How to build the data backbone for direct-to-patient drug sales
This approach to getting medications to patients is gaining traction, but most information systems use a patchwork approach.
As healthcare delivery continues to decentralize, direct-to-patient drug distribution is emerging as a pivotal model for increasing access, transparency and affordability.
Once a niche pilot model, direct-to-patient (DTP) distribution is now rapidly becoming central to how therapies reach patients, how manufacturers capture value and how brands build long-term relationships. This is particularly true, because it’s being driven by the Trump administration's recent Executive Order on healthcare affordability and broader industry trends.
The success of DTP drug programs depends not only on logistics and pharmacy operations, but on how securely, accurately and quickly health data moves across patients, prescribers, payers and manufacturers.
Without the right data infrastructure, DTP efforts can quickly unravel. Fragmented systems, manual verification processes and disconnected APIs create friction that slows fulfillment, drives up administrative costs and erodes patient trust.
To scale effectively, DTP programs require technology architectures purpose-built for interoperability, automation and transparency.
The data infrastructure challenge
Every step in the DTP value chain — from e-prescriptions to benefit verification, prior authorization, fulfillment and adherence — creates both an operational and a data challenge. When handled in silos, each introduces a potential failure point.
Today’s DTP programs often are built on legacy pharmacy systems or hastily bolted-on modules that can’t easily exchange data with EHRs, payer systems or manufacturer analytics platforms. This results in redundant data entry, long turnaround times and inconsistent patient experiences.
To be successful, DTP platforms need to be unified interoperable ecosystems, not isolated applications. This can be achieved by leveraging FHIR-based integrations, robust APIs and data normalization pipelines that ensure information flows seamlessly between all participants.
The goal is to accelerate the transaction while creating a continuous trustworthy data loop that connects prescribing decisions to outcomes, compliance and value-based insights.
Intelligent automation and clinical guardrails
Automation is central to scaling DTP, but it must be done intelligently. To get there, health IT leaders are turning to rules-based automation layered with clinical oversight, enabling efficiency without compromising safety.
For example, automated benefit verification and digital prior authorization can dramatically reduce time-to-fill, but pharmacist review or clinical review is still needed. AI-driven anomaly detection can flag irregular dosing patterns or potential drug interactions before fulfillment, supporting compliance while protecting patients.
In this hybrid model, automation handles repetitive administrative work while clinicians focus on judgment calls. This balance is enabled by data transparency and auditability, building trust with both patients and regulators.
Transparency as a design principle
Transparency is a defining attribute of successful DTP systems. Every stakeholder in the care chain benefits when information is shared in real time and accessible through a unified data layer.
Patients gain visibility into prescription status, delivery timelines and cost details. Prescribers can monitor adherence and outcomes directly from their electronic health records system. Manufacturers can analyze real-world utilization and adjust support programs dynamically. Finally, payers can confirm compliance and utilization without adding friction to the process.
By embracing end-to-end transparency, DTP systems turn data into an active engagement tool, driving adherence, safety and patient confidence.
Building for scale: The data checklist
For organizations supporting or enabling DTP distribution — whether as a health system, technology vendor or manufacturer partner — the key is scalability through integration. Look for the following data and IT capabilities.
Unified data layer. Interoperability with EHRs, payers and pharmacy systems via modern APIs and FHIR standards.
Workflow automation. This facilitates digital prior authorization, benefit verification and payer adjudication.
Clinical oversight logic. This provides automated escalation for exceptions that require pharmacist or physician review.
Advanced analytics. These capabilities make use of dashboards for adherence, refill rates and performance optimization.
Patient engagement frameworks. Communicating with patients is facilitated by omnichannel messaging, delivery tracking and refill notifications.
Compliance and governance. It’s important to have capabilities that ensure end-to-end audit trails, HIPAA compliance and state licensing adherence.
Modular architecture. Flexible design enables new therapy lines and payer integrations without redevelopment.
DTP’s future will be driven by data
Direct-to-patient drug distribution is reshaping the relationship between patients, pharmacies and manufacturers. But for that transformation to endure, it must be powered by health data capable of real-time coordination, predictive insight, and continuous learning.
Organizations that invest now in data-driven DTP infrastructure will gain far more than just operational efficiency; they’ll gain a strategic advantage in patient engagement, brand trust and overall performance.
Matthew Hawkins is the chief technology officer of CaryHealth.