Operations
AuraScore 91/100

Clinical Trial Site Feasibility and Allocation Matrix

Score and rank candidate clinical trial sites using enrollment, regulatory, and cold-chain operational criteria.

Use this template during multi-center study setup to evaluate competing investigator sites. It delivers a structured scoring and operational governance matrix to accelerate study startup while safeguarding compliance.

Template

Role: Principal Director of Global Clinical Operations with 18 years leading multi-center pharmaceutical trials.

Context

  • Protocol reference: {{trial_protocol_id}}
  • Indication domain: {{therapeutic_area}}
  • Evaluated site profiles: {{site_candidate_profiles}}
  • Cohort enrollment quota: {{target_patient_enrollment}}
  • Compliance zones: {{regulatory_jurisdictions}}
  • Biologics handling specs: {{cold_chain_requirements}}

Task

Construct a comprehensive multi-criteria site feasibility scoring matrix that evaluates candidate clinical trial sites, isolates operational bottlenecks, and prescribes tier-ranked site activations to guarantee protocol timelines and compliance.

Method

  1. Parse {{site_candidate_profiles}} against {{target_patient_enrollment}} to compute historical recruitment velocity and screen-failure projections.
  2. Cross-reference local ethics committee turnaround benchmarks across {{regulatory_jurisdictions}} to establish site activation lead times.
  3. Audit equipment capabilities against {{cold_chain_requirements}} for investigational product storage integrity.
  4. Establish weighted scoring dimensions: Enrollment Capacity (30%), Regulatory Speed (25%), Infrastructure & Cold-Chain (25%), and Investigator Experience (20%).
  5. Calculate raw and normalized feasibility indices for each candidate clinical institution.
  6. Map identified operational vulnerabilities to mitigation strategies, highlighting backup recruitment pathways.
  7. Categorize candidate sites into Tier 1 (Immediate Activation), Tier 2 (Conditional Contingency), and Tier 3 (Disqualified).
  8. Synthesize final portfolio resource allocation recommendations into an executive decision summary.

Constraints

  • MUST score every site across all four defined weighting dimensions using a 1-5 integer scale.
  • MUST NOT recommend Tier 1 activation for any site failing {{cold_chain_requirements}} validation.
  • Total weighted feasibility score MUST be mathematically explicit in the matrix.
  • Rationale for site disqualification or contingency placement must be supported by empirical evidence from {{site_candidate_profiles}}.

Output format

Generate the output in two structured sections:

  1. "Feasibility Evaluation Matrix": A markdown table with columns: Site ID, Enrollment Score (1-5), Regulatory Velocity Score (1-5), Cold-Chain Compliance (Pass/Fail), Composite Weighted Score (100-pt scale), and Activation Tier.
  2. "Operational Governance Matrix": A secondary markdown table linking each site to its primary bottleneck, mandatory pre-activation remediation, and designated operational lead.

Self-review

  • Confirm all variables from {{trial_protocol_id}} through {{cold_chain_requirements}} are integrated.
  • Verify mathematical consistency between sub-scores and composite weighted scores.
  • Ensure zero unverified assumptions regarding cold-chain infrastructure viability.
AuraScore breakdown
91/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification14/14 · Strong

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

business-strategy
business-operations
healthcare-life-sciences
clinical-operations
site-feasibility
trial-management