Options for Recognizing and Accommodating Social Risk Factors Helen Burstin, MD, MPH Chief Scientific Officer, NQF SNP Alliance Fall Meeting October 14, 2016

NQF: Lead. Prioritize. Collaborate. Accelerate development of needed measures Reduce, select and endorse measures Drive implementation of prioritized measures Facilitate feedback on what works and what doesn’t Drive measurement that matters to improve quality, safety & affordability

SES Adjustment: At Least Two Divergent Views Adjustment for SES necessary for comparative performance Adjustment for SES will mask disparities

Influence of Healthcare and Risk Factors Outcome due to healthcare and patient-related risk factors Health status Time Healthcare Patient-related factors

Potential Risk Factors Potential risk factors influence outcomes through a variety of pathways: Clinical factors (e.g., diagnoses, conditions and severity) Socioeconomic factors (e.g., poverty, education) Demographic characteristics (e.g., age, sex, ethnicity, language) Health-related behaviors and activities (e.g., tobacco, diet) Community factors (e.g., access to services, poverty)

Findings National Academy of Medicine Report Thus, all other things being equal, the performance of a given health care system (in terms of quality, outcomes, and cost) can undoubtedly be affected by the social composition of the population it serves. What is clear at this point in time is that health literacy and social risk factors have been shown to influence health care use, costs, and health care outcomes in Medicare beneficiaries.

NQF Policy Change The NQF Board approved a two-year trial period prior to a permanent change in NQF policy. Under the new policy, adjustment of measures for SDS factors is no longer prohibited. During the trial period, if SDS adjustment is determined to be appropriate for a given measure, NQF will endorse one measure with specifications to compute: SDS-adjusted measure Non-SDS version of the measure (clinically adjusted only) to allow for stratification of the measure Prior to the trial period, NQF had prohibited consideration of sociodemographic factors in risk adjustment, preferring stratification based on these variables

NQF Policy Change: Trial Period Each measure must be assessed individually to determine if SDS adjustment is appropriate. Not all measures should be adjusted for SDS factors (e.g., central line infection would not be adjusted) Need conceptual basis (logical rationale, theory) and empirical evidence The recommendations apply to any level of analysis including health plans, facilities, and individual clinicians.

Trial Period Update Trial period launched in April 2015 Standing Committees consider whether such measures are appropriately adjusted for SDS factors as part of their evaluation. Newly-submitted measures (e.g., family experience with coordination of care) Previously-endorsed measures undergoing maintenance Measures with conditional endorsement (cost/resource use, readmissions) Measures undergoing endorsement maintenance review during the trial period will also be considered “fair game” for consideration of SDS adjustment. Ad hoc reviews of endorsed measures can be requested by any party, as long is there is adequate evidence to justify the review Ad hoc requests are typically made when : the evidence supporting the measure has changed (e.g. evidence of conceptual relationship between socioeconomic and other social demographic factors (SDS) and the measure’s performance) implementation of the measure results in unintended consequences material changes have been made to the measure, including changes to the measure’s setting and data source.

Preliminary Takeaways and Challenges Using available SES data, many measures with clear conceptual basis for SES adjustment have not demonstrated large effect of SES on outcomes after consideration of clinical risk factors More robust data at the patient and community level are needed to support risk adjustment, as well as stratification to drive targeted improvement Need continued work on risk adjustment and ongoing monitoring for potential unintended consequences Limited availability of patient-level data --Available proxies such as five-digit zip code may not be granular enough or may otherwise not be adequate Concerns about factors selected/analyzed to date --Developers have noted difficulty in accessing nine-digit zip code or census block data. However, Standing Committees have raised concerns that five-digit zip code is not specific enough. --Dual eligible beneficiary status is readily available but may not be granular enough to show meaningful differences. --The appropriateness of including race as variable has been questioned. Call for a more prescriptive approach Historically, NQF has not been prescriptive in its approach to the variables included in risk adjustment models. Measure developers are responsible for the selection of the variables included in the model and for defending the selection of those variables to the Standing Committees. This approach applies to both the selection of clinical and sociodemographic factors. Questions have arose about whether NQF should establish guidelines for what SDS factors should be considered to ensure a more consistent and thorough trial period.

Updates on Related Efforts IMPACT Act of 2014: By October 2016: HHS must report to Congress on the impact of SES on quality and resource use in Medicare using measures from existing data By October 2019: HHS must report to Congress on the impact on SES on quality and resource use in Medicare use measures from other data sources ASPE analyses by CMS program using available SES adjustors (dual eligibility, low income subsidy) were presented at AcademyHealth research meeting: Significant impact of social risk in many CMS programs Importance of unmeasured clinical complexity

Next Generation Measurement

Next Generation Risk Adjustment Move beyond reliance on claims-based risk adjustment Better account for unmeasured clinical complexity: Patient frailty Risk-based grouping of multiple chronic conditions Risk differences within clinical conditions Patient complexity: frailty, disability, poor functional status, and multiple chronic conditions

Purpose of Measurement: Improve Healthcare Quality

The Quality Imperative Not everything that counts can be counted, and not everything that can be counted counts ~Albert Einstein (William Bruce Cameron) But….. You can’t improve what you don’t measure ~ W. Edwards Deming

Helen Burstin, MD, MPH, FACP hburstin@qualityforum.org @HelenBurstin