Manufacturers of specialty therapies aim to ensure patients can access, initiate, and remain on therapy while demonstrating clinical and economic value to payers, providers, and regulators. That means measuring time-to-therapy, initiation rates, prior-authorization success, adherence/persistence, patient support program (PSP) engagement, clinical outcomes, and downstream resource use — and linking those metrics to commercial and health-outcome impact to support coverage, contracting, and launch strategy. Real-world evidence (RWE) and integrated data are central to these objectives, but persistent fragmentation limits actionable insight.

Where Manufacturers Use Claims, EMR/EHR, and Their Own Data Today

• Claims data (commercial, Medicare/Medicaid, and specialty pharmacy claims) are the backbone for utilization, payer mix, fill/dispense events, gross-to-net (GTN) and payer reimbursement analyses, and longitudinal cost/resource use. Claims are used for cohort identification, adherence and persistence proxies (fills, days-supply), and detecting channel shifts (buy-and-bill vs specialty pharmacy). Advantages include broad coverage and payer/payment context; disadvantages include clinical detail gaps and lag.
• EHR / EMR data provide clinical depth — diagnosis, lab results, clinician notes, vital signs, and administration records — which manufacturers rely on for disease severity, treatment response, and safety signals. EHRs are crucial for linking therapy to clinical outcomes but are patchy across systems and inconsistent in structure and capture. Combining EHRs with claims yields richer evidence, but integration and standardization are nontrivial.
• Manufacturer / proprietary data include patient support program (PSP) and hub service data (enrollment, copay assistance, adherence outreach), prior-authorization and appeals workflows, sample and starter pack distribution, call center notes, and specialty pharmacy partner reports. These operational data are closest to the patient experience and often contain the most timely signals about access barriers — but they are frequently siloed in vendor platforms or subject to consent, privacy, and contractual constraints.

Key Gaps That Prevent a Complete Patient-Journey View

1. Linkage and identity resolution across siloed sources
   A single patient’s journey often spans multiple payers, EHRs, specialty pharmacies, and manufacturer systems. Without robust, privacy-preserving linkage (and standardized identifiers), traces are fragmented.
2. Timeliness: open versus closed claims and operational latency
   Manufacturers need near-real-time signals (e.g., prior-authorization denials, therapy drop-offs) to intervene. Many traditional closed claims datasets arrive months after events.
3. Specialty channel complexity and blind spots
   Specialty medications move through diverse channels (buy-and-bill, white-bag/clear-bag, specialty pharmacies, 340B clinic dispenses). Claims and shipment data do not uniformly capture channel or site-of-care nuances, complicating GTN, leakage analytics, and accurate attribution of pull-through.
4. Lack of comprehensive prior-authorization and appeals data
   Prior-authorization (PA) and step-therapy processes are major access barriers for specialty drugs. Manufacturers rarely have standardized, complete feeds of PA submissions, denials, reasons, and time-to-resolution across payers and provider EHR workflows.
5. Clinical outcome and lab data gaps in claims; PROs and SDOH missing
   Claims lack lab values, imaging, and symptom scales; EHRs have those elements but only for patients contained within a health system and often in free text. Patient-reported outcomes (PROs), functional measures, and social determinants of health (SDOH) are rarely captured systematically in any single dataset.
6. Small samples and censoring in rare diseases / niche specialties
   Many specialty therapies target small or fragmented populations. Even large claims pools may produce small analyzable cohorts when applying strict inclusion criteria, making KPI measurement noisy and limiting statistical power for subgroup analyses.

Consequences for KPI Evaluation

Because of the gaps above, common KPIs are either delayed, biased, or incomplete. This forces manufacturers into proxy measures, small sample pilots, or expensive bespoke data collection (chart review, registries) — costly approaches that scale poorly across multiple assets or indications. Regulatory and payer decision-making increasingly expects rigorous RWE; incomplete journey data undermines those conversations.

Practical Levers to Close Gaps

• Invest in privacy-preserving linkage platforms and partnerships that can stitch claims, EHR, and PSP data at the patient level.
• Prioritize near-real-time operational feeds (open claims, hub events, PA logs) for early intervention KPIs.
• Standardize and ingest PSP/hub data into analytics stacks to map interventions to downstream utilization.
• Augment datasets with PROs and SDOH via digital tools, registries, and targeted chart abstraction.
• Use hybrid approaches (claims + EHR + manufacturer data) and transparent data-quality frameworks to support payer/regulatory conversations on RWE validity.

Bottom Line

Manufacturers have the right KPIs in mind, and the component datasets exist, but fragmentation in identity linkage, channel capture, timeliness, and clinical/detail completeness creates measurement blind spots. Closing these gaps requires investment in linkage, operational telemetry, standardized PSP ingestion, and pragmatic hybrid-RWE designs so access interventions can be measured, optimized, and credibly communicated to payers and regulators.

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References:

Datavant. (2025). Privacy-preserving data linkage solutions for healthcare. https://www.datavant.com

IQVIA. (2025). Real-world evidence and patient journey analytics in specialty therapies. https://www.iqvia.com

McKinsey & Company. (2024). Addressing data fragmentation in specialty pharmaceuticals. https://www.mckinsey.com

National Council for Prescription Drug Programs (NCPDP). (2024). Standards and interoperability for patient support programs. https://www.ncpdp.org

PubMed Central. (2025). Challenges in patient-journey measurement for specialty therapies. https://www.ncbi.nlm.nih.gov/pmc

U.S. Food and Drug Administration. (2024). Real-world evidence framework for regulatory decision-making. https://www.fda.gov