priority issues from the medical device alarms summit. Arlington, VA: Association for the Advancement of Medical Instrumentation; Available at: 2011
  • 6 Ariosto D. Factors contributing to CPOE opiate allergy alert overrides. AMIA Annu Symp Proc. 2014; 2014: 256-265
  • 7 Topaz M, Seger DL, Lai K. , et al. High override rate for opioid drug-allergy interaction alerts: current trends and recommendations for future. Stud Health Technol Inform 2015; 216: 242-246
  • 8 Humphrey K, Jorina M, Harper M, Dodson B, Kim SY, Ozonoff A. An investigation of drug-drug interaction alert overrides at a pediatric hospital. Hosp Pediatr 2018; 8 (05) 293-299
  • 9 Nanji KC, Seger DL, Slight SP. , et al. Medication-related clinical decision support alert overrides in inpatients. J Am Med Inform Assoc 2018; 25 (05) 476-481
  • 10 Zenziper Straichman Y, Kurnik D, Matok I. , et al. Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients. Int J Med Inform 2017; 107: 70-75
  • 11 Rayo MF, Kowalczyk N, Liston BW, Sanders EB, White S, Patterson ES. Comparing the effectiveness of alerts and dynamically annotated visualizations (DAVs) in improving clinical decision making. Hum Factors 2015; 57 (06) 1002-1014
  • 12 Westbrook JI, Coiera E, Dunsmuir WT. , et al. The impact of interruptions on clinical task completion. Qual Saf Health Care 2010; 19 (04) 284-289
  • 13 Grundgeiger T, Sanderson P. Interruptions in healthcare: theoretical views. Int J Med Inform 2009; 78 (05) 293-307
  • 14 Ashcroft DM, Quinlan P, Blenkinsopp A. Prospective study of the incidence, nature and causes of dispensing errors in community pharmacies. Pharmacoepidemiol Drug Saf 2005; 14 (05) 327-332
  • 15 Westbrook JI, Woods A, Rob MI, Dunsmuir WT, Day RO. Association of interruptions with an increased risk and severity of medication administration errors. Arch Intern Med 2010; 170 (08) 683-690
  • 16 Group TL. Clinical Decision Support Related to the CPOE Evaluation Tool. [PDF]. Available at: . Accessed July 29, 2019
  • 17 Kaplan A. The conduct of inquiry; methodology for behavioral science. San Francisco, CA: Chandler Pub. Co.; 1964
  • 18 Quality IET. Institute for Healthcare Improvement. Available at: 2017
  • 19 Scoville R, Little K. Comparing Lean and Quality Improvement. Cambridge, Massachusetts: Institute for Healthcare Improvement; Available at: 2014
  • 20 Nielsen J. Enhancing the explanatory power of usability heuristics. Human Factors in Computing Systems, Chi '94 Conference Proceedings - Celebrating Interdependence. 1994: 152-158 . Available at:
  • 21 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381
  • 22 Harris PA, Taylor R, Minor BL. , et al; REDCap Consortium. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019; 95: 103208
  • 23 Brilli RJ, McClead Jr RE, Davis T, Stoverock L, Rayburn A, Berry JC. The Preventable Harm Index: an effective motivator to facilitate the drive to zero. J Pediatr 2010; 157 (04) 681-683
  • 24 Minneci PC, Brilli RJ. The power of “we”: successful quality improvement requires a team approach. Pediatr Crit Care Med 2013; 14 (05) 551-553
  • 25 Brilli RJ, Davis JT. Pediatric quality and safety come of age. J Healthc Qual 2018; 40 (02) 67-68
  • 26 Cuéllar Monreal MJ, Reig Aguado J, Font Noguera I, Poveda Andrés JL. Reduction in alert fatigue in an assisted electronic prescribing system, through the Lean Six Sigma methodology. Farm Hosp 2017; 41 (01) 14-30
  • 27 Simpao AF, Ahumada LM, Desai BR. , et al. Optimization of drug-drug interaction alert rules in a pediatric hospital's electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015; 22 (02) 361-369
  • 28 McCoy AB, Thomas EJ, Krousel-Wood M, Sittig DF. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J 2014; 14 (02) 195-202
  • 29 Wright A, Hickman TT, McEvoy D. , et al. Analysis of clinical decision support system malfunctions: a case series and survey. J Am Med Inform Assoc 2016; 23 (06) 1068-1076
  • 30 Zimmerman CR, Jackson A, Chaffee B, O'Reilly M. A dashboard model for monitoring alert effectiveness and bandwidth. AMIA Annu. Symp. Proc. 2007; 1176
  • 31 Czock D, Konias M, Seidling HM. , et al. Tailoring of alerts substantially reduces the alert burden in computerized clinical decision support for drugs that should be avoided in patients with renal disease. J Am Med Inform Assoc 2015; 22 (04) 881-887
  • 32 Horsky J, Drucker EA, Ramelson HZ. Higher accuracy of complex medication reconciliation through improved design of electronic tools. J Am Med Inform Assoc 2018; 25 (05) 465-475
  • 33 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
  • 34 Melton BL, Zillich AJ, Russell SA. , et al. Reducing prescribing errors through creatinine clearance alert redesign. Am J Med 2015; 128 (10) 1117-1125
  • 35 Miller K, Mosby D, Capan M. , et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc 2018; 25 (05) 585-592
  • 36 Russ AL, Zillich AJ, McManus MS, Doebbeling BN, Saleem JJ. A human factors investigation of medication alerts: barriers to prescriber decision-making and clinical workflow. AMIA Annu Symp Proc 2009; 2009: 548-552
  • 37 Phansalkar S, Edworthy J, Hellier E. , et al. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc 2010; 17 (05) 493-501
  • 38 Horsky J, Schiff GD, Johnston D, Mercincavage L, Bell D, Middleton B. Interface design principles for usable decision support: a targeted review of best practices for clinical prescribing interventions. J Biomed Inform 2012; 45 (06) 1202-1216
  • 39 Middleton B, Bloomrosen M, Dente MA. , et al; American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013; 20 (e1): e2-e8
  • 40 Rubins D, Dutta S, Wright A, Zuccotti G. Continuous improvement of clinical decision support via an embedded survey tool. Stud Health Technol Inform 2019; 264: 1763-1764
  • 41 Aaron S, McEvoy DS, Ray S, Hickman TT, Wright A. Cranky comments: detecting clinical decision support malfunctions through free-text override reasons. J Am Med Inform Assoc 2019; 26 (01) 37-43
  • 42 Gregory ME, Russo E, Singh H. Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017; 8 (03) 686-697
  • 43 Bliss JP, Gilson RD, Deaton JE. Human probability matching behaviour in response to alarms of varying reliability. Ergonomics 1995; 38 (11) 2300-2312
  • 44 Baysari MT, Tariq A, Day RO, Westbrook JI. Alert override as a habitual behavior - a new perspective on a persistent problem. J Am Med Inform Assoc 2017; 24 (02) 409-412
  • 45 Zachariah M, Phansalkar S, Seidling HM. , et al. Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems--I-MeDeSA. J Am Med Inform Assoc 2011; 18 (Suppl. 01) i62-i72