Biomarkers as a Resource Allocation Mechanism in Healthcare

Quantitative biomarkers of health and disease dictate who gets care and what types of healthcare is paid for. How to align innovations in biomarkers with incentives of government and payers?

When I entered medical school as an aspiring physician-scientist I was intrigued by how physiological and biochemical measurements could serve as insight mechanisms for improving patient and population health. The concept of quantifying health, interpreting lab data, problem solving, and then deciding on the best course of action for each patient was exciting.

When I encountered EHRs, treatment algorithms, “care pathways,” and insurance I realized my idealistic concept of measurement -> individualized care had several more regulatory steps that had little to do with physiology or health.

Turns out clinical practice is enforcedly evidence-based and guideline directed, supported by randomized controlled trials and population level outcomes data. Government and payer incentives have a substantial influence on who gets what treatment and when—these organizations require specific standards that ultimately convert individual patient-level problem-solving to measurement-driven actions standardized to a the population average.

Standardization of medicine is determined by the toolkit that takes decades of evidence to generate.

These tools include:

• Biomarkers: what measures can be associated with health, disease, and mortality outcomes at the population level?

• Clinical Trial Design: E.g. RCTs are the dominant mechanism of gold standard evidence, but treat individuals as a representative “average person” within a population

• Population-Level Databases: E.g. Framingham Heart Study, longitudinal profiling in the NIH All-of-Us, UK Biobank, and even the Apple Health Study

And, put simply, medicine evolves only as fast as its measurement tools do.

Significant technological developments are rapidly changing the toolkit of medicine, including omics-based biomarker discovery and development, N-of-1 clinical trial designs gaining momentum, and the integration of wearables, clinical data, and novel biomarkers to determine health outcomes at scale. TBH, this is the type of medicine I’d be excited to practice.

However, the practical application of these tools still must be integrated into the existing incentive structure by the systems that dictate healthcare resource allocation in the US (e.g. government and payers). (Alt view: consumers decide what data they find valuable, and pay themselves—lets save this exciting topic for another discussion)

The Influence of Government Incentives

“U.S. health care spending grew 7.5 percent in 2023, reaching $4.9 trillion or $14,570 per person. As a share of the nation’s Gross Domestic Product, health spending accounted for 17.6 percent.” – CMS National Health Statistics Group

For government agencies, the primary incentive is—and always has been—cost containment. The Centers for Medicare & Medicaid Services (CMS) and other public payers operate under the paradox of delivering comprehensive healthcare while maintaining fiscal sustainability. Every policy lever, from reimbursement structure to quality measure design, is aimed at bending the cost curve without compromising population health outcomes.

But “keeping costs low” ends up presenting as an engineering problem. By shaping incentives through mechanisms like Star Ratings, value-based payment models, and population-level quality metrics, the government effectively dictates the flow of healthcare capital. Plans and providers that prevent disease (at least as defined by current biomarker thresholds), reduce hospitalizations, and manage chronic conditions efficiently are rewarded; those that don’t are penalized through lower payments or lost contracts.

At a deeper level, these incentives push the healthcare system toward data-driven accountability. When a biomarker like HbA1c or blood pressure becomes a proxy for reimbursement, it aligns the government’s cost-saving imperative with the individual’s health optimization. The more precisely we can measure and predict physiological change, the more efficiently public dollars can be allocated. In that sense, biomarkers don’t just measure health—they define the economic architecture of modern medicine.

Keep in mind these biomarkers are assumed to be meaningful proxies of health status.

Payer Incentives

For payers (e.g. Humana, Aetna, United), the North Star—literally—is the CMS Star Ratings system, which determines not just how plans are perceived but how they get paid. Medicare Advantage plans that score four stars or higher receive substantial quality bonus payments, allowing them to offer richer benefits and attract more members. Those that fall short see declining reimbursement and enrollment—an existential problem in an increasingly quality-linked ecosystem.

The 2026 update to the Star Ratings program marks a shift from member satisfaction to measurable health outcomes. CMS is rewarding results rooted in biology: blood pressure control, glycemic stability, kidney health, and medication adherence now carry the heaviest weights in determining plan performance. These are, in essence, biomarker-driven incentives—where a population’s collective clinical data directly translates into financial outcomes for the payer.

In this system, payers are motivated to close care gaps not just for optics, but for economics. Capturing, tracking, and improving biomarkers like HbA1c, LDL-C, and systolic blood pressure isn’t just good care—it’s good business. And as these measures expand to include functional (physical activity monitoring made it on the list) and behavioral health metrics, the next generation of Star Ratings seem to be inching closer to aligning payer incentives with genuine physiological improvement, but often lag behind cutting edge innovation.

So How to Use Biomarker Innovation to Improve Healthcare at Scale?

As mentioned above, biomarkers like hemoglobin A1C, LDL, fasting glucose, creatinine, blood pressure determine what drugs are recommended, what lifestyle interventions are covered by insurance, who should see a specialist, etc. This toolkit has existed in the healthcare system for decades yet has clearly not sufficiently mitigated the downstream consequences of chronic disease morbidity, mortality, AND economic strain.

Current clinical practice treats each of these biomarkers as distinct, targetable, and treatable disease entities. Modern clinical bodies are beginning to realize that they are lagging indicators of a more comprehensive physiological process. This has led to the naming of disease entities such as “cardio-renal-metabolic syndrome” (Ndumele et al 2023, Koufakis et al 2025).

Recommended treatments include GLP1-agonists, which do not target an LDL-Creatinine-A1C axis via multi-target pharmaceutical agonism, but rather through significant reductions in upstream physiological parameters—feeding behavior, body weight and fat mass. Still, clinical trials are run to demonstrate the effects of GLP1 agonists to improve long term cardiovascular health, metabolic health, liver health, addiction, etc. which treat each disease as a specific entity.

If single interventions can have multi-systems-level health benefits, why shouldn’t the next generation of resource-allocation (e.g. covered interventions, therapeutic development, etc.) be determined off multi-systems-level biomarkers?

By guiding interventions towards upstream structural and functional biomarkers (e.g. body composition, cardiorespiratory fitness, & insulin sensitivity) clinicians can directly target functional decline that underlies numerous chronic diseases.

So, how do we measure these?

A New Paradigm in Biomarker-Driven Resource Allocation will Target Upstream Functional Measures

By targeting the upstream biology, the traditional reductionist (single “disease” focused) biomarkers will also improve

Gold standard measures of body composition exist today with whole-body imaging including DXA and MRI (to assess fat mass, visceral fat content, muscle mass, and bone density). Cardiorespiratory fitness can be measured via a maximal exercise test (e.g. VO2max, or cardiopulmonary exercise testing (CPET)). Insulin sensitivity could be directly assessed via a hyperinsulinemic-euglycemic clamp or approximated by oral glucose tolerance tests.

If a patient with obesity, high cholesterol, high blood pressure, and diabetes improves body composition (loses body fat, increases muscle mass index), improves insulin sensitivity, and improves their VO2max, I’d be shocked to learn that their A1C, fasting glucose, LDL, blood pressure, etc. didn’t also improve.

While these measures would be fantastic to have as a part of every primary care exam, they are practically infeasible at population scale (at least today). If the insights from these tests could be captured in blood tests that seamlessly integrate into clinical practice, a scalable biomarker toolkit could exist to drive alignment of incentives between patients, clinicians, governments, and payers.

With rapid advances in functional omics based technologies (e.g. transcriptomics, proteomics, and metabolomics), coupled with omics-based measurements in population level databases (e.g. UK Biobank, China-Kadoorie, etc.) I envision this new clinical toolkit coming very soon.

I also believe that improvements in AI and machine learning (driven largely by training on high quality, clinically-meaningful, datasets) may allow for personalized care pathways that take into account an individual’s physiology and health status, to make precision medicine a reality for chronic disease.

Not only will biomarkers measure health (rather than solely absence of disease) but will allow for cost-effective allocation of resources that preempt & intercept disease early.