Specialty data enables the creation of sub-cohorts to increase your understanding of therapy adoption and patient adherence, persistence, and outcomes.
Improving health outcomes for a patient population defined by a single ICD-10-CM code has never been an easy, one-size-fits-all proposition. Rather, Commercial teams must understand a multitude of variables that can impact both a patient’s access and response to a therapy.
Obtaining these insights isn’t easy.
While administrative claims data connects the dots for understanding the various pathways to a diagnosis and treatment decision, it lacks the context that could explain, for example, why a diagnosis is often delayed, what led to a therapy choice, and why one patient responded to a therapy when another didn’t.
To answer these questions, specialty data such as lab results, genomics, clinical variables, health insurance, social determinants of health, and race and ethnicity (to name a few) must be integrated with claims data. This enables a multidimensional view that surfaces sub-cohorts within a patient population. Commercial teams can then take a more targeted, better-informed approach to HCP engagement.
A recent Komodo webinar featured the power of integrating data sources. Here are some key takeaways:
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New data sources — including those outside of the healthcare system — have the potential to predict and prevent health events, impacting 80% of health outcomes.
Clinical data from EHRs, genomics, and wearables is already being integrated with administrative claims data to generate a deeper understanding of population health, identify patients with rare disease, and assess how a therapy impacts various sub-cohorts. As the use of digital devices continues to grow, so does the potential for richer insights.
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Life Sciences companies can streamline access to multiple types of specialty data by working with a data partner that has pre-integrated data sources, accelerating speed to insights. Accessing and integrating specialty data is no small feat. A 2023 industry survey revealed the process of linking, deduplicating, cleaning, and standardizing data takes seven months, on average. Organizations can shave four to nine months off their data-management timeline by accessing pre-integrated data from one source.
Data layering enables deeper, richer insight into the market landscape and patient journey. Commercial teams can access specialty data to support a variety of use cases. For example, teams can track brand performance in highly specific patient populations by including genomic/biomarker data, or leverage predictive modeling to identify HCPs who are most likely to see patients in a highly defined cohort:
If you’d like more information on how Komodo’s MapEnhance™ gives you both a more holistic and granular view of a patient population, just let us know.
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