Deep Dive

Pre-AI Clinical Trial Supply Management

Before the advent of AI, clinical trial supply management relied heavily on manual tracking, spreadsheets, and legacy software systems, resulting in limited visibility, reactive decision-making, and vulnerability to stockouts or excessive waste. Decisions were often based on static forecasts rather than adaptive real-time data, leading to inefficiencies, delayed trial timelines, and increased costs.

Key challenges with traditional methods include:

• Limited visibility into real-time site-level consumption, causing inventory misalignment.
• Slow responses to demand fluctuations or unforeseen supply disruptions.
• High reliance on manual monitoring, making it difficult to proactively manage risks.

AI-Enhanced Clinical Trial Supply Management

Integrating AI, including both "frontier" generative and agentic AI, alongside machine learning, allows new clinical trial supply prioritization tooling to further optimize clinical trial supply chain management, enabling more effective proactive prioritization and resource allocation.

Comprehensive Data Integration

Platforms such as HASH can aggregate diverse data streams from systems in real-time, integrating structured data such as inventory levels, patient enrollment numbers, shipping status, and IoT sensor data (temperature, humidity, GPS tracking). Information can be drawn from a range of systems, including:

• Interactive Response Technology (IRT) Systems
• Clinical Trial Management Systems (CTMS)
• Enterprise Resource Planning (ERP) systems

In addition, unstructured data from communications (emails, site updates) and external intelligence (regulatory alerts, geopolitical risks) can be brought together in HASH, which specializes in integrating unstructured data alongside existing structured data, resulting in highly-trustable knowledge graphs which power dashboards, internal tools, and AI models.

This integrated data in HASH provides:

• Immediate, actionable insights into supply usage trends and inventory levels across trial sites.
• Early alerts about potential disruptions based on geopolitical events, regulatory changes, or transportation issues.

Such tools serve as a centralized, trusted source of information, acting as a unified data hub and single source of truth for trial supply information, enabling informed decision-making by clinical operations teams. HASH's centralized platform also supports digital twin simulations of clinical supply scenarios, enabling planners to run “what if” analyses, such as exploring outcomes from increased patient enrollment or unexpected site closures, and instantly view implications on inventory and risk.

Predictive & Prescriptive Analytics

Machine learning models can be trained which forecast patient enrollment rates, medication consumption patterns, and potential supply chain disruptions, prescribing optimal shipment prioritization decisions. This enables:

• Dynamic allocation of supplies to sites based on real-time consumption rates.
• Reduction of waste and stockouts by predicting shortages and surpluses before they occur.
• Automated inventory replenishment tailored precisely to each site's evolving requirements.

AI Copilots For Clinical Trial Supply

Generative AI copilots within the platform allow clinical supply planners to query information using natural language (“Which trial sites are at risk of shortage next month?” or “Recommend shipment adjustments to balance inventory across sites.”), significantly enhancing responsiveness.

These copilots vsn also:

• Automate reporting on inventory status, consumption trends, and predicted risks.
• Draft preemptive communications with trial sites to address supply challenges proactively.
• Suggest optimal routing and timing of shipments based on predictive insights.

Foundation For Agentic AI

The centralized and reliable data structure allows for deployment of agentic AI, empowering automated workflows, such as adjusting shipment schedules, reallocating inventory dynamically, and proactively initiating supply replenishments without human intervention. This approach significantly enhances operational efficiency and trial continuity.

Implementation & Enhancement

Selecting A Solution

When adopting clinical trial supply prioritization tools, consider:

• Data accuracy and timeliness: ensuring seamless integration and real-time updates from diverse sources.
• User adoption: incremental roll-out to demonstrate early value and build trust among clinical trial teams.
• Security: maintaining rigorous data privacy and compliance standards suitable for clinical research settings.
• Cloud vs. on-premises hosting: evaluating operational preferences, data sovereignty, and maintenance considerations.

HASH is an open-source platform capable of integrating information from any source, both structured and unstructured. HASH was built from the ground-up to utilize AI, deeply integrating both traditional machine learning and new generative AI. To find out more about our platform, visit hash.ai or contact us to learn more about how our technology and services can support your supply chain.

Roadmap To Value

A typical deployment pathway would involve:

• Connect & standardize data: integrate clinical trial management systems, IoT sensors, site updates, and external alerts.
• Launch real-time dashboards: immediate visibility into site-level inventories, shipment status, and consumption trends.
• Deploy predictive analytics: forecasting enrollment rates, supply consumption, and disruptions.
• Activate autonomous agents: automated shipment prioritization and inventory adjustments.
• Continuously optimize: integrate ongoing feedback to refine forecasting and decision-making accuracy.

AI-driven clinical trial supply prioritization tools help transform clinical trial supply management, delivering increased efficiency, reduced risk, improved trial continuity, and optimized inventory utilization.