Appl Clin Inform 2020; 11(01): 001-012
DOI: 10.1055/s-0039-3402715
Georg Thieme Verlag KG Stuttgart · New York

Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems

John D. McGreevey III
1   Office of the CMIO, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
2   Section of Hospital Medicine, Division of General Internal Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
Colleen P. Mallozzi
1   Office of the CMIO, University of Pennsylvania Health System, Philadelphia, Pennsylvania, United States
Randa M. Perkins
3   H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States
Eric Shelov
4   Division of General Pediatrics, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
Richard Schreiber
5   Physician Informatics and Department of Medicine, Geisinger Health System, Geisinger Holy Spirit, Camp Hill, Pennsylvania, United States
› Author Affiliations
Further Information

Publication History

19 August 2019

12 November 2019

Publication Date:
01 January 2020 (online)


Background Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan.

Objectives The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts.

Methods This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.S. health care institutions that presented their experiences and recommendations at the American Medical Informatics Association 2019 Clinical Informatics Conference in Atlanta, Georgia, United States. The assembled health care institution leaders represent academic, pediatric, community, and specialized care domains. We describe governance and management, structural concepts and components, and human–computer interactions with alerts, and make recommendations regarding these domains based on our experience supplemented with literature review. This paper focuses on alerts that impact bedside clinicians.

Results The manuscript addresses the range of considerations relevant to alert management including a summary of the background literature about alerts, alert governance, alert metrics, starting an alert management program, approaches to evaluating alerts prior to deployment, and optimization of existing alerts. The manuscript includes examples of alert optimization successes at two of the represented institutions. In addition, we review limitations on the ability to evaluate alerts in the current state and identify opportunities for further scholarship.

Conclusion Ultimately, alert management programs must strive to meet common goals of improving patient care, while at the same time decreasing the alert burden on clinicians. In so doing, organizations have an opportunity to promote the wellness of patients, clinicians, and EHRs themselves.

Protection of Human and Animal Subjects

No human subjects were involved in this project and Institutional Review Board approval was not required.

  • References

  • 1 Gawande A. Why doctors hate their computers. The New Yorker. Available at: . Accessed October 18, 2019
  • 2 Jha AK, Iliff AR, Chaoui AA. , et al. A crisis in health care: A call to action on physician burnout. Available at: . Accessed October 18, 2019
  • 3 Schulte F, Fry E. Death by 1,000 clicks: Where electronic health records went wrong. Kaiser Health News. Available at: . Accessed October 18, 2019
  • 4 Kawamanto K, Flynn MC, Kukhareva P. , et al. A pragmatic guide to establishing clinical decision support governance and addressing decision support fatigue: a case study. AMIA Annu Symp Proc 2018; 2018: 624-633
  • 5 Monica K. 5 ways to prevent physician burnout in the age of the EHR System. Available at: . Accessed October 18, 2019
  • 6 Howe JL, Adams KT, Hettinger AZ, Ratwani RM. Electronic health record usability issues and potential contribution to patient harm. JAMA 2018; 319 (12) 1276-1278
  • 7 Wears RL, Berg M. Computer technology and clinical work: still waiting for Godot. JAMA 2005; 293 (10) 1261-1263
  • 8 Downing NL, Bates DW, Longhurst CA. Physician burnout in the electronic health record era: Are we ignoring the real cause?. Ann Intern Med 2018; 169 (01) 50-51
  • 9 Verghese A. How tech can turn doctors into clerical workers. The New York Times Magazine. Available at: . Accessed October 18, 2019
  • 10 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
  • 11 Topaz M, Seger DL, Slight SP. , et al. Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience. J Am Med Inform Assoc 2016; 23 (03) 601-608
  • 12 Schreiber R, Gregoire JA, Shaha JE, Shaha SH. Think time: A novel approach to analysis of clinician's behavior after reduction of DDI alerts. Int J Med Inform 2017; 97: 59-67
  • 13 Saiyed SM, Greco PJ, Fernandes G, Kaelber DC. Optimizing drug-dose alerts using commercial software throughout an integrated health care system. J Am Med Inform Assoc 2017; 24 (06) 1149-1154
  • 14 Silbernagel G, Spirk D, Hager A, Baumgartner I, Kucher N. Electronic alert system for improving stroke prevention among hospitalized oral-anticoagulation-naïve patients with atrial fibrillation: A randomized trial. J Am Heart Assoc 2016; 5 (07) e003776
  • 15 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
  • 16 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. ; with the HITEC Investigators. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak 2017; 17 (01) 36-44
  • 17 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
  • 18 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. J Am Med Inform Assoc 2012; 19 (e1): e145-e148
  • 19 McDaniel RB, Burlison JD, Baker DK. , et al. Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts. J Am Med Inform Assoc 2016; 23 (e1): e138-e141
  • 20 Peterson JF, Bates DW. Preventable medication errors: identifying and eliminating serious drug interactions. J Am Pharm Assoc (Wash) 2001; 41 (02) 159-160
  • 21 Singh H, Spitzmueller C, Petersen NJ, Sawhney MK, Sittig DF. Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 2013; 173 (08) 702-704
  • 22 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
  • 23 Payne TH. EHR-related alert fatigue: minimal progress to date, but much more can be done. BMJ Qual Saf 2019; 28 (01) 1-2
  • 24 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
  • 25 McEvoy DS, Sittig DF, Hickman T-T. , et al. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017; 24 (02) 331-338
  • 26 Lee EK, Wu TL, Senior T, Jose J. Medical alert management: a real-time adaptive decision support tool to reduce alert fatigue. AMIA Annu Symp Proc 2014; 2014: 845-854
  • 27 Payne TH, Hines LE, Chan RC. , et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc 2015; 22 (06) 1243-1250
  • 28 Phansalkar S, Desai AA, Bell D. , et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc 2012; 19 (05) 735-743
  • 29 Carspecken CW, Sharek PJ, Longhurst C, Pageler NM. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics 2013; 131 (06) e1970-e1973
  • 30 Grissinger M. Medication errors involving overrides of healthcare technology. Pennsylvania Patient Safety Advisory 2015; 12 (04) 141-148
  • 31 Wright A, Sittig DF, Ash JS. , et al. Governance for clinical decision support: case studies and recommended practices from leading institutions. J Am Med Inform Assoc 2011; 18 (02) 187-194
  • 32 Alexander B. Schreiber R. WakeMed Health & Hospitals, Raleigh NC. Personal Communication
  • 33 Classen DC, Resar R, Griffin F. , et al. ‘Global trigger tool’ shows that adverse events in hospitals may be ten times greater than previously measured. Health Aff (Millwood) 2011; 30 (04) 581-589
  • 34 Wright A, Ai A, Ash J. , et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2018; 25 (05) 496-506
  • 35 Wright A, Ash JS, Aaron S. , et al. Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study. Int J Med Inform 2018; 118: 78-85
  • 36 Koppel R. Is healthcare information technology based on evidence?. Yearb Med Inform 2013; 8: 7-12
  • 37 Koppel R, Lehmann CU. Implications of an emerging EHR monoculture for hospitals and healthcare systems. J Am Med Inform Assoc 2015; 22 (02) 465-471
  • 38 Khaliq AA, Thompson DM, Walston SL. Perceptions of hospital CEOs about the effects of CEO turnover. Hosp Top 2006; 84 (04) 21-27
  • 39 Blenko MW, Mankins M, Rogers P. The decision-driven organization. Available at: . Accessed October 18, 2019
  • 40 Kuperman GJ, Bobb A, Payne TH. , et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007; 14 (01) 29-40
  • 41 Murphy DR, Reis B, Sittig DF, Singh H. Notifications received by primary care practitioners in electronic health records: a taxonomy and time analysis. Am J Med 2012; 125 (02) 209.e1-209.e7
  • 42 Longhurst C, Sharp C. Implementation and transition to operations. In: Payne TH. , ed. Practical Guide to Clinical Computing Systems: Design, Operations, and Infrastructure. 2nd ed. Amsterdam: Elsevier; 2015: 99-110
  • 43 Schreiber R, Knapp J. Premature condemnation of clinical decision support as a useful tool for patient safety in computerized provider order entry. J Am Geriatr Soc 2009; 57 (10) 1941-1942
  • 44 Aaron S, McEvoy DS, Ray S, Hickman T-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
  • 45 Covey SR. The 7 Habits of Highly Effective People. New York, NY: Simon & Schuster; 2013
  • 46 Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform 2016; (Suppl. 01) S103-S116
  • 47 Truong Q. Strategies to the five rights of clinical decision support. Epic User Web 82831. Verona, WI: Epic Systems Corporation;
  • 48 Wright A, McEvoy DS, Aaron S. , et al. Structured override reasons for drug-drug interaction alerts in electronic health records. J Am Med Inform Assoc 2019; 26 (10) 934-942
  • 49 Horsky J, Phansalkar S, Desai A, Bell D, Middleton B. Design of decision support interventions for medication prescribing. Int J Med Inform 2013; 82 (06) 492-503
  • 50 Feblowitz J, Henkin S, Pang J. , et al. Provider use of and attitudes towards an active clinical alert: a case study in decision support. Appl Clin Inform 2013; 4 (01) 144-152
  • 51 Dowding D, Merrill JA. The development of heuristics for evaluation of dashboard visualization. Appl Clin Inform 2018; 9 (03) 511-518
  • 52 Wright A, Aaron S, Sittig DF. Testing electronic health records in the “production” environment: an essential step in the journey to a safe and effective health care system. J Am Med Inform Assoc 2017; 24 (01) 188-192
  • 53 Ashton M. Getting rid of stupid stuff. N Engl J Med 2018; 379 (19) 1789-1791
  • 54 Hussain MI, Reynolds TL, Zheng K. Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review. J Am Med Inform Assoc 2019; 26 (10) 1141-1149
  • 55 Hasan H. Combating physician burnout: Five insights to help restore the balance. Available at:|Vanity|LB|PhysicianIssues|2017Jan26|Burnout| . Accessed August 10, 2019
  • 56 Phrase Health; analytics and governance for decision support. Available at: . Accessed October 18, 2019
  • 57 Kane-Gill SL, O'Connor MF, Rothschild JM. , et al. Technological distractions (part 1): summary of approaches to manage alert quantity with intent to reduce alert fatigue and suggestions for alert fatigue metrics. Crit Care Med 2017; 45 (09) 1481-1488
  • 58 Health Research & Educational Trust; U.S. Department of Health and Human Services. Implementation guide to reducing harm from high-alert medications. Available at: . Accessed October 18, 2019