The economics of clinical development all point the same way. More than $100 billion spent a year, 9 in 10 programs failing, $535,000 for a single Phase III amendment, most trials running late. Our conversations with sponsors keep landing on the same source, and it sits upstream, in how high-consequence decisions get made and carried across a trial. Each call is made with care, but it passes through clinical, biostatistics, operations, regulatory, data science, CRO partners, and investigators, and somewhere in those handoffs the decision trail comes apart.

We call what accumulates in its absence Decision Debt: the unresolved trade-offs, lost rationale, scattered assumptions, and delayed signals that pile up every time a decision leaves its context behind. It comes due later, as protocol amendments, delayed timelines, and readouts where the reasoning has to be reconstructed after the fact  Our new whitepaper, The Scientific Intelligence Layer for Connected Clinical Trial Decisions, traces where Decision Debt accrues and what changes when the work stays connected. A few of those places:

Planning: Stress-Test the Design Before It Hardens 

Population, endpoint, sample size, control, feasibility, and power are interdependent choices, yet they get settled in separate tools by separate teams. In an early-phase ALS study, applying the sponsor's exact eligibility criteria to our historical dataset enabled the team to weigh competing designs against the same patients before enrollment opened. One path was materially leaner than the protocol on the table at equal power, and adding digital twins widened the gap further. At roughly $250,000 per patient, the difference was not academic. 

Monitoring: Catch Drift Before It Reaches the Dataset 

Every trial runs on assumptions: that patients progress as the protocol expects, that the control arm tracks its historical reference, that raters score the same in month nine as in month one. Any of these can drift without tripping a data-quality check, because the values are still valid; they just no longer describe the trial you designed. Retrospectively tested on the ADCS DHA Alzheimer's trial, our approach detected anomalies on a core cognitive subtest early in enrollment, well before the trial's own safety review surfaced them. 

Analysis: Match the Method to the Design 

Analysis is where the earlier choices come due, and for AI-supported methods the credibility has to be built into the data, model, and documentation well before database lock. This is where Unlearn's regulatory footing with the EMA and FDA matters. What earning that footing actually requires is the paper's through-line.

When planning, monitoring, and analysis stay connected, the result is Decision Compounding: A sharper planning decision sets better expectations for monitoring; what monitoring surfaces makes the analysis easier to defend; what the analysis shows shapes the next protocol. Regulatory reasoning stops being buried history and becomes context the next team can reuse. And because the digital twins, harmonized data, and rationale carry over across programs and stages, the next trial starts from what the last one learned. It is the structural answer to Decision Debt, and it is how sponsors get rigorous trials to patients sooner.

Read the full whitepaper