Do You Know How to Bring Healthcare Data Processing to Scale?
Massive amounts of healthcare data are available in the real-world data (RWD) space. While electronic health records are typically the first stop in terms of data gathering, there are a number of other important data sources, including structured healthcare claims data and unstructured patient-reported outcomes. Companies use this information to generate novel insights and data repositories that lead to real-world evidence (RWE).
Accurate and timely processing of such data is imperative to creating robust analytical datasets that can be used in the RWE setting. This includes being able to link data for specific patients from different sources, incrementally adding data as new sources become available, deriving inferences for missing data, and creating derived data elements from existing data. Long-term, observational data from disease state registries mean that new information is constantly added over months and years – from the files of thousands of consented patients.
The sheer volume of information involved with this type of research means that manual approaches simply are not feasible. Scalability must be incorporated into the initial study design stages to address the complexity of this research. This whitepaper delves deeply into:
- Current challenges when dealing with the scope and variability of health data available to researchers
- Considerations for performing analysis at scale, including securing informed consent and protecting patient confidentiality
- A scalability pipeline and how Target RWE approaches these challenges
When done well, this approach can yield truly meaningful results and generate insights that can change the trajectory of a drug or research program. More importantly, insights borne of rich and comprehensive analysis can ultimately help improve patient care.
Download our whitepaper today to explore and read more!