Appl Clin Inform 2018; 09(01): 163-173
DOI: 10.1055/s-0038-1632397
State of the Art/Best Practice Paper
Schattauer GmbH Stuttgart

The Value of Monitoring Clinical Decision Support Interventions

Eileen Yoshida
Shirley Fei
Karen Bavuso
Charles Lagor
Saverio Maviglia
Further Information

Publication History

31 October 2017

08 January 2018

Publication Date:
07 March 2018 (online)


Background Well-functioning clinical decision support (CDS) can facilitate provider workflow, improve patient care, promote better outcomes, and reduce costs. However, poorly functioning CDS may lead to alert fatigue, cause providers to ignore important CDS interventions, and increase provider dissatisfaction.

Objective The purpose of this article is to describe one institution's experience in implementing a program to create and maintain properly functioning CDS by systematically monitoring CDS firing rates and patterns.

Methods Four types of CDS monitoring activities were implemented as part of the CDS lifecycle. One type of monitoring occurs prior to releasing active CDS, while the other types occur at different points after CDS activation.

Results Two hundred and forty-eight CDS interventions were monitored over a 2-year period. The rate of detecting a malfunction or significant opportunity for improvement was 37% during preactivation and 18% during immediate postactivation monitoring. Monitoring also informed the process of responding to user feedback about alerts. Finally, an automated alert detection tool identified 128 instances of alert pattern change over the same period. A subset of cases was evaluated by knowledge engineers to identify true and false positives, the results of which were used to optimize the tool's pattern detection algorithms.

Conclusion CDS monitoring can identify malfunctions and/or significant improvement opportunities even after careful design and robust testing. CDS monitoring provides information when responding to user feedback. Ongoing, continuous, and automated monitoring can detect malfunctions in real time, before users report problems. Therefore, CDS monitoring should be part of any systematic program of implementing and maintaining CDS.


All the authors were affiliated with Clinical Informatics, Partners HealthCare System Inc., Boston, Massachusetts, United States at the time of the study.

Protection of Human and Animal Subjects

Neither humans nor animal subjects were included in this project.

  • References

  • 1 Mishuris RG, Linder JA, Bates DW, Bitton A. Using electronic health record clinical decision support is associated with improved quality of care. Am J Manag Care 2014; 20 (10) e445-e452
  • 2 Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly 2014; 144: w14073
  • 3 Kassakian SZ, Yackel TR, Deloughery T, Dorr DA. Clinical decision support reduces overuse of red blood cell transfusions: interrupted time series analysis. Am J Med 2016; 129 (06) 636.e13-636.e20
  • 4 Procop GW, Yerian LM, Wyllie R, Harrison AM, Kottke-Marchant K. Duplicate laboratory test reduction using a clinical decision support tool. Am J Clin Pathol 2014; 141 (05) 718-723
  • 5 Kassakian SZ, Yackel TR, Gorman PN, Dorr DA. Clinical decisions support malfunctions in a commercial electronic health record. Appl Clin Inform 2017; 8 (03) 910-923
  • 6 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
  • 7 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
  • 8 Wright A, Ai A, Ash J. , et al. Clinical decision support alert malfunctions: analysis and empirically derived taxonomy. J Am Med Inform Assoc 2017; DOI: 10.1093/jamia/ocx106.
  • 9 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
  • 10 R core team. R: A language and environment for statistical computing. [Internet]. R Foundation for Statistical Computing, Vienna, Austria; 2013. Available at:
  • 11 Health Care IT. Advisor. Enhancing CDS Effectiveness: Monitoring Alert Performance at Stanford Keys to Success; 2017: 17-19
  • 12 Ash JS, Sittig DF, Guappone KP. , et al. Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Med Inform Decis Mak 2012; 12 (01) 6
  • 13 Wright A, Ash JS, Erickson JL. , et al. A qualitative study of the activities performed by people involved in clinical decision support: recommended practices for success. J Am Med Inform Assoc 2014; 21 (03) 464-472
  • 14 Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc 2006; 13 (05) 547-556
  • 15 Institute of Medicine. The path to continuously learning health care. Vol. 29, Issues in Science and Technology. Washington, DC: National Academies Press; 2013: 18