How to Use ICD-10 Data for Clinical Trial Site Selection (and Identify the Right Physicians)
Why ICD-10-Based Site Selection Is Replacing Traditional Feasibility Models
Clinical trial site selection has historically been built on a relatively narrow set of signals. Sponsors and CROs have typically prioritized sites with proven enrollment histories, experienced investigators, and established infrastructure. While this approach reduces perceived risk, it is fundamentally backward-looking. It assumes that past performance is the best predictor of future success, even as patient populations, care delivery models, and referral patterns continue to evolve.
This limitation has become increasingly costly. A large proportion of clinical trials experience delays due to slow patient recruitment, with poor site selection cited as one of the primary causes. Industry analyses consistently show that many selected sites underperform or fail to enroll a single patient. As a result, there is a growing shift toward data-driven feasibility models that prioritize real-time patient access over historical participation.
This is where ICD-10 data becomes particularly valuable. Instead of relying on where trials have succeeded in the past, ICD-10-based approaches allow teams to identify where relevant patients are being diagnosed today. This shift—from investigator-centric to patient-centric site selection—represents one of the most important changes in modern clinical development strategy.
What Are ICD-10 Codes and Why They Matter for Clinical Trials
ICD-10 codes, defined by the World Health Organization, provide a standardized framework for classifying diseases and health conditions across healthcare systems. These codes are used globally in clinical documentation, billing, and reporting, making them one of the most consistent and widely available sources of diagnostic data.
What makes ICD-10 data uniquely valuable for clinical trials is its position in the patient journey. Diagnosis occurs before treatment decisions are made, before prescriptions are written, and often before patients are referred to specialists. This means ICD-10 data captures the earliest possible signal of patient availability.
For example, consider a trial targeting patients with moderate-to-severe psoriasis. Prescription data might identify dermatologists who are already treating advanced cases with biologics, but ICD-10 data can reveal a broader group of physicians diagnosing psoriasis at earlier stages. These physicians represent a critical opportunity to identify patients before they progress or are absorbed into existing treatment pathways.
This early visibility aligns with recommendations from the Clinical Trials Transformation Initiative, which emphasizes the importance of using real-world data during trial planning to better estimate eligible patient populations and improve recruitment strategies (Clinical Trials Transformation Initiative).
The Core Problem with Traditional Clinical Trial Site Selection
Traditional site selection processes are heavily influenced by familiarity. Sponsors often return to the same sites and investigators because they have performed well in previous trials. While this reduces uncertainty, it also creates systemic inefficiencies.
One common issue is geographic mismatch. A site that enrolled well five years ago may no longer have access to the same patient population due to demographic shifts, changes in referral patterns, or new competitors entering the market. Despite this, the site may still be selected based on historical performance.
Another issue is the exclusion of emerging providers. Physicians who are actively diagnosing large numbers of relevant patients may be overlooked simply because they have not previously participated in trials. This is particularly problematic in fast-evolving therapeutic areas, where new treatment centers and care pathways are constantly emerging.
A third issue is the disconnect between diagnosis and treatment. Patients often move through multiple providers before reaching a trial site. If site selection focuses only on treatment centers, it may miss the earlier stages of the patient journey where recruitment opportunities are strongest.
Best Practices for Clinical Trial Site Selection Using Real-World Data
Modern site selection strategies increasingly begin with patient identification rather than site identification. This means starting with data that reveals where eligible patients exist and then mapping those patients to providers and institutions.
For instance, in a cardiovascular trial targeting heart failure patients, ICD-10 codes such as I50 (heart failure) can be used to identify physicians diagnosing large numbers of relevant patients. These physicians may include general cardiologists, internists, or even primary care providers. By mapping where these diagnoses occur, teams can identify geographic clusters of patients that may not align with traditional trial sites.
Best practices also emphasize the importance of validating feasibility early in the trial design process. Regulatory and industry bodies, including the U.S. Food and Drug Administration, have increasingly encouraged the use of real-world data to ensure that eligibility criteria reflect actual patient populations. This reduces the risk of designing trials that are difficult or impossible to enroll.
Another key practice is incorporating referral networks into site selection. Patients rarely remain with a single provider throughout their care journey. Instead, they move through networks of diagnosing physicians, specialists, and treatment centers. Understanding these referral pathways can reveal which providers act as gateways to patient populations.
How ICD-10 Data Translates Into Real Clinical Trial Strategy
The real power of ICD-10 data emerges when it is connected to providers, institutions, and networks.
Consider an oncology trial targeting non-small cell lung cancer. By analyzing ICD-10 codes such as C34 (malignant neoplasm of bronchus and lung), a sponsor can identify not only major cancer centers but also community pulmonologists and oncologists diagnosing new cases.
In many cases, these community providers are the first point of contact for patients. They diagnose the condition and then refer patients to larger treatment centers. If site selection focuses only on those larger centers, it may miss opportunities to engage earlier in the patient journey.
By contrast, an ICD-10-driven approach might identify a regional network where several pulmonologists diagnose high volumes of lung cancer and consistently refer patients to a specific oncology group. Selecting both the diagnosing providers and the treatment center as part of a coordinated site strategy can significantly improve recruitment.
How Alpha Sophia Enables Physician-Level Enrichment
While ICD-10 data provides critical insight into where patients are being diagnosed, it does not, on its own, provide enough context to make optimal site selection decisions.
Diagnosis counts do not reveal whether a physician treats or refers patients, how they are connected to other providers, or what role they play within a healthcare system. This is where physician-level enrichment becomes essential.
Platforms like Alpha Sophia integrate ICD-10 data with claims, referral networks, and organizational affiliations to create a more complete picture of each provider. This allows teams to distinguish between physicians who diagnose and manage patients directly and those who primarily refer them onward.
For example, in a rare disease trial, a physician who diagnoses only a small number of patients may still be highly valuable if they are a central node in a referral network that directs patients to specialized treatment centers. Without network context, this physician might appear insignificant. With enrichment, their strategic importance becomes clear.
Alpha Sophia’s broader approach to unified provider intelligence illustrates how these different data layers can be combined into a single, actionable model:
From Static Feasibility to Continuous Site Optimization
Traditional feasibility assessments are typically conducted once, at the beginning of a trial. Data is collected, analyzed, and used to select sites. After that, the strategy remains largely fixed.
This static approach does not reflect the dynamic nature of healthcare delivery.
ICD-10-driven models enable continuous monitoring of diagnosis trends. For example, if a new cluster of patients begins to emerge in a particular region, additional sites can be activated to capture that opportunity. Conversely, if a selected site shows declining diagnosis activity, resources can be reallocated.
This shift toward continuous optimization is increasingly supported by real-world data platforms and has been shown to improve enrollment timelines and reduce trial delays (https://trinetx.com/blog/clinical-trial-optimization-key-strategies-for-protocol-feasibility-and-site-selection).
Why ICD-10-Based Site Selection Matters Now
Several broader trends are making ICD-10-driven site selection not just advantageous, but necessary.
Clinical trials are becoming more targeted, with narrower inclusion criteria and more specific patient populations. At the same time, competition for patients is increasing, particularly in high-demand therapeutic areas such as oncology and cardiology.
There is also growing regulatory emphasis on diversity and representativeness in clinical trials. ICD-10 data can help identify patient populations in community settings that may be underrepresented in traditional site networks.
Finally, the availability of real-world data has increased dramatically. The challenge is no longer accessing data, but integrating and interpreting it effectively.
Conclusion: A Shift Toward Patient-Centric Trial Design
The use of ICD-10 data in clinical trial site selection reflects a broader shift toward patient-centric trial design.
Rather than relying on historical site performance, sponsors can now identify where patients are entering the healthcare system and build site strategies around real-world care pathways.
When combined with physician-level enrichment and platforms like Alpha Sophia, this approach enables a more accurate, flexible, and effective model of site selection.
The result is not only improved enrollment, but a more efficient and representative clinical development process.
FAQ Section
How are ICD-10 codes used in clinical trial site selection?
ICD-10 codes are used to identify where patients with specific conditions are being diagnosed, allowing sponsors to select trial sites based on real-time patient availability rather than historical enrollment data.
Why is diagnosis data more useful than prescription data for trials?
Diagnosis data captures patients earlier in the care journey, providing a forward-looking view of potential recruitment opportunities before treatment decisions are made.
What is physician-level enrichment in site selection?
Physician-level enrichment involves adding context such as referral networks, institutional affiliations, and clinical activity to diagnosis data, enabling more accurate identification of high-value investigators.
How does Alpha Sophia support clinical trial site selection?
Alpha Sophia integrates diagnosis data with provider activity and network relationships, allowing teams to identify high-potential sites based on real-world patient flow.
What are the biggest challenges in clinical trial site selection today?
The main challenges include poor patient access, over-reliance on historical sites, and lack of visibility into real-world care pathways.
