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Population Health Measures

The United States (U.S.) spends nearly twice the average of other Organization for Economic Co-

operation and Development (OECD) countries expenditures on health, but has the lowest average life

expectancy, performs worse than average on many population health outcomes, and has more

outcome-related disparities compared to peer OECD countries (OECD, 2019 ; Tikkanen, & Abrams,

2020, January ). A recent analysis of 2020 Commonwealth Fund International Health Policy Survey

data found lower income adults in the U.S. fare relatively worse on affordability and access to primary

care and income-related disparities across domains than those in ten other high-income countries

(Doty, Tikkanen, FitzGerald, Fields, & Williams, 2020 ).

The U.S. and CMS acknowledge the importance of quality measurement and that quality reporting and

incentive programs have improved outcomes and how measured entities deliver care. Additionally,

population health measurement is critical to improving the nation’s overall health. As such, CMS is

committed to four principles for improving population health:

• Establish health equity as a strategic priority.

• Empower and enable measured entities and other stakeholders to take a data-driven approach

to measuring and improving population health.

• Leverage state

innovation and local leadership through partnerships.

• Address all determinants of health including clinical, social, behavioral, and environmental

factors.

References to states include the District of Columbia and the territories.

1 Introduction to Population Health ........................ 2

2 Approach to Population Health

Measurement and Improvement .......................... 3

3 Considerations for Developing, Evaluating,

and Maintaining Population Health

Measures ............................................................... 5

3.1 Measure Conceptualization ......................... 5

3.2 Measure Specification .................................. 8

3.3 Measure Testing ......................................... 10

3.4 Measure Implementation .......................... 11

3.5 Measure Use, Continuing Evaluation, and

Maintenance .............................................. 12

4 Key Points ............................................................ 12

References ................................................................ 13

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This document provides a high-level overview and definition of population health. It addresses

considerations for population health quality measures with respect to the Measure Lifecycle. As

population health measures evolve, so will this document.

1 INTRODUCTION TO POPULATION HEALTH

CMS defines population health as health behaviors and outcomes of a broad group of individuals,

including the distribution of such outcomes affected by the contextual factors within the group. The

definition is a slight variation from the widely cited 2003 Kindig & Stoddard definition of population

health

adopted by many including the U.S. Department of Health and Human Services (HHS), Office of

the National Coordinator for Health Information Technology (ONC) (ONC, 2020 , p.7). Note that CMS’s

definition does not delineate how to define the groups themselves. Therefore, when developing

population health measures clarity of the denominator is critical for measurement. The definition also

does not delineate the contextual factors. The current approach for commonly published summary

measures of population health, such as mortality rates, primarily uses geopolitical areas. However,

other population identifiers may include panels of patients (e.g., persons assigned to a specific

measured entity or measured entity team), members of a health plan, or members of a specific social

demographic (e.g., women of color). Social determinants of health (SDOH) (e.g., economic stability,

education, social and community context, health and health care, and neighborhood and built

environment), and social risk factors (e.g., food and housing insecurity, lack of transportation), also

impact population health significantly (Green & Zook, 2019 ).

CMS defines a population health measure as a broadly applicable indicator that reflects the quality of a

group’s overall health and well-being. Examples of measure topics include access to care, clinical

outcomes, coordination of care and community services, health behaviors, preventive care and

screening, and utilization of health services. Without guidance as to how to define a group, these

working definitions reflect important distinctions between population health measures and quality

measures. The current intent of quality measures is to assess the quality, cost, or efficiency of particular

services to individuals by healthcare setting, so there is an attachment of quality measures to particular

services and specific types of measured entities. Population health measures would not necessarily have

these restrictions. Population health measures are more expansive in that they include what is

happening outside the direct healthcare system.

Section 1890 of the Social Security Act (the Act) requires the consensus-based entity (CBE), currently

National Quality Forum (NQF), to report annually on its work to Congress and the HHS Secretary. Section

§1890(b)(5)(A) of the Act also requires the CBE to include descriptions of matters related to convening

multistakeholder groups to provide input on national priorities for improvement in population health.

The 2019 report of the NQF Prevention and Population Health Standing Committee identified six

population health measure gaps in the NQF portfolio:

• measures that detect differences in quality across institutions or in relation to certain

benchmarks, but also differences in quality among populations or social groups

• measures that assess access to care

• measures that assess environmental factors

• measures that address food insecurity

“the health outcomes of a group of individuals, including the distribution of such outcomes within the group”

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• measures that address language and literacy (e.g., health literacy)

• measures that address social cohesion

2 APPROACH TO POPULATION HEALTH MEASUREMENT AND IMPROVEMENT

The population health of a group is dependent upon the interplay of several factors (e.g., economic,

social, environmental, cultural, behavioral), of which clinical care represents only a portion

(Stoto, 2014 ). As such, population health depends on a multiplicity of factors, many of which are not

within CMS’s traditional role to address as a healthcare services payer. Because of this, the achievement

of measurement and improvement in population health depends upon innovation, collaboration, and

coordination across stakeholders. These include local, tribal, state, national government agencies, and

the community, including but not limited to, members of the care team, payors, hospitals, and nursing

homes in delivering care to the target population(s), as well as community members and organizations.

Figure 1 reflects this overlap of roles in improving population health and showing the joint influence on

population health outcomes by healthcare, government, the community, and the private sector.

Figure 1. Population Health and the Triple Aim

No single entity in the public or private sector has sole capacity or responsibility for overall population

health improvement. Multiple organizations, public and private, perform public health activities. As

opposed to other sectors with interorganizational partnerships and alliances, these public health

activities are largely uncoordinated, leading to gaps, inefficiencies, and inequities (Mays & Scutckfield,

2010 ). Systems thinking—understanding the collective effect of multiple actors and actions—is

necessary to organize and sustain population health improvement (Woulfe, Oliver, Zahner, & Siemering,

2010 ). There must be “a shared measurement system.” By extension, a multi-sector approach is

essential to addressing the multiple determinants of population health. Emerging partnerships between

Population health improvement

requires a multisector approach.

Government agencies, including tribal

agencies, measured entities and

payors, community service providers,

and the private sector can join

together to improve the health of

every person and population in their

communities, together through

measurement, innovation,

collaboration, and improvement to

achieve the triple aim goals of better

care, smarter spending, and healthier

people and communities.

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measured entities, federal, state, tribal, and local health agencies, community service providers, and

multiple other organizations (e.g., education systems and justice system), and the private sector can

help call attention to underlying problems, shift resources to increase returns on investments, and

sustain population-level improvements in health.

Peter Drucker, among others, stated that “you cannot improve what you do not measure.”

Stoto (2014 ) noted measurement is critical to improving population health. The Institute of Medicine

(IOM), now known as the National Academy of Medicine, said “Without a strong measurement

capability, the nation cannot learn what initiatives and programs work best, resources cannot be guided

toward the most promising strategies, and there is little ability to promote accountability in results”

(IOM, 2013a , p. 2).

Parrish (2010 ) identified three approaches to measuring population health:

• aggregating health outcome measurements made on people into summary statistics, such as

population averages or medians

• assessing the distribution of individual health outcome measures in a population and among

specific population subgroups

• measuring the function and well-being of the population or society itself, as opposed to

individuals

In 2013(b), the IOM , identified criteria for selecting and prioritizing measures of quality for use in

population health improvement:

Conditions or outcomes for measurement should be

• reflective of a high preventable burden

• actionable at the appropriate level for intervention

Measures should be

• timely

• usable for assessing various populations

• understandable

• methodologically rigorous

• accepted and harmonized

Of particular importance is CMS’s partnerships with state agencies, Medicaid in particular. Because all

healthcare is local, states are in the best position to assess the unique needs of their respective

Medicaid-eligible populations and drive reforms that result in better health outcomes. CMS is

committed to ushering in a new era for the federal and state Medicaid partnership, where states have

more freedom to design programs that meet the spectrum of diverse needs of their Medicaid

population. CMS aims to empower all states to advance the next wave of innovative solutions to

Medicaid’s challenges – solutions that focus on improving quality, accessibility, and outcomes in the

most cost-effective and equitable manner. Working together, through local organizations, tribal

agencies, state agencies, other parts of HHS (e.g., Indian Health Service), and federal partners such as

the Departments of Education, Agriculture, Transportation, Housing and Urban Development, and

Veterans’ Affairs, CMS believes they can collectively manage and improve population health for all

individuals and families served by CMS programs.

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Current CMS initiatives that seek to focus on improving population health and do not focus solely on the

quality of care rendered by a singular measured entity include

• Accountable Care Organizations (ACOs): ACOs are responsible for clinical care, costs, and

outcomes in a particular population of Medicare patients.

• Accountable Health Communities (AHCs) Model: AHCs address a gap between clinical care and

community services in the healthcare delivery system by testing whether identifying and

addressing the health-related social needs of Medicare and Medicaid beneficiaries through

screening, referral, and community navigation services will impact healthcare costs and reduce

healthcare utilization.

• Medicare Advantage Organizations (MAOs): MAOs are responsible for care in the population of

enrollees. MAOs may provide additional services not covered by traditional Medicare

(Tompkins, Higgins, Perloff, & Veselovskiy, 2013 ), such as transportation to appointments and

non-permanent home modifications to allow beneficiaries to age in place.

• Program of All-Inclusive Care for the Elderly (PACE) : PACE is a Medicare and Medicaid

program that provides comprehensive medical and social services to certain frail, elderly people

living in the community. PACE helps people meet their healthcare and social needs in the

community instead of going to a nursing home or other care facility.

3 CONSIDERATIONS FOR DEVELOPING, EVALUATING, AND MAINTAINING

POPULATION HEALTH MEASURES

3.1 MEASURE CONCEPTUALIZATION

Conceptualization of population health measures should identify opportunities for improvement at the

population level, rather than only seeking to identify gaps or variations in clinical care. Similarly,

information gathering and business case development should be at the population level to identify

health differences among populations, including disparities among subpopulations. During measure

conceptualization, measure developers should always consider whether to stratify and/or risk adjust the

measure(s). Measure developers should estimate the potential for population level improvement as well

as the potential benefits, burdens, and costs of achieving the population health goals.

Conceptualization of population health measures presents unique challenges for measure developers.

CMS notes that the current healthcare delivery system lacks an incentive structure to support local

problem-solving. For example, insurers do not usually pay measured entities and systems for their

efforts, e.g., screening for social risk factors or coordinating with local community providers and

governments beyond the clinical setting. Existing value-based purchasing programs do not reward

coordinated community health improvement efforts. Although population health improvement is a

priority goal, there are limited incentives tied to improvements or disincentives to worsening of

population health.

Although the focus of population health measures differs from clinical quality measures, measure

development should address alignment of the population measures with existing or potential measures

of clinical care and other drivers of population health improvement. These may include individual

behaviors, prevention, and social determinants of health, e.g., housing, transportation, food security,

economic stability, education, social and community context, access to healthcare, and neighborhood

environment.

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3.1.1 Environmental Scan

Where should measure developers go to find population health areas needing improvement? Measure

developers need to expand their environmental scan search criteria beyond their usual sources for

quality measures, e.g., the CMS Measures Inventory Tool and NQF Quality Positioning System . For

example, the OECD has 80 key indicators for population health and health system performance. These

key indicators use data from official national statistics to compare countries in terms of health status

and health-seeking behavior, access to and quality of healthcare, and the resources available for health

(OECD, 2019 ). These key indicators may provide insights to population health areas needing further

investigation and offer ideas for measure concepts.

3.1.1.1 Community Health Needs Assessment

The Patient Protection and Affordable Care Act of 2010 requires a community health needs assessment

(CHNA) and implementation strategy every three years for all Section 501(c)(3) nonprofit hospitals

(charitable hospitals) working with public health agencies and community members (Internal Revenue

Service [IRS], n.d. ). The IRS requires hospitals to submit their needs assessment and implementation

strategy with their IRS Form 990 and provide an annual description of how the hospital is addressing the

needs identified in their CHNA and implementation strategy. The IRS also requires hospitals to make

their CHNA and implementation strategy publicly available, which is usually only on the individual

hospital’s website. There is no oversight on the content of these CHNAs, no central repository, no state-

based repository, and no widely available measures focused on measuring the impact of the hospitals’

implementation strategy on the population of the community. These CHNAs and implementation

strategies have the potential to provide ideas for improvement opportunities in population health.

However, a review of the first CHNAs in Texas (Pennel, McLeroy, Burdine, Matarrita-Cascante, & Wang,

2016 ) found that few included health improvement or program goals and objectives. The Association

of State and Territorial Health Officials (ASTHO) website provides information on how to conduct

CHNAs. The ASTHO website also includes some case studies. These case studies provide a high-level

overview of the project, steps taken, results, lessons learned, long-term goals, and sustainability. All

these projects have the goals of improving coordination of community benefits and improving the

health of the community. Similar to the Texas study, these case studies lack measures to determine

whether the projects met their goals, especially for improving the health of the community. Measure

developers can review these case studies and identify measure concepts to assess whether the

community’s health has improved.

3.1.1.2 Research Social Determinants of Health and Social Risk Factors

Research into the effects of SDOH and social risk factors may also provide measure developers with

ideas for population health measure concepts. For example, a recent analysis of state and local

government spending on non-healthcare services (e.g., education, social services, environment, and

housing) noted an association between increased spending and lower infant mortality rates among

certain high-risk populations (Goldstein, Palumbo, Bellamy, Purtle, & Locke, 2020 ).

3.1.1.3 Healthy People

Healthy People 2020/2030 provides a set of broad population level goals and objectives broken down

into five categories: health conditions, health behaviors, populations, settings and systems, and social

determinants of health (HHS, Office of Disease Prevention and Health Promotion, n.d. ). These goals

and objectives may also be concepts for population health measure developers to consider. Healthy

People 2020 also addresses SDOH and includes a list of examples of social determinants and links to

other federal SDOH initiatives and resources. Healthy People 2030 identifies five SDOH domains each

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with multiple objectives: economic stability, education access and quality, healthcare access and

quality, neighborhood and built environment, and social and community context.

3.1.1.4 Existing Population Health Measures

Measure developers should look for existing measures currently identified as population health

measures, but these may not meet the CMS definition. In 2015, IOM identified several existing

measures that are indicators for population health, for example, life expectancy, overweight and obesity

rates, and teenage pregnancy rates. The usual databases of measures, e.g., CMS Measures Inventory

Tool (CMIT) and Quality Positioning System (QPS) , have historically not included these types of

measures. CMIT and QPS can also help identify existing measures that are proxies for population health,

e.g., communication between measured entities and patients for patient-centered care.

A recent scan focusing on existing clinician-level measures applicable to population health identified 248

measures. The reviewers then categorized the measures according to level of analysis using

clinician/clinician group, facility, health plan, integrated delivery system, and population. Some

measures applied to more than one level of analysis. Table 1 shows the number of measures identified

for each conceptual topic and the number of measures with population as the level of analysis (CMS,

Health Services Advisory Group, 2020). For brevity, Table 1 omits numbers for other levels of analysis.

Table 1. Existing Quality Measures at Population Level of Analysis

Conceptual Topic

Number of Quality Measures

Identified

Number of quality measures with

population as level of analysis

Access to care

Clinical outcomes

108

Coordination of care and

community services

Health behaviors

Preventive care and screening

Utilization of health services

3.1.2 Stakeholder Engagement

Given the broad nature of population health measures, it is critical to include community members and

organizations early in the development process. These would include local community organizations and

local governments that address social needs. Community organizations, e.g., soup kitchens and

homeless shelters, can provide important input as to the gaps in population health, the drivers of

improvement, and the benefits of improvement. Measure developers should adhere to the latest Person

and Family Engagement (PFE) toolkit at the earliest stages of developing population health measures.

3.1.3 Public Comment

Soliciting public comments for population health measures should occur frequently throughout the

Measure Lifecycle. The timing in which the measure developer solicits comments in the Measure

Lifecycle may be different than other quality measures. The measure developer may want to target

representatives of the populations and/or communities for measurement. These groups are not the

usual readers and responders to the Federal Register and websites requesting comments from the

public, so concerted targeting may be necessary.

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3.1.4 Technical Expert Panel (TEP)

The composition of a TEP for a population health measure may vary from other measure development

TEPs. The TEP should include representatives of the proposed population, group, and/or community for

measurement. Again, as with public comment, concerted outreach may be necessary.

3.2 MEASURE SPECIFICATION

The general processes for specifying population health measures are no different than other types of

measures. There are, however, some specification building blocks that need closer consideration. The

measure developer must distinguish population health measures from clinical quality measures.

3.2.1 Target/Initial population

3.2.1.1 Population level specification – geography-based

The geographic level of specification may include zip code, county, city, state, national, or other

geographically-based areas.

Example – all adults, 18 years and older, living in zip code 20500

3.2.1.2 Population level specification – patient panel-based

Denominator specifications may be beneficial for use in assessing population health improvement based

on the work of healthcare delivery systems. When specifying at a patient panel level, it is critical to avoid

limiting the measure denominator to only patients who receive specific services. In other words, the

denominator should include all patients in the patient panel without regard to particular services

rendered or patient encounters that occur. Otherwise, the measures no longer address the health of the

entire patient panel population, but rather only those that receive certain services. As such, they

become clinical quality (measure entity-focused) measures assessing quality of the services rendered

rather than an assessment of the health of the population.

Example – all Medicare beneficiaries in Acme ACO

3.2.2 Stratification

Most population health measures will need a stratification plan. As noted by IOM (2015 ), factors

outside of healthcare substantially shape the health of populations, e.g., social, environmental,

individual behaviors. Stratification is necessary to provide actionable information to measured entities

and policymakers, beyond the collection and sharing of data. Stratifying the data by race, ethnicity,

language preference, sexual orientation, gender identity, age, disability, and SDOH, including those

related to education and literacy, social and community context, economic stability, and neighborhood

and built environment, can enable focused quality improvement activities.

As CMS moves toward population-based payments and shared risk, it is increasingly important for

measured entities and payers to be able to quantify and address differences and disparities among the

communities and populations served by CMS programs. Stratified data is the critical first step to

improving the health of all individuals and families.

As part of the Reducing Provider and Patient Burden proposed rule released December 10, 2020, CMS

included a request for information on barriers to adopting standards, and opportunities to accelerate

adoption of standards, related to social risk factors. CMS acknowledges that healthcare “providers in

value-based payment arrangements rely on comprehensive, high-quality data to identify opportunities

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to improve patient care and drive value.” The goal is to standardize and liberate these data for multiple

reasons, such as to decrease patient reporting burden and increase the chances of connecting patients

with appropriate community care and support.

3.2.3 Data Sources

As with clinical quality measures, measure developers must specify data sources for population health

measures. Data sources may include clinical data (electronic health records [EHRs], registries, paper

patient records), claims, surveys, patient assessments (e.g., Minimum Data Set), screening tools (e.g.,

the Accountable Health Communities Health-related Social Needs screening tool [Billioux, Verlander,

Anthony, & Alley, 2017 ]), and administrative data that can include census data, crime data, birth and

death records, etc. The Gravity Project is working to change inconsistencies in existing SDOH data

collection processes. The Gravity Project "convenes stakeholders across the country through an open

and transparent collaborative process where they develop and test consensus-based standards to

facilitate SDOH data capture and exchange across a variety of systems and settings of care and social

services” (The Gravity Project, n.d. ).

However, medical record and other clinical data are unable to describe population health for the total

population group, and therefore, are insufficient. For example, although the measure developer may

consider immunization status a measure of population health, if a measure denominator includes solely

patients who receive certain clinical services or have documented patient encounters, the measure

becomes one of clinical care quality. Immunization status for a population must consider the population

as a whole. As such, public health data sources, such as those provided by CDC, or other repurposed

data, e.g., crime statistics, number of grocery stores, community health assessments, and community

health needs assessments, may be more valuable than the typical patient care data used for clinical

quality measures. CDC’s National Center for Health Statistics administers and/or collects data from

multiple surveys, e.g., National Health and Nutrition Examination Survey (NHANES) and the National

Health Interview Survey, and vital statistics which can provide population-level data for comparison. In

the absence of appropriate data sources, survey development and implementation may be necessary.

3.2.4 Level of Analysis

The level of analysis for population health measures should be at the population level and not limited

solely to patients who receive particular services. The measure developer must clearly define the

population in the target/initial population.

3.2.5 Time Interval

What is the appropriate time interval for population health measures? Is one year long enough or do

these measures require a longer time interval to determine significant changes? Measure developers

should consider the appropriate time interval for measurement, bearing in mind a longer period of time

may be necessary to identify significant changes at the population level.

3.2.6 Risk Adjustment

Outcome measures typically require risk adjustment where the purpose is to assess clinical quality.

The intent of population health measures is to produce true values without adjustment. However,

measure developers should consider risk adjustment.

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3.3 MEASURE TESTING

Measure testing may be challenging due to the potential use of multiple data sources in a single

measure and a lack of data, especially SDOH data. Lack of interoperability among data sources is likely.

Consider the (in)completeness of data sources and data elements (e.g., incomplete SDOH data). The

measure developer needs to be creative with their testing plan and should partner with a variety of

stakeholders including data owners.

For the purposes of testing measures of population health, the nature of the quality construct

(inferences about underlying processes or structures [Messick, 1987 ]) determines the testing

approach (Table 2). The first consideration is which system owns the quality construct. Until recently,

the distinction in attribution was more well defined. Hospitals and physicians provided clinical care;

public health and social service agencies did not provide clinical services or they were very limited. With

the increased emphasis on the importance of social risk factors, healthcare systems are more directly

involved in addressing these social risk factors through the direct provision of or referral to services like

housing or food security, and/or are formalizing collaborations with entities outside the healthcare

system. For the purposes of defining the quality construct for measure testing, the measure developer

needs to expand attribution for traditional measured entities to include the delivery of public health and

social services, and/or to expand the measured entity to include both healthcare and non-healthcare

providers.

Table 2. The nature of the quality construct for population health measures

System owning the quality construct

The quality construct is a public

good

The quality construct is not a

public good

Healthcare

Population health

Preventive healthcare

Non-healthcare

Public health

Social services

Assuming the quality construct is not a public good, once the measure developer defines the quality

construct, then measure testing would proceed as with any quality measure with the focus on

importance, scientific acceptability (reliability and validity), feasibility, and usability. A

public good is one for which consumption of the good by one person does not preclude consumption of

the same good by another person (e.g., a city park, clean air).

Measure validation is critical. If the intervention is a public good, then the validation should

demonstrate that. If the focus is on allocative efficiency,

then there is no need for validation. If the

focus is on some population characteristic like “cohesion,” then the focus is on that construct. The

reliability focus would be whether there is detectable variation in the quality construct across measured

entities (signal), or whether there is overwhelming variation in factors independent of the quality

construct (noise). Because there is an extended pathway between, for example, food (in)security-to-

clinical care-to-outcome, one might surmise a greater influence of factors independent (e.g.,

transportation availability) of the quality construct, thereby reducing reliability. The measure developer

might need to consider strategies to increase reliability, such as increasing the effective sample size,

e.g., borrowing statistical strength of related process or outcome measures (National Quality

Allocative efficiency is when the right share of resources is being devoted to healthcare versus other goods in the economy (Baicker & Chandra,

2011 ).

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Forum, 2015 ) or enhancing the information context (e.g., incorporating structural measures in

reliability adjustment

The second consideration is whether the quality construct is a public good. For example, testing for lead

poisoning and removing lead from city water pipes are both interventions that might improve adverse

outcomes. The second intervention is a public good, the first is not. In the testing for lead poisoning

intervention, the evidence for validity is the same as any quality measure. There must be a person-level

process/outcome relationship established with rigorous evidence and there must be an entity-level

(e.g., ACO) demonstration of validity of the quality construct which shows that there is alignment

between the behavioral response of persons and clinicians to the measure with the end user intent.

However, in the second public good intervention there is no person-level process/outcome relationship

(e.g., an individual’s health might improve whether that individual consumes the good or not). The

effectiveness of the intervention is only determined by examining population level outcomes. Measure

testing might be cross-sectional (e.g., geopolitical areas with and without the intervention that are

similar in other measurable aspects) or temporal (e.g., the same geopolitical area before and after

implementation of the intervention). The focus on measure testing is more about characterizing the

attributes of the population and attempting to demonstrate that the attribute that matters for variation

in population level outcomes is the intervention of interest: removing lead from city water pipes. Testing

might focus on statistical significance of the assertion about the attribute, but reliability conceptualized

as to whether there is detectable variation in the quality construct across measured entities is not

relevant (i.e., there is no within and between variation).

A final testing consideration is the nature of the population level outcome. In productive efficiency,

the

focus is on maximizing the individual’s outcome for a given amount of healthcare or social services. A

population level outcome might be like those used by OECD such as cancer deaths per 1,000 persons. An

intervention to improve the population level outcome would be to improve maternal mortality for

individuals. In allocative efficiency, the focus is on maximizing the outcomes for a population by

allocating or distributing a given amount of healthcare or social services to the best marginal use. A

population level outcome might examine the distribution of outcomes across population subgroups and

consider whether increasing access to healthcare or social services for certain subgroups would have the

largest impact on outcomes. Testing would focus on demonstrating the hypothesis by, for example,

demonstrating that the geopolitical areas where those subgroups have better access have better

outcomes.

3.4 MEASURE IMPLEMENTATION

Because population health measures are not setting specific, their adoption would primarily be into CMS

programs such as the Medicare Shared Savings Program (MSSP), Marketplace Quality Rating System,

and Medicare Advantage program. CMS is proposing to use population health measures in Merit-Based

Incentive Payment System Medicare Value Pathways. Commercial insurers could adopt population

health measures for continuous quality improvement and serve as a comparison with other commercial

insurers. Communities could adopt population health measures to assess the success of pertinent

Although risk adjustment considers differences in patient disease severity and case mix, reliability adjustment allows for repeatability of

estimates related to the relative number of cases and outcomes used to calculate the indicator of interest (Wakeam & Hyder, 2016 ).

Productive efficiency is when health care resources are put to the best use possible and produce as much health as they can

(Baicker & Chandra, 2011 ).

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implemented community programs. Measure results could also serve as input to community resource

and intervention planning and impact policy decisions at the local, state, and national levels.

The ideal plan is to use population health measures broadly, report them at the community level, and

share results among participating clinicians, public health, community, and other organizations.

Consistent with the notion of a “shared measurement system,” this arrangement increases the

likelihood that the multiple stakeholders coalesce around addressing the measure concept.

3.5 MEASURE USE, CONTINUING EVALUATION, AND MAINTENANCE

Population health measures are subject to the same three types of measure maintenance reviews as

other types of measures – annual, triennial comprehensive, and early maintenance – using the measure

evaluation criteria outlined in the CMS MMS Blueprint .

4 KEY POINTS

Most current population health measures summarize population outcomes at a geographic level.

Generally, these encompass health outcomes based on mortality or life expectancy, and survey-based

measures of subjective health status, psychological state, or ability to function (Parish, 2010 ).

Measure developers may need to expand their stakeholder outreach, e.g., community organizations.

Multiple data sources may be necessary to include non-healthcare sources.

There is much more to learn about population health and population health measurement. While the

principle of rigor in measure development remains the same, learning the details will come by doing and

iterating.

Supplemental Material to the CMS MMS Blueprint Population Health Measures

September 2021 Page 13

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