Clarify Health has expanded its strategic partnership with Datavant to help life sciences companies connect first-party and real-world data.
Linking healthcare data at the patient level gives clinical researchers and commercial teams a fuller view of the longitudinal patient journey, thereby supporting the identification of recruitment and adoption opportunities for patients. However, life sciences companies face challenges when trying to realize the benefits.
“High-scale data management has become even more complex as the demand for more and more patient-level insights has accelerated the demand on digital transformation in the life sciences industry. With this rapid evolution, leaders must keep data security and patient privacy top of mind,” said Todd Gottula, co-founder and president of Clarify.
Clarify has responded to the opportunities and challenges by partnering with Datavant, the developer of a HIPAA-compliant, privacy-preserving tokenization technology. By working with Datavant, Clarify aims to guard protected health information while connecting life sciences companies’ first-party data to its Atlas Platform.
The Atlas Platform maps over 4b care journeys across more than 300m unique individuals. Gottula sees value in helping life sciences companies to integrate their data with the real-world data in Clarify’s Atlas Platform.
“Clarify enables [clinical researchers] to measure long-term effectiveness and outcomes of new therapies in a higher fidelity way because these teams are able to leverage critical private data with what Clarify has sourced in the market. Similarly, this linked data empowers commercial teams to better understand patterns in treatment sequencing, therapy adoption, and patient access,” said the Clarify president.
News of the Datavant partnership comes four months after Clarify disclosed a $150m USD Series D financing round. The funding positions Clarify to build on its progress to date, which includes the development of a tool to enable life sciences researchers to explore treatment patterns across years of longitudinal data and characterize patient cohorts across hundreds of medical, social, and genomic factors.
