Category Archives: Utilization

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.

Utilization in Indiana

A new article in Modern Healthcare looks at the Healthy Indiana Plan, a Medicaid expansion program that has yielded some interesting results. Here’s an excerpt from the Modern Healthcare piece:

While the jury is still out on how well the health savings account and preventive-care incentive are working, analysts have looked at utilization trends among the newly insured and found that those signing up for the program are sicker and more frequent users of healthcare than those enrolled in commercial, employer-sponsored health plans.

The Healthy Indiana Plan “population used more care than the typical commercial population in Indiana with the same age and gender characteristics,” says Rob Damler, principal at Milliman, a consulting and actuarial firm. Damler is the consulting actuary to the state of Indiana on the health plan.

Childless adults enrolled in Healthy Indiana, for instance, had nearly three times as many inpatient services as private plan members in the first year. And pharmacy use was nearly 50% higher than a typical commercially insured population.

This newly enrolled group was also sicker than the general population. Their relative morbidity was 65% greater than their peers covered by private health insurance. The earliest enrollees to the program also proved to be the sickest, with the highest healthcare costs, Damler says.

This phenomenon is called anti-selection, where the least healthy population seeks healthcare coverage available to them, driving up the costs to insurers and the population covered.

The Healthy Indiana Plan offers some considerations for national reform, Damler says. “One of the issues that needs to be understood is pent-up demand,” he says. “We need to be prepared that the newly insured may cost more in the first 12 to 24 months than the insured population.”

Not surprisingly, insurance companies say that without a federal law requiring everyone to carry health insurance, national healthcare reform won’t work because the chronically ill will sign up for coverage in large numbers, driving up costs, while the healthy will stay on the sidelines.

“It only works if everyone’s covered,” says Alissa Fox, senior vice president of policy at the Blue Cross and Blue Shield Association.

Healthy Indiana Plan: Enrollee utilization

The Healthy Indiana Plan (HIP) is a Medicaid expansion program that offers perspective on the cost and utilization patterns of the uninsured as they enroll for coverage and access care. What follows is an analysis of the experience data from this program.  


Illustrating cost patterns during initial period of enrollment

The HIP populations also followed a particular pattern of utilization during the initial enrollment period. Figures 7 and 8 show measurements of inpatient, outpatient, pharmacy, and physician expenditures relative to average PMPM costs, first for caretakers and then for non-caretakers (for explanation of these populations, see the full paper). The 100% line measures the average PMPM for the first year of coverage for the population represented.


Figure 7: Caretakers



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