Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial

Abstract

Background

Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs).

Objective

To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings.

Design

Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia.

Setting

Two large academic health system primary care networks with a mix of providers.

Participants

All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group.

Interventions

Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment.

Main Outcome(s)

Change in overall antibiotic prescription rates.

Measure(s)

Frequency, rates, and type of antibiotics prescribed in intervention and controls groups.

Results

33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P = 0.11) or chest X-rays (RR, 1.01; P = 0.999) between groups.

Conclusions

The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts.

Trial Registration

Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov

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Figure 1

References

  1. 1.

    Fleming-Dutra KE, Hersh AL, Shapiro DJ, et al. Prevalence of Inappropriate Antibiotic Prescriptions Among US Ambulatory Care Visits, 2010-2011Inappropriate Antibiotic Prescriptions Among Ambulatory Care Visits, 2010-2011Inappropriate Antibiotic Prescriptions Among Ambulatory Care Visits, 2010-2011. JAMA. 2016;315(17):1864-1873.

    CAS  Article  Google Scholar 

  2. 2.

    Barnett ML, Linder JA. Antibiotic prescribing to adults with sore throat in the united states, 1997-2010. JAMA Intern Med 2014;174(1):138-140.

    Article  Google Scholar 

  3. 3.

    Barnett ML, Linder JA. Antibiotic prescribing for adults with acute bronchitis in the United States, 1996-2010. JAMA. 2014;311(19):2020-2022.

    CAS  Article  Google Scholar 

  4. 4.

    Mainous AG 3rd, Lambourne CA, Nietert PJ. Impact of a clinical decision support system on antibiotic prescribing for acute respiratory infections in primary care: quasi-experimental trial. J Am Med Inform Assoc. 2013;20(2):317-324.

  5. 5.

    Terry A. Do Clinical Decision Support Systems Reduce Inappropriate Antibiotic Prescribing For Acute Bronchitis? On-Line Journal of Nursing Informatics. 2017;21(1).

  6. 6.

    Gonzales R ATMCE, et al. A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis. JAMA Intern Med 2013;173(4):267-273.

    Article  Google Scholar 

  7. 7.

    Linder JA, Schnipper JL, Tsurikova R, et al. Electronic health record feedback to improve antibiotic prescribing for acute respiratory infections. Am J Manag Care. 2010;16(12 Suppl HIT):e311-319.

    PubMed  Google Scholar 

  8. 8.

    Linder JA, Schnipper JL, Tsurikova R, et al. Documentation-based clinical decision support to improve antibiotic prescribing for acute respiratory infections in primary care: a cluster randomised controlled trial. Inform Prim Care 2009;17(4):231-240.

    PubMed  Google Scholar 

  9. 9.

    McCullough JM, Zimmerman FJ, Rodriguez HP. Impact of clinical decision support on receipt of antibiotic prescriptions for acute bronchitis and upper respiratory tract infection. J Am Med Inform Assoc : JAMIA 2014;21(6):1091-1097.

    Article  Google Scholar 

  10. 10.

    Meeker D, Linder JA, Fox CR, et al. Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: A randomized clinical trial. JAMA. 2016;315(6):562-570.

    CAS  Article  Google Scholar 

  11. 11.

    McGinn TG, McCullagh L, Kannry J, et al. Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Intern Med 2013;173(17):1584-1591.

    Article  Google Scholar 

  12. 12.

    Feldstein DA, Hess R, McGinn T, et al. Design and implementation of electronic health record integrated clinical prediction rules (iCPR): a randomized trial in diverse primary care settings. Implement Sci 2017;12(1):37.

    Article  Google Scholar 

  13. 13.

    Richardson S, Rebecca Mishuris, Alexander O’Connell, David Feldstein, Rachel Hess, Paul Smith, Lauren McCullagh, Thomas McGinn, and Devin Mann. “Think aloud” and “Near live” usability testing of two complex clinical decision support tools. Int J Med Inform. 2017;106:1-8.

    Article  Google Scholar 

  14. 14.

    Mann D, Hess R, McGinn T, et al. Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research. Digit Health. 2019;5:2055207619827716

  15. 15.

    Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The Value of Monitoring Clinical Decision Support Interventions. Appl Clin Inform 2018;9(1):163-173.

    Article  Google Scholar 

  16. 16.

    Ancker JS, Edwards A, Nosal S, et al. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Making 2017;17(1):36-36.

    Article  Google Scholar 

  17. 17.

    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 : JAMIA 2013;20(3):489-493.

    Article  Google Scholar 

  18. 18.

    Carli D, Fahrni G, Bonnabry P, Lovis C. Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review. JMIR Med Inform 2018;6(1):e3-e3.

    Article  Google Scholar 

  19. 19.

    Ackerman SL, Gonzales R, Stahl MS, Metlay JP. One size does not fit all: evaluating an intervention to reduce antibiotic prescribing for acute bronchitis. BMC Health Serv Res. 2013;13:462.

  20. 20.

    Gibbs RS, Wieber C, Myers L, Jenkins T. A Continuing Medical Education Campaign to Improve Use of Antibiotics in Primary Care. J Biomed Educ. 2014;2014:6.

    Article  Google Scholar 

  21. 21.

    Litvin CB, Ornstein SM, Wessell AM, Nemeth LS, Nietert PJ. Use of an electronic health record clinical decision support tool to improve antibiotic prescribing for acute respiratory infections: the ABX-TRIP study. J Gen Intern Med. 2013;28(6):810-816.

  22. 22.

    McIsaac WJ, White D, Tannenbaum D, Low DE. A clinical score to reduce unnecessary antibiotic use in patients with sore throat. CMAJ: Journal de l'Association medicale canadienne. 1998;158(1):75-83.

  23. 23.

    Centor RM, Witherspoon JM, Dalton HP, Brody CE, Link K. The diagnosis of strep throat in adults in the emergency room. Med Decis Making. 1981;1(3):239-246.

  24. 24.

    Heckerling PS, Tape TG, Wigton RS, et al. Clinical prediction rule for pulmonary infiltrates. Ann Intern Med 1990;113(9):664-670.

    CAS  Article  Google Scholar 

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Funding

NIH NIAID: R01AI108680

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Correspondence to Devin Mann MD, MS.

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

Ethics Approval

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by New York University School of Medicine’s Institutional Review Board. No animals were included in this study.

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Mann, D., Hess, R., McGinn, T. et al. Impact of Clinical Decision Support on Antibiotic Prescribing for Acute Respiratory Infections: a Cluster Randomized Implementation Trial. J GEN INTERN MED 35, 788–795 (2020). https://doi.org/10.1007/s11606-020-06096-3

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KEY WORDS

  • user-centered design
  • clinical decision support
  • usability
  • health informatics
  • provider adoption