Health Analytics Collective

Advancing real world evidence insights through technology and expertise.

Combining the brainpower of MIT, the Center for Translational Medicine at the University of Maryland, Julia Computing, and MMS.

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When forming in 2018, the Health Analytics Collective was brought together with an understanding that the current timeline to drug approval and marketing was lengthy, delaying the ability to address unmet needs and proving to be a significant financial burden. With the use of real world evidence in the industry new and evolving methods and regulatory expectations unclear, the Health Analytics Collective began a journey to bring proven regulatory expertise, strong processes, and the latest technology to provide data-driven insights that shorten drug development timelines.

Using the Julia Software Platform, the collective provides an end-to-end solution for data science and real world evidence analytics, allowing for the ingestion, transformation, and analysis of all data in one system.

Compared to others, the Health Analytics Collective is one of the only groups to offer:

  • Expertise across clinical development and regulatory strategy with direct health authority experience
  • Data science service methodology
  • A validated and industry standard compliant software platform
  • Unique and flexible computing language
  • Strong connections with regulatory agencies, academia, pharma, and healthcare organizations

With the Health Analytics Collective, new opportunities and a better understanding of data is not only possible, it’s a reality.

Health Analytics Collective

Creating the Collective

Creating the Collective

The team aims to leverage real-world evidence, observational data that are generated during routine clinical practice, and patient health care databases to augment label claims and/or support new drug applications with leading-edge software and algorithms and a depth of regulatory and clinical experience.

Learn more

Shortened Trial Duration

Learn how collective members used data science methodologies to shorten the clinical trial duration and discovered an enhanced endpoint in acute schizophrenia.

Read the case study