Integration of Novel Diagnostics into Clinical Practice

John Schneider, Ph.D

By John Schneider, Ph.D

Similar to drug and device markets, a critical part of the success of novel diagnostics is educating providers on clinical and therapeutic utility.  However, unlike pharmaceutical drugs, the ordering of a diagnostic test does not necessarily imply a change in provider behavior or a change in treatment strategy.   It’s a bit like whether a tree falling in the forest makes any noise.

Medicine is as much art as science, and many physicians continue to rely disproportionately on intuition, traditional forms of diagnosis, and the standard practices of the medical community—all of which are reinforced by practice cultures which vary by setting, geography, training, and so on.

With the exception of diseases and conditions in which clinical practice guidelines (CPGs) strongly promote the use of a particular diagnostic, not only are physicians under no obligation to order a test, but they are under no obligation to take action based on a test result.

For a diagnostic to have “value”—clinical or economic—the results of the test must in some way lead to a change in provider behavior in the form of an altered or augmented treatment approach, the ordering of additional tests, or referral to additional services.

This may be less of a problem for companion diagnostics, where the bundling of tests and treatments explicitly links test results and provider behavior, as in the case of, for example, the companion diagnostic HER2/neu in treatment of breast cancer. HER2/neu helps identify patients who will benefit from the oncology drug trastuzumab (Herceptin®),[1] and oncology guidelines have explicitly recognized this.[2]  But other molecular diagnostics are only effective if provider behavior changes in response to test results.  Again, the existence of an ordered test does not necessarily compel caregivers to deviate from the status quo.

The path toward moving genomic diagnostics from the laboratory into clinical practice is heavily dependent on the generation of reliable evidence of clinical utility.  A diagnostic test has clinical utility if its use leads to clinician decision making resulting in improved patient outcomes.[3]  With sufficient evidence, clinicians in leadership roles are more likely to promote the use of novel diagnostics, and such promotion is a critical first step toward incorporating biomarkers into clinical protocols and CPGs.[4]


-John E. Schneider, PhD & Cara M. Scheibling

Avalon Health Economics



[1] J. Cohen, A. Wilson, and K. Manzolillo, “Clinical and Economic Challenges Facing Pharmacogenomics,” Pharmacogenomics J 13, no. 4 (2013).

[2] S. H. Giordano et al., “Systemic Therapy for Patients with Advanced Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline,” J Clin Oncol 32, no. 19 (2014).

[3] M. R. Trusheim et al., “Uncertain Prognosis for High-Quality Diagnostics: Clinical Challenges, Economic Barriers and Needed Reforms,” Pharmacogenomics 14, no. 3 (2013).

[4] R. Simon, “Lost in Translation: Problems and Pitfalls in Translating Laboratory Observations to Clinical Utility,” Eur J Cancer 44, no. 18 (2008); S. E. Taube, J. W. Jacobson, and T. G. Lively, “Cancer Diagnostics: Decision Criteria for Marker Utilization in the Clinic,” Am J Pharmacogenomics 5, no. 6 (2005).

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Removing Bias from HEOR: Standardizing the Costs of Adverse Events

Jim Davis

By Jim Davis

What are the total costs of prescribing a particular drug? At its core, all health economics and outcomes research (HEOR) studies are designed to answer this fundamental question. And it is this answer that helps to remove barriers to market access. However, economic models are often cited as the least helpful aspect of dossiers that are provided to Managed Care.

Why? It’s simple – bias. In a recent survey conducted by Dymaxium via the AMCP eDossier system, presented at the May 2015 ISPOR Philadelphia Workshop session, “Making Better Use of Company Pharmacoeconomic Models”, the top reason for not using models provided by life sciences organizations is over concern with potential bias.

Historically, hard to calculate data points that power economic assessments, such as the costs of drug side effects, have come solely from manufacturers which because of their inherent conflict of interest, makes it fundamentally flawed. It is just smart marketing to ensure the numbers work out in favor of the manufacturer’s product. However, healthcare decision makers involved in reimbursement decisions are smart. They saw through attempts to spin data, and subsequently have paid little attention to HEOR that is sponsored by pharma. This however, did not negate their desire and need for unbiased HEOR data.

In order to fill this need and to correct for this bias, managed care has sought out independent data sources. Sources that rely on big data and real world evidence that have allowed for comparative effectiveness (including safety) research to take great leaps forward.  The inherent bias and the availability of these independent, real world data, has pushed value assessment outside of the control of manufacturers and in turn, healthcare decision makers simply no longer have to rely on manufacturer controlled data.

In a world where the payer has more control over the success, and more importantly the failure, of manufacturers’ products, it is of vital importance to give the customer what they want.  We’ve seen this first hand in our business at AdverseEvents. Upon launch in January 2014, pharma was less than thrilled with our disruption of the drug safety status quo. But as uptake at managed care organizations has grown, we are now seeing those same detractors looking for ways to use our data and analytics in their studies.

Why is standardized data, such as the costing of adverse events so valuable? Because making comparisons by class, indication, or mechanism of action are direct and plain. Methodologies are transparent and replicable. And most importantly healthcare decision makers can more quickly and accurately make important decisions.

In the case of a standardized adverse event costing metric such as RxCost®*, which calculates the average downstream medical cost from adverse events, it allows the user to track the economic results of emerging safety issues in new drugs, compare those to more established drugs, and even quickly formulate a financial model for novel developments like biosimiliars.

The ability to quickly obtain, easily review, and plug these data into cost effectiveness models, budget impact models and other supporting information for formulary decisions will dramatically improve patient outcomes and lower downstream medical costs. It’s that simple. And with an estimated $25 billion in avoidable serious events and negative patient outcomes from drug adverse events hitting the U.S. healthcare system in 2013, it seems the introduction of standardized costing methodologies, and other independent, real world evidence is right on time.

To learn more, download the RxCost white paper or contact us directly.

*RxCost® is part of the AdverseEvents Explorer platform that is being used by managed care organizations to ensure that healthcare decision makers have vital independent adverse event and outcomes costing data available during their drug purchasing and formulary management processes. This means they now have the tools to be proactive instead of reactive in their attempts to mitigate this enormous cost burden that adverse events have on their bottom lines.


Written by Jim Davis

EVP, AdverseEvents

As Executive Vice President, Jim manages the company’s global sales and business development efforts. Jim brings over 12 years of experience in commercial strategy, global sales management and execution, business development, and product development. He has over 9 years of specific domain expertise in biopharma market research, intelligence, and data.

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