While each rare disease individually impacts only 200,000 people or less, with 7,000 known rare diseases, 30 million Americans, or 1 in 10 people, suffer from a rare condition.1 Half of those are children, many of which won’t even live to see their fifth birthday. Passage of the Orphan Drug Act (ODA) in 1983 to incentivize development of treatments for rare diseases has led to over 600 orphan drugs being approved, compared to just 10 the decade prior to the ODAs enactment.1 However, 40 years later, only 10% of rare diseases have approved treatments because of the many challenges rare conditions pose.1
Barriers in the Road to Treatment
The inherent small population of patients with a particular rare disease makes them difficult to find and recruit for clinical trials. This is further exacerbated by the fact that people with rare diseases are often misdiagnosed or under diagnosed. On average, it takes seven years or more for someone to receive an accurate rare disease diagnosis.1
This in turn means not as much is known about these complex diseases and their natural history. There is scarce medical literature and physicians aren’t as familiar with the conditions. Many of the diseases have no known cause, have multiple interrelated causes or were only just identified as being a rare disease. There is also a lack of diagnosis codes for rare diseases, with only approximately 500 of the 7,000 known rare diseases having proper codes, making it even harder to find patients to study.
Clearing the Path to Discovery
Along the rare disease patient’s long and arduous path to diagnosis, there will have been numerous doctor’s visits, medical procedures, hospital stays, lab tests and prescriptions. Accurately linking all of those real-world interactions across the various siloed data sources can reveal the rare disease patient journey, helping to identify patients, better understand the disease and accelerate development of treatments.
HealthVerity uses advanced identity resolution techniques to accurately match like patient records, creating a unique identifier, or HVID, that remains persistent over time and across data sources, including medical and pharmacy claims, electronic medical records, hospital chargemaster data, lab and pathology tests, and more, allowing researchers to connect all of this siloed data to help tell the patient’s story. Researchers can even link their primary study data to these other sources.
Within HealthVerity Marketplace, our vast healthcare and consumer data ecosystem consisting of over 75 unique data sources representing more than 330 million patients and 150 billion de-identified transactions, you can type the name of the rare disease you’re studying into the search bar and receive a drop down list with search options for potential diagnoses codes, drugs, procedures, biomarkers or lab tests that could indicate a person has that particular condition. By selecting the areas of interest, you can build a custom cohort that instantly tells you how many patients it includes and where there is overlap. Then you can license only the data you want and apply it to a variety of use cases:
Locate Rare Disease Patients and Their Health Care Providers - HealthVerity worked with a major pharmaceutical company studying an often misdiagnosed rare disease for which there was no diagnosis code. We were able to help them source data on particular medical procedures and lab tests indicative of the disease. This revealed healthcare providers treating potential patients that the pharma company had not previously been aware of, providing increased clinical trial participant prospects.
In another situation, we worked with a top 20 pharma company pursuing an under-diagnosed rare disease by providing a custom dataset comprised of medical claims and electronic medical records that was fed into a predictive model. Using the data and predictive model, the organization was able to create weekly tracking reports that identified potential patients and their treating physicians. With additional insights into comorbidities and treatment regimens, the company was able to further narrow the list of likely patients of interest and specialists who could be targeted for education.
With the right data, you can build a cohort of known patients and a control group for a predictive model that demonstrates the differences between the two groups to gain a better understanding of potential behaviors that may indicate someone who has yet to be diagnosed has the disease. This helps you identify them earlier in their journey for education and interventions.
Fill in the Gaps - Many rare diseases have registries that serve as a resource for diagnosed patients, connecting them with other patients, expert healthcare providers, and information on treatment options and managing their condition. Patients voluntarily enter their information into these registries so there could be gaps. They may not complete all of the fields or provide vague details. Additionally, the registry might not ask for information about other comorbidities or areas of interest to your study. Linking this data to real-world data (RWD) sources, you can fill in those gaps to expand your understanding of the patient journey.
Build Synthetic Control Arms - With small populations and the potential for a life-saving treatment, synthetic control arms are often needed in clinical trials for orphan drugs. This was the case for a top 20 pharma company designing a clinical trial for a new rare disease drug. Using advanced statistical methods, HealthVerity was able to help the company develop a custom RWD patient cohort that resembled the clinical trial population. This was able to be incorporated into the trial analysis plan as a synthetic control for submitting the trial findings to the FDA.
Target the Right Patients - Once an orphan drug goes to market, it is important to ensure it reaches the right patients at the right time. If there’s a diagnosis code, RWD can be used to find physicians treating patients with the correct diagnosis to educate them on treatment options. However, if there’s not a diagnosis code or there is a need to inform healthcare providers on the treatment option before other therapies are started, trigger programs can be established using RWD to alert you when a procedure is performed or a lab test is ordered or a particular result received.
Understand the Economic Impact - Using RWD to better understand the rare patient journey also gives insight into the total cost of care associated with a rare disease and related comorbidities. This information can be used to demonstrate the value of new treatment options to payers and regulators.
With all of the challenges that rare diseases pose, it can take nearly four years longer for an orphan drug to be approved compared to treatments for more common conditions.1 However, being able to accurately link a variety of RWD sources can reveal the rare disease patient journey and help clear the path to life-saving treatment options.
1 Pharmaceutical Research and Manufacturers Association. 2012-2021: A Decade of Innovation in Rare Diseases. February 28, 2022. https://phrma.org/-/media/Project/PhRMA/PhRMA-Org/PhRMA-Org/PDF/P-R/PhRMA_RD_Report_R9_Final_Updated-2-28-22.pdf
