Why Clinical Trials Need Decision Intelligence
Over the past three decades in clinical research, development outsourcing, and patient access, I have watched trial execution steadily improve. Systems are more sophisticated. Automation is more prevalent. Operational discipline is stronger.
Yet we still do not reliably understand, capture, or reuse how the best decisions are made. Decision-making quality, consistency, and traceability have not advanced at the same pace
Today’s clinical infrastructure benefits from robust systems—such as Clinical Trial Management Systems (CTMS), Enterprise Resource Planning (ERP), Quality Management Systems (QMS), financial tools, and contract lifecycle management. But, the economic and governance decisions that shape these systems remain fragmented, siloed, and overly reliant on intuition rather than structured logic.
The consequences are familiar
- Incentives misaligned between sponsors, CROs, and sites.
- Budget challenges arise late in the process.
- Amendments can undermine leadership confidence.
- Outcomes of negotiation and collaboration vary widely across programs.
- Participant burdens often manifest only after retention issues become apparent. These challenges are not operational hurdles; they stem from gaps in decision-making structures.
A Case in Point Consider a practical example from an oncology program in which the reimbursable Target Product Profile (rTPP) evolved during development. While payer positioning broadened, the expectations for evidence grew more stringent. Although the scientific logic was sound, a structured approach to economic realignment was missing.
This oversight led to a cascade of reactive actions:
- Trial complexity increased to support payer-relevant endpoints
- Sample sample size and cost assumptions shifted late
- CRO and vendors scope and responsibilities expanded without recalibrated performance incentives
- Clinical Operations, Finance, and Outsourcing teams operated under different assumptions.
- Market access strategies difrated from development cost assumptions.
Individually, each decision made sense; however, collectively, they introduced variability, quality, data compliance risks, and uncertainty regarding capital.
A Decision Intelligence Layer What is needed is a Clinical Trial Decision Intelligence layer. A structured layer that strengthens how economic design, negotiation logic, and governance alignment are shaped — before execution systems take over
At RHIEOS, we focus on three pillars:
- Enhance Economic Design
When the rTPP shifts, economic implications are modelled structurally—budget prescriptions, incentive alignment, payment logic, and participant burdens—before negotiations start.
- Improve Negotiation Intelligence
Concession pathways and potential economic implications are illuminated upfront, reducing defensive posture and lowring amendment risk.
- Establish Governance Coherence
Structured decision logic is captured and made reusable across studies and programs, aligning Clinical, Finance, Outsourcing, and Leadership around shared reasoning
Over time, these decisions can transform into program-level intelligence rather than remaining isolated transactions.
Why This Matters Now While AI systems will continue to expedite operations, poorly structured decisions made faster can inherently increase risk. The next phase of clinical infrastructure must prioritize not only efficiency but also effective coherence. Decision coherence reduces variability, aligns incentives, lowers amendment risk, and strengthens capital confidence It is about strengthening how trials are economically designed, negotiated, and governed.
Begin with One Study:
- Improve budget confidence.
- Enhance negotiation consistency.
- Align incentives from the outset.
- Make participant burdens transparent during the design phase.
If decision-making becomes structured and reusable, value compounds across programs. In my experience, this approach leads to improved outcomes for all stakeholders—not just improved processes.