Purpose

In our two-part series, Metrics that Moved Systems, we examined six historical cases in which disease metrics drove transformative, system-level change and drew direct implications for the design of the Biomedical Health Efficiency (BHE) framework. In this BHE Perspective, we shift to clearly define BHE and how the use of BHE reveals both visible and hidden system barriers to fully leverage the value of biomedical innovation. Ultimately, BHE and quality measures do different jobs at different stages of a system's development, in pursuit of the same goal:  better outcomes for patients and good stewardship of resources. Quality measures track how well the right things are being done. BHE makes it easier for all stakeholders to do the right things in the first place.

Defining what BHE is, and is not

Biomedical Health Efficiency (BHE) is a multi-stakeholder measurement framework for assessing how efficiently a health system delivers the benefit of biomedical innovations such as medicines, diagnostics, devices, and other interventions to the patients they were developed to serve.

BHE evaluates system performance in two dimensions and a third that cuts across both:  Outcomes (is the innovation reaching patients and producing real-world benefit?), Resources (are system capacity, workforce, and infrastructure as well as time, financial, and other costs being used effectively?), and Equitability (does the ratio hold across populations and settings?). Together, these define what it means  for a biomedical system to function efficiently — not just clinically, but operationally, with consistency.

Expressed as a ratio, BHE calculates the outcomes achieved relative to the resources required (BHE = Outcomes ÷ Resources) and computes this ratio separately for various populations, settings, and geographies to determine whether the ratio holds for everyone. A ratio is used instead of a scorecard or a single metric, such as the percentage of patients with HbA1c below a given threshold (e.g., < 7.0%), because an outcome measure with no resource denominator cannot be compared across sites with different levels of available capacity (e.g., workforce, number of MRI machines).

BHE is disease-agnostic. The same framework, underlying methodology, and design principles can be applied to any therapeutic area where a biomedical innovation exists but is not reaching patients at the scale, speed, or equity that the science makes possible.

BHE is four things simultaneously:

Filling these four roles is what makes BHE both compelling and important. It also makes the work of developing the correct measures challenging, yet necessary, to help systems improve their ability to ensure biomedical innovations reach the populations they are intended to help in a sustainable fashion.

Complementing, Not Competing With, Quality Measures

A common misconception is that BHE is yet another quality metric. This is not the case.

Quality measures assess how good care is. They track and evaluate specific, micro-level clinical practices and direct results for patients and provide actionable data to benchmark clinical outcomes, ensure patient safety, and guide continuous improvement initiatives. Most quality frameworks are based on Donabedian’s work and organized into structure, process, and outcome categories. Quality indicators are essential tools for identifying problems (pinpointing areas of care that lead to complications or readmissions), accountability (transparent data that regulatory bodies or accreditation organizations use to evaluate facilities), and patient choice (empowering consumers with data to make informed choices about where they receive care).

BHE is a macro-level measure of system performance, evaluating how well a healthcare system meets the broader needs (access, workforce capacity, equity, and efficiency) involved in delivering biomedical innovations to the patients who can benefit from them.

The focus on efficiency is a key component of BHE. Efficiency measures how well resources are used to produce a particular result. They typically assess the relationship between inputs and outputs (productive efficiency), asking whether a provider or system produces a given output with fewer resources or more output with the same resources. This is important given the real-world limitations on costs, system infrastructure, and workforce capacity.

Equity tracks disparities in outcomes that are based on factors such as race, income, or geography. It is recognized as a core dimension of healthcare quality by organizations such as CMS and AHRQ. In traditional quality measures, equity has been typically considered as an isolated and separate domain. In contrast, BHE builds equity into both the outcomes (numerator) and the resources (denominator), identifying whether the BHE ratio applies to everyone. This is critical in exploring where resource allocation may be unevenly applied and impacting outcomes, or where other factors may need to be considered in disparate outcomes.

Different Roads, Different Purposes

Standard quality measurement and BHE architecture differ in the direction of their process flow. The direction of flow matters because it begins with different starting questions. Standard quality measurements tend to be developed retrospectively. That is, they begin with observed outcomes and work toward standardization for use in accountability and payment policy. BHE, on the other hand, is prospective, starting with a specific policy or system-change decision that needs to be made and working backward to determine the evidence needed to steer behavior change and support decision-making. To this end, BHE metrics are designed with input from the decision-makers who would act on the evidence, helping to ensure that the metrics are fit for purpose.

The evidence needed to serve different purposes also differs. Quality measures tend to focus on clinical processes and outcomes such as adherence to clinical guidelines, hospital readmission rates, and biometric measures of disease management such as blood pressure and HbA1c. While clinical processes and outcomes are critically important, the evidence mix that BHE considers is broader and dependent upon the decisions being targeted. Clinical outcomes are included if the decision-maker requires them, yet other types of evidence such as access patterns, cost trajectories and resource utilization, and indicators that infrastructure can support care delivery may be needed for other decisions. BHE methodology is metric-agnostic in the abstract but becomes metric-specific in its application to a given disease or system challenge.

Both sequences are legitimate. They serve different purposes. Quality measures center on conformance. That is, they presume a settled standard of care and measure fidelity to this standard within the system that exists today. However, this means that structurally they are trailing: a measure cannot exist until the underlying science and standard have stabilized, allowing something settled to be conformed to. BHE, on the other hand, is centered on transformation, making a desired system state that does not yet exist visible and therefore steerable before the standard is stabilized. BHE is structurally leading; it does not wait for the standard because forming the standard is part of its work. In other words, BHE is most powerful where standard approaches fall short—where decisions require evidence that has not yet been collected.

The clearest evidence that BHE is not a quality measure is when it is used. A conformance measure can only be applied after a care model exists and has stabilized. BHE begins before that point and continues through and past it.

In the NEWDIGS innovation process, BHE work begins while a system problem is still being diagnosed and a solution designed (in the Elucidate and Design phases of the NEWDIGS innovation process) and continues as the shared measurement spine of the Readiness Network once implementation is underway. By the time a solution reaches Implementation, its measurement architecture is already part of the design rather than an evaluation bolted on afterward. BHE then runs continuously through Enable Implementation and Monitor & Learn as the shared measurement spine of the Readiness Network.

This is why BHE is best understood as a prospective system design tool that also produces measurement, rather than a measurement model that happens to be used early. It what a system must be able to achieve before that system has been built – work that a conformance instrument structurally cannot do.

Quality measures and BHE differ in target, construction, and assumptions. By broadening the framework to include outcomes, resources, and equitability, BHE does not replace standard quality measurements but instead complements them.

The relationship between them is sequential, not competitive. BHE does the upstream system-formation work that conformance instruments cannot, and as a clinical pathway matures and stabilizes. BHE metrics can feed into quality measurement systems (e.g., HEDIS, Star ratings, MIPS) where the reporting requirements and incentives sit. A subset will make that transition; many will stay where they are, serving as learning signals for the sites doing the work rather than as accountability measures.

Summary: BHE vs. Quality Measures at a Glance

Mature BHE metrics feed forward into quality measurement systems as pathways stabilize