Measures that matter

Measures That Matter

The Center’s aim in developing Measures That Matter for primary care practices is to better align assessment and payment policies with what patients and clinicians know to be valuable, to reduce burden, and to reduce burnout.

Measures that matter

Measures That Matter

The Center’s aim in developing Measures That Matter for primary care practices is to better align assessment and payment policies with what patients and clinicians know to be valuable, to reduce burden, and to reduce burnout.

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Clinical Quality Measures are a mechanism for assessing observations, treatment, processes, experience, and outcomes of patient care that relate to one or more quality aims for health care such as effective, safe, efficient, patient-centered, equitable and timely care. Measuring the quality of patient care helps to drive improvements in health care and can:

  • Improve patient-centered care
  • Identify areas for quality improvement
  • Identify differences in care/outcomes among various populations
  • Improve care coordination between health care providers
  • Align physician assessment and payment to produce high value care
  • Reduce physician burden
  • Reduce high-cost behaviors
  • Enable assessment and comparison of health systems

Well-designed and supported primary care is an important source of improved outcomes in high performing health systems even though it may produce lower disease-based clinical quality measures. This has been called the Paradox of Primary Care. This does not mean that clinical quality measures or payment schemes that use them are bad, but it does suggest a need for better alignment between measures that matter and providing sufficient resources to address them.

The Center aims to assess and promote the most meaningful clinical quality measures in several health care sectors starting with primary care. The Center aims to produce comprehensiveness measures, continuity measures as well as total cost of care and low-value care measures. Additionally, the American Board of Family Medicine Foundation funded research with the Larry A. Green Center that produced the Patient Centered Primary Care Measure (PCPCM), a Patient Reported Outcome Measure (PROM), which won the National Quality Forum’s (NQF) 2019 Patient-Reported Outcomes Abstract Award. The research underpinning this Patient Reported Outcome (PRO) demonstrated close association with continuity and comprehensiveness, and strong endorsement by both patients and providers.

The Person-Centered Primary Care Measure (PCPCM)

The Person-centered Primary Care Measure (PCPCM) is a patient-reported measure of exemplary primary care that has been developed by the Larry A. Green Center based on extensive development work with patients, clinicians and health care payers. The measure is the winner in the Patient-Reported Outcomes category of the National Quality Forum (NQF) Next-Generation Innovator Abstract Award.

The PCPCM focuses attention and support on the integrating, personalizing, and prioritizing functions that patients and clinicians say are important. A measure based on these principles may reduce both the de-personalization experienced by patients, and the measurement burden, burnout and crisis of meaning experienced by clinicians.

The PCPCM uses a survey to ask patients to assess 11 distinct yet highly interrelated items regarding their assessment of the care they receive. The 11 items were developed with input from hundreds of patients and physicians, and are associated with better personal and population health, equity, quality and costs.

The PCPCM is now featured in the PRIME Registry Measure Set and available for use as a MIPS (Merit-based Incentive Payment System) measure.

The Continuity of Care Measure

Continuity of Care was developed in collaboration with the Robert Graham Center and uses the Bice-Boxerman Continuity of Care Primary Care Physician Measure. At a patient-level, Bice-Boxerman Continuity of Care is a measure that considers the dispersion of primary care visits across providers, such that patients with higher scores have most of their primary care visits to the same provider or a small number of providers while those lower scores see a larger number providers.

Continuity of Care Measure is also featured in the PRIME Registry Measure Set, and available for use as a MIPS (Merit-based Incentive Payment System) measure.

Comprehensiveness

The comprehensiveness measure is currently in the conceptualization phase in collaboration with the Robert Graham Center. Comprehensiveness is lauded as 1 of the 5 core virtues of primary care, but its relationship with outcomes is unclear. When measuring associations between variations in comprehensiveness of practice among family physicians and healthcare utilization and costs for their Medicare beneficiaries, we found that increasing family physician comprehensiveness of care, especially as measured by claims measures, is associated with decreasing Medicare costs and hospitalizations.

Low Value Care

The Low-Value Care measure is also in the conceptualization phase in collaboration with Mount Sinai. Low-Value Care is an attempt to give feedback on modifiable behavior as a mechanism to improve primary care’s well documented moderation of total health care spending. Key to the measurement and reporting of total cost of care is our effort to develop and test Low-Value Care measures that can help clinicians identify specific, modifiable behaviors. Testing of this measure sets up capacity for long-term evaluation of feedback on practicing clinicians and their behaviors and on total cost of care.

Phase 1: Conceptualization

Information Gathering

Measure development begins with information gathering – the measure developer conducts an environmental scan, develops a business case and requests input from a broad group of stakeholders including patients. The developer then narrows down the concepts to specific measures.

Phase 2: Specification

Draft Specifications

After the information gathering phase, we begin to draft the measure specifications. Measure specifications provide the comprehensive details that allow the measure to be collected and implemented consistently, reliably, and effectively. The specifications identify the population, the recommended practice, the expected outcome and determine how it will be measured. They also may include age ranges, performance time period and allowable values for medical conditions or procedures, code systems, descriptions.

Measure technical specifications will address the following questions: How will the measure be named? Does the name mean anything to people when they read it? Do they understand what that measure is about? What would the setting of the measure be (e.g., ambulatory office)? How will the data be collected? These questions have to be answered before testing begins.

Harmonization is all about reducing burden. Look at measures currently in practice and determine if there are places where our measure could be harmonized with the existing measure(s).

Phase 3: Testing

Measure Testing

Measure testing assesses the suitability of the quality measure’s technical specifications and acquires empirical evidence to help assess the strengths and weaknesses of a measure. Measure testing involves testing the components of the quality measure such as the data elements, the scales (and items in the scales if applicable), and the performance score.

There are two parts to measure testing: alpha and the beta testing.

Alpha testing helps identify early issues and often begins as early as the conceptualization step and is repeated during the development of the measure specifications.

Beta testing, which is also referred to as field testing, generally occurs after the initial specifications have been developed, and strives for representative sample sizes – multiple sites/settings. The primary purpose for beta testing is to understand the usability of the measure and to test the scientific acceptability of the measure.

After the testing ends, the results are analyzed with a return to the specification phase, or even the conceptualization phase, to rework the measure before testing again.

The PRIME registry is ABFM’s Qualified Clinical Data Registry (QCDR) and serves as our measure testing bed.

Phase 4: Implementation

Measure Implementation

The measure is then submitted for NQF endorsement (not a requirement for use by CMS) and for use in the 18 CMS quality reporting and payment programs.

What’s the difference between submitting to CMS versus submitting to NQF?

The National Quality Forum (NQF) submission is about the endorsement process where a consensus-based entity reviews the measure using five evaluation criteria to assess the measure on its own merit and independent of a CMS program. It essentially gives it that stamp of approval, and so endorsement/NQF submission is separate from CMS implementation. NQF endorsement is valued for measures in CMS programs, but it is not a requirement.

The CMS implementation process takes the measure from being in development to being actively used in 18 of the CMS quality payment programs (QPP).

Phase 5: Use, Continuing Evaluation, and Maintenance

Re-evaluation

This step ensures that the measure continues to add value to quality reporting measurement programs and that its construction continues to be sound. The regular reevaluation of measures is vital as the science and other factors are always changing (e.g., development of new clinical guidelines, new technologies for data collection, discovering a better way to calculate measure results). Continually reviewing the measure will ensure it remains relevant and meaningful. Measures that stop being useful are retired.

Interaction Among Measure Lifecycle Phases

The Measure development lifecycle is not a linear process. Once the measure is conceptualized, it can move throughout the various phases in the measure development lifecycle.

Decision Criteria

The following decision criteria throughout the measure development cycle ensures a measure meets the applicable standards before moving to the next phase:

Importance to measure and report—including analysis of opportunities for improvement such as reducing variability in comparison groups or disparities in healthcare related to race, ethnicity, age, or other classifications.

Scientific acceptability—including analysis of reliability, validity, and exclusion appropriateness.

Feasibility—including evaluation of reported costs or perceived burden, frequency of missing data, and description of data availability.

Usability—including planned analyses to demonstrate that the measure is meaningful and useful to the target audience. This may be accomplished by the Technical Expert Panel (TEP) reviewing the measure results such as means and detectable differences, dispersion of comparison groups, etc. More formal testing, if requested by CMS, may require assessment via structured surveys or focus groups to evaluate the usability of the measure (e.g., clinical impact of detectable differences, evaluation of the variability among groups).

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