Tackling Challenges Faced by Life Sciences Organizations
Since joining Clarify Health last year, I have been working with senior leaders at life sciences companies to better understand the challenges they face in navigating today’s fragmented and siloed healthcare data ecosystem. Even with my ten years’ experience in the clinical trials industry, these conversations have opened my eyes to the opportunities we still have ahead of us in the way we accelerate clinical development and ensure the right patients receive the right therapies at the right time by better analyzing all the real-world data we have available today.
Clarify Health addresses these challenges head-on, sitting at the forefront of the growing movement to improve interoperability, data availability, and machine learning applications within healthcare. Our solutions leverage both the medical expertise of clinicians and the technological prowess of engineers from the financial services industry – a space known for its data integration and analytical speed despite heavy regulations and sensitive information. In addition, our analytics platform is powered by a data set of over 200M longitudinal patient lives that link across clinical, claims, social determinant, lab, and prescription data sources. Together these forces have allowed us to develop a set of solutions that give our customers a more accurate understanding of the impact that therapies have in the real world, with respect to healthcare resources utilization, treatment patterns and total costs for specific patient cohorts.
While we support life sciences companies today, Clarify started out serving healthcare payers and providers. Over time we honed our ability to answer critical questions from real-world data (RWD) through proprietary methods that have been tested and trusted by these stakeholders. We decided to expand our customer-base upon realizing that the life sciences industry was also interested in learning from those same, foundational questions that we have been answering on the cost of care, patient outcomes, utilization, and patterns of care delivery. Today, Clarify is dedicated to making all stakeholders within the healthcare system smarter by illuminating the holistic patient experience across therapeutic area, geography, and site of care.
Within Life Sciences, our new offerings are designed to help commercial and market access teams better understand the performance and impact of their therapies, as well as to empower clinical development teams to design, set up, and execute trials as efficiently as possible.
As part of our recent introduction of nine solutions, two are specifically designed to benefit our life sciences customers:
- Disease Maps:
- Real-time filtering and analysis for trial design, protocol feasibility, and site selection.
- Using a trial’s specific inclusion and exclusion criteria, users can access heatmaps and data extracts for key decisions in clinical operations.
- This tool accelerates clinical trial design, setup, and execution across therapeutic areas, and enables scenario modeling to test the feasibility of protocol and design options
- Predictive analytics that drive understanding of which providers are likely to perform best in trials, and which patients are most likely to enroll and remain adherent
- Therapy Intelligence:
- Dynamic access to a comprehensive, actionable, and segmented view of the market, including heatmaps with predictive analytics on patients most likely to be appropriate for a given therapy and providers most likely to switch therapies
- Rapid, scalable approach to analyzing patient journeys and their clinical and economic differences across patient cohorts, providers and payers.
- Analysis of the impact of treatments on disease trajectory and total cost of care for a patient population.
Working with Clarify Health
At Clarify, we enable RWD to be used at scale via a robust technology stack that includes comprehensive grouper technology, enabling precise understanding of indications, proper attribution of medical codes, and ability to process total cost of care. We also control for the variability across different populations — our case-mix adjustment methodology takes into account 400+ patient factors and incorporates social and behavioral determinants of health — for far more advanced and actionable patient cohort comparisons. In just over twelve months, we’ve made great strides to bring pragmatic AI and machine learning to clinical trials, helping to lead the industry’s push toward more personalized solutions.