Interview of Avik Pal, CEO, CliniOps
In this quest for a paradigm shift in the age-old process of drug development, Devendra Mishra, Executive Director of BSMA, reached out to Avik Pal, CEO at CliniOps, an execution-first clinical trial platform supporting modern Hybrid and Decentralized Clinical Trials (Hybrid DCT), currently supporting studies across more than 35 countries. The answers obtained in the interview regarding the standardization of clinical study protocols, real time data capture at every node of the clinical supply chain, real-time monitoring, logistics strategies for global reach, and adherence to regulatory matters were remarkable.
He regularly contributes to Forbes Councils and has been featured in USA Today, The CEO Magazine, and other global media outlets. Avik is passionate about global health, social entrepreneurship, and impact-driven innovation.
CliniOps is a technology and data company transforming clinical trial operations through an execution-first platform built for modern Hybrid and Decentralized Clinical Trials (Hybrid DCT).
Q1: Sponsors already rely on systems such as IRT, EDC, and ERP. Where do these systems fall short from an operational execution and supply chain perspective, and how does CliniOps address those gaps?
The reality is that a significant portion of clinical data is still captured on paper at the point of care. This happens for practical reasons: unreliable connectivity, site SOPs, infrastructure constraints, or even security/PHI restrictions in advanced hospitals. That data is then transcribed later into EDC, often days or weeks after the actual visit.
From a supply chain perspective, this creates a fundamental problem: decisions such as drug allocation, resupply, and forecasting are based on delayed and sometimes inaccurate data. CliniOps operates at the execution layer. We capture data directly at the point of care, on mobile devices in a fully offline model, and enforce protocol logic in real time, during execution.
This ensures that data is clean at source, enrollment and visit activity are visible immediately, and downstream systems like IRT and ERP receive high-quality data in near-real-time. Essentially, we transform the supply chain from being reactive to responsive, because it is now driven by real-time execution data rather than delayed transcription data.
Q2: Clinical trial data is often delayed, incomplete, or inconsistent across systems. How does CliniOps improve data reliability in real time, and why does that matter specifically for downstream supply chain decisions?
By the time it is usable, operational decisions have already been made. CliniOps eliminates this lag by capturing data once, at source, enforcing validation and protocol compliance during execution, and making data available immediately after capture.
This has direct implications for supply chain operations:
- Enrollment signals are real-time, improving demand forecasting
- Visit completion triggers can drive automated resupply decisions
- Inventory allocation becomes more precise
- The risk of overstocking or stockouts is significantly reduced
In essence, high-quality, real-time data becomes the control system for the clinical supply chain, not a delayed reporting artifact.
Q3: As hybrid and decentralized trials expand, operational complexity increases across sites, home health providers, and logistics networks. From your perspective, where do breakdowns most frequently occur in the clinical supply chain today?
This creates fragmentation in both workflows and data capture. The most common failure points are delayed or incomplete capture of visit data, lack of synchronization across multiple actors (sites, CROs, home health), transcription lag into systems like EDC and IRT, and inconsistent visibility into patient progression
Supply chains then operate reactively, because they depend on signals like randomization and visit completion, which are delayed. For example, if enrollment or visit completion data is delayed by even a few days, kit allocation and resupply decisions may already be out of sync with actual patient activity.
CliniOps addresses this through a unified execution layer that orchestrates data capture across all modalities. By ensuring that execution data is captured and validated in real time, we eliminate the latency that drives downstream supply chain inefficiencies.
Q4: How does CliniOps integrate with existing sponsor infrastructure, and what level of process change is typically required for organizations to realize value?
These systems remain systems of record, while CliniOps ensures that the data entering them is timely, complete and accurate. From a process standpoint, the shift is less about adding complexity and more about removing inefficiency. It is about eliminating paper source, reducing transcription workflows, and reducing reconciliation cycles.
For supply chain teams, the biggest change is not operational burden, it is improved visibility and predictability. The CliniOps platform enables faster, more reliable signals without requiring major downstream process redesign.
Q5: Can you share examples of how improved operational visibility or data accuracy has translated into measurable supply chain outcomes, such as reduced drug wastage, improved forecasting, or prevention of stockouts?
- Industry eSource implementations have reported query reductions of approximately 50–60% and near elimination of transcription errors in certain workflows.
- Near real-time data availability instead of delays of days or weeks
From a supply chain standpoint, this translates into:
- More accurate demand forecasting based on real enrollment velocity
- Reduced drug wastage due to better alignment of supply with actual patient activity
- Fewer emergency shipments and expedited logistics
- Improved kit allocation accuracy through enforced execution workflows
Additionally, execution-level controls such as enforced randomization and drug accountability directly reduce mis-dispensing and protocol deviations, which are major hidden drivers of supply inefficiency.
Q6: You describe CliniOps as “execution-first.” How does this differ from traditional monitoring and reconciliation approaches, and what impact does this shift have on clinical supply operations?
This is inherently reactive and labor-intensive. An execution-first model assumes that errors should not occur in the first place. CliniOps embeds controls directly into workflows, enforced by QR codes, that are fully functional even in offline mode:
- Correct patient, visit, and form selection enforced at execution
- Protocol logic validated in real time
- Drug allocation and accountability enforced at the point of care
For supply chain operations, this has a profound impact:
- Fewer downstream corrections and rework
- Fewer protocol deviations affecting supply planning
- Reduced need for buffer inventory
- More deterministic and predictable supply flows
Instead of reacting to errors discovered much later in the process, the supply chain operates on validated, execution-grade data in near real-time.
Q7: Across different trial types and geographies, where have you seen the strongest product-market fit for CliniOps, particularly in relation to complex or resource-constrained environments?
In these settings, traditional assumptions around reliable internet, centralized workflows, and consistent site infrastructure often do not hold. CliniOps’ offline-first, mobile-first architecture bridges this gap by enabling data capture without dependency on connectivity, consistent execution across sites, homes, and field settings, and reliable operation in real-world conditions.
From a supply chain perspective, these are also the environments where inefficiencies are most costly, making execution-layer control particularly valuable.
Q8: Transitioning to a more proactive, execution-driven model requires organizational change. What are the main barriers you see within clinical operations and supply chain teams when adopting this approach?
- Entrenched workflows built around systems of record and post-hoc correction
- Organizational silos between clinical operations, data management, and supply chain
- Comfort with monitoring-heavy models that have been institutionalized for decades
- Misaligned incentives, particularly in service-based delivery models
For supply chain teams specifically, the shift requires:
- Trusting real-time execution data rather than reconciled datasets
- Aligning more closely with clinical operations workflows
- Moving from buffer-driven planning to signal-driven planning: that transition requires organizations to trust real-time execution signals instead of relying on delayed reconciliation cycles and excess inventory buffers.
There is also a broader mindset shift – from managing variability to eliminating it at the source. Once organizations experience the operational clarity and predictability that execution-first models provide, adoption tends to accelerate.
Q9: Looking ahead, how do you see clinical supply chains evolving over the next 5–10 years, particularly in terms of data integration, automation, and cross-stakeholder coordination? Where does CliniOps fit into that future?
- tighter integration between clinical operations and supply chain systems
- Increased automation driven by real-time execution data
- Reduced reliance on manual reconciliation and monitoring
- Greater transparency across sponsors, CROs, and partners
- AI-driven optimization, but only where data is available in real-time with high data-quality
The limiting factor will not be algorithms, but it will be the quality and timeliness of execution-layer data. CliniOps fits into this future as the execution-layer infrastructure that generates high-quality, real-time data, provides the foundation for coordinated, responsive supply chains, and enables automation and AI, by ensuring a clean foundational data layer.
Organizations that become data-native at the execution layer will define the next generation of clinical development.
Q10: Clinical supply chains often struggle with limited end-to-end visibility across stakeholders. How does CliniOps enable better coordination between sponsors, CROs, sites, and logistics partners, and what impact does that have on overall trial execution?
- All stakeholders operate on the same real-time data
- Execution events are captured once and shared across systems, without requiring repeated reconciliation across stakeholders
- Workflows are coordinated across sites, field teams, and partners
This enables real-time dashboards for sponsors and CROs, earlier detection of operational risks, synchronized decision-making across stakeholders, and reduced latency in supply chain responses
For supply chains, this means better alignment between demand signals and supply actions, fewer surprises and last-minute interventions, and more efficient logistics and inventory management. Ultimately, improved coordination reduces friction across the ecosystem and enables trials to execute more predictably, efficiently, and at scale.