How Medtech Can Benefit From Health Economic Analyses And Cost Effectiveness Data

By Edward Black, founder and principal, Reimbursement Strategies

By Daniel A. Lace, Reimbursement Strategies, LLC.

As part of a comprehensive reimbursement plan, consider creating a health economics analysis (HEA) for payers. This article will review the definition of a health economics analysis, the different types of analyses, when to use a HEA, and some insights into a payer’s perspective on health economic analyses.

Along with the move toward evidence based-medicine, health economics has become a larger part of the current health care discussion. This is based upon concern for the sustainability of the U.S. healthcare system, providing more care to more people, reducing costs on a per capita basis, and focusing more on net health outcomes and quality. A HEA examines the balance between a technology’s additional health benefits and the costs associated with achieving those benefits. The HEA helps to answer the question “is it worth it?”  It provides a retort to the concern that "just because we can, does not mean we should."

There are several ways to create a HEA:

• Cost-Minimization – This method examines the least costly technology among one or more alternatives, all of which are assumed to be of equal benefit. This works well if it has been previously established that all of the technologies are substantially equivalent.  For example, generic drugs may be substituted for brand name pharmaceuticals based on similar outcomes with lower costs.
• Cost-Utility – In this method, technology costs are compared with quality of life (utility) adjusted outcomes, such as quality-adjusted life years (QALYs), which reflect patient preference for certain states of health. A more complex comparison is to calculate the incremental cost effectiveness ratio (ICER), which compares the difference in the therapies’ costs and the difference in their clinical effectiveness. Payers may be unwilling to pay for therapies that have a high cost without an associated high clinical benefit, as that balance does not represent a good value for the expenditure. QALYs, as a metric, offer the advantage of being a common unit that can be used for direct comparisons across different fields of medicine. 
• Cost-Effectiveness – This method compares costs of alternative technologies with clinical outcomes, measured in “natural” units, such as life expectancy or years of disease avoided. Costs are expressed financially, while health benefits for each technology are expressed in units like life-years gained. For example, a value of $100,000 may be assigned to each life year gained. The payer then can decide whether a differential of $100,000 between alternative therapies will be money well spent. Although the concept is straightforward to understand, it is not as straightforward to apply within the U.S. healthcare system. Cost-effectiveness and cost utility tend to be used interchangeably, although they have subtle differences and the term cost-effectiveness is most common. 

In cost-effectiveness, benefits analyses for two or more therapies are compared, looking at their effectiveness from a clinical perspective, as well as the costs to implement the therapy. For example, if existing Therapy 1 and new Therapy 2 have similar clinical outcomes, but Therapy 1 is less expensive than Therapy 2, then Therapy 1 would be considered to be more cost-effective. 

Rarely are the comparisons of two therapies this straightforward, though. If Therapy 2 is much more effective than Therapy 1, but costs two to three times as much, can it be cost-effective? In the realm of health economic analysis, it can. For example, if Therapy 2 is a one-time intervention that has lasting health benefits, the extra upfront costs may be a good tradeoff, compared to the periodic ongoing treatment necessary for Therapy 1. Continuing with that example, consider a one-time surgical procedure to treat a condition like benign prostatic hyperplasia, compared to multi-year pharmaceutical therapy.

Additionally, a health economic analysis can be performed from a variety of perspectives — societal, payer, patient, and provider. The perspective from which the analysis is performed will determine what costs are included. For example, from a payer perspective, the direct costs of the medical intervention, including the cost of drugs, procedures, inpatient and outpatient services, complications, and disease management may be included. From a societal perspective, the indirect costs may also be included: the cost of missed work days, additional transportation to obtain healthcare services, child care costs, lower productivity, early retirement, or premature death, as these all reflect costs associated with the disease state and medical intervention.

Obtaining accurate cost data can be a challenge, as well as finding data that span long time periods. For these reasons, health economic analyses usually are done using a Markov model. A Markov model provides a logical framework into which clinical outcomes and costs are entered, using short- and medium-term data to project long-term outcomes and costs. Within the model, a subject is placed in a health state and then moved to other health states to simulate chronic disease progression, as well as the different treatment choices that can be made, based upon the probability of this movement, from published clinical evidence. The transition between health states can include risk factors, complications, and expected patient outcomes.  Using short- and medium-term data, the model is manipulated to show how various treatment choices can influence costs and long-term outcomes.

However, Markov models have been challenged as a way to predict a given therapy’s long-term benefits within cost parameters. Markov models are only as accurate as the information that is used to create them, based on published clinical studies, demographic data, and epidemiological data. While studies may report the primary therapeutic outcome, there also may be secondary outcomes that can contribute to or detract from the patient’s health. Patient experience also may be included, as many studies record patients’ perceptions of their well-being; their satisfaction with care and the way in which that care was delivered.

Additionally, a sensitivity analysis can be performed using the model by varying the inputs in order to see how outcomes are affected. If variables are changed without altering the conclusion, then one can reasonably expect that the model accurately reflects comparative cost-effectiveness of the therapies. These models assist the healthcare community in understanding the price, as well as the value, of a therapy.

Payers look at cost comparisons to determine reimbursement for episodes of care, and a payer may determine that certain therapies should precede others. A good example is when a patient is prescribed a course of drug therapy before undergoing surgery, with the goal of controlling the condition over time at a lower cost than an expensive surgical option. Another example is implementation of an aggressive nutrition, dietary, and behavior modification program, before or in lieu of bariatric surgery.  

Cost effectiveness comparisons are an attempt to objectively compare therapies without intervening opinions and emotions. Such comparisons are designed to determine how best to do the most with the least amount of resources, optimizing value while still providing quality care.  These comparisons are becoming more common, and should be considered as part of a comprehensive reimbursement plan. Amidst the dearth of data in the medical device arena, a robust health economic analysis and solid cost-effectiveness data can be a strong competitive advantage.

About The Author

Daniel A. Lace, MD, CPE, FACPE is chief medical officer at Reimbursement Strategies, LLC. He has over 20 years of experience as a healthcare executive across a range of managed care, pharmaceutical, device, diagnostic and consulting businesses, having served as chief medical officer and other senior leadership positions in global pharmaceutical and device companies, as well as in national managed care organizations. He can be contacted at DLace@Reimbursement-Strategies.com