Predictive EFL Screening Model for Ventilator Triage

The patent applies preliminary action by using a predictive model to pre-identify patients who are likely to have EFL before performing the actual ventilator-based FOT detection. The model uses demographic data, spirometry results, and patient-reported features to classify patients into high-probability EFL groups, ensuring that expensive ventilator-based testing is only performed on patients most likely to benefit from it, thereby improving resource utilization efficiency while maintaining detection accuracy.

The patent introduces an intermediary approach by using a predictive model as a filter between patient presentation and invasive/ventilator-based detection. The model processes readily available data (demographics, spirometry, patient-reported outcomes) to predict EFL probability, creating a triage system that directs only the most likely candidates to undergo invasive or ventilator-based testing. This intermediary layer maintains measurement precision for those who need it while significantly improving patient comfort by avoiding unnecessary invasive procedures.

The patent applies local quality by transitioning from a uniform approach (applying ventilator-based FOT to all patients) to a differentiated approach where the intensity and type of testing are localized to individual patient needs. The predictive model assesses each patient's specific characteristics and assigns an EFL probability, thereby localizing the comprehensive ventilator-based testing only to patients with high probability of EFL. This maintains detection coverage for those who need it while reducing overall device complexity and cost by avoiding unnecessary equipment deployment.