A Structural Shift in Where Clinical Trials Happen

For decades, the default clinical trial site model was either a large academic medical center or a stand-alone, dedicated research clinic with no other clinical function. In the last several years, a third model has grown rapidly: the embedded research site, where clinical trial infrastructure is built directly inside an existing care delivery network, such as a primary care group, urgent care network, or multi-specialty practice, rather than as a separate free-standing entity. Understanding why this model is growing, and what makes it work operationally, matters for any health system or care network evaluating whether to build a research division of its own.

Why Health Systems Are Building In-House Research Networks

The core economic and operational logic behind embedded research sites comes down to patient population access. A health system or urgent care network with an existing base of tens or hundreds of thousands of patients already has, embedded within its own EHR, a characterized population that can be queried against study eligibility criteria far faster than a stand-alone research site could ever recruit from a cold start. This structural advantage shows up directly in feasibility metrics that sponsors care about most: time to first patient enrolled, and total enrollment achieved within a study's active recruitment window.

Beyond patient access, embedded models offer several additional advantages that are driving adoption:

  • Diversified revenue for the health system, since clinical trial revenue provides a source of income that is largely uncorrelated with traditional payer reimbursement cycles and insurance mix
  • Physician engagement and retention, since offering physicians the opportunity to serve as sub-investigators or principal investigators on clinical trials is an increasingly valued professional development and compensation lever in a competitive physician recruitment market
  • Patient access to novel therapies, which health systems increasingly market as a genuine differentiator for patient acquisition and retention in competitive local markets

The Operational Build-Out Challenge

Standing up an embedded research division inside an existing care network is a genuinely different operational challenge than opening a stand-alone research clinic, because it requires integrating research workflows into a clinical operation that was not originally designed to support them. The core build-out components typically include:

  • A dedicated research operations team, distinct from clinical staff, including at minimum a director of clinical research, study coordinators, and a regulatory or quality lead, even if some of these roles start as shared or part-time responsibilities
  • IRB relationships, typically through a central or commercial IRB rather than building an internal IRB from scratch, which is faster and less resource-intensive for a network in its early build-out phase
  • A defined referral pathway from clinical staff to research staff, since the biggest source of patient identification in an embedded model is often physicians and clinical staff who are seeing eligible patients in the normal course of care and need a simple, low-friction way to flag them for the research team
  • Contract and finance infrastructure, including the ability to negotiate and manage Clinical Trial Agreements, per-patient budgets, and sponsor payment reconciliation, which is typically new territory for a health system's existing finance function

Technology Infrastructure for the Embedded Model

Embedded research divisions depend heavily on how well the underlying clinical technology infrastructure, particularly the EHR system, supports research workflows layered on top of standard clinical care. Networks building this infrastructure should plan for a few specific technical needs early rather than retrofitting them later: a reliable method for flagging research-eligible patients within the clinical workflow without disrupting standard care documentation, a clean separation between clinical and research billing codes to avoid the compliance issues described below, and a research-specific data extraction capability that allows the research team to query the broader network's patient population for feasibility and enrollment purposes without requiring a manual chart-by-chart review process. Networks that address these technical requirements during initial build-out, working closely with existing IT and EHR administration teams, avoid a substantial amount of rework that otherwise surfaces once the first few studies are underway and workflow gaps become apparent under real operational pressure.

The Sub-Investigator Pathway as a Growth Engine

One of the most effective ways embedded research networks scale beyond a single principal investigator is by building a structured sub-investigator pathway, where physicians already practicing within the network can be trained and credentialed to support studies under an experienced PI's oversight, gradually taking on more responsibility and eventually becoming principal investigators themselves for future studies. This model does two things simultaneously: it increases the network's overall enrollment capacity without needing to recruit external research-only physicians, and it builds a pipeline of investigator depth that materially strengthens the network's feasibility profile with sponsors, who explicitly value multi-investigator sites as a hedge against single-PI availability risk.

Business Development Advantages of the Embedded Model

From a pure business development standpoint, embedded research networks have a genuinely differentiated pitch to sponsors compared to stand-alone research sites. Rather than competing purely on investigator experience or facility quality, an embedded network can point to a specific, quantifiable patient population, for example, a documented count of patients meeting a BMI threshold and a relevant diagnosis code across a network's entire patient base, and translate that directly into a credible enrollment projection that a sponsor's own predictive models can independently verify against claims and EHR penetration data.

This population-based pitch is particularly powerful in high-demand therapeutic areas currently experiencing intense sponsor competition for site allocation, such as obesity and metabolic disease, where the size and specificity of an addressable population is often the deciding factor in feasibility scoring.

Common Pitfalls in the Embedded Model

Health systems building embedded research networks encounter a few recurring challenges worth planning for in advance:

  • Underestimating the cultural change management required, since clinical staff accustomed to a purely clinical care workflow need training and incentive alignment to consistently identify and refer research-eligible patients, which does not happen automatically just because the infrastructure exists
  • Regulatory and billing compliance complexity, since clinical trial billing requires careful separation from standard clinical billing to avoid compliance issues, and this needs dedicated expertise rather than being handled by a standard clinical billing team unfamiliar with research billing rules
  • Scaling too quickly across multiple locations before core processes are proven, which can dilute quality and create inconsistent data across sites before the network has validated its operational model at a smaller scale

Governance Structure: Where Research Reports Within the Organization

One of the less obvious but consequential early decisions in building an embedded research division is where it sits within the broader organizational structure. Networks generally choose between a few governance models, each with distinct trade-offs:

  • Research as a direct clinical service line, reporting through the same clinical leadership structure as other specialties, which tends to produce strong physician engagement but can create friction when research priorities compete with clinical productivity metrics for physician time
  • Research as an independent business unit, reporting directly to executive leadership with its own P&L, which gives the research division more autonomy to make investment and business development decisions quickly, but requires deliberate effort to maintain strong referral relationships with clinical staff who now sit in a separate reporting line
  • A hybrid dyad leadership model, pairing a clinical leader, often a practicing physician serving as a medical director, with an operational or business development leader who manages the day-to-day research operation, which many networks find balances clinical credibility with the operational discipline research administration requires

There is no universally correct choice here, but networks that make this decision deliberately, rather than defaulting to wherever research happens to land organizationally during initial build-out, tend to avoid the governance ambiguity that otherwise slows decision-making as the division scales.

Financial Considerations and the Path to Profitability

Health system leadership evaluating an embedded research investment reasonably wants to understand the financial trajectory before committing significant resources. Most embedded research divisions follow a broadly similar financial arc: an initial investment period of twelve to twenty-four months where infrastructure costs, including staffing, regulatory setup, and initial business development, exceed study revenue, followed by a transition to profitability as the sponsor relationship pipeline matures and the network's feasibility win rate improves with a growing track record.

A few financial factors materially affect how quickly a network reaches profitability:

  • Per-patient budget negotiation discipline, since embedded networks that accept the first budget offered by a sponsor without negotiating based on their actual cost structure and enrollment performance consistently under-monetize their patient population relative to networks that negotiate systematically
  • Study volume diversification across multiple sponsors, since a network dependent on a single sponsor relationship carries significant revenue concentration risk if that relationship changes or that sponsor's pipeline slows
  • Overhead allocation clarity, ensuring the research division's cost accounting accurately reflects shared resources, such as regulatory or finance staff time, so that study-level profitability numbers are trustworthy for future investment decisions

Networks that build financial tracking discipline early, rather than treating research as a loosely tracked side activity within the broader health system budget, are able to make much clearer decisions about when and how aggressively to scale.

A Practical Build-Out Sequence for New Embedded Research Divisions

Health systems evaluating whether and how to launch an embedded research division benefit from a clear, sequenced build-out plan rather than attempting to stand up every component simultaneously:

  • Begin with a population analysis identifying the network's strongest therapeutic area candidates based on documented patient volume and diagnosis prevalence
  • Establish IRB relationships and core regulatory infrastructure before pursuing specific sponsor relationships, so the network can respond quickly once opportunities arise
  • Recruit or credential an initial principal investigator with genuine interest and credibility in the target therapeutic area, ideally someone already practicing within the network
  • Build the referral pathway and staff training needed for clinical staff to identify and flag potentially eligible patients as a normal part of their workflow
  • Pursue an initial study, even a smaller or lower-budget one, primarily to validate operational processes before scaling to multiple simultaneous studies
  • Use the lessons from that first study to refine staffing, referral, and data processes before expanding to additional therapeutic areas or locations

Frequently Asked Questions on Embedded Research Sites

How long does it typically take to build a functioning embedded research division from scratch?

Most networks see twelve to eighteen months from initial planning to a first activated study, with the timeline heavily dependent on how quickly IRB relationships, regulatory infrastructure, and initial PI credentialing can be established.

What is the minimum patient population size that makes an embedded research model economically viable?

There is no universal threshold, since it depends heavily on therapeutic area focus, but networks with fewer than roughly 20,000 to 30,000 patients often find it more efficient to focus on a small number of high-prevalence therapeutic areas rather than attempting broad multi-indication coverage from the start.

Does an embedded research model compete with or complement a health system's existing academic medical center relationships?

Generally complements. Sponsors often value having both a large academic site and a community-based embedded network in the same trial, since the two site types typically draw from different patient populations and offer different enrollment speed and diversity profiles.

Conclusion

The embedded research site model represents one of the more significant structural shifts in where and how clinical trials are conducted, driven by a straightforward business logic: health systems and care networks already sit on top of the patient populations sponsors most want access to, and building the operational infrastructure to activate that population for research has become a genuine competitive advantage rather than a niche side business. Networks that invest deliberately in the underlying operational, regulatory, and cultural infrastructure, rather than assuming patient access alone is sufficient, are the ones building embedded research divisions that scale successfully over time.

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