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Appl Clin Inform 2025; 16(02): 393-401
DOI: 10.1055/a-2508-7086
Research Article

Academic Detailing to Enhance Adoption of Clinical Decision Support for Patients at Risk of Opioid Overdose

Sarah Hussain
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Harold Lehmann
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Megan E. Buresh
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Timothy M. Niessen
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Michael I. Fingerhood
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
,
Nazeer Ahmed
2   Department of Pharmacy, Johns Hopkins Bayview Medical Center, Baltimore, Maryland
,
Kelly Cavallio
3   Maryland Primary Care Physicians, Baltimore, Maryland
,
Andrew Maslen
4   Epic Application Team, Johns Hopkins Health System, Baltimore, Maryland
,
Amy M. Knight
1   Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
› Author Affiliations Funding None.
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Abstract

Background Not all patients at risk for opioid overdose are prescribed naloxone when discharged from the emergency department or hospital. Clinical decision support (CDS) can be used to promote clinical best practices, such as naloxone prescribing; however, it may be ignored due to knowledge deficiencies or alert fatigue.

Objectives Assess the effect of academic detailing on responses to a CDS alert recommending a naloxone prescription for patients at risk of opioid overdose.

Methods A pre/postquality improvement study of 2,161 active providers at a 400-bed academic medical center. The first intervention was an educational email to all providers. The second intervention was individual emails to 150 providers who infrequently ordered naloxone in response to the alert. The main outcome measure was prescription-to-alert ratios, defined as the number of naloxone prescriptions signed in response to the alert divided by the number of times the alert fired.

Results The first academic detailing intervention resulted in a prescription-to-alert ratio increase from 32.6 to 51.7%, a 19.1% absolute increase when comparing the approximately 8 months before and after the email was sent (95% confidence interval [CI]: 16.3–21.9%, p < 0.001). The second intervention resulted in an increased prescription-to-alert ratio from 9.3 to 50.6%, an absolute increase of 41.3% when comparing the nearly 8 months before and after the emails were sent (95% CI: 36.9–45.7%, p < 0.001). Improvements were seen across all services and all provider roles, particularly for advanced practice providers, and were sustained for 8 months.

Conclusion Academic detailing can be used to augment responses to CDS for patients with opioid dependence. Further study is needed to see if this effect can be replicated with CDS for other high priority conditions, and whether academic detailing with one alert might improve responses to other alerts as well, potentially decreasing alert fatigue.

Keywords

formative feedback - clinical decision support systems - alert fatigue - opioid-related disorders - naloxone

Protection of Human and Animal Subjects

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 approved by the Johns Hopkins Medicine Institutional Review Board.


Supplementary Material



Publication History

Received: 30 August 2024

Accepted: 27 December 2024

Article published online:
07 May 2025

© 2025. Thieme. All rights reserved.

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

 

  • References

  • 1 Hedegaard H, Miniño AM, Warner M. Drug overdose deaths in the United States, 1999–2017. NCHS Data Brief 2018; 329 (329) 1-8
    PubMedSearch in Google Scholar
  • 2 Kochanek KD, Murphy SL, Xu J, Arias E. Deaths: final data for 2017. Natl Vital Stat Rep 2019; 68 (09) 1-77
    PubMedSearch in Google Scholar
  • 3 Hargan ED. Determination that a public health emergency exists. Accessed April 19, 2024 at: https://www.cms.gov/about-cms/agency-information/emergency/downloads/october_26_2017_public_health_declaration_for_opioids_crisis.pdf
    PubMed
  • 4 Smalley CM, Willner MA, Muir MR. et al. Electronic medical record-based interventions to encourage opioid prescribing best practices in the emergency department. Am J Emerg Med 2020; 38 (08) 1647-1651
    PubMedSearch in Google Scholar
  • 5 Wally MK, Thompson ME, Odum S, Kazemi DM, Hsu JR, Seymour RB. PRIMUM Group. Opioid Prescribing for chronic musculoskeletal conditions: trends over time and implementation of safe opioid-prescribing practices. Appl Clin Inform 2023; 14 (05) 961-972
    PubMedSearch in Google Scholar
  • 6 Pierce RP, Eskridge B, Ross B, Wright M, Selva T. Impact of a vendor-developed opioid clinical decision support intervention on adherence to prescribing guidelines, opioid prescribing, and rates of opioid-related encounters. Appl Clin Inform 2022; 13 (02) 419-430
    Thieme ConnectPubMedSearch in Google Scholar
  • 7 Donnell A, Unnithan C, Tyndall J, Hanna F. Digital interventions to save lives from the opioid crisis prior and during the SARS COVID-19 pandemic: a scoping review of Australian and Canadian experiences. Front Public Health 2022; 10: 900733
    PubMedSearch in Google Scholar
  • 8 U.S. Department of Health and Human Services (HHS), Office of the Surgeon General. Facing addiction in America: the Surgeon General's Spotlight on opioids; 2018
    PubMed
  • 9 Oteo A, Daneshvar H, Baldacchino A, Matheson C. Overdose alert and response technologies: state-of-the-art review. J Med Internet Res 2023; 25: e40389
    PubMedSearch in Google Scholar
  • 10 Xuan Z, Walley AY, Yan S, Chatterjee A, Green TG, Pollini RA. Pharmacy naloxone standing order and community opioid fatality rates over time. JAMA Netw Open 2024; 7 (08) e2427236
    PubMedSearch in Google Scholar
  • 11 Sohn M, Delcher C, Talbert JC. et al. The impact of naloxone coprescribing mandates on opioid-involved overdose deaths. Am J Prev Med 2023; 64 (04) 483-491
    PubMedSearch in Google Scholar
  • 12 Wilson N, Kariisa M, Seth P, Smith IV H, Davis NL. Drug and opioid-involved overdose deaths - United States, 2017-2018. MMWR Morb Mortal Wkly Rep 2020; 69 (11) 290-297
    CrossrefPubMedSearch in Google Scholar
  • 13 Srikumar JK, Daniel K, Balasanova AA. Implementation of a naloxone best practice advisory into an electronic health record. J Addict Med 2023; 17 (03) 346-348
    CrossrefPubMedSearch in Google Scholar
  • 14 Wu R, Foster E, Zhang Q, Eynatian T, Mishuris RG, Cordella N. Iterative development of a clinical decision support tool to enhance naloxone co-prescribing. Appl Clin Inform 2024;
    Thieme ConnectPubMedSearch in Google Scholar
  • 15 Sommers SW, Tolle HJ, Trinkley KE. et al. Clinical decision support to increase emergency department naloxone coprescribing: implementation report. JMIR Med Inform 2024; 12: e58276
    PubMedSearch in Google Scholar
  • 16 Nelson SD, McCoy AB, Rector H. et al. Assessment of a naloxone coprescribing alert for patients at risk of opioid overdose: a quality improvement project. Anesth Analg 2022; 135 (01) 26-34
    CrossrefPubMedSearch in Google Scholar
  • 17 Heiman E, Lanh S, Moran TP, Steck A, Carpenter J. Electronic advisories increase naloxone prescribing across health care settings. J Gen Intern Med 2023; 38 (06) 1402-1409
    CrossrefPubMedSearch in Google Scholar
  • 18 Siff JE, Margolius D, Papp J, Boulanger B, Watts B. A healthcare system-level intervention to increase naloxone availability for patients with opioid prescriptions. Am J Addict 2021; 30 (02) 179-182
    CrossrefPubMedSearch in Google Scholar
  • 19 Preventing opioid overdose deaths with naloxone co-prescribing. Accessed September 20, 2022 at: https://galaxy.epic.com/Redirect.aspx?DocumentID=100041157&PrefDocID=123363
    PubMed
  • 20 Kouri A, Yamada J, Lam Shin Cheung J, Van de Velde S, Gupta S. Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake. Implement Sci 2022; 17 (01) 21-23
    CrossrefPubMedSearch in Google Scholar
  • 21 Kwan JL, Lo L, Ferguson J. et al. Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ 2020; 370: m3216
    CrossrefPubMedSearch in Google Scholar
  • 22 Westerbeek L, Ploegmakers KJ, de Bruijn GJ. et al. Barriers and facilitators influencing medication-related CDSS acceptance according to clinicians: a systematic review. Int J Med Inform 2021; 152: 104506
    PubMedSearch in Google Scholar
  • 23 Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010; 17 (01) 25-33
    CrossrefPubMedSearch in Google Scholar
  • 24 Ford E, Edelman N, Somers L. et al. Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Med Inform Decis Mak 2021; 21 (01) 193
    CrossrefPubMedSearch in Google Scholar
  • 25 Kortteisto T, Komulainen J, Mäkelä M, Kunnamo I, Kaila M. Clinical decision support must be useful, functional is not enough: a qualitative study of computer-based clinical decision support in primary care. BMC Health Serv Res 2012; 12: 349-349
    PubMedSearch in Google Scholar
  • 26 Avorn J. Academic detailing: “marketing” the best evidence to clinicians. JAMA 2017; 317 (04) 361-362
    PubMedSearch in Google Scholar
  • 27 Ivers N, Jamtvedt G, Flottorp S. et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev 2012; 2012 (06) CD000259
    PubMedSearch in Google Scholar
  • 28 Avorn J, Soumerai SB. Improving drug-therapy decisions through educational outreach. A randomized controlled trial of academically based “detailing”. N Engl J Med 1983; 308 (24) 1457-1463
    PubMedSearch in Google Scholar
  • 29 Langaas HC, Hurley E, Dyrkorn R, Spigset O. Effectiveness of an academic detailing intervention in primary care on the prescribing of non-steroidal anti-inflammatory drugs. Eur J Clin Pharmacol 2019; 75 (04) 577-586
    PubMedSearch in Google Scholar
  • 30 Kisuule F, Wright S, Barreto J, Zenilman J. Improving antibiotic utilization among hospitalists: a pilot academic detailing project with a public health approach. J Hosp Med 2008; 3 (01) 64-70
    PubMedSearch in Google Scholar
  • 31 Schwartz KL, Ivers N, Langford BJ. et al. Effect of antibiotic-prescribing feedback to high-volume primary care physicians on number of antibiotic prescriptions: a randomized clinical trial. JAMA Intern Med 2021; 181 (09) 1165-1173
    PubMedSearch in Google Scholar
  • 32 Sampedro A. BMC Inpatient Discharges with Coded Opioid Substance Abuse; 2024
    PubMed
  • 33 Seiler L. Johns Hopkins Bayview Medical Center; 2019
    PubMed
  • 34 CDC. CDC guideline for prescribing opioids for chronic pain—United States, 2016. Accessed May 27, 2024 at: https://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm
    PubMed
  • 35 Sommers S, Tolle H, Napier C, Hoppe J. Targeted messaging to improve the adoption of clinical decision support for prescription drug monitoring program use. J Am Med Inform Assoc 2023; 30 (10) 1711-1716
    PubMedSearch in Google Scholar
  • 36 Preiksaitis C, Ashenburg N, Bunney G. et al. The role of large language models in transforming emergency medicine: scoping review. JMIR Med Inform 2024; 12: e53787
    CrossrefPubMedSearch in Google Scholar
  • 37 Lüscher TF, Wenzl FA, D'Ascenzo F, Friedman PA, Antoniades C. Artificial intelligence in cardiovascular medicine: clinical applications. Eur Heart J 2024; 45 (40) 4291-4304
    PubMedSearch in Google Scholar
  • 38 Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med 2019; 132 (07) 795-801
    CrossrefPubMedSearch in Google Scholar
  • 39 Phansalkar S, Edworthy J, Hellier E. et al. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc 2010; 17 (05) 493-501
    CrossrefPubMedSearch in Google Scholar
  • 40 Osheroff JA, Teich J, Levick D. et al. Improving Outcomes with Clinical Decision Support: An Implementer's Guide. 2nd ed.. Chicago, IL: HIMSS; 2012
    Search in Google Scholar
  • 41 Middleton B, Sittig DF, Wright A. Clinical decision support: a 25 year retrospective and a 25 year vision. Yearb Med Inform 2016; 1 (Suppl 1, Suppl 1): S103-S116
    PubMedSearch in Google Scholar
  • 42 Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly 2014; 144: w14073
    PubMedSearch in Google Scholar
  • 43 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
    CrossrefPubMedSearch in Google Scholar
  • 44 Tolley CL, Slight SP, Husband AK, Watson N, Bates DW. Improving medication-related clinical decision support. Am J Health Syst Pharm 2018; 75 (04) 239-246
    CrossrefPubMedSearch in Google Scholar
  • 45 Hussain MI, Reynolds TL, Zheng K. Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review. J Am Med Inform Assoc 2019; 26 (10) 1141-1149
    CrossrefPubMedSearch in Google Scholar
  • 46 Knight AM, Maygers J, Foltz KA, John IS, Yeh HC, Brotman DJ. The effect of eliminating intermediate severity drug-drug interaction alerts on overall medication alert burden and acceptance rate. Appl Clin Inform 2019; 10 (05) 927-934
    Thieme ConnectPubMedSearch in Google Scholar
  • 47 James MT, Har BJ, Tyrrell BD. et al. Effect of clinical decision support with audit and feedback on prevention of acute kidney injury in patients undergoing coronary angiography: a randomized clinical trial. JAMA 2022; 328 (09) 839-849
    PubMedSearch in Google Scholar
  • 48 James MT, Har BJ, Tyrrell BD. et al. Clinical decision support to reduce contrast-induced kidney injury during cardiac catheterization: design of a randomized stepped-wedge trial. Can J Cardiol 2019; 35 (09) 1124-1133
    PubMedSearch in Google Scholar
  • 49 Holland WC, Nath B, Li F. et al. Interrupted time series of user-centered clinical decision support implementation for emergency department-initiated buprenorphine for opioid use disorder. Acad Emerg Med 2020; 27 (08) 753-763
    PubMedSearch in Google Scholar
  • 50 Roberts GW, Farmer CJ, Cheney PC. et al. Clinical decision support implemented with academic detailing improves prescribing of key renally cleared drugs in the hospital setting. J Am Med Inform Assoc 2010; 17 (03) 308-312
    PubMedSearch in Google Scholar
  • 51 Barton HJ, Maru A, Leaf MA. et al. Academic detailing as a health information technology implementation method: supporting the design and implementation of an emergency department-based clinical decision support tool to prevent future falls. JMIR Hum Factors 2024; 11: e52592
    PubMedSearch in Google Scholar
  • 52 Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate?. J Am Med Inform Assoc 2014; 21 (01) 23-26
    PubMedSearch in Google Scholar
  • 53 Cho I, Bates DW. Behavioral economics interventions in clinical decision support systems. Yearb Med Inform 2018; 27 (01) 114-121
    Thieme ConnectPubMedSearch in Google Scholar
  • 54 Foy R, Skrypak M, Alderson S. et al. Revitalising audit and feedback to improve patient care. BMJ 2020; 368: m213
    PubMedSearch in Google Scholar
  • 55 Tuti T, Nzinga J, Njoroge M. et al. A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory. Implement Sci 2017; 12 (01) 61
    CrossrefPubMedSearch in Google Scholar
  • 56 Crawshaw J, Meyer C, Antonopoulou V. et al. Identifying behaviour change techniques in 287 randomized controlled trials of audit and feedback interventions targeting practice change among healthcare professionals. Implement Sci 2023; 18 (01) 63-68
    PubMedSearch in Google Scholar
  • 57 Sedgwick P, Greenwood N. Understanding the Hawthorne effect. BMJ 2015; 351: h4672
    CrossrefPubMedSearch in Google Scholar
  • 58 Hysong SJ. Meta-analysis: audit and feedback features impact effectiveness on care quality. Med Care 2009; 47 (03) 356-363
    CrossrefPubMedSearch in Google Scholar
  • 59 Gude WT, Brown B, van der Veer SN. et al. Clinical performance comparators in audit and feedback: a review of theory and evidence. Implement Sci 2019; 14 (01) 39-1
    PubMedSearch in Google Scholar
  • 60 McCullagh L, Mann D, Rosen L, Kannry J, McGinn T. Longitudinal adoption rates of complex decision support tools in primary care. Evid Based Med 2014; 19 (06) 204-209
    PubMedSearch in Google Scholar
  • 61 Khanal S, Ibrahim MIBM, Shankar PR, Palaian S, Mishra P. Evaluation of academic detailing programme on childhood diarrhoea management by primary healthcare providers in Banke district of Nepal. J Health Popul Nutr 2013; 31 (02) 231-242
    PubMedSearch in Google Scholar
  • 62 Scott A, Sivey P, Ait Ouakrim D. et al. The effect of financial incentives on the quality of health care provided by primary care physicians. Cochrane Database Syst Rev 2011; 9 (09) CD008451
    PubMedSearch in Google Scholar
  • 63 Vyas D, Hozain AE. Clinical peer review in the United States: history, legal development and subsequent abuse. World J Gastroenterol 2014; 20 (21) 6357-6363
    PubMedSearch in Google Scholar