A New Era for Feasibility in Clinical Research
In the high-stakes world of pharma R&D, selecting the right clinical trial sites is both critical and complex. The stakes are high: the right site can fast-track study enrollment, while the wrong one can derail timelines and inflate costs. Yet, feasibility assessments and site selection are still largely manual, inconsistent, and often disconnected from the broader clinical ecosystem.
This is where process automation comes in. By unifying systems, standardizing data, and delivering real-time insights, process automation platforms offer a smarter, faster way to evaluate and select sites that are truly trial-ready.
Whether you’re a clinical operations director, a feasibility specialist, or a data manager, you’ll find insights here on how automation (and increasingly, AI) can transform today’s complex research landscape.
Rethinking How We Approach Feasibility
Life sciences organizations are navigating a dynamic environment. To stay competitive and compliant, they must constantly evolve their approach to site feasibility in order to:
- Advance protocol designs
- Adapt to evolving regulatory landscapes
- Accelerate time-to-market
- Identify and engage high-performing sites
- Reach diverse patient populations
Despite these demands, feasibility and site selection are often:
- Disconnected from real-time data sources
- Reliant on siloed or outdated systems
- Dependent on manual workflows and subjective judgment
Automation offers a way to not only keep up with these demands but also proactively meet them. And with AI layered into the process, feasibility teams can shift from reactive decisions to proactive predictions.
Why Automation Is a Game-Changer for Feasibility Teams
Process Automation platforms like Appian can unify disparate tools and data sources (internal and external) into a single, streamlined workflow using Data Fabric – a smart “overlay” that connects all your existing systems, from modern cloud apps to decades-old legacy platforms, so they act like one unified environment. Data Fabric connects seamlessly with systems such as CTMS, EDC, safety databases, and more, often through pre-built connectors and APIs. This means you can integrate and activate your existing data without overhauling your entire tech stack.
More than just integration, these platforms offer:
- Customizable dashboards to display site performance, patient demographics, and regulatory readiness
- Visual workflows that map to your team’s unique processes
- Built-in collaboration tools that bring together sponsors, CROs, investigators, and regulators
High-Quality Data, Right from the Start
Clean, consistent data is the foundation of good feasibility. Process Automation platforms offer built-in tools for:
- Detecting and correcting inconsistencies in data formats
- Applying standardized terminologies across sources
- Flagging data quality issues through validation rules
- Enforcing governance policies across systems
By ensuring high-quality data from the beginning, you can reduce rework, improve forecasting, and support more confident decision-making.
What Unified Data Actually Delivers
Unified data views provide a single perspective of all relevant information no matter the underlying systems or databases where that data physically resides. With process automation, companies can quickly pull data from various sources (internal databases and third party content), make it consistent, and present it to match the specific needs of feasibility assessment teams.
This might include creating dashboards that display site performance metrics, patient demographics, and regulatory compliance information all in one place. The visual nature of low-code development in process automation platforms makes it easier to design these views. When all feasibility-related data is accessible in one place, the benefits are immediate:
- Faster access to critical information: No more switching between tools or chasing spreadsheets.
- More comprehensive assessments: Unified views ensure no red flags or patient population gaps are missed.
- Improved decision-making: Holistic views allow feasibility teams to act faster and with greater confidence.
- Reduced time and cost: Automated processes cut down on effort spent on low-value tasks.
- Better collaboration: A single source of truth supports more effective communication across teams.
- Increased agility: As protocols change, your workflows and data views can adapt quickly.
Benefits for Every Stakeholder
A process automation approach doesn’t just benefit feasibility teams. It creates value across the clinical trial ecosystem:
Investigators
- Faster access to comprehensive site and patient data
- Improved ability to assess study feasibility at their site
- Enhanced communication with sponsors and CROs
Sponsors
- More accurate and efficient site selection
- Better oversight of trial progress across multiple sites
- Improved resource allocation and planning
CROs
- Streamlined management of multiple trials and sites
- Enhanced reporting to sponsors
- Greater operational efficiency
Regulatory Teams
- Easier access to standardized data for review
- Better ability to assess protocol adherence
- Faster submission timelines supported by complete data views
Driving Smarter Decisions, Faster
Study feasibility and site selection don’t have to be manual, fragmented, or slow. With process automation (supported by Data Fabric), you can unify systems, clean your data, and deliver real-time insights that lead to faster, smarter decisions. And with AI, you can go one step further – from insights to foresight.
Think about it: instead of simply reviewing past site performance, AI models can analyze vast datasets (historic enrollment rates, investigator experience, patient demographics, even social determinants of health) to predict which sites are most likely to deliver results for your specific trial.
For example:
- An AI model can flag a site that looks strong on paper but has struggled with patient retention in similar trials.
- It can highlight under-the-radar community sites with strong access to diverse patient populations that manual screening might overlook.
- Natural language processing can even scan past investigator notes and regulatory submissions to surface subtle risks before they become issues.
The result? Feasibility teams make faster, more confident decisions that balance speed, quality, and patient diversity. Instead of just reacting to problems after a trial begins, you’re proactively setting it up for success from day one.
In a landscape where speed, quality, and patient diversity are critical, automation helps clinical teams move with confidence and precision, right from the start.
Whether you’re preparing for your next study or re-evaluating your current workflows, now is the time to bring intelligent automation into your feasibility strategy.
Test with a Proof of Concept
Do you have a specific use case in mind? I invite you to brainstorm with our team of experts and use our Innovation Lab to see what a potential solution could look like.
Want to see Process Automation in action? Explore solution demos of clinical use cases that bring together Data Fabric and AI – including Clinical Complaint Management, Real World Evidence, Clinical Supplies Shipping, and more. Visit the Innovation Lab to learn more.”
