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The American Journal of Managed Care June 2019
Reports of the Demise of Chemotherapy Have Been Greatly Exaggerated
Bruce Feinberg, DO; Jonathan Kish, PhD, MPH; Igoni Dokubo, MD; Jeff Wojtynek, PharmD; and Kevin Lord, PhD, MHS
From the Editorial Board: Patrick H. Conway, MD, MSc
Patrick H. Conway, MD, MSc
Association of Decision Support for Hospital Discharge Disposition With Outcomes
Winthrop F. Whitcomb, MD; Joseph E. Lucas, PhD; Rachel Tornheim, MBA; Jennifer L. Chiu, MPH; and Peter Hayward, PhD
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Anita Chawla, PhD; Kimberly Westrich, MA; Angela Dai, BS, BA; Sarah Mantels, MA; and Robert W. Dubois, MD, PhD
Understanding Price Growth in the Market for Targeted Oncology Therapies
Jesse Sussell, PhD; Jacqueline Vanderpuye-Orgle, PhD; Diana Vania, MSc; Hans-Peter Goertz, MPH; and Darius Lakdawalla, PhD
Cancer Care Spending and Use by Site of Provider-Administered Chemotherapy in Medicare
Andrew Shooshtari, BS; Yamini Kalidindi, MHA; and Jeah Jung, PhD
Will 2019 Kick Off a New Era in Person-Centered Care?
Ann Hwang, MD; and Marc A. Cohen, PhD
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Ross G. Hewitt, MD; Debra Williams, EdD; Richard Adule; Ira Feldman, MPS; and Moe Alsumidaie, MBA, MSF
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Jennifer N. Goldstein, MD, MSc; Merwah Shinwari, BS; Paul Kolm, PhD; Daniel J. Elliott, MD, MSCE; William S. Weintraub, MD; and LeRoi S. Hicks, MD, MPH
Changing Electronic Formats Is Associated With Changes in Number of Laboratory Tests Ordered
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Kathleen Carey, PhD; Omid Ameli, MD, MPH; Brigid Garrity, MS, MPH; James Rothendler, MD; Howard Cabral, PhD; Christine McDonough, PhD; Michael Stein, MD; Robert Saper, MD, MPH; and Lewis Kazis, ScD
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Improving Quality Measure Maintenance: Navigating the Complexities of Evolving Evidence
Thomas B. Valuck, MD, JD; Sarah Sampsel, MPH; David M. Sloan, PhD; and Jennifer Van Meter, PharmD

Improving Quality Measure Maintenance: Navigating the Complexities of Evolving Evidence

Thomas B. Valuck, MD, JD; Sarah Sampsel, MPH; David M. Sloan, PhD; and Jennifer Van Meter, PharmD
An exploration of potential negative effects from delays in measure maintenance when changes in clinical evidence affect measure use found that delays may affect patient care and outcomes.

To be effective, healthcare quality measures must communicate clear, evidence-based standards to promote improved quality of care and outcomes. When the evidentiary foundation for measures changes, revisions must be made quickly and communicated clearly; otherwise, measures can confuse providers who are trying to reconcile the evidence-based care they deliver with outdated measure specifications. Outdated measures can also affect clinical decision making, potentially harming patients if the measures promote care that is not the best treatment for their condition according to the most recent evidence.

This case study focuses on 2 measures for which the evidence base changed, yet implementation of revised specifications lagged and subsequently affected the payment programs in which the measures are used. The case study is shared to motivate collaboration among quality measurement stakeholders to advance shared responsibility for timely measure updates when evidence changes and to avoid confusion in measure implementation.

Multiple parties share the responsibility for ensuring that measures are updated and aligned with evidence and practice recommendations. Issues of coordination among clinical experts, measure developers or stewards, and program implementers, including health plans, are not unique to any steward or implementer. The timing of new evidence releases and guidelines for the condition, service, or product being measured will always vary regardless of the measure update cycle for any one program. Changes to measure maintenance processes cannot totally negate these underlying challenges but can mitigate their impact. This case study calls for a national conversation to address opportunities for measure update process improvements.

Am J Manag Care. 2019;25(6):e188-e191
Takeaway Points

Quality measures play an essential role in driving improvement; however, measurement is fraught with challenges that make consistent application difficult. In this article, we review how approval of the heart failure drug sacubitril/valsartan tablets introduced complexities in the measure life cycle for 2 value-based payment heart failure measures.
  • Effective measures must communicate clear, evidence-based standards of quality care so that providers can understand their role in achieving quality.
  • Inaccurate measures can be confusing when reconciling evidence-based quality of care with outdated specifications.
  • Three principles should be considered to promote coordination between measure developers and implementers: communication, flexibility, and streamlining.
Quality measures are important tools for guiding, assessing, and rewarding improvement in healthcare delivery and outcomes. For measures to be effective, they must communicate clear, evidence-based standards of quality care so that those providers being measured can understand what they must do to meet those standards. When the evidentiary foundation for a measure changes, revisions to the measure must be made quickly and communicated clearly; otherwise, measures can confuse providers who are trying to reconcile the evidence-based quality of care they deliver with outdated specifications. Beyond frustrating providers, outdated measures can affect clinical decision making, potentially harming patients or prohibiting patients from receiving treatment that could optimize intended clinical outcomes. This case study examines the paths of 2 measures used in value-based payment programs where specification revisions lagged significantly behind clinical guideline updates and subsequently generated frustration and confusion from misalignment.

The responsibility for ensuring that measures are updated and aligned with evidence and practice recommendations is shared by multiple parties. Responsibility may begin with measure stewards but ultimately requires action on the part of program implementers, including health plans, to ensure that providers are reporting on currently accurate specifications. Ideally, the timelines and processes of the measure stewards who maintain measures and those who implement measures in programs are coordinated. However, synchronization between measure stewards and implementers has proved difficult to achieve in practice. Stewards have their own update schedules and rigorous processes for evaluating evidence, guidelines, and feedback about technical issues with their measures. Likewise, each program has its own schedule and means for receiving updates from different stewards, often on an annual or semiannual basis. Measures may come in multiple formats (eg, claims based or electronic health record [EHR] based), each of which must be updated and implemented in slightly different ways, adding further complexity. With so many variables at play, updates needed to keep measures current can be delayed, frustrating providers rather than facilitating higher-quality care.

This case study illustrates how an update to 2 measures prompted by new guidelines was delayed, resulting in implementation confusion. The case focuses on 2 heart failure measures included in Medicare programs, but it is relevant across the healthcare system, including to health plans. The examples presented could be replicated anytime the evidence base for a measure is significantly altered. In addition, there may be multiple opportunities to improve coordination between measurement stakeholders, and this case study attempts to provoke collaboration on a broader scale to shorten the time lag between evidence emergence and application to measures. The case highlights the necessity for measure stewards and implementers to communicate more effectively, remain flexible, and streamline processes.


Medicare Quality Measurement Programs

The Medicare Access and CHIP Reauthorization Act of 2015 requires eligible clinicians to report quality data to CMS to earn financial incentives under the Quality Payment Program (QPP). One track of the QPP is the Merit-based Incentive Payment System (MIPS). MIPS participants earn a performance-based payment adjustment, in part based on their performance on quality measures. Clinicians can choose which quality measures to report from a measure set selected by CMS. The overarching objective of the QPP is to improve Medicare outcomes by rewarding clinicians for higher-quality care and outcomes.

Updating the measure sets for value-based payment programs, such as MIPS, is a complex process that is primarily dictated by CMS’ annual rulemaking and standardized specification release. Given the complexity of the measure update processes, coordination between measure stewards and CMS is critical to ensure that updates are made in a timely manner and communicated to the providers participating in the quality programs.

Heart Failure Measures

The MIPS measure set includes 2 measures designed to promote better outcomes for patients with reduced cardiac ejection fraction by assessing use of appropriate drug therapy (see Table). These measures are designated as 0066 and 0081 by the National Quality Forum (NQF). Specifically, the measures address the prescription of angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs), which are drug classes indicated for lowering blood pressure and increasing blood flow in patients with heart failure.1 Both measures have been endorsed by the NQF.2,3 The NQF endorses and updates measures through a voluntary consensus process that includes rigorous endorsement criteria.

The data for both measures are reportable to CMS through qualified registries or qualified clinical data registries, such as the American College of Cardiology (ACC) PINNACLE clinical data registry.4 The data for measure 0081 are also reportable as an electronic clinical quality measure (eCQM),5 meaning that the data can be obtained and reported through a clinician’s EHR system. Both measures are reviewed on regular cycles by their respective stewards to update their specifications when necessary.

For eCQM reporting, the lists of drugs that are considered part of the ACE inhibitor and ARB classes are maintained in value sets. Value sets include the specific codes (eg, RxNorm, Current Procedural Terminology, and International Classification of Diseases, Tenth Revision) used to calculate the measure and are housed in the Value Set Authority Center (VSAC).6 However, for registry reporting, the lists of drugs are coded within the registry itself, and registries do not always use the value sets found within the VSAC.

The New Evidence and Need for Reassessment

In July 2015, the FDA approved a new drug, sacubitril/valsartan tablets, for use in patients with chronic heart failure (New York Heart Association class II-IV) and reduced ejection fraction. In a clinical trial, sacubitril/valsartan was found to significantly reduce the risk of cardiovascular death and hospitalizations related to heart failure. This new class of drug is called an angiotensin receptor antagonist–neprilysin inhibitor (ARNI). An ARNI is an ARB with an additional active ingredient to further relax blood vessels and decrease sodium and fluid in the body. Sacubitril/valsartan is the only product currently in the ARNI class.7

As is customary as evidence evolves, in May 2016, the ACC/American Heart Association (AHA) Task Force on Clinical Practice Guidelines issued a focused update on pharmacological therapy for heart failure with reduced ejection fraction, based on study data that had been recently published.8 The guideline update indicated that ARNIs could be appropriately prescribed in place of ACE inhibitors or ARBs. The recommendation was class I (strong) and was supported by moderate-quality evidence.

Guideline updates often motivate review of existing performance measures to ensure alignment of the evidentiary foundation between measures and clinical guidelines. The ACC/AHA focused update presented an opportunity for the stewards of measures 0066 and 0081 to consider a specification revision. If ARNIs are appropriate treatment in place of ACE inhibitors or ARBs under the new guidelines, how should an ARNI prescription be captured by clinical quality measures?

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