Reducing Imaging Utilization in Primary Care Through Implementation of a Peer Comparison Dashboard



High clinical variation has been linked to decreased quality of care, increased costs, and decreased patient satisfaction. We present the implementation and analysis of a peer comparison intervention to reduce clinical variation within a large primary care network.


Evaluate existing variation in radiology ordering within a primary care network and determine whether peer comparison feedback reduces variation or changes practice patterns.


Radiology ordering data was analyzed to evaluate baseline variation in imaging rates. A utilization dashboard was shared monthly with providers for a year, and imaging rates pre- and post-intervention were retrospectively analyzed.


Providers within the primary care network spanning 1,358,644 outpatient encounters and 159 providers over a 3-year period.


The inclusion of radiology utilization data as part of a provider’s monthly quality and productivity dashboards. This information allows providers to compare their practice patterns with those of their colleagues.

Main Measures

We measured provider imaging rates, stratified by modality, as well as order variation over time.

Key Results

We observed significant variation in imaging rates among providers in the network, with the top decile ordering an average of 4.2 times more than the lowest decile in the two years prior to intervention. Provider experience and training were not significantly associated with imaging utilization. In the first year after sharing utilization data with providers, we saw a 17.3% decrease in median imaging rate (p < 0.001) and a 21.4% reduction in provider variation between top and bottom deciles. Median ordering rate for more costly cross-sectional imaging, including CT, MRI, and nuclear medicine studies, decreased by 30.4% (p < 0.001), 20.2% (p = 0.008), and 41.8% (p = 0.002), respectively.


Peer comparison feedback can shape provider imaging behavior even in the absence of targets or financial incentives. Peer comparison is a low-touch, low-cost intervention for influencing provider ordering and may have applicability in other clinical areas.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3


  1. 1.

    Corallo AN, et al. A systematic review of medical practice variation in OECD countries. Health Policy. 2014;114(1): p. 5-14.

    Article  Google Scholar 

  2. 2.

    Fisher ES, et al. The implications of regional variations in Medicare spending. Part 1: The content, quality, and accessibility of care. Ann Intern Med. 2003;138(4):273-I36.

    Article  Google Scholar 

  3. 3.

    Fisher ES et al. The implications of regional variations in Medicare spending. Part 2: Health outcomes and satisfaction with care. Ann Intern Med. 2003;138(4):288-I49.

    Article  Google Scholar 

  4. 4.

    Iglehart JK. Health Insurers and Medical-Imaging Policy — A Work in Progress. N Engl J Med. 2009;360(10):1030-1037.

    CAS  Article  Google Scholar 

  5. 5.

    Ip IK, et al. Use of Public Data to Target Variation in Providers’ Use of CT and MR Imaging among Medicare Beneficiaries. Radiology. 2015;275(3):718-24.

    Article  Google Scholar 

  6. 6.

    Parker L, et al. Geographic variation in the utilization of noninvasive diagnostic imaging: national medicare data, 1998-2007. AJR Am J Roentgenol. 2010;194(4):1034-9.

    Article  Google Scholar 

  7. 7.

    Iglehart JK. The New Era of Medical Imaging — Progress and Pitfalls. N Engl J Med. 2006;354(26):2822-2828.

    CAS  Article  Google Scholar 

  8. 8.

    Bernardy M, et al. Strategies for Managing Imaging Utilization. J Am Coll Radiol. 2009;6(12):844-850.

    Article  Google Scholar 

  9. 9.

    Thrall JH. Appropriateness and Imaging Utilization: Computerized Provider Order Entry and Decision Support. Acad Radiol. 2014;21(9):1083-1087.

    Article  Google Scholar 

  10. 10.

    Levin DC, Rao VM. Reducing Inappropriate Use of Diagnostic Imaging Through the Choosing Wisely Initiative. J Am Coll Radiol. 2017;14(9):1245-1252.

    Article  Google Scholar 

  11. 11.

    Garg AX, et al. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient OutcomesA Systematic Review. JAMA. 2005;293(10):1223-1238.

    CAS  Article  Google Scholar 

  12. 12.

    Hussey PS, et al. Appropriateness of Advanced Diagnostic Imaging Ordering Before and After Implementation of Clinical Decision Support Systems. JAMA. 2015;313(21):2181-2182.

    CAS  Article  Google Scholar 

  13. 13.

    Blackmore CC, Mecklenburg RS, Kaplan GS. Effectiveness of Clinical Decision Support in Controlling Inappropriate Imaging. J Am Coll Radiol. 2011;8(1):19-25.

    Article  Google Scholar 

  14. 14.

    Cassel CK, Guest JA., Choosing Wisely: Helping Physicians and Patients Make Smart Decisions About Their Care. JAMA. 2012;307(17):1801-1802.

    CAS  Article  Google Scholar 

  15. 15.

    Curry L, Reed MH. Electronic decision support for diagnostic imaging in a primary care setting. J Am Med Inform Assoc. 2011;18(3):267-270.

    Article  Google Scholar 

  16. 16.

    Festinger L. A Theory of Social Comparison Processes. Hum Relat. 1954;7(2):117-140.

    Article  Google Scholar 

  17. 17.

    Garcia SM, Tor A, Gonzalez R. Ranks and Rivals: A Theory of Competition. Personal Soc Psychol Bull. 2006;32(7):970-982.

    Article  Google Scholar 

  18. 18.

    Brown B, et al. Clinical Performance Feedback Intervention Theory (CP-FIT): a new theory for designing, implementing, and evaluating feedback in health care based on a systematic review and meta-synthesis of qualitative research. Implement Sci. 2019;14(1):40.

    Article  Google Scholar 

  19. 19.

    Huang Y-S, et al. The effect of peer influence on the use of CT by emergency physicians for patients with headaches. Am J Emerg Med. 2019;37(4):710-714.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to David J. Halpern MD MPH.

Ethics declarations

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

David J. Halpern and Adrian Clark-Randall are co-first authors.

Electronic Supplementary Material


(DOCX 3813 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Halpern, D.J., Clark-Randall, A., Woodall, J. et al. Reducing Imaging Utilization in Primary Care Through Implementation of a Peer Comparison Dashboard. J GEN INTERN MED 36, 108–113 (2021).

Download citation