What makes an analytical workflow trustworthy after the design is fixed: data quality, reproducibility, lineage, and the engineering discipline that lets an independent reviewer arrive at the same result.
AI-generated text, classifications and summaries can re-enter health-data and research systems as if they were ordinary observations. Without provenance, future models and analyses may learn from prior model outputs rather than from the world they are intended to represent.
Mortality-data quality starts before ICD coding. Certificate content, registration completeness, temporal consistency and fitness for purpose determine what coding can preserve.
Computational reproducibility asks whether the same data and methods produce the same result. Validity asks whether the result supports the intended scientific inference.
Reconstructing an analysis requires more than saving scripts and datasets. The audit trail should preserve entities, transformations, versions, exclusions, parameters, alternatives and accountable decisions.
AI can multiply drafts, analyses and candidate decisions faster than humans can remember how they were produced. Acceleration increases the value of provenance.
Reproducibility requires more than saved code. The exact data state, transformations, software environment, parameters, randomness, manual decisions, and expected outputs must be identifiable — and the workflow must be independently rerunnable within legitimate access constraints.
A single "run analysis" button that returns a chart hides more than it reveals. Lab keeps quality, computation, and review as distinct, inspectable stages instead.