Somewhere in a health system right now, a patient with a treatable condition is waiting for a diagnosis that may not come for years. Their symptoms have been coded broadly, their lab work sits in a record no one has connected to a therapy, and the specialist who could change their trajectory does not yet know the patient exists. This is the reality for individuals living with rare diseases, difficult-to-diagnose cancers, and complex immunological conditions. For the Emerging Biopharma companies developing therapies for these potential patients, the challenge is just as stark: a small field force, a narrow indication, and eligible patients scattered across hundreds of health systems. When a patient who could benefit from a therapy goes unidentified, the clinical consequence and the commercial consequence are the same. An opportunity lost.

In this tricky environment, optimizing Next Best Action (NBA) is necessary but not sufficient. The more impactful activity, and the one that actually drives patient outcomes and launch performance, is to determine around which patients the entire launch engine should be oriented right now. That is what Next Best Patient (NBP) is built to address. It puts the patient at the center of commercial strategy, ensuring that the patients most likely to benefit from a therapy are identified and connected with the HCPs who can act on their behalf.

From Next Best Action to Next Best Patient

NBA has become a core concept in life sciences engagement. By applying AI and advanced analytics, NBA systems recommend the optimal next interaction with each HCP. Most large pharma companies now run NBA programs to deliver more personalized, targeted experiences at scale.¹

For Emerging Biopharma (EBP) launching targeted oncology therapies, rare disease treatments, or specialty biologics, knowing what a rep should do next is only part of the equation. The more challenging step is to determine which patients need attention most urgently and which HCPs are treating them. Traditional broad reach targeting and static call plans were not built for that. An NBP approach picks up where these approaches leave off by identifying and ranking patients on how they match the product’s label, clinical value, and access criteria, then connecting those patients to the right HCPs resulting in a launch strategy focused around patient need rather than territory coverage.

Why Next Best Patient Matters

In oncology, rare disease, and specialty biologics, a single identifiable patient can change the course of a clinical practice and a commercial launch. Major cancer centers have already started building AI-enabled, multimodal data platforms to find, phenotype, and manage high-impact patients in real time.^2–4 In rare disease, the stakes are even more acute. With diagnostic journeys averaging five to seven years, eligible patients may sit unidentified inside health systems where diagnosis is delayed.

EBP launches compound the problem. Fragmented care pathways, delayed diagnoses, uneven biomarker or genetic testing, and complex specialty pharmacy requirements scatter eligible patients across health systems. Many of these patients carry non-specific diagnosis codes that would flag them in a conventional targeting model. These gaps cluster in particular geographies, care settings, and patient populations where unmet clinical need runs deepest. An NBP engine is designed to close that gap, identifying patients at clinical inflection points and surfacing those who are likely undiagnosed, misdiagnosed, or undertreated, so they can connect with HCPs who can take appropriate action.

The Data Foundation: EHR, Genomic, Claims, and Registries

NBP depends on fusing multiple real-world sources into a unified analytic layer. The evidence on this is clear, and it is consistent: platforms that combine EHR, genomic, imaging, and registry data enable more precise prediction, patient matching, and trial design than any single source working in isolation. ^5–8

EHR data forms the backbone. Diagnosis codes, lines of therapy, lab values, and visit patterns identify patients matching precise clinical phenotypes or approaching treatment decision points. Genomic and NGS data supply biomarker and mutation status, indispensable in modern oncology but still unevenly adopted without structured frameworks. Claims and administrative data add longitudinal visibility into utilization, procedures, comorbidities, and prescribing behavior, feeding both patient finding and HCP segmentation models. Registries and specialty databases can reveal pockets of concentrated unmet need that never show up in traditional data streams.

Where standard ICD-10 coding is non-specific or inconsistently applied, this data foundation matters even more. Structured EHR data, clinical terminologies such as SNOMED, and laboratory results can precisely define patient cohorts that conventional code-based approaches would miss entirely. Think rare diseases, heterogeneous conditions, and any market where the gap between clinical reality and administrative coding is wide.

Linking these sources at the patient and HCP level, within strict privacy and compliance boundaries, allows EBP to move from static eligibility estimates to dynamic, continuously updated patient opportunity maps.

Scoring the Next Best Patient

At a high level, the process starts by defining what a confirmed patient looks like based on clinical history and treatment patterns, then employs training models to recognize similar signals across a broader population. Scoring features typically include disease stage, prior treatments, biomarker test status, care site characteristics, and HCP behavior patterns tied to adoption of similar therapies. What the models pick up are the subtle combinations of clinical events that rules-based targeting would never catch.

This is not a one-and-done exercise. As new claims and clinical data come in, patient scores are refreshed on a recurring cadence, typically monthly, keeping the picture current as patients move through their care journeys. Patient scores then map to HCPs, care teams, and sites of care. Behind every high-scoring patient is a real person who may be approaching a treatment decision. The score connects that person to the specific HCP and care setting where appropriate action can happen.

Once data is integrated, machine learning models can assign patient-level opportunity scores that account for clinical fit and how actionable the opportunity is. Leading oncology centers already use AI this way to predict prognosis and guide decisions at the individual patient level.^{5–6, 9–10}

What This Looks Like in Practice

A field team supporting a newly approved targeted oncology therapy no longer works from a static territory list. Instead, the team receives a morning alert. For example: three patients at a community oncology practice have just completed second-line therapy and show biomarker profiles consistent with the product’s label. The NBP engine has already flagged the treating oncologist, surfaced recent prescribing patterns, and queued relevant clinical content. That afternoon’s call is a focused, patient-relevant conversation that respects the physician’s time and addresses an immediate clinical decision, one that could put a patient on a therapy better suited to their disease.

In rare disease scenario, a specialty field team launching an enzyme replacement therapy receives an alert that a metabolic specialist at a regional children’s hospital has two patients with recent genetic testing results confirming a diagnosis that the product addresses. The NBP system has identified that this physician has not yet prescribed the therapy and flags relevant peer-reviewed data and patient support resources. What might have been a month-long awareness campaign becomes a timely, clinically grounded conversation that could shorten the diagnostic journey for patients who have already waited years for answers.

NBP as a Standalone Solution and CRM Accelerator

NBP is a standalone analytic solution that delivers value on its own: HCP-level target lists ranked by patient opportunity, predicted patient counts segmented by likelihood, and the clinical rationale behind each score. Commercial teams can act on outputs immediately, to shape call plans, prioritize accounts, and focus field activity where patient need is greatest.^11–14

NBP outputs are designed to integrate into the CRM platforms whether a commercial organization runs on Veeva, Salesforce, or another system. NBP signals can flow directly into existing workflows without requiring a technology overhaul, opening up additional layers of coordination across the commercial model.

Field CRM workspaces can surface prioritized HCP and account lists enriched with patient opportunity indicators that guide call planning, sequencing, and discussion focus. On the digital side, marketing and remote engagement systems can use NBP-aligned triggers to ensure HCPs receive content relevant to patients they are actively managing. Medical affairs workflows alert MSLs to complex patient clusters or centers of excellence that warrant deeper scientific exchange, while omnichannel journeys can coordinate timing and channel mix so that field, email, and self-service experiences reinforce one another around a consistent, patient-centered message.

Research across B2B and MedTech markets consistently shows that companies with mature omnichannel capabilities grow faster and outperform peers. But the foundation is the analytic layer itself. The CRM and omnichannel integration amplifies the signal; the NBP engine is what creates it.

The Role of Partnerships and Platforms

Building full NBP capability in-house is often difficult for EBPs given constraints in data engineering, AI talent, and technology investment. Successful launch teams instead assemble a network of partners. That starts with real world data providers supplying de-identified EHR, claims, genomic, and registry data with appropriate linkages and governance. It extends to analytics and AI specialists skilled in patient finding and recommendation engines. And it includes life sciences engagement platforms that natively support NBP coordination across field and digital execution.^11,15–16

Academic and industry collaborations, such as those integrating de-identified cancer data at scale, show that these networks are maturing quickly.^{2–3, 5–7} Patient finding solutions designed for this use case can be operational within a single quarter, which matters when launch timelines are measured in months, not years. The build versus buy debate misses the point for most Emerging BioPharma companies. What matters is how to assemble the right partners into a capability that actually works together.

The Path Forward

For EBPs launching oncology, rare disease, or specialty biologic therapies, the measure of a successful launch is whether the patients who needs therapy actually receives it. A Next Best Patient engine, backed by integrated real-world data, advanced analytics, and coordinated engagement, is how that happens in practice.

The question is whether your launch strategy is designed to find patients who can benefit from your therapy.

When every patient counts, partnering with IQVIA Emerging Biopharma Analytics empowers you with integrated data, analytics, and industry-leading expertise to identify and reach the next best patient with confidence. To learn how a Next Best Patient approach can support your launch strategy, contact us.

References:

1. IQVIA. Mastering the Art of Next Best Action: A Beginner’s Guide for the Pharmaceutical Industry. IQVIA. Published 2023. Accessed December 3, 2025.
2. Stanford Medicine. Advancing cancer care through artificial intelligence. Published 2025. Accessed December 1, 2025.
3. Stanford Medicine. The bridge between cancer patient data and real-world research. Published 2025. Accessed December 1, 2025.
4. RWJBarnabas Health, Rutgers Cancer Institute, Tufts Medical Center, University of Manchester. International prognostic model for Hodgkin lymphoma. Published 2025. Accessed December 5, 2025.
5. Stanford Medicine. Advancing cancer care through artificial intelligence. Published 2025. Accessed December 1, 2025.
6. Stanford Medicine. The bridge between cancer patient data and real-world research. Published 2025. Accessed December 1, 2025.
7. RWJBarnabas Health, Rutgers Cancer Institute, Tufts Medical Center, University of Manchester. International prognostic model for Hodgkin lymphoma. Published 2025. Accessed December 5, 2025.
8. Rifat A, et al. Cancer trial eligibility and accrual enhanced by clinical trial alert (ACTEA). J Med Internet Res. 2023. PMCID: PMC10655164. Accessed December 4, 2025.
9. MIT Sloan. 3 ways AI helps to empower health care clinicians. Published 2025. Accessed December 6, 2025.
10. Stanford Medicine. Unique Stanford-designed AI predicts cancer prognoses. 2022. PMCID: PMC8794143. Accessed December 2, 2025.
11. IQVIA. Omnichannel Engagement and Insights. IQVIA. Published 2024. Accessed December 7, 2025.
12. Jain R. Why Omni-channel Personalization Is the Future of Marketing. Knowledge@Wharton. Published 2025. Accessed December 4, 2025.
13. McKinsey & Company. How medtechs can meet industry demand for omnichannel engagement. Published 2023. Accessed December 2, 2025.
14. McKinsey & Company. McKinsey research confirms omnichannel is the leading approach to B2B sales. Published 2021. Accessed December 2, 2025.
15. Davenport T, et al. Mayo Clinic’s healthy model for AI success. MIT Sloan Management Review. Published 2024. Accessed December 5, 2025.
16. Stanford Medicine. The bridge between cancer patient data and real-world research. Published 2025. Accessed December 1, 2025.