The Future of Connected Clinical Research Infrastructure

Introduction

Drug development has a data problem that goes deeper than any single technology can fix. Sponsors, sites, CROs, and regulators each generate and hold data that the others urgently need, yet that data flows between them slowly, manually, and incompletely. A 2024 Special Communication published in JAMA by Franklin, Marra, Abebe, and colleagues from the JAMA Summit on Clinical Trials found that the ways in which clinical trials access, acquire, and use data "have evolved very little over time, resulting in a fragmented and inefficient system that limits the amount and quality of evidence that can be generated" ^[1]. The infrastructure for clinical trial data collection "remains expensive and labor intensive," they observed, while demand for evidence from randomized trials to support regulatory, payment, and clinical care decisions keeps rising ^[1].

The failure is not about computing power or storage capacity. It is about architecture: disconnected systems that were digitized without being integrated, each solving one problem well and creating connection problems everywhere else.

This article examines where connected clinical research infrastructure stands today, which standards are converging to change it, what regulatory agencies have signaled to sponsors, and what genuine interoperability would mean in practice for trial timelines, data quality, and patient access.

Why Fragmentation Is the Default State

Clinical trial data fragmentation was not built through negligence. The regulatory and privacy frameworks that govern clinical research created compartments around data to protect participants and ensure integrity, and those compartments became structural. Each sponsor, each site, and each CRO built systems optimized for their own workflow. EDC platforms, eTMF systems, CTMS applications, and safety databases proliferated independently, each solving one problem and creating a connection problem everywhere adjacent to it.

The clinical trial industry compounded this by digitizing paper-based processes without re-engineering the underlying data models or workflows. The result, as the JAMA Summit communication put it, is a system where "substantial public and industry investment in advancing electronic health record interoperability, data standardization, and the technology systems used for data capture" has not yet produced infrastructure capable of meeting current evidentiary demands ^[1].

The operational consequences trace directly to study startup. According to a 2025 WCG survey of hundreds of research sites globally, 31% of sites cited study start-up as a top challenge, with contract negotiations, coverage analysis, and budget review identified as the principal sources of delay ^[2]. This is consistent with patterns Tufts CSDD has tracked over more than a decade. A 2024 Tufts CSDD analysis published in Therapeutic Innovation & Regulatory Science by Smith, DiMasi, and Getz, drawing on 409 clinical trial budgets, found that the average direct daily cost of conducting a Phase III trial is approximately $55,716 (in 2023 USD) as reported in the accompanying Tufts CSDD Impact Report, meaning every week of preventable startup delay costs sponsors more than $390,000 in direct trial expenses, before accounting for opportunity costs from delayed drug sales ^[3].

The deeper cost is scientific rather than operational. When datasets are siloed across EDC systems, imaging platforms, biomarker repositories, and patient-reported outcome tools, cross-dataset analysis becomes manually intensive or, within a trial's timeline, simply impossible. Safety signals that would be apparent in a unified dataset remain invisible. Dosing insights that emerge from combining pharmacokinetic data with genomic subgroups never surface. Franklin et al. observed that current infrastructure "limits the amount of evidence that can be collected to inform whether and how interventions work for different patient populations": not because the data is absent, but because the architecture cannot connect it ^[1].

The Standards Converging Around This Problem

Three parallel standardization efforts are now sufficiently mature that their combined effect on clinical trial infrastructure is becoming concrete rather than aspirational. Each operates at a different layer of the data flow, and their deliberate alignment is one of the more significant infrastructure developments of the past two years.

Standards Layer Map

Before examining each standard individually, it is useful to understand where each one sits:

These standards are complementary, not competing. M11 defines what the protocol says. USDM defines how the study is structured for system configuration. FHIR defines how patient data from healthcare settings enters the trial workflow. SDTM and ADaM define how collected data is formatted for regulatory review. The alignment between them (particularly between USDM v4.0 and M11) is the architectural foundation on which connected infrastructure is being built.

CDISC's Digital Data Flow and the Unified Study Definitions Model

The Clinical Data Interchange Standards Consortium (CDISC), in collaboration with TransCelerate Biopharma, has been developing the Digital Data Flow (DDF) initiative, centered on the Unified Study Definitions Model (USDM). The USDM provides a machine-readable, standardized framework for defining clinical trial structure: the protocol design, schedule of assessments, endpoints, and data collection specifications in a format that downstream systems can directly consume ^[4].

TransCelerate's DDF initiative describes the USDM as "a mechanism to digitize clinical study components to enable automation, interoperability, and reuse across the study lifecycle," with the goal of enabling automated study asset creation and system configuration to support trial execution ^[5]. The practical implication, when implemented correctly, is that a study defined once in USDM format could be imported into an EDC and used as the basis for CRF configuration, reducing or eliminating manual re-entry of study design elements that have already been captured in the structured definition.

CDISC released USDM version 4.0 on June 3, 2025, aligned specifically with ICH M11 and including a full implementation guide, conformance rule specifications, and API specifications ^[6]. Phase 5 development ran through April 2026, expanding the model's scope ^[6].

The USDM addresses a structural problem that has accumulated over decades. Every new trial currently requires manual re-entry of protocol content into EDC systems, CTMS configurations, and clinical trial registries, each requiring multiple transcription steps, each introducing opportunities for inconsistency and each consuming data management time that could go toward trial execution. A USDM-aligned workflow replaces these steps with automated propagation from a single source definition.

ICH M11 and the Machine-Readable Protocol

The International Council for Harmonisation finalized the M11 Clinical Electronic Structured Harmonised Protocol (CeSHarP) guideline at Step 4 on November 19, 2025 ^[7]. EMA's CHMP adopted the technical specification on December 11, 2025, with a date for coming into effect of June 11, 2026 ^[8]. In the United States, FDA published a Federal Register notice announcing the guidance's availability on May 22, 2026 ^[9].

ICH M11 creates an internationally harmonized standard for the content and electronic exchange of clinical trial protocols. The Technical Specification defines data elements and technical attributes that enable interoperable electronic exchange of protocol content across regulators, sponsors, ethical oversight bodies, and investigators ^[7]. The explicit intent, as stated in the final guideline, is to create a standard that "facilitates review and assessment by regulators, sponsors, ethical oversight bodies, investigators, and other stakeholders" ^[7].

For the first time, a clinical trial protocol can be structured so that a machine can read it and act on it consistently. A machine-readable M11-compliant protocol could, in principle, be consumed by a site's CTMS to configure feasibility workflows, by an EDC to pre-populate study definitions, by a regulatory agency to support more efficient review workflows, and by a patient screening platform to match eligible candidates, all from a single source document, without re-keying. USDM v4.0 is specifically aligned with M11, and the two standards together create a single, structured, digital starting point for a trial that can propagate downstream without manual transcription at each step. It is worth noting that M11 adoption timelines and regulatory acceptance of M11-formatted protocols will vary by jurisdiction; the EMA effective date of June 11, 2026 ^[8] and the May 2026 FDA notice ^[9] signal direction rather than immediate uniform enforcement across all regional regulators.

FHIR and EHR-to-Trial Connectivity

HL7 FHIR R4, finalized in December 2018, has become the dominant technical standard for healthcare data interoperability, providing a consistent, API-based mechanism for exchanging clinical data across systems. In the United States, ONC's HTI-1 Final Rule, issued in December 2023, adopted USCDI Version 3 as the new baseline standard within the ONC Health IT Certification Program effective January 1, 2026, and mandated updated FHIR-based API requirements to align with USCDI v3 data elements by that date ^[10].

For clinical research, FHIR's role has become increasingly concrete. FDA has identified eSource, meaning data initially recorded in electronic format and used directly without manual re-entry, as an approach that can eliminate unnecessary data duplication, reduce transcription errors, and support real-time data review ^[11]. FHIR APIs are the primary mechanism through which EHR-generated eSource data can flow into clinical trial systems without manual transcription.

In April 2025, FDA formally opened a public docket (FDA-2025-N-0287) to gather input on challenges in using FHIR for submission of clinical study data collected from real-world data sources ^[12]. The docket's scope (evaluating FHIR not just for data collection but as a potential input format for regulatory submissions) reflects how seriously FDA is examining the EHR-to-submission data flow. As of the comment deadline in June 2025, at least one major stakeholder formally recommended that FDA adopt a dual-standards approach: FHIR for EHR data ingestion and CDISC for submission output, with alignment between them to avoid requiring separate manual transformations ^[13]. (The recommendation came from Datavant, a health data linkage company, submitted as a public comment to the FDA docket; it is a stakeholder position, not a regulatory determination.)

The Regulatory Dimension: RWE, EHR Data, and Submission Requirements

The regulatory environment for connected clinical research data shifted materially in 2024 and 2025. Sponsors building connected infrastructure need to understand the specific scope and binding status of each relevant guidance.

FDA's Final RWE Guidance for EHRs and Claims Data

On July 25, 2024, FDA finalized its guidance "Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products" ^[14]. This guidance, which finalizes the September 2021 draft with targeted clarifications, outlines considerations for sponsors who wish to use EHR and claims data in clinical studies to support regulatory decisions on safety and effectiveness.

As with most FDA guidance documents, this is nonbinding and describes FDA's current thinking rather than establishing legally enforceable requirements. Its practical value is that it articulates what FDA will look for if sponsors submit EHR-based evidence, and that guidance is specific. Sponsors must demonstrate that their data sources are appropriate for the study question, that the data captures relevant populations, exposures, covariates, and outcomes, and that data traceability and quality are maintained through accrual, curation, and incorporation into the final dataset ^[14]. For EHR data specifically, FDA acknowledges a core limitation: EHRs are captured in the course of routine care, not under a prespecified research protocol, and may not comprehensively capture care across settings ^[14]. It is also important to note that the scope of this guidance covers EHR data used in clinical studies to support regulatory decisions on safety and effectiveness. It does not govern EHR data used solely for recruitment identification or study feasibility assessment, which operate under separate regulatory frameworks and applicable privacy regulations.

The guidance also recommends that sponsors planning to use EHR or claims data in regulatory submissions submit their protocols and statistical analysis plans to FDA before initiating the study, to receive feedback before substantial data collection begins ^[14].

21 CFR Part 11 and Audit Trail Continuity

21 CFR Part 11 governs the use of electronic records and electronic signatures in FDA-regulated studies. Under 21 CFR Part 11.10(e), the regulation requires complete, computer-generated, time-stamped audit trails of operator entries and actions that create, modify, or delete electronic records ^[15]. In a connected infrastructure, FDA's inspection and recordkeeping expectations under Part 11 and related regulations require sponsors to design their systems so that auditability and traceability are preserved across every transfer point, not only within each individual application. When data moves between an EHR, an eSource layer, an EDC, and an eTMF, each handoff must preserve the chain of custody in a way that supports source data verification and inspection. Infrastructure designed for interoperability must be designed with equal care for auditability, or the connectivity that improves data flow can introduce gaps in the audit trail.

FDA's Approach to Standardized Study Data

FDA requires the use of standardized study data for submissions of applications for new drugs, biologics, and certain other regulated products, as described in its Study Data Technical Conformance Guide. Section 745A(a) of the FD&C Act requires use of FDA-supported data standards as specified in FDA's Data Standards Catalog ^[16]. For electronic submissions, this generally means CDISC-formatted datasets (specifically SDTM for tabulations and ADaM for analysis) as specified in the Catalog for each submission type. When implemented consistently throughout a trial, USDM can support upstream alignment with SDTM trial design concepts, potentially reducing the downstream mapping burden during data curation compared to studies where EDC configuration and SDTM mapping begin from separate, disconnected source definitions.

Where DCTs Fit: The Infrastructure Demands of Decentralized Models

Decentralized clinical trials (DCTs) intensify the infrastructure problem because they disperse data collection across geographies, devices, and time zones simultaneously. FDA's 2024 final guidance "Conducting Clinical Trials with Decentralized Elements" acknowledges that most trials exist on a spectrum of site-based and remote activities rather than being fully decentralized or fully traditional ^[17]. That operational reality creates concrete infrastructure demands.

A March 2026 analysis in DIA Global Forum identified what it termed a governance gap in modern trials: when sponsors deploy study-specific technology outside a site's standard-of-care environment, investigators may lack authority to train staff or govern data generated on study-specific devices ^[18]. When DHTs transmit data directly to sponsors or vendors without structured integration into investigator review workflows, the safety oversight chain weakens. FDA's 2024 DCT guidance recommends that sponsors include plans for how DHT-derived data will be handled and reviewed as part of the safety monitoring plan for a decentralized study ^[17]. FDA's 2023 DHT guidance separately recommends that the safety monitoring plan specify how abnormal DHT measurements will be reviewed and managed ^[20]. Both recommendations are easier to implement when trial systems are integrated into a site's standard environment rather than operating as isolated, sponsor-specific deployments.

The practical lesson from DCT implementation is that decentralization without infrastructure integration does not expand patient access; it fragments oversight. FHIR-connected patient screening, USDM-aligned study configuration, and M11-compliant protocol exchange create conditions under which data collected outside a traditional site can flow into standardized, auditable, regulatory-grade datasets rather than accumulating in separate systems that must be manually reconciled after data lock.

Consider a concrete workflow: A sponsor builds a protocol in an M11-compliant authoring tool, which generates a USDM-formatted study definition. The EDC imports the study definition automatically, configuring CRFs and eligibility criteria without manual re-entry. A FHIR-connected patient screening layer queries EHR data at participating sites against those eligibility criteria, identifying candidates before a coordinator manually reviews records. Remote visit data from digital health devices flows through FHIR APIs into the EDC, with timestamps and audit trails preserved. At trial close, SDTM datasets are generated from source definitions consistent with those used to configure the EDC. Each step in this workflow exists in some implementations today. What does not yet exist reliably is the full chain: integrated, validated, and consistently operational across a multisite trial.

What AI Can and Cannot Do in a Connected Infrastructure

Automation and AI tools have a clearer and more productive role in a connected infrastructure than in a fragmented one, but the dependency runs in one direction: AI quality is a function of infrastructure quality, not a substitute for it.

When a protocol is structured in M11 format, AI-assisted drafting tools have a defined schema to work against, which may reduce manual review burden and inconsistency risk in document generation, and could make it easier to propagate protocol changes across documents when amendments occur. When FHIR-connected EHR data flows into a patient matching layer, AI-driven eligibility screening can operate against structured, standardized patient records rather than relying on manual chart pulls or unstructured notes. When CDISC standards govern data collection from EDC through to submission, AI assistance in SDTM mapping can draw on a consistent source structure rather than reconciling inconsistent field definitions across systems.

These operational benefits are real, but they are conditional on data quality and standards compliance. AI applied to fragmented, non-standardized data inherits all of the fragmentation's problems: inconsistent field definitions, missing values, non-comparable coding across sites, and audit trail gaps. Franklin et al. noted that the infrastructure gap limits "the amount of evidence that can be collected to inform whether and how interventions work for different patient populations," a constraint that AI models, which depend on consistent labeled data, face as directly as human analysts do ^[1].

Vendors making AI performance claims in clinical research should be evaluated against their specific operational context. Published, independently validated evidence on AI-assisted clinical document generation, patient matching, and protocol review is still limited and context-dependent. Accuracy figures from vendor environments may not transfer directly to a sponsor's own data infrastructure.

Regulatory and Documentation Considerations for Connected Infrastructure

Building connected clinical research infrastructure creates documentation and compliance obligations that extend beyond individual system validation.

Protocol and quality management documentation. Under ICH E6(R3), adopted at Step 4 on January 6, 2025, sponsors are required to implement quality management systems that address risks to essential functions throughout the trial ^[19]. Connected infrastructure that changes how protocol information flows between systems (for example, an M11-compliant authoring tool pushing definitions to an EDC via API) requires documentation of those data flows as part of the quality management system, with validation activities proportionate to the risk posed by each integration point.

Cross-document consistency. Regulatory submissions require that information presented in the protocol, the Investigator's Brochure, the Informed Consent Form, the DSUR, and the Clinical Study Report be mutually consistent. When these documents are generated by disconnected teams working from different source files, inconsistencies are predictable and common: different patient populations described in the protocol and ICF, visit schedules that differ between the protocol and the CSR, eligibility criteria that evolved through amendments but were not propagated to the IB. Connected infrastructure where documents draw from shared, structured source definitions reduces this failure mode at the architectural level rather than depending on manual cross-document QC after drafting is complete.

CDISC-formatted submissions. FDA's Data Standards Catalog specifies CDISC-formatted datasets as the required standard for electronic submissions under FD&C Act Section 745A(a) ^[16]. This requirement applies to new drug applications, biologics license applications, and certain abbreviated applications as specified in the Catalog. When implemented consistently throughout a trial, USDM can support upstream alignment with SDTM trial design concepts and may reduce the downstream data curation burden, though USDM adoption does not automatically produce submission-ready datasets; that still requires correct and consistent implementation at every step.

How Kitsa Fits Into This Problem

Based on publicly available product descriptions (kitsa.ai), Kitsa describes its platform as AI-native clinical research infrastructure designed to connect sponsors, CROs, SMOs, sites, and medical writers across the trial lifecycle. KScribe is described as an AI-powered regulatory document generation tool built to produce clinical regulatory documents including Protocol, ICF, IB, and CSR in a connected workflow, with cross-document consistency as a design property. KScreener is described as using FHIR-connected patient data for eligibility pre-screening, at the intersection of EHR interoperability and trial operations that FDA's July 2024 RWE guidance and the April 2025 FHIR docket are actively shaping. KScout addresses site selection and feasibility intelligence, where sponsor-to-site data flows remain one of the most persistent interoperability gaps during study startup.

The infrastructure challenges described throughout this article: fragmentation, inconsistent standards adoption, audit trail complexity in distributed systems, cross-document consistency failures, are the operational problems that Kitsa's design intent targets. Organizations evaluating clinical research platforms should treat interoperability against current and emerging standards (FHIR, USDM, M11, SDTM) as a first-order procurement requirement, not a future roadmap item.

Evaluating Connected Clinical Research Infrastructure: Questions Sponsors Should Ask

Before selecting or building clinical research infrastructure, sponsors and their technology evaluation teams should consider the following:

Key Takeaways

• A 2024 JAMA Special Communication by Franklin et al., drawing on the JAMA Summit on Clinical Trials, confirmed that clinical trial data infrastructure has evolved very little over time despite decades of investment, limiting the volume and quality of evidence the system can produce.
• Three standards are converging to create connected infrastructure: ICH M11 CeSHarP (finalized November 2025, EMA effective June 2026) for machine-readable protocol exchange; CDISC USDM v4.0 (June 2025) for study definition and automated system configuration; and HL7 FHIR for structured EHR-to-trial data transfer. They operate at different layers and are deliberately aligned.
• FDA finalized guidance on using EHR and claims data for regulatory decision-making in July 2024 and opened a formal docket (FDA-2025-N-0287) in April 2025 to evaluate FHIR for clinical study data submissions from real-world data sources. Neither constitutes a binding requirement; both signal that FDA is actively evaluating this direction.
• Study startup is the most operationally costly fragmentation point. Tufts CSDD's 2024 analysis (Smith, DiMasi, Getz) found the average direct cost of a Phase III trial at $55,716 per day, meaning every additional week of preventable startup delay costs more than $390,000 in direct expenses before opportunity costs.
• AI tools in clinical research are dependent on infrastructure quality. Applied to USDM-aligned, FHIR-connected, M11-compliant data flows, they can deliver consistent efficiency gains; applied to fragmented, non-standardized data, they inherit the fragmentation's inconsistencies.
• FDA's inspection and recordkeeping expectations under 21 CFR Part 11 require sponsors to design connected systems so that auditability and traceability are preserved across every integration point, not only within each individual application. Compliance within a single system does not automatically satisfy Part 11 for data that transfers between systems.
• Cross-document regulatory consistency (across Protocol, ICF, IB, DSUR, CSR) is structurally more reliable when documents draw from shared source definitions. Disconnected authoring from separate source files is one of the most common and preventable drivers of regulatory submission deficiencies.

FAQ

References

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Suggested internal links: KScreener (FHIR-based patient pre-screening), KScribe (AI regulatory document generation), KScout (site selection and feasibility intelligence).