National Institutes of Health Taps EVERSANA for Research on the Impact of Rare Disease on Patients, Healthcare System

The National Institutes of Health (NIH) has selected EVERSANA, a leading service provider to the life sciences industry, to conduct the next phase of research on the impact of rare diseases on patients and the overall healthcare system in America. Through its vast array of experts and data & analytics solutions, EVERSANA will build a computation tool to identify patients with rare diseases while alerting clinicians in clinical care settings to improve treatment options. 

It’s a break throughout concept that has the potential to make a massive impact on the lives of patients. And here’s how it all started. 

Purpose Behind the Initiative 

The partnership with EVERSANA ties into NIH’s National Center for Advancing Translational Sciences (NCATS) and its mission to improve health through smarter science, which results in better treatments faster for all diseases, rare or common. The NCATS has a division called the Division of Rare Diseases Research Innovation (DRDRI) whose purpose is to advance rare disease research to benefit patients. Currently, there are between 7,000 and 10,000 known rare diseases that affect people in the United States, yet only a few hundred (less than 5%) have a treatment, and even fewer a cure.  

A rare disease is defined in the United States as a disease or condition that affects fewer than 200,000 people. Most rare diseases impact far fewer patients than this, with most affecting only a few hundred to thousands of patients. Collectively, however, rare diseases are not rare at all and, in total, are estimated to touch the lives of between 25 and 30 million patients in the United States, making rare diseases a large public health consideration. 

Given the large number of rare diseases, assessing the true impact of rare diseases on healthcare systems in the United States is challenging. Many patients with rare diseases have difficulty obtaining a timely and accurate diagnosis (referred to as the diagnostic odyssey) and there is a lack of data regarding the likely high number of patients who have not yet received a rare disease diagnosis. 

A History of Collaboration 

The research initiative, with results expected by early 2025, is commissioned by the NIH’s NCATS’ DRDRI and builds on previous projects with EVERSANA. 

The company has conducted preliminary feasibility assessments of medical utilization in an initial set of 14 representative rare diseases and then assessed the impacts (utilization and cost) of rare diseases on healthcare systems by leveraging large health databases with the team’s data science and medical expertise.  

This next iteration of the IDeaS (Impact of Rare Diseases on Patients and Healthcare Systems) study will produce results for a pilot program to inform new methods for clinical decision support tools. These will then alert clinicians to possible cases of rare diseases, helping to establish earlier treatment options that could reduce financial burdens. 

The Power of Data to Drive Change 

So what is EVERSANA doing to help the NIH? The company’s Data & Analytics team, supported by Pierantonio Russo, MD, FCCP, FAAP, STS, the company’s chief medical officer, along with Ramaa Nathan, PhD., Vice President of Data Science & Real-World Evidence, Alex Moore, Senior Data Scientist and their teams will use machine learning, and advanced techniques, like vector embedding vector and clusters to showcase ways to accelerate the identification of patients with rare diseases. 

 More specifically, the team will build a computational tool to identify potential rare patients in clinical care settings. The goal is to develop a tool that can alert clinicians to possible cases of rare diseases and to help doctors when a patient may present with characteristics that belong to those of a known rare disease. 

Each group of characteristics, or features, is considered a cluster in the study. The characteristics can be a collection of disparate characteristics, such as ICD codes, prescriptions, procedures, tests, physician visits, temporal patterns (visits, prescriptions, dosage, frequency of visits), imagery, associated genomic data, phenotype(s), location (as supported by the data sources), and demographics. The collection of these characteristics is then transformed into a series of numbers, or a vector, referred to as the embedding vector. If a patient presents with characteristics that are similar to that of a rare disease, the query will return the rare disease(s) that present similar (but not exact) characteristics of the patient. 

“The power of data insights and analysis is only as strong as the source of the data,” said Russo. “Through our experiences and sophisticated tools, we can conduct unmatched research to help physicians bring new ways of identifying patients with rare diseases. As a physician, there is no greater joy than finding new ways to help people.” 

To learn more about EVERSANA and its leading Data & Analytics offering, click here. 

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