{"entity":{"id":"idea-data-order-set-defaults-30-days","kind":"idea","name":"Update hospital order sets within 30 days of a guideline change","aka":[],"tldr":"The pre-built treatment menus in hospital computers often stay unchanged for years. Require them to be updated within a month of any guideline change, using the machine-readable guideline feed.","summary":"Order sets and chemotherapy protocol libraries are the actual determinant of what gets prescribed, and they are updated by local committees on their own schedule. The proposal ties protocol libraries to the computable guideline feed with a 30-day service-level requirement, tracked and published per centre, and provides shared, validated protocol content (as the Cancer Care Ontario and NHS regimen libraries do) so each centre does not rebuild it.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (Knowledge reaches practice too slowly): Morris, Wooding & Grant, The answer is 17 years, what is the question (JRSM 2011)","url":"https://doi.org/10.1258/jrsm.2011.110180"}],"tags":[],"related":["idea-data-computable-living-guidelines","idea-data-guideline-concordance-dashboards"],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-knowledge-diffusion"],"keyPapers":["paper-morris-j-r-soc-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Centres meeting a 30-day order-set update requirement will show adoption of new standards months earlier than centres with ad hoc updates, measured in structured prescribing data.","rationale":"Default options strongly shape prescribing; changing the default is the cheapest and most effective implementation lever available, and it is currently unmanaged.","test":"Measure order-set update latency for the last five practice changes across 30 centres; implement the requirement with shared content in half; compare adoption curves.","maturity":"early-clinical","actor":"clinic","cost":"small","horizonYears":1},"route":"/ideas/idea-data-order-set-defaults-30-days/","neighbours":{"idea":[{"id":"idea-data-computable-living-guidelines","kind":"idea","name":"Living guidelines published as versioned, computable rules","route":"/ideas/idea-data-computable-living-guidelines/"},{"id":"idea-data-guideline-concordance-dashboards","kind":"idea","name":"Real-time guideline-concordance feedback for every cancer centre","route":"/ideas/idea-data-guideline-concordance-dashboards/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-knowledge-diffusion","kind":"bottleneck","name":"Knowledge reaches practice too slowly","route":"/bottlenecks/b-knowledge-diffusion/"}],"paper":[{"id":"paper-morris-j-r-soc-med","kind":"paper","name":"The answer is 17 years, what is the question: understanding time lags in translational research","route":"/key-papers/paper-morris-j-r-soc-med/"}]}}