ABSTRACT
Population health is the most complex, most consequential, and most underperformed domain of government mission. The United States spends more per capita on healthcare than any other developed nation and achieves health outcomes that rank below most peer countries — a paradox that reflects decades of structural underinvestment in the social determinants that drive 80% of health outcomes, systematic bias toward treatment over prevention, and the absence of a coherent performance management framework that connects government investments to population health results.
This article provides a comprehensive framework for health profit measurement and management in government: the social determinants of health that government programs most directly influence, a 24-metric health profit library organized into five clinical domains, agency-specific OKR examples for state health departments, Medicaid programs, behavioral health agencies, local health departments, and the VA, a prevention ROI table demonstrating the evidence-based financial returns to seven evidence-based interventions, seven principles of health equity analysis, a map of eight government health data infrastructure systems, and a practical guide for connecting health data feeds to Profit.co OKR dashboards. The article challenges the conventional framing of healthcare as a cost to be managed and reframes population health as one of the highest-return investments in the government’s portfolio — with measurable returns that justify systematic investment and rigorous accountability.
- $4.5T U.S. Annual Healthcare Spend 17.3% of GDP — highest in the developed world
- 80% of Health Outcomes Driven by social determinants, not healthcare access
- 24 Health Profit Metrics across 5 clinical domains with data sources
- 44:1 Vaccination Program ROI CDC estimate for childhood immunization programs
1. Reframing Healthcare as Health Profit Investment
Why the conventional framing of healthcare as a cost to be managed is wrong — and what the investment framing reveals about government’s real leverage points.
The United States spent $4.5 trillion on healthcare in 2023 — 17.3% of GDP, more than any other country in the world by a significant margin. Yet American life expectancy ranks 46th globally, below most peer nations, and continues to decline in the communities with the greatest government investment in healthcare services. The paradox is real and its causes are understood: the U.S. dramatically underinvests in the social determinants that drive the majority of health outcomes — housing stability, food security, early childhood development, environmental quality, economic mobility — while over-investing in tertiary care that addresses the consequences of that underinvestment at enormous cost and limited effectiveness.
For government health agency leaders, this paradox contains a strategic insight that is counterintuitive but well-supported by decades of research: the most powerful levers for improving population health are not within the healthcare system. They are in housing policy, early childhood programs, criminal justice reform, food assistance, environmental regulation, and the economic development investments that determine whether people can afford to make healthy choices. A county health department that focuses exclusively on what happens inside the healthcare system is optimizing 20% of the problem and ignoring 80%.
Health profit is the concept that reframes this challenge: instead of asking “How do we manage healthcare costs?” it asks “How do we generate the maximum improvement in population health per dollar of public investment — across all government systems, not just the healthcare system?” This reframing changes everything: which agencies are relevant to health outcomes (all of them), which investments count (housing and early childhood alongside vaccination and primary care), which metrics matter (population health outcomes alongside process measures), and which officials are accountable (mayors, city managers, and governors alongside health commissioners).
2. The Social Determinants of Health: Government’s Highest Leverage Points
The six domains of social influence on health outcomes where government programs have the greatest impact — and the specific metrics that capture government’s contribution to each.
The social determinants of health (SDOH) are the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. They are the upstream factors that shape the probability of disease, injury, and premature death long before a person ever enters the healthcare system.
Healthy People 2030 — the federal government’s ten-year national health objectives — organizes SDOH into five domains. For government health performance management, a sixth domain — mental and behavioral health — is sufficiently distinct in its measurement challenges and policy levers to warrant separate treatment. The six domains below constitute the social determinants framework that government agencies should use to design cross-sector health profit OKRs.
Economic Stability
Income, employment, food security, and housing stability. The strongest and most consistent predictor of health outcomes across populations — poverty is the leading risk factor for premature mortality in the United States.
Key measures
% of households above 200% FPL; unemployment rate; food insecurity rate; housing cost burden (>30% income on rent)
Education Access & Quality
Educational attainment, early childhood education quality, and literacy. Education is the social determinant most amenable to government intervention and most strongly predictive of long-term health outcomes including life expectancy.
Key measures
High school graduation rate; 3rd-grade reading proficiency; kindergarten readiness; adult literacy rate
Healthcare Access & Quality
Insurance coverage, primary care access, preventive service utilization, and healthcare quality. The most direct government lever — but research consistently shows healthcare access explains less than 20% of health outcome variation.
Key measures
% with health insurance; % with primary care provider; preventive screening rates; avoidable hospitalization rate
Neighborhood & Built Environment
Air and water quality, housing quality, access to healthy food, green space, walkability, and exposure to violence and environmental toxins. The physical context in which people live their daily lives.
Key measures
Air quality index; % with access to healthy food within 1 mile; walkability score; housing quality index; environmental justice index
Social & Community Context
Social support networks, community cohesion, discrimination experiences, incarceration history, and civic participation. The relational context that buffers stress and enables healthy behavior.
Key measures
Social isolation rate; discrimination experience score; incarceration rate; voter participation rate; community trust index
Mental & Behavioral Health
Mental health condition prevalence, substance use disorder rates, and access to behavioral health services. The most rapidly deteriorating dimension of U.S. population health — and the domain where the government capacity gap is largest.
Key measures
Suicide rate; opioid overdose mortality rate; % with unmet mental health need; depression prevalence (PHQ screening)
Figure 1: Six Social Determinants of Health Domains — government’s role in each, and key metrics for health profit measurement
3. The Health Profit Metric Library
Twenty-four evidence-based, data-source-linked metrics organized into five clinical domains — ready for use as OKR Key Results.
The health profit metric library below provides twenty-four specific metrics across five clinical domains, each with its data source, measurement frequency, and implementation notes. The metrics are selected based on four criteria: they are outcome-oriented (measuring health results, not just activity), they are data-available (drawing on existing government health data systems), they are action-linked (amenable to improvement through government intervention), and they are equity-sensitive (capable of disaggregation to reveal disparities that population averages obscure).
| Metric | Data Source | Frequency | Implementation Notes |
|---|---|---|---|
| MORTALITY & LONGEVITY | |||
| Life expectancy at birth (by census tract, race, sex) | CDC Wonder / state vital statistics | Annual | Ultimate outcome measure; 1-year change = significant population health shift |
| Premature mortality rate (deaths <75 per 100,000) | CDC Wonder / state vital statistics | Annual | Captures deaths that public health intervention can most plausibly prevent |
| Infant mortality rate per 1,000 live births | State vital statistics; NCHS | Annual | Global standard for population health; highly sensitive to maternal and infant health investment quality |
| Years of potential life lost (YPLL) before age 75 | CDC PLACES; state vital statistics | Annual | Weights premature deaths by years lost; highlights impact on younger populations |
| PREVENTABLE ILLNESS & CHRONIC DISEASE | |||
| Preventable emergency department visit rate per 1,000 | HCUP State Inpatient Databases; Medicaid claims | Quarterly | Leading indicator: high rates signal primary care access failure and chronic disease mismanagement |
| Diabetes prevalence rate (diagnosed) per 100 adults | CDC PLACES; BRFSS | Annual | Most costly and prevalent preventable chronic condition; strong predictor of downstream health costs |
| Hypertension control rate (% with BP <140/90) | HEDIS quality measures; EHR data | Annual | Process measure with strong outcome linkage; amenable to primary care quality improvement |
| Childhood obesity rate (BMI ≥95th percentile) | State BRFSS; school health data | Annual | Leading indicator for adult chronic disease burden 20–30 years later |
| Cancer screening rates (mammography, colorectal) | HEDIS; Medicare/Medicaid claims; BRFSS | Annual | Process measure predictive of late-stage cancer incidence and treatment cost |
| Adult smoking prevalence (% adults) | CDC BRFSS; state surveys | Annual | Leading modifiable mortality risk; strong linkage to cancer, CVD, and respiratory burden; equity-sensitive by geography and income |
| BEHAVIORAL & MENTAL HEALTH | |||
| Suicide rate per 100,000 (age-adjusted) | CDC Wonder; state vital statistics | Annual | Most severe behavioral health outcome; requires disaggregation by method, age, and geography |
| Opioid overdose death rate per 100,000 | CDC Wonder; state drug poisoning data | Monthly (provisional) | Fast-moving crisis indicator; monthly provisional data enables real-time response |
| % with unmet mental health need (past 12 months) | SAMHSA NSDUH; state BRFSS modules | Annual | Demand-supply gap measure; critical for workforce and access planning |
| Behavioral health crisis visit rate per 1,000 | ED data; crisis call center data | Monthly | Leading indicator for community behavioral health infrastructure adequacy |
| Depression prevalence rate (PHQ-9 screening) | Primary care EHR data; BRFSS | Annual | Most prevalent behavioral health condition; highly amenable to primary care intervention |
| MATERNAL & INFANT HEALTH | |||
| Maternal mortality rate per 100,000 live births | CDC Pregnancy Mortality Surveillance; state vital statistics | Annual | Major U.S. outlier vs. peer nations; significant racial disparities require disaggregation |
| Severe maternal morbidity rate | HCUP hospital data; Medicaid claims | Annual | Broader measure of near-miss events; more statistically stable than mortality in smaller jurisdictions |
| Preterm birth rate (% births <37 weeks) | NCHS National Vital Statistics; state vital statistics | Annual | Leading indicator for infant health outcomes and NICU costs; amenable to prenatal care quality improvement |
| % of births with adequate prenatal care (Kotelchuck index) | State vital statistics; Medicaid claims | Annual | Process measure strongly predictive of birth outcomes; equity gaps highly actionable |
| Low birth weight rate (% live births <2500g) | NCHS / state vital statistics | Annual | Core infant outcome; sensitive to maternal health, prenatal access, and social risk; compare by race/ethnicity and geography |
| HEALTH EQUITY | |||
| Life expectancy gap between highest and lowest census tracts | CDC PLACES; state vital statistics | Annual | Single most powerful summary equity measure; 20+ year gaps common within single counties |
| Avoidable hospitalization rate disparity (by race/ethnicity) | HCUP; Medicaid claims; HEDIS | Annual | Reveals primary care access inequity; drives highest-cost care for lowest-income populations |
| % of racial/ethnic minority populations receiving preventive screening | HEDIS; Medicaid EPSDT data | Annual | Process equity measure; gaps are highly actionable through targeted outreach and access programs |
| Environmental justice index (pollution burden × population vulnerability) | EPA EJScreen; CalEnviroScreen | Annual | Intersectional measure linking environmental and health inequity |
Figure 2: Health Profit Metric Library — 24 metrics across 5 domains with data sources, frequency, and implementation notes
4. Health Profit OKRs: Agency-Specific Examples
Complete OKR templates for five government agency types — from state health departments to the VA — demonstrating how health profit metrics become management accountability.
Health profit OKRs must connect the broad population health aspirations of senior leadership to the specific, measurable, time-bound commitments that program managers can actually manage. The examples below demonstrate this connection for five distinct government agency contexts, each with an ambitious Objective and four sample Key Results drawn directly from the health profit metric library.
| Agency Type | Objective | Sample Key Results |
|---|---|---|
| State / County Health Department | Eliminate the racial life expectancy gap within our jurisdiction within 15 years |
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| Medicaid / Managed Care Program | Maximize health outcomes per dollar of Medicaid investment in FY26 |
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| Behavioral Health Agency | Build a behavioral health system that meets community need without crisis as the default entry point |
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| Local Health Department (City/County) | Reduce the preventable chronic disease burden in the five highest-mortality zip codes |
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| VA / Veterans Health Administration | Eliminate veteran suicide as an unresolvable outcome of military service |
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Figure 3: Health Profit OKR Examples — five agency types with Objectives and Key Results grounded in the metric library
4.1 Building Cross-Agency Health Profit OKRs
The most powerful health profit OKRs are those that cut across agency boundaries, reflecting the cross-determinant nature of health outcomes. A county committed to reducing diabetes incidence in high-burden zip codes might create a shared Objective owned by the health department, the parks department (physical activity infrastructure), the planning department (healthy food access), the social services department (food assistance outreach), and the schools (nutrition education and breakfast programs). Each department contributes Key Results from within its domain; all are visible on a shared mission profit dashboard.
Profit.co’s alignment tree feature supports exactly this kind of multi-agency health OKR structure. A single population health Objective can have contributing Key Results owned by five different departments, with each department’s KR automatically aggregating into the composite health profit score. This creates the visibility and shared accountability that population health requires — without requiring a new organizational structure or reporting relationship.
5. The Prevention Investment Case: ROI by Intervention
The financial evidence for shifting government health investment from treatment to prevention — with ROI ratios for seven evidence-based interventions.
The most powerful argument for reorienting government health investment toward prevention is financial: evidence-based prevention programs consistently produce returns that dwarf what the same dollars invested in treatment can achieve. The following table presents ROI estimates for seven high-evidence prevention and early intervention programs, each with a specific cost-per-unit, avoided-cost-per-unit, and ROI ratio drawn from published research.
These ratios are not merely academic — they are the foundation of the budget justification argument that health agencies need to make to legislative appropriators who are accustomed to funding treatment systems with immediate visible constituents and resistant to funding prevention programs whose payoffs are diffuse and delayed. An ROI-based prevention investment case, built on published research and presented in the OKR accountability framework, is significantly more persuasive than a narrative appeal to prevention as inherently worthwhile.
| Intervention | Type | Cost per Unit | Avoided Cost per Unit | ROI Ratio | Notes |
|---|---|---|---|---|---|
| Hypertension screening + medication management | Primary prevention / secondary prevention | $400–$800/year per patient | $4,200–$8,600/year per patient managed in hospital setting | 5–7:1 | Most cost-effective chronic disease intervention; high-volume opportunity in Medicaid |
| Childhood vaccination program (full schedule) | Primary prevention | $450 per child (full schedule) | $11,000+ per case of prevented measles hospitalization; $250,000+ per lifetime prevented polio case | 10–44:1 | Among the highest-ROI health investments in public health history (CDC VFC program) |
| Nurse-Family Partnership (prenatal home visiting) | Primary prevention / early intervention | $12,500 per family | $34,148 government savings per family (reduced welfare, justice, healthcare costs) | 2.88:1 government; 5.7:1 societal | Two-generation intervention; benefits extend 15+ years; strongest evidence base in maternal health |
| Naloxone (Narcan) distribution program | Harm reduction / overdose reversal | $50–$150 per kit distributed | $1.47M per overdose death averted (VSL methodology) + $34,000 average hospitalization avoided | >100:1 (estimated) | Among highest ROI public health programs given opioid crisis scale; each naloxone kit has ~10% probability of being used |
| Evidence-based smoking cessation program | Secondary prevention | $1,200 per quit attempt supported | $2.3M value per smoking-attributable death prevented; $15,000+ per COPD hospitalization prevented | 4–8:1 | 40-year investment thesis; most benefit realized 10–20 years post-cessation |
| Preventive dental care (Medicaid EPSDT) | Primary prevention | $220 per child per year | $750–$1,400 per preventable dental ER visit avoided; $3,200+ per general anesthesia dental case avoided | 3–6:1 | High return in pediatric Medicaid; dental ER visits are frequent and entirely preventable |
| Chronic disease self-management program (CDSMP/Stanford) | Secondary prevention / self-management support | $400–$600 per participant | $2,300 reduction in healthcare costs per participant at 6 months; $4,800+ at 12 months for high-risk | 4–8:1 | Scalable, evidence-based; deployable through health departments, libraries, and community organizations |
Figure 4: Prevention Investment ROI — seven evidence-based interventions with cost, avoided cost, and return on investment ratios
6. Health Equity Analysis: Seven Principles for Fair Measurement
The analytical principles that distinguish health equity measurement from standard population health reporting — and why they are non-negotiable for government agencies committed to reducing health disparities.
Population health averages are politically useful and analytically misleading. A state that reports a life expectancy of 76.4 years may simultaneously contain census tracts where life expectancy is 61 years and tracts where it is 86 years. A Medicaid program that achieves 68% hypertension control across its enrolled population may be achieving 81% for non-Hispanic White enrollees and 52% for Black enrollees. Health equity measurement exists to make these disparities visible — because disparities that are invisible cannot be addressed, and disparities that are visible create the accountability necessary for action.
| Analytical Principle | Why It Matters | How to Implement |
|---|---|---|
| Disaggregate by Race/Ethnicity | Every health metric must be reported disaggregated by race and ethnicity, not just as a population average. Population averages mask disparities that can span 10–20 years of life expectancy within a single county. | CMS OMB race/ethnicity standards; REAL protocol for data collection; at minimum: Non-Hispanic White, Black/African American, Hispanic/Latino, Asian/Pacific Islander, American Indian/Alaska Native, Multiracial |
| Disaggregate by Geography | Census tract-level analysis is essential for identifying health profit hotspots and cold spots. Zip code aggregation loses critical information; neighborhood-level differences in life expectancy often exceed 20 years within a single city. | CDC PLACES data (census tract); American Community Survey; State vital statistics with geocoding |
| Disaggregate by Socioeconomic Status | Income, education, and insurance status are among the strongest determinants of health outcomes. Metrics reported only at the population level cannot identify whether disparities are driven by access barriers, social determinants, or healthcare quality differences. | Medicaid vs. commercial insurance comparisons; income quintile analysis using ACS data; educational attainment stratification |
| Measure Access Separately from Utilization | A high utilization rate may reflect high need rather than good access. An agency that serves a severely ill population will show high ED utilization regardless of how well it manages access. Access measures (% with PCP, enrollment rates, wait times) must be tracked alongside utilization. | Health Plan Finder enrollment rates; PCMH enrollment; wait time data from provider systems; HEDIS access measures |
| Track Both Incidence and Prevalence | Incidence (new cases) measures whether prevention is working. Prevalence (existing cases) measures disease burden. Programs that focus only on incidence can miss the growing burden of existing chronic disease; programs that focus only on prevalence cannot detect whether prevention investments are paying off. | CDC PLACES for prevalence; sentinel surveillance systems for incidence; notifiable disease reporting systems |
| Use Appropriate Denominators | Health rates must use the right denominator population. Using total population when a program serves only Medicaid enrollees produces a metric that is mathematically incomparable to the program’s actual reach. Define the denominator precisely and consistently. | Census denominators for population health; program enrollment denominators for program-specific metrics; age-adjusted denominators for temporal comparisons |
| Account for Confounding | Comparison of health outcomes across agencies or time periods without controlling for population characteristics can produce misleading results. A health department serving a sicker, poorer, or older population will show worse outcomes than one serving a healthier population — regardless of program quality. | Risk adjustment using CMS HCC methodology; age-adjustment for temporal comparisons; multivariate analysis for program evaluation |
Figure 5: Seven Principles of Health Equity Analysis — why each matters and how to implement it in government health measurement systems
7. The Health Data Infrastructure: Eight Systems Every Agency Must Know
A map of the government health data ecosystem — the systems that generate health profit metrics, their custodians, and how to connect them to Profit.co dashboards.
One of the most practical barriers to health profit measurement is data access: understanding which systems contain the metrics you need, who controls access, what the data quality limitations are, and how to connect these systems to a performance management platform. The eight data systems below constitute the core of the government health data infrastructure — the sources that enable the health profit metrics in the library above.
| Data System | What It Contains | Frequency | Custodian | Health Profit Metrics It Enables |
|---|---|---|---|---|
| Vital Statistics System | Birth and death certificates; cause of death coding; maternal/infant birth outcomes | Annual (death); near real-time (birth) | State health departments; CDC NCHS | Life expectancy; infant mortality; cause-specific mortality rates; maternal mortality |
| Electronic Health Record (EHR) Data | Clinical encounter data; diagnosis codes; medication records; lab values; screening documentation | Near real-time via HL7/FHIR | Health Information Exchanges (HIEs); hospital systems; FQHC data systems | Chronic disease prevalence; hypertension control; diabetes management; screening rates |
| Medicaid / Medicare Claims | Insurance claims data capturing all covered services; diagnosis codes; procedure codes; provider information | Monthly (claims lag 30–90 days) | CMS; State Medicaid agencies; managed care organizations | Avoidable hospitalizations; ED utilization; preventive care rates; care gaps by population |
| Behavioral Risk Factor Surveillance System (BRFSS) | State-level annual survey capturing health behaviors, chronic conditions, and preventive care use | Annual | CDC; State health departments | Obesity; smoking; physical inactivity; depression; health status self-report |
| CDC PLACES / 500 Cities | Census tract-level health estimates derived from BRFSS using small area estimation | Annual | CDC | Diabetes; hypertension; obesity; mental health; access to health care — at census tract level |
| Emergency Department Data (HCUP) | Hospital discharge records; ED visit records; diagnosis codes; charges | Quarterly to annual | AHRQ HCUP; state hospital associations | Preventable ED visits; behavioral health crisis visits; injury surveillance; overdose tracking |
| Syndromic Surveillance | Near real-time ED visit data coded for syndrome categories; overdose; influenza-like illness; respiratory illness | Daily / weekly | State health departments; CDC BioSense platform | Overdose trends; disease outbreak detection; behavioral health crisis volume |
| Public Health Laboratory Data | Communicable disease testing; HIV viral load; STI testing; newborn screening | Variable — disease-specific reporting timelines | State public health labs; CDC NNDSS | Communicable disease incidence; HIV viral suppression; newborn screening outcomes |
Figure 6: Eight Government Health Data Systems — contents, frequency, custodians, and health profit metrics they enable
7.1 Connecting Health Data to Profit.co
Profit.co’s government platform supports multiple data integration pathways for health metrics. For metrics with established APIs (CDC PLACES, CMS claims data, state vital statistics systems), automated data feeds can be configured to update Key Result progress scores in near-real-time, eliminating the manual data entry burden that causes health OKR programs to stall. For metrics that require data extraction from legacy systems, Profit.co’s CSV import feature supports structured uploads on a defined schedule.
The most common integration architecture for government health agencies involves three tiers: a real-time feed from syndromic surveillance and ED data systems for leading indicators (overdose trends, behavioral health crisis volume, influenza-like illness); a monthly automated pull from Medicaid claims for process measures (preventive care rates, ED utilization, care gaps); and an annual upload from vital statistics for outcome measures (mortality rates, life expectancy). Together, these three tiers provide the multi-frequency data architecture that connects weekly operational management to annual population health accountability.
8. Getting Started: A Health Profit Implementation Roadmap
A practical sequence for health agencies beginning their health profit measurement and management journey — from data audit to live OKR dashboard.
- Step 1: Conduct a population health data audit (Month 1–2): Inventory all health data systems your agency has access to. Map each system to the health profit metrics it can produce. Identify the highest-priority measurement gaps — metrics that are theoretically available but not currently being extracted and monitored.
- Step 2: Define your population health baseline (Month 2–3): For each priority metric, extract the current baseline value disaggregated by race/ethnicity and geography. This baseline is the foundation of every health profit Key Result — you cannot set a meaningful target without knowing where you start.
- Step 3: Identify your highest-burden geographies and populations (Month 3): Conduct a geographic and demographic analysis to identify the census tracts and population groups with the greatest health profit deficits. These become the target populations for your initial OKR cycle.
- Step 4: Set your health profit OKRs (Month 3–4): Use the metric library and OKR examples in this article as starting points. Set a 3–5 Objective structure for your agency and the departments you can influence, with 3–5 Key Results each. Ensure each KR has a named owner, a baseline value, and a stretch target.
- Step 5: Configure Profit.co for health OKR management (Month 4–5): Set up your OKR structure in the platform. Configure available data feeds for automated KR updates. Establish the monthly review cadence with a live health profit dashboard as the anchor for leadership conversations.
- Step 6: Launch public health equity reporting (Month 5–6): Publish your health profit baseline disaggregated by race and geography. Making disparities public creates accountability that internal management processes alone cannot produce — and demonstrates the agency’s commitment to health equity as a management value, not just a rhetorical one.
9. Conclusion: Health as Mission, Not Cost
The conventional framing of population health in government is cost management: how do we provide adequate care to our beneficiaries at the lowest possible cost to the taxpayer? This framing produces a performance management system focused on utilization rates, cost per member, and compliance with coverage requirements — none of which measure whether people are actually healthier.
The health profit framing produces a fundamentally different performance management system: one focused on whether the government’s investments — across all agencies, not just the health department — are generating measurable improvement in population health outcomes, with equity across populations as a non-negotiable design requirement. This framing connects preventive investment to long-horizon returns, makes the social determinants visible as performance management levers, and creates the cross-agency accountability that population health requires.
The agencies that adopt this framing — that measure health profit with the rigor applied to financial performance, that set ambitious OKRs for life expectancy and chronic disease burden alongside their process measures, that build the data infrastructure to track progress in real time, and that publish their results publicly disaggregated by race and geography — are the agencies that will generate the most health value from the extraordinary public investment that government makes in the health of its population. The measurement is not the goal. Healthier, longer, more equitable lives are the goal. But without the measurement, the goal is just aspiration.