Imagine you’re an oncologist faced with a difficult treatment decision for a patient. At your computer, you sift through all the data in their electronic health record—their demographics, cancer diagnosis, and clinical history. But one critical component is missing: the patient’s molecular profile, which is the key to personalized treatments. Then you hear the screech of the fax machine.
Most oncologists don’t have to imagine this. It’s standard practice for molecular test results to be faxed, or downloaded as a PDF then printed in a wide range of layouts that vary by testing provider. This makes it nearly impossible for oncologists to take full advantage of this crucial data, and deprives patients of the best care.
Molecular testing labs use some of the most advanced software-connected machine and analytics on earth, yet have managed to turn structured digital genomic data into paper!
At MedCity’s CONVERGE conference July 31-August 1 in Philadelphia next week, I’m speaking on this issue and other challenges that are holding back precision medicine.
Precision medicine already works, and health systems are using it successfully today to deliver better treatments to patients. But to truly scale precision medicine to providers across the country and fight cancer today, we need to modernize how molecular test results are structured, transferred, and shared. This would give providers, payers, and policymakers the real-world evidence needed to shift rapidly toward precision medicine.
Molecular tests, which give critical insights into the genomic makeup of a patient’s condition, are the foundation of precision medicine. But they’re newer and more complex than other lab tests, and the result data are not standardized, portable, consistent, or structured like common blood tests and almost all other “routine” lab results.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
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Standardizing this data will enable multiple health systems to communicate and exchange information. This improves the quality of care by helping physicians uncover new insights and saving them time that they can dedicate to more critical tasks.
Oncologists need to be able to easily access molecular data in the same system as the patient’s clinical data—not as PDF attachments or faxes—so that patients can get the personalized care they deserve. They can then make more effective treatment decisions, and more accurately and quickly match patients to the right drugs or right clinical trials at the right time. Precision medicine is already changing how health systems treat certain forms of cancer, and standardizing molecular data will accelerate this.
Standardizing molecular data has broader implications, too.
One of the biggest questions around precision medicine is the economics. Can you deliver it cost-effectively? The answer is yes, and many case studies demonstrate the value to patients, providers, and payers. But for payers to buy in, they need real-world evidence on a much larger scale. Standardizing molecular data would help address this.
It would also facilitate better data sharing among providers. In the past year, health systems have rapidly moved toward sharing de-identified cancer data, largely spurred by former Vice President Biden’s Cancer Moonshot initiative. But you can’t share data at any meaningful scale when that data is faxed from department to department.
We now know that precision medicine not only works but can lead to better outcomes and quality of life. These benefits will expand exponentially as more providers adopt precision medicine, as they will have a much larger pool of real-world data to inform their insights. Now we need to make it easier—not harder—for organizations to access and share the real-world data that will enable this shift.
