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When an oncologist opens the order screen to prescribe a new line of treatment, the record would show the trials this patient may fit, with the nearest open site and a one-click referral.
Eligibility criteria are encoded in a structured, computable form (mCODE/FHIR profiles for stage, biomarkers, prior lines, performance status) and matched against the patient's record inside the EHR order-set workflow, not in a separate portal. Alerts fire only at decision points (new line of therapy, progression documented) to avoid fatigue. Several AI matching tools exist; the missing piece is embedding at the point of decision with a referral action.
Shares Cancer Commons, ClinicalTrials.gov, AI trial matching & clinical decision support, Trials enrol too few, too slowly.
Shares AI trial matching & clinical decision support, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, Trial modernisation roadmap: the randomised trial → platforms and adaptive designs → decentralised, pragmatic and always-on.
Shares Cancer Commons, AI trial matching & clinical decision support.
Shares AI trial matching & clinical decision support, Trial modernisation roadmap: the randomised trial → platforms and adaptive designs → decentralised, pragmatic and always-on, Trials enrol too few, too slowly.
Shares Tempus AI, AI trial matching & clinical decision support.
Shares ClinicalTrials.gov, Trial modernisation roadmap: the randomised trial → platforms and adaptive designs → decentralised, pragmatic and always-on, Trials enrol too few, too slowly.
Shares Cancer Commons, Data silos.
Shares Tempus AI, ClinicalTrials.gov, AI trial matching & clinical decision support, Trials enrol too few, too slowly.