The concept of “fit for purpose” is frequently used in regulated environments, particularly under frameworks such as ICH E6(R2) and ICH E6(R3). Within this context, systems must ensure data integrity, reliability, traceability, and appropriate documentation. However, regulatory compliance alone does not automatically imply operational adequacy. A system may meet validation standards and still fall short in representing the real operational complexity of a study.
To evaluate functional coverage properly, the starting point should not be the software itself, but the protocol. The protocol defines everything that will happen to a participant: visit schedules, procedures, laboratory assessments, safety monitoring, endpoints, and follow-up requirements. It establishes the scientific and ethical foundation of the trial. Increasingly, initiatives such as ICH M11 seek to standardize protocol structure in a way that supports consistent interpretation and digital reuse. Regardless of format, the protocol remains the primary source of operational requirements.
Every procedural element described in the protocol generates downstream activity. Monitoring visits must be scheduled and conducted. Case report forms must be reviewed and cleaned. Safety events must be reconciled across systems. Documents must be filed in the eTMF. Vendors must be coordinated. Budgets must reflect workload. These operational realities form the true functional requirements of the study. The essential question, therefore, is whether the digital system landscape mirrors this operational architecture or merely captures fragments of it.
In many environments, functionality is distributed across specialized platforms. Data collection may be handled in systems such as Medidata Rave or Oracle Clinical. Documentation may reside in Veeva Vault eTMF. Portfolio planning and financial projections may be maintained in tools such as Planisware. Each system performs its domain function effectively. The challenge emerges in the space between them. Cross-system dependencies, workload forecasting, amendment impact, and financial transparency often require manual reconciliation outside the core platforms. The persistence of parallel spreadsheets and tracking files is not simply a habit; it is frequently a structural signal that functional coverage is incomplete.
A practical way to define “fit for purpose” is to examine four structural dimensions. First, coverage: does the system environment represent all operational activities derived from the protocol? Second, traceability: can executed actions be linked back to specific protocol requirements? Third, alignment: are timelines, workload, and cost logically connected, or are they tracked independently? Fourth, amendment resilience: when the protocol changes, can the operational and financial consequences be quantified without extensive manual reconstruction? When weaknesses appear in these dimensions, the issue is rarely a lack of features. More often, it reflects a misalignment between scientific design and digital representation.
This distinction becomes increasingly important as trials grow in complexity. Multi-regional studies, adaptive designs, decentralized components, and intensified safety oversight place pressure on project governance. Software environments may evolve technologically, yet structural fragmentation can persist if the underlying operational logic is not explicitly modeled. Fit for purpose, in this sense, is not defined by the length of a feature list. It is defined by whether the system landscape faithfully represents how the study actually operates.
For project owners, the perspective shift is subtle but significant. Instead of asking what a platform can do, the more relevant question may be what the study must achieve operationally and whether that reality is digitally mirrored. When required functionality is defined clearly at the protocol-to-execution interface, system evaluation becomes more objective. When it is not, software decisions risk being driven by vendor capability rather than study logic.
Clinical trial software has undoubtedly matured over the past decade. Integration capabilities, dashboards, and cloud scalability have improved. Yet maturity at the technical level does not automatically resolve structural misalignment. In a regulated environment where patient safety, data integrity, and financial stewardship intersect, defining functional coverage explicitly may be one of the most underappreciated responsibilities of project leadership.
Fit for purpose is therefore less a certification outcome and more a governance question. It requires clarity about what the study demands and transparency about how that demand is digitally represented. Only when those two layers are aligned can software truly be considered supportive of clinical trial execution rather than merely adjacent to it.
Digital transformation in clinical research is often discussed in terms of innovation and integration. Less attention is given to structural coherence. For project owners, clarity about required functionality may be the most practical starting point. When scientific intent, operational workload, and digital representation share the same architecture, governance becomes more transparent and execution more predictable.
Further readings:
- Inan, O.T., Tenaerts, P., Prindiville, S.A. et al. Digitizing clinical trials. npj Digit. Med. 3, 101 (2020). https://doi.org/10.1038/s41746-020-0302-y
- Getz KA, Campo RA. New Benchmarks Characterizing Growth in Protocol Design Complexity. Ther Innov Regul Sci. 2018 Jan;52(1):22-28. doi: 10.1177/2168479017713039. Epub 2017 Jun 23. PMID: 29714620.
- Adams A, Adelfio A, Barnes B, Berlien R, Branco D, Coogan A, Garson L, Ramirez N, Stansbury N, Stewart J, Worman G, Butler PJ, Brown D. Risk-Based Monitoring in Clinical Trials: 2021 Update. Ther Innov Regul Sci. 2023 May;57(3):529-537. doi: 10.1007/s43441-022-00496-9. Epub 2023 Jan 9. PMID: 36622566; PMCID: PMC9829217.
