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.
Objectives: This study sought to formulate a consolidation of guidelines representing best practices related to office-based opioid treatment (OBOT) of opioid use disorder (OUD) using buprenorphine. It also demonstrates how a set of evidence-based guidelines may be linked with claims data to leverage analytic techniques that drive cost-effective, positive health outcomes.
Study Design: Literature review of US and international guidelines for OBOT using buprenorphine for OUD.
Methods: The study conducted a review of currently available US and several international guidelines from 2009 to 2018 published on OUD and the use of buprenorphine in OBOT. Guidelines were consolidated based on common elements. The process of correlating common elements with available commercial and state Medicaid claims data is described, including which elements are amenable to analysis along with relative complexity.
Results: Seven guidelines met inclusion criteria and are presented as 3 tables, organized by clinical themes and phase of care related to OBOT use of buprenorphine for OUD. Themes included establishing care, monitoring treatment stability and engagement, and nonpharmacologic treatment to improve outcomes. Areas of agreement and divergence between guidelines are highlighted. Specific components are identified as they relate to metrics of interest to public and private payers.
Conclusions: Among US and international guidelines for treatment of OUD, common themes are readily identified and may indicate agreement in regard to interventions. Linking pharmacy and medical billing claims data to evidence-supported best practices provides public and private payers the ability to track individual patients, facilitate high-quality care, and monitor outcomes.
Am J Manag Care. 2019;25(3):e88-e97Takeaway Points
Despite widespread efforts by private- and public-sector healthcare providers, organizations, and policy makers, the opioid epidemic in the United States has shown few signs of abating. Widespread opioid prescribing has resulted in many patients developing opioid use disorder (OUD), which is described by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, as “a problematic pattern of opioid use leading to clinically significant impairment or distress” with at least 2 specific criteria within a 12-month period.1 Based on cross-sectional data from between 2006 and 2011 from the National Survey on Drug Use and Health (NSDUH), the number of individuals in the United States with OUD was estimated to be 2.1 million.2 Furthermore, 2016 NSDUH results showed that approximately 3.3 million Americans engaged in nonmedical use of prescription opioids within the past month, suggesting that the number of additional individuals at risk for developing OUD is substantial.3
The CDC reported 2016 data showing 42,249 opioid-related drug overdose deaths, driven primarily by an increase in deaths involving heroin and synthetic opioids believed to be fentanyl derivatives.4 This marks a continuing trend of a rising death toll from opioid-related deaths over the preceding decade, with growing focus on illicit drug use and less on opioid prescribing alone.5 Even with the ongoing emphasis on healthcare provider education, increased regulations limiting prescription opioids, and expansion of prescribing limits for buprenorphine, OUD remains undertreated and prevalence continues to climb, secondary to illicit opioid use.
OUD and opioid-related adverse events including overdose are associated with significant societal costs, morbidity, and mortality. The economic burden of opioid overdose, abuse, and dependence includes medical and OUD treatment costs, lost work productivity, and the societal costs of those who enter the criminal justice system. Using 2013 data, this total economic burden was estimated at $78.5 billion annually, with $28.9 billion associated with healthcare and substance abuse treatment costs.6 However, a 2017 report by the US Council of Economic Advisors states that previous figures underestimated the total healthcare burden of OUD by not including the economic costs of overdose deaths and illicit use, and their analysis reflects a cost of $504 billion in 2015.7 Results of analyses using commercial insurance databases have shown that opioid abuse results in excess healthcare costs per patient of between $10,627 and $14,810 annually.8,9 A 2015 review of the available literature using data from 2009 to 2014 regarding increased healthcare costs for opioid users suggested that the average cost to a payer for a patient with OUD is $23,000 to $25,000 per year, of which increased costs attributable to OUD are approximately $15,000 per patient annually.10
OUD is fundamentally a public health problem, and its high economic burden makes it a source of financial incentive for both commercial and public (ie, Medicare or state Medicaid) health plans and accountable care organizations—all hereafter referred to as payers—to facilitate adequate treatment of patients with OUD. In early 2017, the chief executive officers of several prominent payer organizations approached this study’s sponsor specifically requesting assistance to monitor OUD treatment and identify providers of high-quality care using claims data. The comprehensive review presented herein represents the initial analysis critical to the creation of a surveillance solution for OUD using data analytics. High-quality care guidance must begin with a consolidation of available evidence to identify and implement best practices, yet multiple national and international stakeholder organizations publish OUD treatment guidelines. Practice pattern variation is inevitable when each organization promotes the recommendations of its own subject matter experts. Nevertheless, harmonious definitions can be leveraged to identify and track individuals with OUD to provide them with appropriate treatment resources, prioritize early intervention, and promote sustained recovery. Effectively achieving this aim requires completing several crucial tasks: (1) tracking members who are at risk for OUD or currently diagnosed with OUD, (2) coordinating care and facilitating access to high-quality providers, and (3) measuring treatment outcomes for these patients as well as determining the impact of providers.
Our proposed solution for these problems employs a summary of widely recognized treatment guidelines and clinically validated best practices. We present a review of the major US, and several international, OUD treatment guidelines where consensus among common elements leads to best practice and areas of discrepancy may account for practice pattern variation. By culling the shared elements across a number of guidelines, we are able to focus on aspects that are measurable and actionable for a payer that desires an evidence-based, consistent approach to managing OUD. Tracking actionable aspects of the consolidated common elements among these guidelines requires linking them to relevant medical billing and pharmacy claims data. This framework provides a foundation for defining the criteria by which at-risk patients are identified and the quality of treatment outcomes is assessed. Payers can utilize this data management and solutions approach to identify high-value providers and subsequently create a network of higher-quality and accessible recovery centers to which patients with OUD may be directed; this would thus drive high-quality outcomes and lower healthcare costs among individuals with OUD.
PubMed, Embase, and Google Scholar were searched using the terms opioid use disorder, medication assisted treatment, opioid addiction, opioid dependence, and buprenorphine prescribing, filtering search results for systematic reviews and guidelines. Fifteen guidelines were initially identified, including 10 from the United States and 5 international. US guidelines were included if they specifically addressed office-based opioid treatment (OBOT) with buprenorphine and were excluded if they were written for individual states or regions, health conditions, or age groups. International guidelines were included if medications and treatment paradigms resembled practice in the United States. For example, international guidelines were excluded if they promoted pharmaceutically prepared heroin as a treatment option or if prescribing and dispensing of medication-assisted treatment (MAT) did not require a medical license. Guidelines were independently reviewed by the authors and categorized according to common themes modeled after the CDC’s “Common Elements in Guidelines for Prescribing Opioids for Chronic Pain.”11 An initial list of common elements for MAT and OBOT was created, with topics commonly proposed by the individual guidelines retained and those that seemed to be contextually unique to a particular guideline omitted. The collection of common elements was individually verified through searches of available state Medicaid and commercial medical and pharmacy claims data provided by axialHealthcare, Inc. Simple measurement strategies were sufficient for many of the common elements because they were readily identifiable in claims data. However, others required creation of definitions and application of specialized analytics.
Seven guidelines met inclusion criteria and were included in the summary of common elements. The 4 from the United States were those of the American Society of Addiction Medicine (ASAM),12 Substance Abuse and Mental Health Services Administration,13,14 Veterans Affairs/Department of Defense,15 and Federation of State Medical Boards.16 Three additional guidelines met inclusion criteria and represented the international community, including those of the World Health Organization,17 the British Columbia Centre on Substance Abuse,18 and the Australian Alcohol and Drug Information Services.19 The common elements are presented here as 3 separate tables and organized by themes that emerged from the MAT guidelines.
Table 112-19 compares recommendations when patients establish care with an MAT provider in an OBOT setting. Guidelines are consistent in recommending a comprehensive medical history, physical examination, mental health assessment, and independent confirmation of OUD diagnosis. A social history and a psychosocial needs assessment are also consistently recommended. Tracking these recommended assessments is not possible in claims data alone without access to the electronic health record. A unique feature in US guidelines is patient selection for OBOT versus an opioid treatment program (OTP), for which an assessment of appropriateness is recommended. OBOT is generally recommended for more stable patients who are motivated and more adherent to treatment. Severe psychiatric disorders, polydrug use, and multiple failed treatment attempts may indicate that a patient is more appropriate for an environment with increased supervision and observed dosing (ie, OTP). Although international guidelines do not reflect the structure of OBOT, they assert that more stable patients may be candidates for “carry” or take-home doses similar to OTPs, which generally require daily observed administration of medications. As the risk and complexity of treatment increase, patient placement is critical, and detailed resources like ASAM’s criteria exist to provide additional guidance.12 Examination of the common elements in Table 112-19 reveals that only a few binary outcomes (eg, new patient visits, prescriptions, or laboratory tests for OUD) can be tracked through claims data, and these are generally available with a single International Classification of Diseases, Tenth Revision, code or procedure code.
Criteria associated with treatment follow-up and monitoring are found in Table 2.12-19 There is consistency in recommended frequency of follow-up for office visits and prescription renewals: Patients are seen frequently at first and then less often over time as adherence to MAT is demonstrated. Frequency of visits may require periodic adjustment in response to changes in patient stability. Significant disagreement exists between guidelines regarding appropriate dosing of buprenorphine. As multiple buprenorphine formulations are now available with varying potency and amounts of naloxone, it should be noted that references to specific doses in the guidelines use buprenorphine-naloxone (Suboxone) as the reference standard. Guidelines agree that most patients achieve stable maintenance doses between 8 mg and 16 mg daily; however, the appropriateness of daily doses higher than 24 mg is more controversial. Some guidelines cite a lack of evidence for improved efficacy and increased risk of diversion at doses greater than 24 mg daily, whereas others indicate that some patients may require up to 32 mg daily.
Monitoring recommendations vary considerably between guidelines, with several specifically recommending verifying abstinence with the state prescription drug monitoring program (PDMP) and utilizing pill counts, but the majority of available guidelines do not provide specific recommendations in this area. Pill counts and verification PDMP queries are not available in claims data. (Please see eAppendix [available at ajmc.com] for additional details.) Guidelines are consistent in recommending urine drug testing (UDT) at baseline and frequently throughout treatment, but they begin to vary in recommendations for additional testing frequency throughout treatment. UDT utilization is readily available in claims data, but these data generally lack information on the outcome and interpretation of UDT. The recommended duration of MAT is relatively consistent across guidelines, where time limits are discouraged and patient preference is emphasized, among other factors. However, longer treatment courses are associated with improved outcomes—one guideline recommends at least 1 year of treatment14 and another recommends a minimum of 2 years.18 Follow-up visits and prescription information, including prescribed dose, are readily available in medical and pharmacy claims data that enable tracking of individual patients during treatment. Duration of treatment can be assessed, but accuracy is highly dependent on length of enrollment with a specific payer.
Table 312-19 examines common elements of nonpharmacologic treatment that are recommended to improve outcomes. Psychosocial interventions and case management are strongly recommended in every guideline. These criteria encompass numerous data elements that are available in claims. The guidelines generally agree that basic needs such as housing, employment, family, and legal concerns can significantly affect treatment and providers should be aware of available community resources. Case management or care coordination services providing assistance are increasingly utilized and/or covered by many benefits providers.20-22 Although there is consensus regarding the value of psychosocial interventions in treatment, the specific therapies recommended vary considerably. Mutual help or 12-step facilitation programs are embraced and recommended within some guidelines14 and not considered equivalent to professional psychotherapy treatment in others.12 In addition, these groups are anonymous and patient involvement is not visible in claims. Relapse is a critical indicator of successful treatment because OUD is a chronic relapsing disorder, with guideline consensus that treatment should be intensified with adjustments to follow-up frequency, length of prescription renewals, and psychosocial interventions in the event of relapse. Tracking relapse through medical claims data is elusive, but achievable, with accurate definitions of measurable related events captured through intelligent design of analytic engines. After multiple relapses despite treatment adjustments, the appropriate treatment setting may need to be reconsidered along with overall risk status. High-risk MAT patients include those with comorbid alcohol abuse, benzodiazepine use, or dependence on a sedative, hypnotic, or anxiolytic, due to their additive central nervous system depressant effects and increased risk of overdose.
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: firstname.lastname@example.org.REFERENCES
1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Association; 2013.
2. Kirson NY, Shei A, Rice JB, et al. The burden of undiagnosed opioid abuse among commercially insured individuals. Pain Med. 2015;16(7):1325-1332. doi: 10.1111/pme.12768.
3. Key substance use and mental health indicators in the United States: results from the 2016 National Survey on Drug Use and Health. Substance Abuse and Mental Health Services Administration website. samhsa.gov/data/sites/default/files/NSDUH-FFR1-2016/NSDUH-FFR1-2016.htm. Published September 2017. Accessed September 9, 2018.
4. Seth P, Rudd RA, Noonan RK, Haegerich TM. Quantifying the epidemic of prescription opioid overdose deaths. Am J Public Health. 2018;108(4):500-502. doi: 10.2105/AJPH.2017.304265.
5. Guy GP Jr, 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(26):697-704. doi: 10.15585/mmwr.mm6626a4.
6. Florence CS, Zhou C, Luo F, Xu L. The economic burden of prescription opioid overdose, abuse, and dependence in the United States, 2013. Med Care. 2016;54(10):901-906. doi: 10.1097/MLR.0000000000000625.
7. Council of Economic Advisers. The underestimated cost of the opioid crisis: executive summary. White House website. whitehouse.gov/sites/whitehouse.gov/files/images/The%20Underestimated%20Cost%20of%20the%20Opioid%20Crisis.pdf. Published November 2017. Accessed February 17, 2019.
8. Rice JB, Kirson NY, Shei A, et al. Estimating the costs of opioid abuse and dependence from an employer perspective: a retrospective analysis using administrative claims data. Appl Health Econ Health Policy. 2014;12(4):435-446. doi: 10.1007/s40258-014-0102-0.
9. Kirson NY, Scarpati LM, Enloe CJ, Dincer AP, Birnbaum HG, Mayne TJ. The economic burden of opioid abuse: updated findings. J Manag Care Spec Pharm. 2017;23(4):427-445. doi: 10.18553/jmcp.2017.16265.
10. Oderda GM, Lake J, Rüdell K, Roland CL, Masters ET. Economic burden of prescription opioid misuse and abuse: a systematic review. J Pain Palliat Care Pharmacother. 2015;29(4):388-400. doi: 10.3109/15360288.2015.1101641.
11. Common elements in guidelines for prescribing opioids for chronic pain. CDC website. cdc.gov/drugoverdose/pdf/common_elements_in_guidelines_for_prescribing_opioids-20160125-a.pdf. Published December 15, 2015. Accessed July 12, 2018.
12. The national practice guideline for the use of medications in the treatment of addiction involving opioid use. American Society of Addiction Medicine website. asam.org/docs/default-source/practice-support/guidelines-and-consensus-docs/asam-national-practice-guideline-supplement.pdf. Published June 1, 2015. Accessed March 11, 2017.
13. Medications for opioid use disorder: for healthcare and addiction professionals, policymakers, patients, and families. Substance Abuse and Mental Health Services Administration website. store.samhsa.gov/system/files/sma18-5063fulldoc.pdf. Published February 2018. Accessed October 17, 2018.
14. McNicholas L. TIP 40: Clinical Guidelines for the Use of Buprenorphine in the Treatment of Opioid Addiction: Treatment Improvement Protocol (TIP) Series 40. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2004. lib.adai.washington.edu/clearinghouse/downloads/TIP-40-Clinical-Guidelines-for-the-Use-of-Buprenorphine-in-the-Treatment-of-Opioid-Addiction-54.pdf. Accessed August 5, 2017.
15. Department of Veterans Affairs; Department of Defense. VA/DoD clinical practice guideline for the management of substance use disorders. Department of Veterans Affairs website. www.healthquality.va.gov/guidelines/MH/sud/VADoDSUDCPGRevised22216.pdf. Published December 2015. Accessed August 5, 2017.
16. Model policy on DATA 2000 and treatment of opioid addiction in the medical office. Federation of State Medical Boards website. fsmb.org/siteassets/advocacy/policies/model-policy-on-data-2000-and-treatment-of-opioid-addiction-in-the-medical-office.pdf. Published April 2013. Accessed August 5, 2017.
17. Guidelines for the Psychosocially Assisted Pharmacological Treatment of Opioid Dependence. Geneva, Switzerland: World Health Organization; 2009. who.int/substance_abuse/publications/opioid_dependence_guidelines.pdf. Accessed August 5, 2017.
18. British Columbia Centre on Substance Use; British Columbia Ministry of Health. A guideline for the clinical management of opioid use disorder. British Columbia Centre on Substance Use website. bccsu.ca/wp-content/uploads/2017/06/BC-OUD-Guidelines_June2017.pdf. Published June 5, 2017. Accessed August 5, 2017.
19. Gowing L, Ali R, Dunlop A, Farrell M, Linzeris N. National Guidelines for Medication-Assisted Treatment of Opioid Dependence. Canberra, Australia: Commonwealth of Australia; 2014. nationaldrugstrategy.gov.au/internet/drugstrategy/Publishing.nsf/content/AD14DA97D8EE00E8CA257CD1001E0E5D/$File/National_Guidelines_2014.pdf. Accessed August 5, 2017.
20. Haas LR, Takahashi PY, Shah N, et al. Risk-stratification methods for identifying patients for care coordination. Am J Manag Care. 2013;19(9):725-732.
21. Di Capua P, Clarke R, Tseng CH, et al. The effect of implementing a care coordination program on team dynamics and the patient experience. Am J Manag Care. 2017;23(8):494-500.
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.
23. Measure evaluation criteria. National Quality Forum website. qualityforum.org/docs/measure_evaluation_criteria.aspx#top. Published 2013. Accessed July 17, 2018. Available at Wayback Machine at: web.archive.org/web/20140929173120/http:/www.qualityforum.org/docs/measure_evaluation_criteria.aspx#top.