The US Department of Health and Human Services (HHS) Secretary Robert F. Kennedy Jr. recently announced plans to study the causes of autism, while the National Institutes of Health (NIH) is moving forward with developing a comprehensive real-world evidence (RWE) dataset to study autism spectrum disorder (ASD). At a recent NIH leadership meeting, newly appointed NIH Director Dr. Jay Bhattacharya, introduced the proposed NIH Real-World Discovery platform. The platform aims to integrate a variety of healthcare datasets into one platform, including CMS data, medical and pharmacy claims data, lab and genomic data, and data from smartphones and fitness trackers.

The current fragmented healthcare data landscape presents challenges when building comprehensive data infrastructures to understand specific therapeutic areas. Below, Avalere Health experts have identified four key challenges that both the NIH and life sciences companies should be aware of to maximize success when navigating the healthcare data landscape:

Duplicative Data Sets

Dr. Bhattacharya highlighted that the NIH routinely pays multiple times for the same data resource. Avalere Health has seen that life sciences companies face a similar issue, often paying for identical claims from multiple data aggregators across various internal stakeholder groups, such as health economics and outcomes research, medical affairs, and commercial teams. Enhanced internal communication and coordination, with participation from procurement, can reduce the risk of duplication and may free up resources that could be put to alternative use.

Siloed Data Acquisition Strategies

To compare individuals diagnosed with ASD with the general population, large samples of individuals as controls will need to be incorporated into the NIH database. These control patients, as well as those diagnosed with ASD, may also present with comorbid conditions of potential interest to NIH researchers. Similarly, life sciences companies often purchase multiple datasets for discrete therapeutic areas, rather than investing in a data strategy that provides a comprehensive, population-wide view that serves the entire enterprise. Purchasing data with a focus on molecules or therapeutic areas may result in duplicated data costs.

Data that is Not “Fit for Purpose”

In recent years, several life sciences companies invested heavily in incorporating data from sensors and wearables into their research data portfolios. This approach is echoed in the recent NIH proposal. While sensor and wearable data can be valuable, there has been a decline in the utilization of this data, as oftentimes the established research questions are not aligned with the data’s structure or intended use. While there can be a desire to incorporate novel data into a portfolio, ensuring that data is “fit for purpose” remains crucial for both NIH and commercial stakeholders.

Complex Datasets

While the NIH ASD data platform aspires to address a variety of research questions, stakeholders should be cognizant of the nuances of highly complex datasets that source data from payers, physician systems, and pharmacy networks. Advanced analytics and artificial intelligence (including machine learning) applied to these datasets must be tailored to address these nuances effectively to avoid misinterpretation or incomplete analysis. 

How Avalere Health Can Help  

Avalere Health’s experts in data optimization, evidence planning, and data comparison support clients in mapping current data assets, identifying data gaps and duplication, and recommending areas for improvement. To learn more about how Avalere Health can help you navigate your 2025 and beyond data strategy, connect with us.