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Appl Clin Inform 2024; 15(01): 101-110
DOI: 10.1055/a-2226-8144
Research Article

Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support

Anne Fallon
1   Division of Pediatric Hospital Medicine, Department of Pediatrics, University of Rochester Medical Center, Rochester, New York, United States
,
Kristina Haralambides
2   Department of Otolaryngology, University of Rochester Medical Center, Rochester, New York, United States
,
Justin Mazzillo
3   Department of Emergency Medicine, University of Rochester Medical Center, Rochester, New York, United States
,
Conrad Gleber
4   Division of Hospital Medicine, Department of Medicine, University of Rochester Medical Center, Rochester, New York, United States
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Abstract

Background Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas.

Objectives Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders.

Methods Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts.

Results At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to −1.3 minutes.

Conclusion CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Keywords

clinical decision support systems - alert fatigue - patient safety - clinical governance

Protection of Human and Animal Subjects

This project was deemed exempt as non-Human Subject Research per our Institutional Review Board. Following query validation, no protected health information was accessed and only aggregated de-identified data were stored and analyzed on an internal institutional OneDrive compliant with the Health Insurance Portability and Accountability Act (HIPAA).


Supplementary Material



Publication History

Received: 24 September 2023

Accepted: 11 December 2023

Accepted Manuscript online:
12 December 2023

Article published online:
31 January 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Bates DW, Kuperman GJ, Wang S. et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc 2003; 10 (06) 523-530
    Google Scholar
  • 2 Osheroff JA, Teich JM, Levick D. et al. Improving Outcomes with Clinical Decision Support: An Implementer's Guide. 2nd ed. Chicago, Illinois: HIMSS Publishing; 2012
    Google Scholar
  • 3 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
    Google Scholar
  • 4 Elias P, Peterson E, Wachter B, Ward C, Poon E, Navar AM. Evaluating the impact of interruptive alerts within a health system: use, response time, and cumulative time burden. Appl Clin Inform 2019; 10 (05) 909-917
    Google Scholar
  • 5 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
    Google Scholar
  • 6 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
    Google Scholar
  • 7 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
    Google Scholar
  • 8 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
    Google Scholar
  • 9 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
    Google Scholar
  • 10 Miller SD, Murphy Z, Gray JH. et al. Human-centered design of a clinical decision support for anemia screening in children with inflammatory bowel disease. Appl Clin Inform 2023; 14 (02) 345-353
    Google Scholar
  • 11 Orenstein EW, Boudreaux J, Rollins M. et al. Formative usability testing reduces severe blood product ordering errors. Appl Clin Inform 2019; 10 (05) 981-990
    Google Scholar
  • 12 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
    Google Scholar
  • 13 Samal L, Wu E, Aaron S. et al. Refining clinical phenotypes to improve clinical decision support and reduce alert fatigue: a feasibility study. Appl Clin Inform 2023; 14 (03) 528-537
    Google Scholar
  • 14 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
    Google Scholar
  • 15 Chaparro JD, Hussain C, Lee JA, Hehmeyer J, Nguyen M, Hoffman J. Reducing interruptive alert burden using quality improvement methodology. Appl Clin Inform 2020; 11 (01) 46-58
    Google Scholar
  • 16 McCoy AB, Russo EM, Johnson KB. et al. Clinician collaboration to improve clinical decision support: the Clickbusters initiative. J Am Med Inform Assoc 2022; 29 (06) 1050-1059
    Google Scholar
  • 17 Chaparro JD, Beus JM, Dziorny AC. et al. Clinical decision support stewardship: best practices and techniques to monitor and improve interruptive alerts. Appl Clin Inform 2022; 13 (03) 560-568
    Google Scholar
  • 18 Van Dort BA, Zheng WY, Sundar V, Baysari MT. Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals. J Am Med Inform Assoc 2021; 28 (01) 177-183
    Google Scholar
  • 19 Ng HJH, Kansal A, Abdul Naseer JF. et al. Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore. JAMIA Open 2023; 6 (03) ooad056
    Google Scholar
  • 20 Ogrinc G, Davies L, Goodman D, Batalden P, Davidoff F, Stevens D. SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process. BMJ Qual Saf 2016; 25 (12) 986-992
    Google Scholar
  • 21 Orenstein EW, Kandaswamy S, Muthu N. et al. Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics. J Am Med Inform Assoc 2021; 28 (12) 2654-2660
    Google Scholar
  • 22 Langley GJ, Moen RD, Nolan KM, Nolan TW, Norman CL, Provost LP. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. 2nd ed. Chichester, England: Jossey-Bass; 2009
    Google Scholar
  • 23 Provost LP, Murray SK. The Health Care Data Guide: Learning from Data for Improvement. 1st ed. San Francisco, California: Jossey-Bass; 2011
    Google Scholar
  • 24 Trinkley KE, Blakeslee WW, Matlock DD. et al. Clinician preferences for computerised clinical decision support for medications in primary care: a focus group study. BMJ Health Care Inform 2019;26(01):0
    Google Scholar
  • 25 Kizzier-Carnahan V, Artis KA, Mohan V, Gold JA. Frequency of passive EHR alerts in the ICU: another form of alert fatigue?. J Patient Saf 2019; 15 (03) 246-250
    Google Scholar
  • 26 Nijor S, Rallis G, Lad N, Gokcen E. Patient safety issues from information overload in electronic medical records. J Patient Saf 2022; 18 (06) e999-e1003
    Google Scholar
  • 27 Montini T, Graham ID. “Entrenched practices and other biases”: unpacking the historical, economic, professional, and social resistance to de-implementation. Implement Sci 2015; 10 (01) 24
    Google Scholar