You are leading a data cleanup effort for a Phase II trial. The data team has identified three high‑priority tasks: (a) Reconcile patient IDs across EHR and the trial database, (b) Standardize adverse event terminology to MedDRA, (c) Verify dosing accuracy in the exposure dataset. Each task requires different resources and has different regulatory implications. How would you prioritize these tasks and justify your decision?

Use a structured framework (e.g., RICE or MECE) to score each task on Reach, Impact, Confidence, Effort. Then rank tasks, justify the top choice with regulatory risk, stakeholder impact, and resource constraints. Outline steps: 1) Gather data on patient volume, regulatory weight, effort estimate; 2) Apply RICE; 3) Rank; 4) Communicate rationale to stakeholders; 5) Plan execution timeline.

I would first quantify each task using the RICE framework. For reconciliation, I’d assess Reach (95% of patients), Impact (high regulatory risk due to patient safety), Confidence (high, based on existing mapping rules), and Effort (moderate). For MedDRA standardization, Reach is 100% of AE data, Impact is moderate (standardization improves reporting but not immediate safety), Confidence is moderate, Effort is high due to manual mapping. For dosing verification, Reach is 100%, Impact is moderate, Confidence is high, Effort is low. Calculating RICE scores yields: Reconciliation = 8.5, MedDRA = 7.2, Dosing = 6.8. I would prioritize reconciliation first because it has the highest score and mitigates the greatest regulatory risk. I would then schedule MedDRA standardization next, leveraging automated mapping tools to reduce effort, and finally address dosing verification. Throughout, I’d document the rationale, involve stakeholders, and track progress against the regulatory timeline.