Pharmaceutical companies invest more than $300 billion in research and development annually, but just 12% of drugs entering clinical trials ultimately receive approval from the U.S. Food and Drug Administration (FDA).
Even when therapies succeed, commercialization hinges on identifying eligible patients and the physicians treating them; it’s a challenge that has intensified as treatments become more targeted and inclusion criteria more complex. Diagnostic data is emerging as a strategic asset.
“Pharma companies can use predictive analytics built on lab results and claims to validate hypotheses, estimate market size, and prioritize physician outreach to educate about the patient and diagnosis journey,” says Parag More, executive director of life sciences data and analytics solutions at Quest Diagnostics. “It leads to a more informed, data-driven launch strategy.”
Predictive analytics uses real-world lab data to help transform go-to-market strategies, expanding eligible patient pools from diagnosed to undiagnosed and underdiagnosed, identifying national provider identifiers (NPIs) treating patients, and accelerating therapy adoption.
The commercialization gap
FDA approval is not the finish line in drug development. Instead, it signals the start of another critical phase: commercialization.
Increasing specialization of therapies, including precision medicine and rare diseases, makes identifying patients more complex. This can be especially true for patients who have diseases with complex or delayed diagnostic pathways, who often receive fragmented care spread across multiple health systems. When the right patients and providers aren’t identified early in the commercialization phase, therapies can struggle to gain traction.
Pharmaceutical companies are increasingly investing in advanced analytics to address these gaps. Data-driven commercialization strategies, including predictive analytics and real-world data analysis, are becoming essential for improving commercial performance and optimizing launches.
“Commercial success depends on identifying where eligible patients are in the healthcare system and which physicians are treating them,” says More. “Without that near-real-time visibility, even groundbreaking therapies can face slower uptake.”
Lab testing generates longitudinal, real-world clinical insights, providing visibility into disease prevalence, testing patterns, and progression. Compared with retrospective analysis, which identifies known eligible patients, predictive modeling identifies “hidden” or undiagnosed patients who fit target profiles, maps disease progression and high-risk cohorts, and identifies physicians treating eligible populations through de-identified patient data to move beyond simple retrospective analyses and toward proactive identification of potential patients. Besides helping commercial teams understand where patient populations are concentrated and which providers are most likely to encounter them in practice, predictive analytics can also inform market sizing and launch planning.
Using diagnostic data as a strategic asset enables more precise education and outreach and supports sponsored testing initiatives to close diagnostic gaps.
“When we use predictive analytics to determine populations that haven’t been diagnosed yet, commercial teams can reach out to their providers to educate them on the patient journey and the diagnostic tests that can help to make a diagnosis,” More says. “As a result, physicians can ensure that patients receive an accurate and timely diagnosis, easing their uncertainty and helping them get on the right treatment path sooner.”
Diagnostic data as a strategic asset
More than 12 billion medical laboratory tests are analyzed every year in the United States, creating a massive, diverse dataset that provides insights into disease prevalence, testing patterns, and progression and bridges the gap between approval and adoption. As therapies grow more targeted and markets become more competitive, commercialization strategies must become more data-driven.
“Diagnostic lab data is among the most valuable and underutilized sources of healthcare insights,” More says. “It captures the diagnostic journey—from early abnormal results to confirmatory testing—and that visibility creates opportunities to identify patients earlier in the care pathway.”
Organizations that treat diagnostic intelligence as a strategic asset are likely to gain a significant advantage. By integrating predictive analytics into commercialization planning, pharmaceutical companies can validate market opportunities, prioritize physician outreach, and ultimately expand access to life-changing therapies.
“Predictive analytics transforms diagnostic data from a retrospective record into a forward-looking strategy tool,” says More. “When pharma companies understand where patients are in the diagnostic journey, they can design launch strategies that reach the right physicians, support earlier diagnoses, and ensure that therapies reach the patients who need them most.”
Healthcare organizations that leverage diagnostic intelligence as a strategic asset will be better positioned to validate market opportunities, prioritize outreach, and improve patient access to therapies.
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