Real-world data (RWD) and real-world evidence (RWE) are coming into increasingly widespread use across the entire pharmaceutical product development lifecycle. The breadth and variety of the available data today is more expansive than ever. From commercial fitness trackers to clinical and administrative health records, there’s a wealth of valuable health information – data that could potentially be synthesized to enable a deeper and more holistic understanding of patients’ lived experiences on a given therapy or the ways that relevant factors impact treatment outcomes.
Innovative leaders in health care research and analytics are currently exploring new possibilities for generating insights by bringing together previously disparate datasets. By leveraging data tokenization and encryption technology, it’s possible to create a complete and highly differentiated picture of individual patient dynamics while maintaining privacy and compliance with the Health Insurance Portability and Accountability Act (HIPAA) and other data protection regulations.
Such a picture could enable researchers to uncover causal connections between factors like patient demographics, clinical characteristics, functional status, disease severity, patient-reported outcome measures, laboratory values, health care resource utilization and economic outcomes. This will allow for a deeper, more accurate understanding of what’s driving the success of a particular therapeutic.
Combining clinical registry data with additional data sources yields rich insights
Biopharmaceutical researchers can source multiple different types of data to answer various research questions. Generally speaking, these data types exist on a continuum that stretches from very broad to very deep. Open claims data, which includes medical and pharmacy claims sourced from clearinghouses, pharmacies and other providers, is extremely broad in scope, incorporating information on large numbers of patients, but it doesn’t offer much depth. Closed claims data is sourced directly from the payer and captures nearly every interaction with the health care system that took place while the patient was enrolled in that health insurance plan. These interactions include both medical and pharmacy visits, and the data includes more fields than open claims data. However, most Americans change insurance providers with some frequency, and closed claims data only covers the enrollment period with one specific payer.
Data sourced from electronic health records (EHR) offers much more depth than either open or closed claims data, but data sets are smaller since they’re sourced only from clinical practices with a particular EHR system. Finally, clinical registry data goes into the greatest detail, but only for very specific, pre-established populations.
Securely combining de-identified medical, pharmacy and laboratory data from open and closed claims sources with patient phenotypes derived from clinical registry data gives researchers the ability to combine the breadth of their claims data with the depth of registry data, providing a uniquely detailed view into the value of therapies and services used to treat the disease in question. Encrypting and tokenizing the patient data removes siloes that have traditionally existed between systems and data sources and protects patient privacy. Abstracting the data into a patient phenotype means that sensitive information, such as adverse event occurrence, is not revealed to third parties.
“We’ve begun partnering with companies to bring together clinical registry data, which is extraordinarily detailed, with open and closed claims data, which is focused on patient journeys and health care resource utilization,” said Heather von Allmen, Senior Director of Data and Licensing at CorEvitas. “These newly combined datasets are expanding researchers’ ability to answer questions like how much more do patients with severe disease spend on treatment than those with moderate disease? Or, are people diagnosed more than a decade ago seeking treatment less often than those diagnosed recently? This creates a new opportunity to build a fuller picture of the patient – one that wouldn’t be available through either dataset by itself.”
Meeting pharma’s expanding need for knowledge
Life sciences companies’ demand for information continues to grow. There’s always a need to better understand how patient behavior impacts a therapy’s effects or how disease severity and health care resource utilization affect treatment outcomes.
“With combined datasets, it’s now possible for pharmaceutical companies to look longitudinally at a very large population while also considering factors like disease activity measures,” says von Allmen. This provides insights that weren’t available previously. Augmenting claims data with clinically rich registry data enhances analyses, adding value and enabling life science companies to make more informed business decisions.”
Today’s collaborations between real-world evidence and payer-sourced data providers are enabling pharmaceutical companies to gain new insights – which will ultimately improve patient health and well-being over the long term.
To learn more about how CorEvitas’ regulatory-grade real-world evidence is powering pharmaceutical development, visit our website