4 ways healthcare payers can better manage oncology drug costs with data analytics

Data analysis in key areas can help health plans and employers shape more effective strategies for managing oncology treatment cost.

By | 5 minute read | August 16, 2021

Oncology patient at home on couch receives hug from another woman

One of the largest drivers of spend in healthcare is specialty medication. In 2014, medical and prescription specialty drug treatment contributed USD 439 in annual allowed PMPY. In five short years, this ballooned to USD 1,025 in 2019 – a 133% increase.1

Many of the leading specialty drugs are oncology treatments, especially in the medical setting. Despite only contributing 9% of specialty drug patients in 2019, oncology treatments lent 26% of spend.1

There are several areas healthcare payers can analyze to better understand trends within their population and identify opportunities to encourage safe, lower-cost options. Including:

1. Biosimilars

Unlike traditional drugs, most specialty medications are manufactured using live organisms (or components from them). Because of this, most specialty medications will never have a traditional generic alternative – only a biosimilar.

Unfortunately, many payers confuse biosimilars for generics and think their vendors are actively managing. Additionally, payers often forget about specialty drugs administered in the medical setting, such as cancer medication. This can lead to missed opportunities to work with their provider network to encourage biosimilar use.

The U.S. Food and Drug Administration (FDA) has approved 30 biosimilars to date and more than half treat cancer.2 In the next few years, an additional 30+ medications are expected to come off patent.3 This will enable more competition across drug manufacturers and thus potentially significant savings for payers who develop a cross-benefit biosimilar strategy.

Monitoring the prescribing patterns and cost of these drugs can help payers find ways to work with providers and pharmacy benefit managers (PBMs) to use more affordable biosimilars, where clinically appropriate. Healthcare payers should also structure benefits to encourage competitive pricing across drug manufacturers.

Learn how Watson Health helps payers use healthcare data analytics to glean actionable insights

2. Provider partnership opportunities

Unlike prescription medication, which typically has a flat price point due to the strict management strategies of PBMs, pricing for oncology medication in the medical setting can be highly variable.  For example, while a majority of providers will charge USD 100 – USD 200 per milligram of J9310 – rituximab, 10 – 15% will charge more than USD 500.1

Usually this price variability is not due to the price of the drug itself, but rather the price of the facility. In 2019, IBM Watson Health found the average price per milligram for specialty drugs often cost half as much in the office setting as in an outpatient hospital setting.1

Healthcare payers should analyze the settings where their populations receive specialty drugs and review the variability in price for services at these locations. Through benefit design and strategic partnerships, employers and health plans can encourage patients to seek treatment in the most cost-effective, clinically appropriate locations.

See how Novartis Business Services used data to make decisions about the location of infusion therapy treatments


3. Clinical policy review and new-to-market drugs

As new oncology drugs come to market, healthcare payers should understand the potential impact of these medications on their population. Analyzing condition prevalence, disease severity and treatment history can help project utilization and spend of new, often very expensive, therapy options.

For example, if a new oncology treatment is approved as second-line therapy for relapsed / remitting patients, payers will want to structure their clinical policy to require failure of one or more therapies before using the new drug. Payers who do not coordinate these policies across both medical and pharmacy benefits are at risk of potentially very high unnecessary spend, where an older, less expensive treatment may be just as successful for the patient.

In one recent study, IBM Watson Health analyzed prescribing patterns in a payer’s network. We found  that certain providers were using newer, more expensive oncology drugs faster over the course of treatment and at a higher rate than other network providers, despite no difference in cancer type, disease progression or treatment length.

It is important for healthcare payers to keep up to date on new FDA approvals. They should confirm PBMs and medical carriers are setting appropriate coverage for new medications. Additionally, payers should consider value-based payment arrangements with providers to encourage oncologists to use the most clinically appropriate, low-cost option available.

4. Transparency of biomarker testing

Most new oncology medications target specific biomarkers (e.g., breast cancer biomarkers include HER2+ or HER2-, HR + or HR -). If patients use these biomarker-specific medications, and they have test results different than the drug is approved for, treatment will likely be unsuccessful.

Unfortunately, due to the lack of specificity in ICD-10 diagnostic coding, healthcare payers are unable to verify biomarker results in medical claims data. Currently, the only means of identifying these sub-types of cancer is by accessing biomarker lab data, which most do not have access to. This means that most healthcare payers cannot determine if these oncology drugs are going to the right patient or if better treatment options are available.

To add to this concern over the lack of transparency, biomarker-specific therapies are one of the largest growing areas for the future of oncology treatment. Soon, instead of an oncology drug treating only a certain type of cancer, it will treat all cancers with a certain biomarker target, regardless of where the cancer manifests.

We’re beginning to see this with the drugs pembrolizumab (Keytruda®) and nivolumab (Opdivo®).4 The FDA originally approved both medications in 2014 for advanced melanoma. They have since expanded to more than 17 types of cancer.5,6 Both drugs and all of the various approvals have one thing in common – a biomarker: PD-1.7 PD-1 is a protein found in high amounts on cancer cells that acts like a brake to the patient’s immune system, preventing T cells from killing the cancer cells.7 Drugs like Keytruda® and Opdivo® target PD-1 proteins to release the brakes and allow the patient’s own immune system to fight the cancer, regardless if the cancer is in the lungs or the kidneys.7

Healthcare payers should work with their medical carriers and lab partners to improve visibility into biomarker testing. This data is critical to develop and evaluate provider partnerships, value-based care arrangements, clinical policies and many other strategies for payers to manage oncology spend.

By analyzing their population in these four areas, healthcare payers can better understand the needs of their oncology patients and opportunities to make treatment more accessible and affordable.

Learn more about IBM® Health Insights® can help payers make the most of healthcare data

  1. IBM Watson Health Commercial MarketScan Benchmarks. Incurred January – December 2019
  2. FDA Biosimilar Product Information. July 2021. www.fda.gov
  3. Friedman, Yali. “DrugPatentWatch” DrugPatentWatch, thinkBiotech, 2021.
  4. Vinay Prasad, Victoria Kaestner. Nivolumab and pembrolizumab: Monoclonal antibodies against programmed cell death-1 (PD-1) that are interchangeable. Seminars in Oncology.Volume 44, Issue 2. 2017. Pages 132-135. ISSN 0093-7754. www.doi.org.
  5. Merck Sharp & Dohme Corp. Keytruda (pembrolizumab) [package insert]. U.S. Food and Drug Administration website. www.accessdata.fda.gov. Revised June 2020.
  6. Bristol-Myers Squibb Company. Opdivo (nivolumab) [package insert]. U.S. Food and Drug Administration website. www.accessdata.fda.gov. Revised January 2021.
  7. American Cancer Society. Immune Checkpoint Inhibitors and Their Side Effects. December 2019.