Purpose Driven RWD Solution
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Quantifying the clinical journey

81qd uses over one billion data points of RWD to create market-sizing metrics of current clinical practice. This allows us to analyze clinical journeys that encompass the great majority of patients within any disease state, along with more than 95% of all clinicians.

We avoid the pitfalls of the traditional primary market approach by going into great depth on the experience of patients along with the breadth of patient/physician interaction. We then include important non-core specialties to delineate multiple journeys in any disease state.

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Leverage the analysis to build a broader understanding of patients and treatment plans

81qd uses real-world claims data to capture how patients flow through HCP specialties and treatments from diagnosis through management. This assessment allows our clients to build a foundational understanding of the patient journey based on factual claims analysis that captures all patient interactions.

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Drive product adoption

81qd analytics go well beyond the historical approaches of assessing likely adoption based on broad segmentation and extrapolation using analog products. We leverages machine learning to assess thousands of patient and HCP features to identify and weigh their roles in driving adoption.

These insights provide our clients with tools to optimize HCP targets and engagement to drive product adoption.

Identify HCPs managing patients likely to adopt a therapy

To predict the practices with patients likely to adopt a specific product, 81qd leverages claims data and machine learning to assess thousands of patient and HCP features to identify and weigh their roles in driving adoption. We identify patients that are likely adopters of a specific product and the HCPs who are currently managing these patients.

Clients can leverage Orion analytics within marketing initiatives and campaigns to engage directly across multiple digital channels.



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Maximize therapy adherence

81qd offers clients the ability to be proactive in efforts to address nonadherence by leveraging machine learning to identify HCP practices with the highest probability of patients likely to be nonadherent to therapy and provide targeted interventions to improve adherence.

Nonadherence to a medication is a complex health care problem, driven by a myriad of both patient- and HCP-related factors, such as:

Patient-driven factors

  • Demographics
  • Ability to follow a medication regimen
  • Willingness to pay for a medication regimen

HCP-driven factors

  • Prescribing patterns
  • Practice attributes
  • Historical adherence patterns

Identify HCPs managing patients likely to be nonadherent to therapy

To identify the practices likely to have nonadherent patients, machine learning structures and finds interrelations between patient and HCP features, then assesses which features are predictive of nonadherence for a specific product and disease. Our clients can then leverage these findings to proactively engage and educate clinicians and patients across channels.

These applications span our clients’ core business needs

  • Effectively target disease management interventions
  • Optimize resource allocation
  • Improve financial support program efficiency and effectiveness