WHY IT MATTERS
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Obtaining granular insights across the patient journey is critical to resolving therapy adoption/adherence hurdles
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AI/ML enables predictive analytics to engage HCPs and patients at the most appropriate time
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Identifying underserved populations to advance care equity is the right thing to do and has the potential to increase market size, market share, and return on investment (ROI)4
CHALLENGE #8
Driving Therapy Adoption
50% of Commercial teams report they face a "significant to very significant challenge" obtaining accurate insights into:
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The care setting where therapy is being prescribed
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Identifying eligible patients at the appropriate time to enable timely HCP engagement
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Patient “drop-offs” (therapy discontinuation)
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Underserved populations
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Patient access barriers, such as denials by payer and payer type
What’s behind the challenge?
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Datasets that don’t include both open and closed claims data
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Hurdles to integrating/harmonizing data sources
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Not using technology solutions enhanced with AI/ML
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A lack of high-fidelity (representative) demographic data
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Limited or no access to specialty data sources
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Using claims data alone to determine primary and secondary insurance coverage and identify access barriers (too many empty fields, inaccuracies, and “Payer Unknown” statuses)