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The American Journal of Managed Care March 2019
Fragmented Ambulatory Care and Subsequent Emergency Department Visits and Hospital Admissions Among Medicaid Beneficiaries
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Common Elements in Opioid Use Disorder Guidelines for Buprenorphine Prescribing
Timothy J. Atkinson, PharmD, BCPS, CPE; Andrew J.B. Pisansky, MD, MS; Katie L. Miller, PharmD, BCPS; and R. Jason Yong, MD, MBA
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Common Elements in Opioid Use Disorder Guidelines for Buprenorphine Prescribing

Timothy J. Atkinson, PharmD, BCPS, CPE; Andrew J.B. Pisansky, MD, MS; Katie L. Miller, PharmD, BCPS; and R. Jason Yong, MD, MBA
This article presents a synthesis of opioid use disorder guidelines and a framework to link them to claims data and recognize higher-quality practice, monitor outcomes, and individualize intervention.

Any approach to a societal problem as pervasive as the uncontrolled growth of OUD requires access to effective OUD treatment that is guided by the intent to improve and individualize care. As stated by ASAM, “In circumstances in which the Practice Guideline is being used as the basis for regulatory or payer decisions, improvement in quality of care should be the goal.”12 Payers, both public and private, have wrestled with the appropriate response to the growing crisis surrounding OUD. Access to care must be assured while mitigating not only excessive healthcare utilization by those engaging in opioid and other substance abuse, but also the effects of unscrupulous practices and/or providers. In order to appropriately understand and respond to the complex needs of individuals with OUD and deploy evidence-based treatment, health systems must develop the ability to identify and track patients in a highly reliable and reproducible manner.

Individualized care requires payer identification of members; however, evaluation of their healthcare utilization has proven a challenge for federal governmental agencies. Utilization assessment systems have inadequately addressed this problem and have not achieved sufficient granularity to create improved outcomes at the patient level. The Drug Abuse Warning Network system, for instance, has provided population-level data for OUD and motivated policy changes through its final published year in 2011. However, these data were aggregated over several years prior to publication and were therefore descriptive public health metrics, but they lacked the specificity and timeliness needed to affect patient outcomes. Similarly, the National Electronic Injury Surveillance System data set has been used to track adverse drug events, but this system is used to retrospectively query data sets that have been deidentified in order to provide population-level statistics. The NSDUH has been performed since the 1970s and examines population trends in drug abuse, but it cannot be harnessed to individualize treatment. In short, the federal government’s tracking systems are designed for trend analysis and are not capable of identifying at-risk patients even if healthcare privacy laws permitted them to act on personally identifiable health information. These limitations have shaped the solutions enacted by the federal government, which focus their efforts on harm-reduction strategies, such as syringe service programs, naloxone distribution, treatment coordination, expanding treatment capacity for OUD, and encouraging the use of opioid prescribing guidelines.

Although visibility of claims data may be limited to individual payers for privacy reasons, the proposed outcomes presented herein are consistent with the evaluation criteria set forth by the National Quality Forum (NQF) for healthcare performance measures.23 Buprenorphine-specific NQF measures include measurement of adherence and access to buprenorphine for OUD. We incorporated these measures and expanded our analyses to include relapse, treatment adherence, psychosocial interventions, case management, high-risk patients, and buprenorphine dose. The relative importance of these outcomes varies; for example, signs of quality treatment would include consistent patient adherence to follow-up (demonstrating treatment stability with their provider), incorporating psychosocial interventions, and providing care coordination services. However, the utility of these outcome measures is somewhat dependent on the presence or absence of relapse, because these outcomes provide insight into potential causes of relapse or areas where treatment may need to be adjusted to decrease the rate of relapse. One might imagine having the ability to compare providers by the relapse rate of their practice. Such a measurement is considerably more meaningful than arbitrary rating systems or risk reports and may assist payers in decreasing practice variation by preferentially routing patients to higher-quality providers, who may then be rewarded with preferred provider status and reimbursement. Not only would this identify higher-quality providers, but underperforming providers would be incentivized to focus on practice improvement to qualify for inclusion and decrease punitive or costly remediation.

When searching for answers to high rates of relapse despite adjustments in treatment, the high-risk criteria begin to provide insight into potential reasons for this pattern by identifying the highest-risk patients. Revisiting risk assessment in this manner may help providers determine which patients may not be appropriate for OBOT and for whom referral to a more intensive treatment program may be necessary to address their individual needs. Similarly, the buprenorphine dose criteria identify high-dose prescribing of buprenorphine, which is controversial. Payers can utilize this information to identify unscrupulous prescribers who are clear outliers, with the majority of their patients receiving high-dose buprenorphine, potentially indicating “pill mill” operations. By evaluating the treatment patterns and outcomes of all insured members within a provider’s patient panel, quality assessment and comparison among healthcare providers becomes a reality.


There are several notable limitations to this type of analysis. The limitations of utilizing medical and pharmacy claims data to measure healthcare quality are well known and include reliance on correct coding of medical claims and that detection is restricted to enrolled members, which may not accurately reflect use of secondary insurance or cash pay for healthcare services or prescriptions.


This paper presents a consolidation of the evidence base for treatment of individuals with OUD in the OBOT setting. It is an outline of common elements among guidelines that, when linked with claims data, could be used to create a framework to follow patients and tailor proprietary solutions in order to provide better care and outcomes for these individuals. The opioid crisis continues to change, characterized by a shift away from prescription abuse toward illicit opioid abuse, driving the increases in opioid overdose deaths and increasing rates of OUD.5 This change therefore requires a paradigm shift in treatment strategy, as efforts to educate stakeholders, restrict prescribing, and legislate solutions to reduce prescription opioid abuse will all be less effective when attempting to address illicit abuse. We propose that any viable solution must be capable of tracking individuals who are engaging in opioid abuse or have diagnosed OUD, facilitate access to high-quality providers through outreach and care coordination, and prospectively measure treatment outcomes for these patients. Only payers have the permitted access to individual claims data and the authority to control reimbursement with their strategic healthcare partnerships. All other stakeholders should consider supporting their efforts through tax incentives and grant funding where possible.


The authors thank John Donahue, chief executive officer of axialHealthcare, Inc, for his passion and commitment to this project from its infancy and the vision to see that it could transform the measurement and management of OUD to improve outcomes for patients and partners in managed care.

Author Affiliations: axialHealthcare, Inc (TJA, KLM), Nashville, TN; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital (AJBP, RJY), Boston, MA.

Source of Funding: axialHealthcare, Inc, funded this project and was involved in data collection, analysis, and approval of the final manuscript.

Author Disclosures: Dr Atkinson reports that he is a member of the Daiichi Sankyo Scientific Advisory Board and Purdue Pharma Epidemiology Scientific Advisory Board, was a presenter at the 2017 American College of Clinical Pharmacy National Conference, and is a consultant for axialHealthcare, Inc, which funded the manuscript and conducted the study. Dr Pisansky reports preparing this manuscript as part of paid consulting to axialHealthcare. Dr Miller is employed by axialHealthcare. The remaining author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (TJA, AJBP, RJY); acquisition of data (TJA, KLM); analysis and interpretation of data (TJA, AJBP, KLM, RJY); drafting of the manuscript (TJA, AJBP); critical revision of the manuscript for important intellectual content (TJA, AJBP, KLM, RJY); provision of patients or study materials (TJA); obtaining funding (KLM); and supervision (TJA, RJY).

Address Correspondence to: Timothy J. Atkinson, PharmD, BCPS, CPE, axialHealthcare, Inc, Cummins Station, 209 S 10th Ave S #332, Nashville, TN 37203. Email:

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22. Stewart KA, Bradley KWV, Zickafoose JS, et al. Care coordination for children with special needs in Medicaid: lessons from Medicare.Am J Manag Care. 2018;24(4):197-202.

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