###### Abstract
Lung protective ventilation (LPV) strategies have been introduced to prevent such harmful effects of ventilation, whereby it is recommended that patients should be ventilated with low tidal volumes typically less than 8 mL/kg of ideal body weight (IBW) [10; 11; 12]. Although there has been a significant volume of work in illustrating the positive aspects of LPV, evidence suggests that at most only half of such ventilated patients undergo LPV in a clinical environment [13; 14; 15]. The need for appropriate use of LPV, inspired our earlier work on VILIAert when we created a clinical decision support system (CDS) with real-time smart alerting capabilities, and Healthcare 4.0 compliance. Its first deployment has been in Belfast's Royal Victoria Hospital, where the vision is to have a continuous monitoring system of a patient's tidal volume (TV), which is evaluated using a set of thresholds from which appropriate real time alerts are triggered if there is a predicted risk of VILI occurring. The ICU in which it was implemented has a maximum capacity for 20 patients and an aspect of earlier research has demonstrated the feasibility of adopting VILIAIert to scale up to a hospital wide scenario [16]. When an alert is raised, it is a major challenge of such smart-alerting CDSs to be able to determine whether the alert is considered to be genuine or not without expert opinion. However, referring every case to the expert, has the potential of causing expert alert fatigue or complacency for the alerts, undermining the whole purpose of the alerts and value [17]. Because of this issue, and due to clinicians' competing work demands, the accuracy assessment is often performed at the end of the study, by manually inspecting each generated alert so a decision can be made on whether they are genuine or not. The main issue with this retrospective performance assessment approach is that it is a very difficult and time consuming process, particularly if we take into account that over time, this can lead to a huge number of generated alerts being accumulated, which highly depends on the quality of alerting algorithm, the number of patients that are involved in the study, and other CDS settings. Therefore, this raises a need for new accuracy assessment techniques, which should accelerate development of novel smart alerting algorithms and lead towards minimising reliance on clinical assistance [6]. In our earlier work, we created the DM-P approach to address this somewhat unsupervised learning challenge where the data generated from the alerts is unlabelled and there is no further information as to whether the alert is genuine or not. The DM in the approach refers to the new defects map method of visualising the alerts, which is combined with p-charts, a type of statistical process control (SPC) charts used in quality control of dichotomous processes, similar in nature to the dichotomous variable of an alert being raised or not. This work had been inspired by [PERSON] et al. [18] who previously illustrated that if the alert was triggered for the TV values greater than 8 mL/kg, these values continue to remain above this threshold until there is clinical intervention. The DM-P approach can be used as a standalone independent module or can be used in conjunction with other healthcare systems. In this current paper, we use the same hypothesis as in our earlier work, that is, the assumption that if the alert was triggered because the TV readings violated the 8 mL/kg threshold, then it is expected that this threshold is going to be continuously violated for some considerable time in the future until there is clinical intervention. We propose an approach based on conditional survival analysis techniques that enables researchers to dynamically estimate the probabilities that will describe for how long this hypothesis will hold the ground truth, suggesting that the longer survival times indicates a better algorithm performance and poorer quality of care.