Triage Vendor Prediction Models with TRIPOD+AI

• The updated TRIPOD+AI adherence tool decomposes prediction model reporting into 37 main items and 136 granular elements, providing distinct forms for model development, external evaluation, and combined studies.

• Use the adherence score as a standardized procurement gate: rigorous documentation helps expose gaps in fairness, missing-data strategies, and code availability—reducing deployment risk before technical validation begins.

• Incomplete reporting creates "black box" risks, making it impossible for pharmacy leaders to assess the safety, applicability, or equity of a model before integration.

• Without a standardized assessment, vendor and literature reviews become ad hoc and labor-intensive, increasing the likelihood of operationalizing opaque or biased tools.

• TRIPOD+AI adds critical AI/ML domains—such as compute requirements, fairness mitigation, and data provenance—offering pharmacy governance a standardized "nutrition label" to screen potential CDS tools.

• Mapped the original TRIPOD-2015 checklist to the new TRIPOD+AI standards, refining the content through iterative expert consensus and alignment with the "Explanation & Elaboration" guidance.

• Piloted the tool with eight independent reviewers across 15 papers (covering both regression and AI/ML methods) to validate wording and scoring logic.

• Developed separate, operational forms for development, evaluation, and combined studies, resulting in a fillable Excel tool with automated scoring rules.

• The final tool operationalizes transparency into 37 main items (labelled 1 to 27c) and 136 granular elements, moving beyond vague descriptions to specific, reproducible metrics.

• Strict scoring logic was established: a main item is "adhered" only if all underlying elements are "yes" or "not applicable," preventing partial transparency from inflating scores.

• The "referenced" loophole was closed: reviewers must now retrieve and verify information in cited sources rather than accepting a citation as proof of reporting.

• The maximum number of applicable items differs by study type (47 for development, 44 for external evaluation, 52 for combined), clarifying the evidence burden for different stages of model maturity.

• Implement TRIPOD+AI as a transparency firewall to screen reports; use the score to triage candidates and focus deep validation resources only on models that are transparent enough to evaluate.

• Demand explicit reporting on data provenance, missing-data handling, and fairness; do not accept "trade secrets" or unverified citations as an excuse for obscuring essential safety parameters.

• Remember that adherence measures completeness of reporting, not model quality—a high score means the authors disclosed their methods, not that the model is valid or safe.

• Standardized use of this tool allows pharmacy departments to benchmark vendors, monitor reporting quality over time, and justify "no-go" decisions on opaque technologies.

Strengths:

• Led by contributors to the original TRIPOD initiatives with iterative consensus, ensuring content validity and alignment with the reporting guideline. Operationalized with granular items, separate forms, and a fillable Excel tool to facilitate reproducible, auditable assessments in pharmacy practice.

Limitations:

• Pilot testing was descriptive and small (15 papers) without reported inter-rater reliability metrics or completion-time data. The tool assesses reporting completeness rather than methodological quality, requiring it to be paired with risk-of-bias tools (like PROBAST+AI) for a full appraisal.

Deploy the TRIPOD+AI adherence tool as a standardized triage gate: it filters out opaque candidates, ensuring you invest deep technical and safety validation only in models that are transparent enough to evaluate.