User-centred clinical decision support to implement emergency department-initiated buprenorphine for opioid use disorder: protocol for the pragmatic group randomised EMBED trial

Edward R Melnick, Molly Moore Jeffery, James D Dziura, Jodi A Mao, Erik P Hess, Timothy F Platts-Mills, Yauheni Solad, Hyung Paek, Shara Martel, Mehul D Patel, Laura Bankowski, Charles Lu, Cynthia Brandt, Gail D'Onofrio, Edward R Melnick, Molly Moore Jeffery, James D Dziura, Jodi A Mao, Erik P Hess, Timothy F Platts-Mills, Yauheni Solad, Hyung Paek, Shara Martel, Mehul D Patel, Laura Bankowski, Charles Lu, Cynthia Brandt, Gail D'Onofrio

Abstract

Introduction: The goal of this trial is to determine whether implementation of a user-centred clinical decision support (CDS) system can increase adoption of initiation of buprenorphine (BUP) into the routine emergency care of individuals with opioid use disorder (OUD).

Methods: A pragmatic cluster randomised trial is planned to be carried out in 20 emergency departments (EDs) across five healthcare systems over 18 months. The intervention consists of a user-centred CDS integrated into ED clinician electronic workflow and available for guidance to: (1) determine whether patients presenting to the ED meet criteria for OUD, (2) assess withdrawal symptoms and (3) ascertain and motivate patient willingness to initiate treatment. The CDS guides the ED clinician to initiate BUP and facilitate follow-up. The primary outcome is the rate of BUP initiated in the ED. Secondary outcomes are: (1) rates of receiving a referral, (2) fidelity with the CDS and (3) rates of clinicians providing any ED-initiated BUP, referral for ongoing treatment and receiving Drug Addiction Act of 2000 training. Primary and secondary outcomes will be analysed using generalised linear mixed models, with fixed effects for intervention status (CDS vs usual care), prespecified site and patient characteristics, and random effects for study site.

Ethics and dissemination: The protocol has been approved by the Western Institutional Review Board. No identifiable private information will be collected from patients. A waiver of informed consent was obtained for the collection of data for clinician prescribing and other activities. As a minimal risk implementation study of established best practices, an Independent Study Monitor will be utilised in place of a Data Safety Monitoring Board. Results will be reported in ClinicalTrials.gov and published in open-access, peer-reviewed journals, presented at national meetings and shared with the clinicians at participating sites via a broadcast email notification of publications.

Trial registration number: NCT03658642; Pre-results.

Keywords: change management; health informatics; quality in health care; substance misuse.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Schematic diagram of parallel, cluster randomised study design.
Figure 2
Figure 2
Clinical algorithm for ED initiation of buprenorphine. ED, emergency department; SL, sublingual.
Figure 3
Figure 3
Graphical user interface of the user-centred CDS EMBED intervention. CDS, clinical decision support; COWS, Clinical Opiate Withdrawal Scale; ED, emergency department; EMBED, Emergency Department-Initiated Buprenorphine for Opioid Use Disorder; OUD, opioid use disorder.
Figure 4
Figure 4
Number of study sites required as a function of coefficient of variation for site size assuming an ICC of 0.03. ICC, intraclass correlation coefficient.

References

    1. Rudd RA, Aleshire N, Zibbell JE, et al. . Increases in drug and opioid overdose deaths--United States, 2000-2014. MMWR Morb Mortal Wkly Rep 2016;64:1378–82. 10.15585/mmwr.mm6450a3
    1. Buchanich JM, Balmert LC, Burke DS. Exponential Growth Of The USA Overdose Epidemic. bioRxiv. 2017. .
    1. Center for Behavioral Health Statistics and Quality. Key Substance Use and Mental Health Indicators in the United States: Results from the 2015 National Survey on Drug Use and Health. Substance Abuse and Mental Health Services Administration. 2016. .
    1. Seth P, Scholl L, Rudd RA, et al. . Overdose deaths involving opioids, cocaine, and psychostimulants - United States, 2015-2016. MMWR Morb Mortal Wkly Rep 2018;67:349–58. 10.15585/mmwr.mm6712a1
    1. Substance Abuse and Mental Health Services Administration. Drug Abuse Warning Network. 2011: National Estimates of Drug-Related Emergency Department Visits. 2013. .
    1. Vivolo-Kantor AM, Seth P, Gladden RM, et al. . Vital signs: trends in emergency department visits for suspected opioid overdoses - United States, July 2016-September 2017. MMWR Morb Mortal Wkly Rep 2018;67:279–85. 10.15585/mmwr.mm6709e1
    1. Sullivan LE, Fiellin DA. Narrative review: buprenorphine for opioid-dependent patients in office practice. Ann Intern Med 2008;148:662–70.
    1. Larochelle MR, Bernson D, Land T, et al. . Medication for opioid use disorder after nonfatal opioid overdose and association with mortality: a cohort study. Ann Intern Med 2018;169 10.7326/M17-3107
    1. Kakko J, Svanborg KD, Kreek MJ, et al. . 1-year retention and social function after buprenorphine-assisted relapse prevention treatment for heroin dependence in Sweden: a randomised, placebo-controlled trial. Lancet 2003;361:662–8. 10.1016/S0140-6736(03)12600-1
    1. Mattick RP, Breen C, Kimber J, et al. . Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst Rev 2014:CD002207 10.1002/14651858.CD002207.pub4
    1. D’Onofrio G, O’Connor PG, Pantalon MV, et al. . Emergency department-initiated buprenorphine/naloxone treatment for opioid dependence: a randomized clinical trial. JAMA 2015;313:1636–44. 10.1001/jama.2015.3474
    1. Goodnough A. This E.R. Treats Opioid Addiction on Demand. That’s Very Rare. (accessed 2 May 2019).
    1. Walsh L. Buprenorphine Waiver Management | SAMHSA - Substance Abuse and Mental Health Services Administration. (accessed 17 Jul 2018).
    1. Duber HC, Barata IA, Cioè-Peña E, et al. . Identification, management, and transition of care for patients with opioid use disorder in the emergency department. Ann Emerg Med 2018;72 10.1016/j.annemergmed.2018.04.007
    1. Houry DE, Haegerich TM, Vivolo-Kantor A. Opportunities for prevention and intervention of opioid overdose in the emergency department. Ann Emerg Med 2018;71:688–90. 10.1016/j.annemergmed.2018.01.052
    1. Kawamoto K, Houlihan CA, Balas EA, et al. . Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005;330:765 10.1136/bmj.38398.500764.8F
    1. Garg AX, Adhikari NK, McDonald H, et al. . Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293:1223–38. 10.1001/jama.293.10.1223
    1. Sittig DF, Wright A, Osheroff JA, et al. . Grand challenges in clinical decision support. J Biomed Inform 2008;41:387–92. 10.1016/j.jbi.2007.09.003
    1. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004;11:104–12. 10.1197/jamia.M1471
    1. Ash JS, Sittig DF, Campbell EM, et al. . Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc 2007:26–30.
    1. Levin S, France DJ, Hemphill R, et al. . Tracking workload in the emergency department. Hum Factors 2006;48:526–39. 10.1518/001872006778606903
    1. Melnick ER, Nielson JA, Finnell JT, et al. . Delphi consensus on the feasibility of translating the ACEP clinical policies into computerized clinical decision support. Ann Emerg Med 2010;56:317–20. 10.1016/j.annemergmed.2010.03.006
    1. Sirajuddin AM, Osheroff JA, Sittig DF, et al. . Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support. Effective CDS is essential for addressing healthcare performance improvement imperatives. J Healthc Inf Manag 2009;23:38–45.
    1. 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;17:493–501. 10.1136/jamia.2010.005264
    1. Horsky J, Phansalkar S, Desai A, et al. . Design of decision support interventions for medication prescribing. Int J Med Inform 2013;82:492–503. 10.1016/j.ijmedinf.2013.02.003
    1. Gellert G, Webster L, Gillean J, et al. . Should US doctors embrace electronic health records? BMJ 2017;356:j242 10.1136/bmj.j242
    1. Roland M, Torgerson DJ. What are pragmatic trials? BMJ 1998;316:285.
    1. Murray DM, Varnell SP, Blitstein JL. Design and analysis of group-randomized trials: a review of recent methodological developments. Am J Public Health 2004;94:423–32.
    1. Li F, Lokhnygina Y, Murray DM, et al. . An evaluation of constrained randomization for the design and analysis of group-randomized trials. Stat Med 2016;35:1565–79. 10.1002/sim.6813
    1. Loudon K, Treweek S, Sullivan F, et al. . The PRECIS-2 tool: designing trials that are fit for purpose. BMJ 2015;350:h2147 10.1136/bmj.h2147
    1. Zuidgeest MGP, Welsing PMJ, van Thiel G, et al. . Series: Pragmatic trials and real world evidence: Paper 5. Usual care and real life comparators. J Clin Epidemiol 2017;90:92–8. 10.1016/j.jclinepi.2017.07.001
    1. Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials 2007;28:182–91. 10.1016/j.cct.2006.05.007
    1. Hemming K, Haines TP, Chilton PJ, et al. . The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ 2015;350:h391 10.1136/bmj.h391
    1. Richesson RL, Hammond WE, Nahm M, et al. . Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc 2013;20:e226–31. 10.1136/amiajnl-2013-001926
    1. Hylan TR, Von Korff M, Saunders K, et al. . Automated prediction of risk for problem opioid use in a primary care setting. J Pain 2015;16:380–7. 10.1016/j.jpain.2015.01.011
    1. Carrell DS, Cronkite D, Palmer RE, et al. . Using natural language processing to identify problem usage of prescription opioids. Int J Med Inform 2015;84:1057–64. 10.1016/j.ijmedinf.2015.09.002
    1. Reardon JM, Harmon KJ, Schult GC, et al. . Use of diagnosis codes for detection of clinically significant opioid poisoning in the emergency department: A retrospective analysis of a surveillance case definition. BMC Emerg Med 2016;16:11 10.1186/s12873-016-0075-4
    1. Yale School of Medicine. ED-Initiated Buprenorphine | Emergency Medicine. (accessed 24 Aug 2018).
    1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub 2013.
    1. Wesson DR, Ling W. The Clinical Opiate Withdrawal Scale (COWS). J Psychoactive Drugs 2003;35:253–9.
    1. Walsh L. Special Circumstances for Providing Buprenorphine | SAMHSA - Substance Abuse and Mental Health Services Administration. (accessed 13 Aug 2018).
    1. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999;89:1322–7.
    1. Glasgow RE, Lichtenstein E, Marcus AC. Why don’t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health 2003;93:1261–7.
    1. LeBlanc A, Ruud KL, Branda ME, et al. . The impact of decision aids to enhance shared decision making for diabetes (the DAD study): protocol of a cluster randomized trial. BMC Health Serv Res 2012;12:130.
    1. National Institutes of Health (NIH). Research methods resources. (accessed 13 Aug 2018).
    1. Eldridge SM, Ashby D, Kerry S. Sample size for cluster randomized trials: effect of coefficient of variation of cluster size and analysis method. Int J Epidemiol 2006;35:1292–300.
    1. Raab GM, Butcher I. Balance in cluster randomized trials. Stat Med 2001;20:351–65.
    1. Gong MN, Schenk L, Gajic O, et al. . Early intervention of patients at risk for acute respiratory failure and prolonged mechanical ventilation with a checklist aimed at the prevention of organ failure: protocol for a pragmatic stepped-wedged cluster trial of PROOFCheck. BMJ Open 2016;6:e011347.
    1. Preisser JS, Lohman KK, Rathouz PJ. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random. Stat Med 2002;21:3035–54.
    1. National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics. The prevention and treatment of missing data in clinical trials: National Academies Press, 2010.
    1. Lachin JM. Statistical considerations in the intent-to-treat principle. Control Clin Trials 2000;21:167–89.
    1. Molenberghs G, Thijs H, Jansen I, et al. . Analyzing incomplete longitudinal clinical trial data. Biostatistics 2004;5:445–64.
    1. McKinney RE, Beskow LM, Ford DE, et al. . Use of altered informed consent in pragmatic clinical research. Clin Trials 2015;12:494–502. 10.1177/1740774515597688
    1. . Office for Human Research Protections (OHRP). 45 CFR 46. 2016. (accessed 16 Aug 2018).
    1. Lantos JD, Wendler D, Septimus E, et al. . Considerations in the evaluation and determination of minimal risk in pragmatic clinical trials. Clin Trials 2015;12:485–93.
    1. Suffoletto B, Lynch M, Pacella CB, et al. . The effect of a statewide mandatory prescription drug monitoring program on opioid prescribing by emergency medicine providers across 15 hospitals in a single health system. J Pain 2018;19:430–8.
    1. ICMJE. Recommendations | Defining the Role of Authors and Contributors. (accessed 8 Mar 2019).

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