Measuring the Effectiveness of the Healthcare Sector


Numerous public and private organizations exist to measure, aggregate, and track data originating from activity within the healthcare sector, and the trends and patterns generated over time. Various stakeholders rely on these data to help predict what the industry can expect in the months and years ahead. These data impact the development of public health policy, influence investment decisions from the private and public sectors, and shape public opinions and discourse. These data are as impactful as they are misunderstood. This author hopes that readers can gain a more curious attitude for future discussions to help drive meaningful change across the sector and improve the quality of life for millions of Americans.

Meaningful improvement should be a measurable improvement—that is, supported by data. Access and affordability are closely interlinked concepts used for evaluating the sector’s effectiveness overall. Within healthcare, effectiveness covers metrics ranging from dollar spend to inclusivity scores. Not only is the range of possible variables quite vast, but the relationships between and among data points are also meaningful. This adds a layer of complexity and an opportunity for a deeper understanding into the “why” of how events unfold.

Diving into those relationships and their historical evolution is a powerful way to glean more nuanced insights, which in turn should yield more accurate predictions to guide public policy development, investment decisions, and other factors influencing the sector overall.

Access to Care

Access to healthcare services refers to the ease with which individuals can obtain timely and appropriate medical care. Care is no longer limited to treating acute disease and injuries; it increasingly includes managing chronic conditions, wellness visits, and other types of preventative care services. Mental health is also being included under the overarching umbrella of necessary medical care, as is coverage for dental, vision, occupational/physical therapy, and nutritional services.

Given the increasing array of specific care services, it should be no surprise that the accompanying library of data points measuring all of them is equally large, and still growing. Some of the most well-established components of this metric are geographic accessibility, the availability of services (and all necessary components to provide them), provider acceptance, and latency.

Geographic Accessibility

Geographic accessibility measures how easily individuals can reach healthcare facilities based on location and transportation access. Rural areas or areas with limited transportation options may have reduced access due to long travel times. Telemedicine has improved access for certain specialties within some remote areas but has yet to prove a panacea to overcoming geographical barriers to meet all care needs.

Availability of Services

There are three sub-components within the availability of services metric, and all three are required to constitute the provision of a medical service. These are:

  1. The presence of healthcare providers trained to provide a particular service
  2. The availability of staffed and licensed clinics, hospitals, and/or facilities in which the providers can perform the service
  3. The specialized equipment, tools, and supplies required by providers and their support staff to perform the service

Provider Acceptance

Provider acceptance can often be the delta between the appearance of service availability, and the reality for patients seeking care. A particular region having the necessary trained physicians, hospitals, and tools to do the job does not mean that those physicians will agree to take on new patients. They may need more bandwidth to do so, or they may no longer be able to break even with a certain type of health insurance plan prominent in the area. Provider acceptance measures the capacity for new patients to take advantage of those available services.


Latency refers to the time delay for a patient seeking medical care to receive it—in short, the wait time to obtain services for non-emergency care. The number of days a provider is booked out before a new patient can be seen is one of the most carefully monitored data points within most health systems and practices because of its close correlation with patient satisfaction surveys and revenue cycles. This metric can represent the difference between a region seemingly having its population health needs met on paper, and a reality of care delays negatively impacting survival, life quality, and other measures of population health outcomes.

Access metrics are not limited to these groupings, but these represent some of the most commonly referenced areas from which data points are gathered for reporting, debate, analytics, and more. The Centers for Medicare and Medicaid Services ( provides a rich repository of datasets on access metrics at no charge to the public.

Affordability of Care

Affordability refers to the financial aspects of healthcare from the vantage point of the individual consumer (or patient). These data are used to examine whether individuals can pay for the care they need without experiencing financial hardship. These areas include out-of-pocket costs, health insurance coverage, and income and socioeconomic factors.

Out-of-Pocket Costs

Out-of-pocket costs are the cluster of expenses individuals must pay directly for healthcare services, including deductibles, copayments, and coinsurance. This is the broadest group within the affordability bucket, such that the two terms are commonly used interchangeably, but do note while they are very similar, they are not the same.

Prescription drug costs would be included in this category, and they also represent a substantial portion of out-of-pocket costs for the average consumer. This is particularly so for aging populations. Prescription costs are especially important for their substantial cost contribution and because traditional Medicare coverage—upon which many aging individuals rely—excludes coverage for prescription medication costs. This is the “donut hole” problem.

Health Insurance Coverage

This term refers to the extent to which individuals are covered by health insurance, either through public programs like Medicare or Medicaid, private insurance plans through an employer, or individual plans (such as those purchased through the ACA marketplace). The percentage of a population with active coverage is a frequently referenced metric that closely ties to health outcomes and particularly so with outcomes for chronic diseases such as diabetes and hypertension.

This group of data variables would also include the percentage of a population that is underinsured. This refers to individuals whose health insurance coverage plans are inadequate for their care needs, such as those plans with an excessively high deductible that must be met before any services being covered, or plans whose covered services list is quite limited. This metric is often overlooked and yet quite relevant, as such plans can deter individuals from accessing care.

Some individuals choose those plans because they cannot afford a higher monthly premium. Others may be stuck with such a plan because it is the only one offered to them through their employer-sponsored coverage. Data on underinsured populations is rapidly gaining the interest of the public and decision-makers alike and is expected to have a growing impact on regulatory decisions and policy development.

Income and Socioeconomic Factors

Growing income inequality has increasingly highlighted the relationship between an individual’s income and their ability to afford healthcare.

Affordability, after all, is indeed quite relative. Lower-income individuals may face more challenges in affording necessary healthcare. This is particularly the case for those who earn too much to qualify for fully subsidized care such as Medicaid, but not enough to afford monthly premiums for a private plan while still being able to cover other necessary costs such as housing, food, and transportation.

Access and Affordability are Intertwined

Within the context of our healthcare sector’s effectiveness, metrics of access and affordability remain tightly intertwined. Historical data have demonstrated these correlations beyond a doubt.

Greater affordability of insurance coverage positively impacts access. Health insurance coverage enhances access by reducing financial barriers in the shorter term. Insured individuals are more likely to seek healthcare when needed, demonstrating a greater ease of access.

Lower affordability from high out-of-pocket costs negatively impacts access. High out-of-pocket costs can be a significant barrier to access, whether from a lack of health insurance coverage, or coverage with a substantial deductible. So even if healthcare services are geographically accessible, they are pragmatically inaccessible if unaffordable.

Preventative care services for insured individuals are increasingly offered without out-of-pocket costs, even with high deductible plan coverage. However, identifying acute issues from a preventative care encounter can entail patient follow-up expenses. Furthermore, uninsured individuals lack even the opportunity for an initial wellness visit. In either case, reduced affordability reduces access to care.

Greater access to preventive care services positively impacts affordability. Preventive care access encourages individuals to seek early interventions and screenings by removing financial and logistical barriers, and in some cases by expanding incentives. Over time, enhanced access to timely preventive services can wholly or partially mitigate many health issues that otherwise would become more severe, and ultimately require more resources to treat in the absence of earlier care.

These are just a few mechanisms demonstrating the interconnectedness between affordability and access for the U.S. healthcare sector. As the capture and aggregation of healthcare data continue to grow exponentially, our understanding of these relationships will also continue to grow. However, greater understanding does not necessarily mean they will be leveraged more thoughtfully.

Conclusion: Creating a More Effective Healthcare Sector

The relationship between access and affordability in assessing the healthcare sector in the US underscores the need for a comprehensive approach to healthcare reform. Policies and initiatives must address both dimensions to ensure that individuals can access necessary healthcare services without financial hardship, ultimately leading to a healthier population and a more efficient and equitable healthcare system. Those are long-term goals.

In the immediate term, pushing for debate that takes a different approach in referencing data may be significantly impactful when arguing for a potential policy approach. When we ignore one of the two sides of the coin—ignoring access when discussing affordability and vice-versa—important nuances of the discussion are lost, and the potential consequences of one-sided policies can be obfuscated.

If we can improve the language in which we conduct debate on this matter, we can improve the odds of generating policy that can effect meaningful and measurable change.

Elizabeth Bradford Kneeland, MBA
Elizabeth Bradford Kneeland, MBA

Elizabeth Kneeland is a writer and entrepreneur living in Philadelphia. As a small business owner, she spends much of her time creating content, researching markets, and refining financial models. Her career has straddled novel operational and financial modeling, and traditional academic research within the healthcare sector, providing her with a unique perspective on programmatic development. She built the first for-profit telemedicine program for the University of Pennsylvania Health System in 2015. She also has helped build and scale sleep medicine startups in the U.S., China, and Taiwan.

Kneeland has co-authored publications in peer-reviewed journals on topics ranging from device validation to clinician-level educational interventions and has been an invited speaker at medical conferences throughout the U.S., China, and Taiwan. She has most recently contributed to discussions on healthcare technology as a research analyst focused on analytics, real-world data, and patient privacy legislation.

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