Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Research Article Volume 7 Issue 5

Alert Fatigue and Alert Override Significance in Relation to CDSS Success

Allison Eagen*

Department of Information Technology, Encompass Health, USA

*Corresponding Author: Allison Eagen, Department of Information Technology, Encompass Health, USA.

Received: March 30, 2023; Published: April 24, 2023

Abstract

Introduction/Background/Significance: Clinical decision support systems (CDSS) use information and communication technologies developed through evidenced-based methodologies such as algorithms and logical regression to provide relevant knowledge and information to providers to assist with the decision making process to support the health care and clinical outcomes of the patient. Common formats of CDSS include alerts and ‘pop-up’ messages. The overall success of the CDSS depends on five stages. Each stage builds on the previous stage and the lowest two factors impacting success include firing rates and override rates. This literature review seeks to evaluate the relationship between the two factors of alert fatigue and alert overrides to the success of CDSS.

Problem/Purpose Statements: The problem is that electronic medical record (EMR) systems which actively depend on CDSS generally have a large quantity of alerts and these alerts have been shown to create alert fatigue for providers. Given the significance of alert fatigue and the potential negative outcomes that result from alert overrides, the aim of this systematic literature review is to answer three questions: Does the use of CDSS when used in the care of a patient improve clinical outcomes?, Is there a threshold with the level or quantity of alerts that has been shown to create provider alert fatigue?, Is there a relationship between the number of CDSS alert messages presentations and the frequency rate of alert overrides?

Methods: The literature search focused on two databases using key search terms and Boolean operators. Sixteen articles (n = 16) were utilized after the inclusion and exclusion criteria were applied, abstract and full-text reviews were assessed for significance to the research purpose and quality assessments were completed. A data collection matrix was used to organize the data.

Results/Findings: Several benefits were found to support the use of CDSS to support clinical outcomes. Precautions and possible consequences were also identified. Several theories were identified which explain the alert fatigue phenomenon. Several recommendations for both CDSS and alert design and implementation were identified.

Discussion/Conclusions: Subgroup analysis did not provide enough evidence at this time to conclude that quantity alone can explain the relationship between CDSS alert messages presentations and the frequency of alert overrides. Best practice guidelines and recommendations are provided for CDSS and alert design and implementation as well as recommendations for the use of CDSS in conjunction with health information technologies (HITs) to support decision making processes. This literature review has implications for designers, implementation specialists, system analysts and additional health informatics (HI) professionals. Additionally, it adds value to the existing knowledge base surrounding CDSS while presenting areas for further research to examine additional heterogeneous factors which may impact the success of CDSS.

 Keywords: Alert Fatigue; Alert Override; Clinical Decision Support (CDS); EMR Alerts

References

  1. Agency for Healthcare Research and Quality. Alert fatigue (2019).
  2. Ancker JS., et al. “Effects of workload, work complexity and repeated alerts on alert fatigue in a clinical decision support system” (2017).
  3. Athena Health. “What is meaningful use?” (2020).
  4. Athena Health. “What is meaningful use stage 2” (2020).
  5. Backman R., et al. “Clinical reminder alert fatigue in healthcare: A systematic literature review protocol using qualitative evidence” (2017).
  6. Basit M., et al. “Agile clinical decision support development and implementation [Powerpoint presentation]”. AMIA Informatics Summit, UT Southwestern Medical Center (2018).
  7. Baysari MT., et al. “Alert override as a habitual behavior-a new perspective on a persistent problem” (2016).
  8. Baysari MT., et al. “An experimental investigation of the impact of alert frequency and relevance on alert dwell time” (2020).
  9. Bryant AD., et al. “Drug interaction alert override rates in the meaningful use era” (2014).
  10. Center for Medicare and Medicaid Services (CMS). “Clinical decision support: More than just ‘alerts’ tipsheet” (2014).
  11. Centers for Medicare and Medicaid Services (CMS). “Promoting interoperability programs” (2020).
  12. Greenes R A. “Clinical decision support: The road to broad adoption (2nd)”. Oxford, UK: Elsevier, Inc (2014).
  13. Heselmans A., et al. “Computerized clinical decision support system for diabetes in primary care does not improve quality of care: A cluster-randomized controlled trial” (2020).
  14. Higgins JPT., et al. “Systematic reviews and meta-analyses: Eligibility criteria” (2020).
  15. Joanna Briggs International. “Checklist for cohort studies” (2020).
  16. Joanna Briggs International. “Checklist for systematic reviews and research syntheses” (2020).
  17. Kane-Gill SL., et al. “Technologic distractions (Part 1): Summary of approaches to manage alert quantity with intent to reduce alert fatigue and suggestions for alert fatigue metrics”. (2017).
  18. Khalifa M and Zabani I. “Improving utilization of clinical decision support systems by reducing alert fatigue: Strategies and recommendations”. (2016).
  19. McCoy AB., et al. “Clinical decision support alert appropriateness: A review and proposal for improvement”. (2014).
  20. McDaniel RB., et al. “Alert dwell time: Introduction of a measure to evaluate interruptive clinical decision support alerts” (2015).
  21. Miller K., et al. “Interface, information, interaction: A narrative review of design and functional requirements for clinical decision support” (2018).
  22. Olakotan OO and Yusof MM. “Evaluating the alert appropriateness of clinical decision support systems in supporting clinical workflow” (2015).
  23. Parke C., et al. “Reduction of clinical support warnings through recategorization of severity levels” (2015).
  24. Slight SP., et al. “Are we heeding the warning signs? Examining providers’ overrides of computerized drug-drug interaction alerts in primary care” (2013).
  25. Sutton RT., et al. “An overview of clinical decision support systems: Benefits, risks, and strategies for success” (2020).
  26. Topaz M., et al. “Rising drug allergy alert overrides in electronic health records: An observational retrospective study of a decade of experience” (2016).
  27. Wadhwa R., et al. “Analysis of a failed clinical decision support system for management of congestive heart failure” (2008).

Citation

Citation: Allison Eagen. “Alert Fatigue and Alert Override Significance in Relation to CDSS Success”.Acta Scientific Medical Sciences 7.5 (2023): 173-185.

Copyright

Copyright: © 2023 Allison Eagen. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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