Category Archives: Utilization

Healthcare industry must evolve with direct-to-consumer genetic testing utilization

The growth of direct-to-consumer (DTC) genetic testing presents several medical and financial implications for the healthcare industry to consider. In this article, Milliman consultant Barbara Culley examines the demand for DTC genetic testing and industry concerns. The excerpt below highlights a few of them.

Providers and regulators have expressed concerns about the DTC process, including the absence of healthcare providers from it, concerns for unnecessary testing, patient anxiety, erroneous test results, and misinterpretation of test results by untrained consumers. Insurers have concerns about the possibility of adverse selection. If a person knows they have a positive indicator for a disease, will those people seek insurance in greater numbers than those without any genetic concerns?

One key factor driving concerns is that not all persons with a positive genetic test will develop the indicated disease. Many variables, including environment, personal lifestyle choices, and other genetic factors, have a significant impact on health. Concern exists that genetic test results may lead to unnecessary treatment. In the case of false positive and false negative outcomes, the added concern is that treatment choices may be made in error. In a 2012 study13 of 179 people from four countries, the average person was found to have about 400 defects in their genes, some associated with disease. However, these people were well. Even if results cause people to seek insurance or care, they may never develop the disease indicated by genetic testing.

Concerns have been raised about the potential for DTC testing to create added costs and consume resources in the healthcare system with little value. For example, positive DTC genetic testing results may lead consumers to follow up with their physicians for treatment based on a test result that may not indicate a genuine health issue.

Barbara also provides some actionable measures that health plans can take to respond effectively to the evolving DTC genetic testing market. The following excerpt summarizes her considerations.

Continued growth in the marketplace is probable with the recent FTC approvals for DTC genetic testing and health risk reporting for diseases. Growing consumer use and subsequent follow-up with healthcare providers and insurers can be anticipated.

Data trends indicate consumers are likely to seek information perceived as helpful in self-management of health. Health plans may wish to consider how to best respond to the evolving impact of DTC genetic testing and subsequent member expectations with education, tools, and medical policy that optimize the use of these tests and support member engagement in their health.

Transgender health coverage considerations

This paper written by Milliman’s Susan Philip and Andrew Naugle highlights recent state and federal policy changes concerning healthcare benefits for transgender individuals. The authors also examine health insurance clinical policies governing the coverage of services related to gender transition. Lastly, Susan and Andrew provide future considerations for healthcare payers, including appropriately capturing data relevant to the healthcare needs of the transgender and gender nonconforming population.

Reduce healthcare’s long-tail problem with telemedicine

Technology has enabled many industries to reduce or eliminate the long-tail problem. Similarly, telemedicine offers the healthcare industry a solution to its long-tail problem—access barriers to healthcare services. A new article entitled “Telemedicine and the long-tail problem in healthcare” by Milliman’s Jeremy Kush and Susan Philip explores the benefits of telemedicine as a mode for healthcare delivery. The authors also analyze current levels of telemedicine utilization and identify five factors limiting adoption.

Introduction to utilization health waste in the U.S. healthcare marketplace

The American healthcare system is experiencing rapid change, largely driven by the recognition by both public and private payors that the trajectory of healthcare spending growth must be slowed. Despite the recent slowdown in healthcare spending growth, which many attribute to the recession, efforts to transform benefit design and payment systems are proceeding rapidly. For example, public payors are both cutting payment rates and experimenting with bundled and global payment models. Private payors are adopting similar payment models and developing more sophisticated benefit designs that encourage patients to seek care from low-cost and maybe high-value providers, and to avoid expensive and maybe low-value services.

In this environment it is crucial to try to eliminate waste. The new payment models allow providers to share some of the savings if utilization of wasteful services can be curtailed. The challenge of course is identifying which services are wasteful. The fact that waste exists in the healthcare system is widely accepted. Berwick and Hackbarth (2012) estimate there is about $200 billion in waste that is due to overtreatment in the U.S. healthcare system, almost 10% of total spending.

But eliminating that waste may be a challenge. Like everything in healthcare, the waste is likely to vary across geography and, more importantly, across providers. Identifying which providers to focus on is a challenge. More fundamentally, aggregate measures of waste are not necessarily helpful to providers. Detailed, operational measures that can be applied to provider systems are needed. Fortunately, there has been a recent increase in effort to identify wasteful services. A number of lists exist. Perhaps the most prominent of these efforts is the Choosing Wisely campaign, sponsored by the American Board of Internal Medicine Foundation, which challenged specialty societies to identify wasteful practices. Other panels, such as the U.S. Preventive Services Task Force, have identified services that might be wasteful. Thus clinically meaningful knowledge of what is wasteful exists.

Translating the knowledge of what is wasteful into tools that can be applied to identify that waste at the system level is difficult. Claims data is not ideal in many cases to identify waste. Often the measures of waste depend on patient history. Clinical knowledge and information technology (IT) expertise are needed. Academic efforts to quantify waste using subsets of available measures are just beginning. For example, using a limited number of services, Schwartz et al. (2014) find that 0.6% to 2.7% of Medicare spending may be wasteful and between 25% and 40% of beneficiaries have received at least one low-value service. Moreover, they found that there was significant regional variation in spending on low-value services, suggesting some providers are more prone to use them than others. Finally, different measures of low-value services were correlated across regions, suggesting that measures of them based on a small number of services may be indicative of broader patterns of waste.

Commercial tools to quantify practice patterns will be crucial to many cost containment activities. Data can help focus efforts on reducing waste and thereby improve value. Such tools could be used to support payment reform, provider education, tiered benefits, or even value-based insurance designs. One way or another, spending growth must be contained. Our goal must be to do so in a way that improves value.

This article first appeared at Milliman MedInsight.

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Identifying potentially overutilized medications

Often lying off the radar screen of many insured populations is the high number of prescriptions for opioid analgesics (or narcotic analgesics). These drugs are prescribed for pain relief for a wide range of conditions and the potential for abuse of these medications has grown over the past several years.

High utilization patterns of these prescription drugs do not always attract attention because, compared to other classes of medications, they are relatively inexpensive. As seen in Figures 1 and 2 below, based on a 50,000-patient commercial dataset, when sorted by total allowed charges, opioid analgesics rank 13 in terms of highest total cost, but when we sort by number of prescriptions, this therapeutic class jumps in rank to 2.

There are several methods for analyzing utilization of drug classes to identify opportunities for intervention. For a broad class of drugs, such as opioid analgesics, it is useful to drill further into the therapeutic classes. In Table 1, we see that the highest number of prescriptions were filled for hydrocodone combinations (including drugs like Vicodin), followed by opioid agonists (a category of very strong analgesics including morphine and Oxycontin) and codeine combinations (including drugs such as Percocet and Percodan.)

Analysts may want to analyze utilization by network or geographic areas to determine if specific markets have higher utilization rates compared to others. Table 2 displays prescription utilization by plan, revealing that Plan 3 had the highest utilization rate for these drugs.

Identifying possible cases of abuse typically involves drilling down to the provider or patient level. Table 3 illustrates an example analyzing utilization by primary care physician (PCP).

A complete analysis would include additional reports to better understand the prescribing physician specialties, the types of conditions they treat (chronic use of pain medications for periods of time may be appropriate for some conditions such as cancer), days supplied, and refill rates. At the patient level, it may also be important to quantify how many different providers have prescribed these drugs, as one physician is not likely to know what other physicians have prescribed for that patient, if the patient has not disclosed that information.

Analgesics are not the only class of drugs that have the potential for abuse. Generic Engineering & Biotechnology News recently “put together a list of 14 top abused prescription drugs, as listed by the [Centers for Disease Control and Prevention, the Food and Drug Administration], and nongovernment nonprofit sources on public websites.”

Their list is as follows (listed by drug brand name):

1. Oxycontin
2. Concerta
3. Ambien
4. Zoloft
5. Ritalin/Focalin
6. Adderal XR
7. Lunesta
8. Opana ER
9. Xanax XR
10. Vicodin
11. Fentora
12. Percocet
13. Valium
14. Ativan

This article first appeared at Milliman MedInsight.