Reducing the default dispense quantity for new opioid analgesic prescriptions: study protocol for a cluster randomised controlled trial

Marcus A Bachhuber, Denis Nash, William N Southern, Moonseong Heo, Matthew Berger, Mark Schepis, Chinazo O Cunningham, Marcus A Bachhuber, Denis Nash, William N Southern, Moonseong Heo, Matthew Berger, Mark Schepis, Chinazo O Cunningham

Abstract

Introduction: As opioid analgesic consumption has grown, so have opioid use disorder and opioid-related overdoses. Reducing the quantity of opioid analgesics prescribed for acute non-cancer pain can potentially reduce risks to the individual receiving the prescription and to others who might unintentionally or intentionally consume any leftover tablets. Reducing the default dispense quantity for new opioid analgesic prescriptions in the electronic health record (EHR) is a promising intervention to reduce prescribing.

Methods and analysis: This study is a prospective cluster randomised controlled trial with two parallel arms. Primary care sites (n=32) and emergency departments (n=4) will be randomised in matched pairs to either a modification of the EHR so that new opioid analgesic prescriptions default to a dispense quantity of 10 tablets (intervention) or to no EHR change (control). The dispense quantity will remain fully modifiable by providers in both arms. From 6 months preintervention to 18 months postintervention, patient-level data will be analysed (ie, the patient is the unit of inference). Patient eligibility criteria are: (A) received a new opioid analgesic prescription, defined as no other opioid analgesic prescription in the prior 6 months; (B) age ≥18 years; and (C) no cancer diagnosis within 1 year prior to the new opioid analgesic prescription. The primary outcome will be the quantity of opioid analgesics prescribed in the initial prescription. Secondary outcomes will include opioid analgesic reorders and health service utilisation within 30 days after the initial prescription. Outcomes will be compared between study arms using a difference-in-differences analysis.

Ethics and dissemination: This study has been approved by the Montefiore Medical Center/Albert Einstein College of Medicine Institutional Review Board with a waiver of informed consent (2016-6036) and is registered on ClinicalTrials.gov (NCT03003832, 6 December 2016). Findings will be disseminated through publication, conferences and meetings with health system leaders.

Trial registration number: NCT03003832; Pre-results.

Keywords: acute pain; default; electronic health record; opioid analgesics; pain management.

Conflict of interest statement

Competing interests: None declared.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

References

    1. Guy GP, Zhang K, Bohm MK, et al. . Vital Signs: Changes in Opioid Prescribing in the United States, 2006-2015. MMWR Morb Mortal Wkly Rep 2017;66:697–704. 10.15585/mmwr.mm6626a4
    1. Rudd RA, Seth P, David F, et al. . Increases in Drug and Opioid-Involved Overdose Deaths - United States, 2010-2015. MMWR Morb Mortal Wkly Rep 2016;65:1445–52. 10.15585/mmwr.mm655051e1
    1. Florence CS, Zhou C, Luo F, et al. . The Economic Burden of Prescription Opioid Overdose, Abuse, and Dependence in the United States, 2013. Med Care 2016;54:901–6. 10.1097/MLR.0000000000000625
    1. Bohnert AS, Valenstein M, Bair MJ, et al. . Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA 2011;305:1315–21. 10.1001/jama.2011.370
    1. Fulton-Kehoe D, Sullivan MD, Turner JA, et al. . Opioid poisonings in Washington State Medicaid: trends, dosing, and guidelines. Med Care 2015;53:679–85. 10.1097/MLR.0000000000000384
    1. Lewis ET, Cucciare MA, Trafton JA. What do patients do with unused opioid medications? Clin J Pain 2014;30:654–62. 10.1097/01.ajp.0000435447.96642.f4
    1. Centers for Disease Control and Prevention (CDC). Adult use of prescription opioid pain medications - Utah, 2008. MMWR Morb Mortal Wkly Rep 2010;59:153–7.
    1. Kennedy-Hendricks A, Gielen A, McDonald E, et al. . Medication sharing, storage, and disposal practices for opioid medications among US Adults. JAMA Intern Med 2016;176:1027–9. 10.1001/jamainternmed.2016.2543
    1. McCabe SE, West BT, Boyd CJ. Leftover prescription opioids and nonmedical use among high school seniors: a multi-cohort national study. J Adolesc Health 2013;52:480–5. 10.1016/j.jadohealth.2012.08.007
    1. Hall AJ, Logan JE, Toblin RL, et al. . Patterns of abuse among unintentional pharmaceutical overdose fatalities. JAMA 2008;300:2613–20. 10.1001/jama.2008.802
    1. Center for Behavioral Health Statistics and Quality. Behavioral health trends in the United States: Results from the 2014 National Survey on Drug Use and Health (HHS Publication No. SMA 15-4927, NSDUH Series H-50). Rockville, MD, 2015.
    1. Voepel-Lewis T, Wagner D, Tait AR. Leftover prescription opioids after minor procedures: an unwitting source for accidental overdose in children. JAMA Pediatr 2015;169:497–8. 10.1001/jamapediatrics.2014.3583
    1. Bailey JE, Campagna E, Dart RC; RADARS System Poison Center Investigators. The underrecognized toll of prescription opioid abuse on young children. Ann Emerg Med 2009;53:419–24. 10.1016/j.annemergmed.2008.07.015
    1. National Conference of State Legislatures. Prescribing Policies: States Confront Opioid Overdose Epidemic. (accessed 29 Jan 2018).
    1. Johnson EJ, Goldstein D. Do Defaults save lives? Science 2003;302:1338–9. 10.1126/science.1091721
    1. Patel MS, Day S, Small DS, et al. . Using default options within the electronic health record to increase the prescribing of generic-equivalent medications: a quasi-experimental study. Ann Intern Med 2014;161(10 Suppl):S44–S52. 10.7326/M13-3001
    1. Zwank MD, Kennedy SM, Stuck LH, et al. . Removing default dispense quantity from opioid prescriptions in the electronic medical record. Am J Emerg Med 2017;35:1567–9. 10.1016/j.ajem.2017.04.002
    1. Von Korff M, Korff MV, Saunders K, et al. . De facto long-term opioid therapy for noncancer pain. Clin J Pain 2008;24:521–7. 10.1097/AJP.0b013e318169d03b
    1. Hooten WM, St Sauver JL, McGree ME, et al. . Incidence and Risk Factors for Progression From Short-term to Episodic or Long-term Opioid Prescribing: A Population-Based Study. Mayo Clin Proc 2015;90:850–6. 10.1016/j.mayocp.2015.04.012
    1. Cantrill SV, Brown MD, Carlisle RJ, et al. . Clinical policy: critical issues in the prescribing of opioids for adult patients in the emergency department. Ann Emerg Med 2012;60:499–525. 10.1016/j.annemergmed.2012.06.013
    1. Epstein H, Hansen C, Thorson D. A protocol for addressing acute pain and prescribing opioids. Minn Med 2014;97:47–51.
    1. Chou R, Qaseem A, Snow V, et al. . Diagnosis and treatment of low back pain: a joint clinical practice guideline from the American College of Physicians and the American Pain Society. Ann Intern Med 2007;147:478–91. 10.7326/0003-4819-147-7-200710020-00006
    1. Harris K, Curtis J, Larsen B, et al. . Opioid pain medication use after dermatologic surgery: a prospective observational study of 212 dermatologic surgery patients. JAMA Dermatol 2013;149:317–21.
    1. Bates C, Laciak R, Southwick A, et al. . Overprescription of postoperative narcotics: a look at postoperative pain medication delivery, consumption and disposal in urological practice. J Urol 2011;185:551–5. 10.1016/j.juro.2010.09.088
    1. Maughan BC, Hersh EV, Shofer FS, et al. . Unused opioid analgesics and drug disposal following outpatient dental surgery: A randomized controlled trial. Drug Alcohol Depend 2016;168:328–34. 10.1016/j.drugalcdep.2016.08.016
    1. Paulozzi LJ, Strickler GK, Kreiner PW, et al. . Controlled Substance Prescribing Patterns-Prescription Behavior Surveillance System, Eight States, 2013. MMWR Surveill Summ 2015;64:1–14. 10.15585/mmwr.ss6409a1
    1. Bohnert AS, Logan JE, Ganoczy D, et al. . A detailed exploration into the association of prescribed opioid dosage and overdose deaths among patients with chronic pain. Med Care 2016;54:435–41. 10.1097/MLR.0000000000000505
    1. Dunn KM, Saunders KW, Rutter CM, et al. . Opioid prescriptions for chronic pain and overdose: a cohort study. Ann Intern Med 2010;152:85–92. 10.7326/0003-4819-152-2-201001190-00006
    1. U.S. Department Of Health And Human Services. National pain strategy: a comprehensive population health-level strategy for pain. Washington, DC, 2016.
    1. Healthcare Cost and Utilization Project (HCUP). Beta clinical classifications software (CCS) for ICD-10-CM/PCS. (accessed 29 Jan 2018).
    1. Donner A, Klar N. Pitfalls of and controversies in cluster randomization trials. Am J Public Health 2004;94:416–22. 10.2105/AJPH.94.3.416
    1. Randolph AG, Haynes RB, Wyatt JC, et al. . Users' Guides to the Medical Literature: XVIII. How to use an article evaluating the clinical impact of a computer-based clinical decision support system. JAMA 1999;282:67–74.
    1. Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA 2003;290:1624–32. 10.1001/jama.290.12.1624
    1. Greevy R, Lu B, Silber JH, et al. . Optimal multivariate matching before randomization. Biostatistics 2004;5:263–75. 10.1093/biostatistics/5.2.263
    1. Meyer BD. Natural and quasi-experiments in economics. Journal of Business & Economic Statistics 1995;13:151–61.
    1. Angrist J, Pischke J. Mostly harmless econometrics: an empiricist’s companion. Princeton, NJ: Princeton University Press, 2009.
    1. Ryan AM, Burgess JF, Dimick JB. Why we should not be indifferent to specification choices for difference-in-differences. Health Serv Res 2015;50:1211–35. 10.1111/1475-6773.12270
    1. Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. Proc Fifth Berkeley Symp Math Statist Prob 1967;1:221–3.
    1. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48:817–38. 10.2307/1912934
    1. Fazzari MJ, Kim MY, Heo M. Sample size determination for three-level randomized clinical trials with randomization at the first or second level. J Biopharm Stat 2014;24:579–99. 10.1080/10543406.2014.888436
    1. Linder JA, Rigotti NA, Schneider LI, et al. . An electronic health record-based intervention to improve tobacco treatment in primary care: a cluster-randomized controlled trial. Arch Intern Med 2009;169:781–7. 10.1001/archinternmed.2009.53
    1. Sequist TD, Gandhi TK, Karson AS, et al. . A randomized trial of electronic clinical reminders to improve quality of care for diabetes and coronary artery disease. J Am Med Inform Assoc 2005;12:431–7. 10.1197/jamia.M1788
    1. Campbell MK, Piaggio G, Elbourne DR, et al. . Consort 2010 statement: extension to cluster randomised trials. BMJ 2012;345:e5661.

Source: PubMed

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