Fixing the Bottlenecks in Clinical Trial Site & Investigator Selection
Introduction: The High Cost of Inefficient Trial Site & PI Selection
Clinical trials are the backbone of drug and medical device development, yet inefficiencies in site and investigator selection are costing the industry billions. In 2023, the global clinical trial market was valued at $51.3 billion (Grand View Research), but studies suggest that poor trial site and investigator selection account for up to 20% of all trial costs (McKinsey & Company). Moreover, 80% of clinical trials fail to meet their enrollment targets on time, causing costly delays and missed revenue opportunities (Deloitte).
Despite advances in real-world data, AI, and decentralized trials, many sponsors still rely on manual, outdated site feasibility processes that lead to poor-performing sites, investigator burnout, and bottlenecks in patient recruitment.
This article breaks down:
The key inefficiencies in trial site & PI selection
The link between poor site selection and patient recruitment failures
Why standardized data models, interoperability, and AI are crucial for optimizing selection
How leading pharma & medtech firms are re-evaluating site feasibility strategies
1. The Real Problem: Why Traditional Site & PI Selection Fails
Reliance on Outdated Investigator Networks
Pharma and medtech companies often select sites and investigators based on historical performance, assuming past success guarantees future efficiency. However, this leads to site saturation, longer recruitment timelines, and trial delays.
🔹 According to a report by Tufts CSDD, 11% of clinical trial sites fail to enroll a single patient. Another 37% underperform, enrolling fewer than the projected number of participants (Tufts Center for the Study of Drug Development).
🔹 Over-reliance on high-volume research sites can reduce diversity and generalizability of study results, a growing concern for regulators like the FDA and EMA (Deloitte).
Industry Insight: Doug Bain, CTO at KCR, emphasizes that data silos and a lack of auto-screening in recruitment slow down the process unnecessarily. Instead of relying on static PI lists, sponsors should dynamically assess real-time investigator availability and site capacity.
Inadequate Feasibility Assessments
Most feasibility surveys are static, manual, and often outdated by the time trials begin. This leads to:
Overestimations of site capacity
Mismatch between protocol complexity and site capabilities
Poor alignment with real-world patient populations
Industry Insight: Jennifer Howell, clinical research nurse at Indiana University School of Medicine, points out a real-world issue: “We have a pediatric trial that we cannot recruit for because the protocol calendar has monthly in-person visits. Every eligible patient has seen the study visit calendar and declined.”
Lack of Real-Time Site Performance Data
Sponsors rarely track real-time site performance metrics, relying instead on historical data that doesn’t reflect current capabilities. This results in:
Sites underperforming due to competing trials
Investigator burnout affecting recruitment
Limited expansion to new, high-potential sites
Industry Insight: Mohammed Ali, Head of Business Performance Data & Analytics at Astellas Pharma, stresses the need for dynamic data sources rather than static lists: “Clinical trial data needs to be captured inside and outside of sites for more equitable centricity.”
2. The Link Between Poor Site Selection and Patient Recruitment Bottlenecks
Selecting the wrong sites directly impacts patient recruitment. A misaligned site may:
Struggle to find eligible patients
Lack the staffing and infrastructure to engage potential participants
Fail to retain enrollees due to poor patient experience
Protocol Design: The Hidden Recruitment Killer
Many trials fail to enroll not because of lack of patients, but because protocols are too restrictive, inconvenient, or poorly designed for real-world settings.
🔹 30% of Phase III failures are due to enrollment difficulties rather than drug inefficacy (McKinsey).
Industry Insight: Ayse Tezcan, PhD, an epidemiologist and clinical consultant, highlights that the limiting factor isn’t patient availability but overly rigid eligibility criteria: “The patient recruitment space is crowded because it’s a low-hanging business opportunity, but systemic changes require brave innovation.”
Heather Armstrong, a Quality Assurance expert, adds: “You can have plenty of interested patients, but if the protocol is restrictive, they won’t get in.”
3. The Path Forward: Structured Data Models, AI & Smarter Recruitment
A data-driven approach to site and investigator selection requires more than AI—it demands standardized data models, interoperability, and structured ontologies.
1. The Role of USDM & Common Data Models
🔹 The Unified Study Data Model (USDM) is a structured framework aimed at harmonizing clinical trial data across different sponsors, sites, and regulatory bodies. By implementing USDM, SNOMED CT, LOINC, and ICD-10, sponsors can ensure that AI models work on clean, structured, and interoperable data.
Industry Insight: Doug Bain emphasizes that AI-driven site selection is meaningless if trial data is fragmented, outdated, or locked in silos.
2. AI-Powered Site & PI Matching (Only When Data Is Standardized)
Once data interoperability is in place, AI can enhance site selection by:
Predicting site performance using historical & real-time metrics
Auto-matching PIs based on expertise, experience & capacity
Enhancing patient identification through real-world evidence (RWE) integration
AI cannot function effectively without high-quality, standardized data inputs.
3. Hybrid & Decentralized Site Models
Expanding beyond traditional research sites to include community healthcare settings
Increasing the use of remote monitoring and decentralized trial elements
Reducing patient burden with more flexible scheduling and digital consent
A hybrid model enables greater patient access, improved diversity, and faster recruitment.
Conclusion: The Need for a Smarter Approach
Clinical trials must evolve beyond static site selection and outdated recruitment models. Pharma and medtech companies that fail to modernize risk trial delays, higher costs, and missed revenue opportunities.
🚀 At Meplis, we’re building solutions that connect the right investigators, sites, and patients to accelerate clinical trial success.
🔗 Want to explore how smarter site selection can drive better recruitment and trial outcomes? Contact Us to learn more.