Tag Archives: Drug Utilization

Opioid prescription patterns affect risk scores

Opioid prescribing nationwide peaked in 2012 at over 80 prescriptions per 100 persons. Between 2012 and 2016, the prescribing rate decreased by almost 20%. Even after this decline, 19% of the U.S. population filled at least one opioid prescription during 2016.

As opioid prescribing declined, many doctors switched to other pain relief drugs. The change in prescribing patterns has potential implications for risk adjustment, because some of the drugs now being used for pain relief were previously flagged in pharmacy-based risk adjustment models as associated with high-cost conditions such as multiple sclerosis.

This brief by Christine Mytelka, Melanie Kuester, Colin Gray, and Lucas Everheart provides data on the decline in opioid prescribing and the increased use of other non-opioid pain relief drugs. Additionally, it addresses the corresponding effect that changing prescribing patterns may have on evaluating population health and risk-adjusted payments in risk-based managed care programs.

Hepatitis C treatments: Emerging trends

The high cost of therapy for patients with chronic hepatitis C (HCV) infection has been an important topic of discussion for key stakeholders in pharmacy benefit design and management. Multiple effective treatments have been introduced, with cure rates approaching 100%.

Although costly, curing HCV early on can prevent serious liver complications, such as hepatic cirrhosis, organ failure, and cancer, for the approximately 2.7 million affected people in the United States.

In 2016, there was a downward cost and utilization trend for the HCV Specialty category. Express Scripts reported in its 2016 Drug Trend Report that utilization of HCV therapies had decreased by 27.3% and the unit cost had decreased by 6.7%. The cost per member per year (PMPY) for HCV drugs decreased to $25.26 PMPY from $38.44 PMPY the previous year.

Why have cost and utilization suddenly decreased after two years of steady growth?

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Prescription drugs propel healthcare costs upward

Weltz-ScottUntil recently, prescription drugs have not materially altered the trajectory of the Milliman Medical Index (MMI). With drug costs being roughly 15.9% of the annual healthcare expense of our typical family of four, drug trends must be very different from other categories of care to materially influence overall healthcare cost trends. Nevertheless, they are doing just that. As discussed below, a combination of forces is creating the perfect pharmaceutical storm. Employers and families alike feel the financial consequences more than ever when a prescription is filled.

Specialty drugs
Specialty drug expenditures have grown over the past 25 years to the point where they now account for one of every three dollars spent on prescription drugs. While various definitions of specialty drugs are used in the industry, they nearly all have a common denominator: They are expensive. Medicare defines specialty drugs as those costing more than $600 per prescription. But the costs can be much higher than that. The category includes the well-publicized treatments for hepatitis C, some of which cost more than $1,000 per dose. Many subclasses of specialty drugs have huge potential to improve health outcomes, but also come at a significant cost. Experts project that the pipeline for specialty drugs is substantial and likely will not subside in the near future as manufacturers focus their efforts on targeted genetic profiles and rare disease states.

Compounded drugs
Compounded drugs have become one of the most costly components of pharmacy spending over the past several years. In fact, several recent national news stories have focused on this phenomenon. The U.S. Food and Drug Administration (FDA) defines compounding as “a practice in which a licensed pharmacist, a licensed physician, or, in the case of an outsourcing facility, a person under the supervision of a licensed pharmacist, combines, mixes, or alters ingredients of a drug to create a medication tailored to the needs of an individual patient.” Many of these drugs (often custom creams and ointments) have questionable clinical value, and compounding pharmacies are receiving much higher prices for compounds than the individual ingredients they comprise. As a result, drug trends have accelerated for compounded drugs, which is due in part to more limited regulation for them than for other drugs, along with increases in the average wholesale price (AWP) of these drugs. Pharmacy benefit managers (PBMs) have responded with specialized programs targeting the inappropriate use of this emerging drug class.

“Patent cliff” aftermath
Over the past few years, many brand-name drugs have lost patent protection, including some of the highest-grossing drugs in history such as Lipitor, Plavix, and Nexium. This “patent cliff” benefited consumers because generic versions became available at much lower costs. Now that many of the heavily utilized brand-name drugs have lost patent protection, the year-over-year price reductions produced by generic shifts have slowed as well. In addition, there is also evidence that generic price trends are on the rise, where in past years they have been flat or even negative. Manufacturer consolidation and reduced competition, particularly among generic manufacturers, may be a contributor to the increase in cost.

AWP increases
By some accounts, the cost of brand-name drugs is accelerating at a rate of over 10% annually, with generics increasing at a slower rate, but still higher than in the recent past. In large part, this has to do with the AWP increases themselves and with the fact that many payers’ contracts are tied to a discount off the AWP. Because most employer prescription drug contracts are based on discounts from the AWP, consumer prices are at the mercy of the manufacturer’s price increases.

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Do not overlook the value of claims auditing

When seeking ways to keep expenses under control, healthcare plan sponsors may overlook the value of claims auditing. Auditing fees may not be inconsequential, but the fact is that an accurate audit including both pharmacy and medical claims has the potential to pay back the investment many times over.

The more members a plan has enrolled and the more complex the plan’s benefit setup, the more likely a plan is to have a greater number of claims payment errors. Every plan will have claims paid in error. It is often tempting simply to assume claims have been paid with a certain degree of accuracy, and then move on without verifying whether the assumption is correct. Nevertheless, there will always be instances of duplicate billing, wrong or missing discounts and rebates, mistakes in member eligibility, incorrect plan setup, or other problems.

Regular audits of medical and pharmacy claims can find these discrepancies, leading to the recovery of overpayments. Even more importantly, audits can identify problems in the way a plan is set up and point the way to eradicating inaccuracies, reducing cost, and preventing waste in the future. Auditing can give plan sponsors vital information for revising and improving contracts with third-party administrators (TPAs) and pharmacy benefit managers (PBMs), which can lead to significant reductions in costs.

Many engagements begin with auditing one plan year, and then extend to multiple plan years based on the results of the first audit. Some plans have implemented processes that include monthly oversight reporting, which provides ongoing auditing and trend metrics. The monthly reporting is set up as an online service so that the reports can be automatically emailed to the health plans and accessed via an encrypted web portal.

Auditing is applicable to all types of healthcare plans, including self-insured plans, Medicaid, Medicare, Taft-Hartley funds, and commercial plans. In our opinion, any organization that is at risk for paying medical or pharmacy claims must consider the value of claims auditing.

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Using administrative claims data for quality reporting

Developing healthcare quality metrics based on administrative claims data has become increasingly common over the past several years. The National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the Centers for Medicare & Medicaid Services (CMS) Pioneer Accountable Care Organization (ACO) program and Oregon Coordinated Care Organization program have included claims-based quality measures as requirements for program participation.

Most claims-based measures are process-based, evaluating if appropriate services are provided for specified groups of patients, or identifying potential overutilization of services, but claims data are not the sole source of quality measurement. Survey data are often used for patient satisfaction and operational measures, and there is increasing use of lab results and electronic health record (EHR) data to expand the clinical components of quality that can be measured—a topic for another posting.

Despite the expansion of claims-based quality measures, some still question their merit. Those citing concerns point out known limitations associated with analyzing claims data, including:

• Potential errors or inconsistencies in coding.
• Availability of required data sources may be constrained if components of benefits are administered by multiple sources.
• Lack of complete clinical information.
• No diagnostic coding for blood pressure, laboratory results, or pathology results.
• Clinical information is limited to conditions for which the patient was treated and submitted a claim. A noncompliant diabetic may have no claim history of the disease.
• Timeliness of data is impacted by claim lag.

However, the advantages of analyzing claims data greatly outweigh the limitations noted above. The advantages include:

• Data are commonly available and relatively inexpensive to analyze
• Data are available for very large populations, allowing for more robust sample sizes
• Coding accuracy has improved dramatically over the past 20 years
• For some types of measures, claims may produce a more accurate picture than even chart reviews

An example of this last point would be measures focusing on patient compliance with medications. A physician may regularly write refill prescriptions for a patient’s hypertension medication, and those refills may be well documented in the patient’s chart, but those data provide no real evidence that the patient filled those prescriptions. Tracking actual claims for prescription refills is a much better measure. Granted, submitting a claim for a hypertension medication does not prove that the patient actually took the medication at the appropriate frequency, but a regular, ongoing refill pattern is a better proxy of medication adherence than chart review information.

Days supplied is commonly available on claims data, making it easy to calculate “possession ratios” to monitor patient compliance from pharmacy claims. A simplistic way (additional conditions can be added to the calculation) to measure possession ratios is demonstrated in Table 1 below. For patients continuously enrolled during a 180-day period and previously diagnosed with hypertension, the possession ratio for each patient is the sum of all days supplied on their prescriptions during the study period, divided by 180 days.

Although claims data are not perfect for clinical reporting, they will continue to be a valuable and important source of data for quality reporting for a selected set of metrics.

This article first appeared at Milliman MedInsight.

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.