•Accelerate R&D and clinical development

• Leverage NLP-derived deep phenotypic patient-level data to create subsegments of patients and better identify those with unmet needs who are most suitable for clinical trials.
• Use longitudinal, patient-level real-world data and advanced analytics methods to identify which symptoms can be attributed to the underlying disease versus which are associated with the effects of external stressors, and learn more about how these factors impact response to treatment.
• Supplement conventional clinical trial analysis methods with data-driven insights to inform R&D strategy, as well as clinical development, protocol optimization, and patient recruitment.

•Boost market access strategy

• Evaluate comparative outcomes and measure drug effectiveness in real-world settings. Identify unmet needs where novel therapeutics may be able to most improve patient outcomes.
• Develop patient-centric endpoints and outcome measures that can elucidate differential responses to drugs and novel therapeutics.
• Accelerate regulatory approvals and pricing model strategy.

•Improve quality, cost, and outcomes

• Inform program design, risk prediction, and quality measurement with unique analytic approaches. These proprietary methods enhance population-level risk and patient targeting, for better management of cost of care and outcomes.

•Health system research and innovation

• Conduct innovative research using real-world data and obtain insights to guide policy designs and regulatory decisions.

•Research partnerships with innovative techniques

• Conduct and publish research projects in behavioral health and analytic methods.