Best Practice Capture & Replication

A wiki stores descriptions; this program transfers results. Every entry is validated against performance data, matched to specific target sites, adopted through a named owner, and measured after landing. The unit of success is a replication, not a page.

Compare performance with and without it, control for context, and where possible pilot it in one receiving site before broad rollout. The evidence bar is deliberately high because the cost of spreading a false positive is network-wide.

Credit and adaptation: name the source team prominently, and let receiving teams adapt the practice to local conditions within defined limits. People resist compliance; they adopt what they were allowed to make partly theirs.

Validated, high-impact practices with clear applicability deserve a mandate from operations leadership; the rest work by pull. A useful middle path is mandatory evaluation: sites must formally assess and answer, adopt or justify.

Trained documenters working with the source team, not the source team alone. Practitioners underdocument what they consider obvious, and the obvious parts are usually where replication fails.

Give every practice a review date and an owner, retire entries whose conditions changed, and track usage. A small, current library outperforms a large archive; staleness is measured in trust lost.

Sum the measured impact of completed replications and watch the performance spread between your best and average sites narrow. Cost per replication against value per replication is the program’s operating ratio.

Q-optimize captures practices with evidence, runs validation before library entry, matches practices to target sites, and tracks adoption and impact per location. Iris AI drafts documentation and surfaces matching opportunities across the network.