## 2 Methods  
### The VILIAlert System Architecture  
The VILIAlert system was deployed to operate in parallel with the EMR to monitor the ventilation process in continuous time and is therefore relevant for process mining. Its purpose is to notify clinicians when there is a high risk of VILI in ventilated patients. Such risk is according to the configured settings of the system. Its design consists of two components (Figure 1):  
**Patient Data**--patient information that is collected routinely and stored anonymously including patient ID, gender, height, and ventilator readings received at minute inter  
Figure 1: An architectural overview of the VILIAlert System [19]. vals (TV). The time of alert generations is also recorded so that post-hoc analysis can be performed on the system's performance. **Analytics Kernel--the continual collection of data allows the analytics kernel to be formed so that the patient TV can be continually monitored and the kernel generates an alert in the event of a violation of the threshold metrics. A violation is considered to happen when the average TV values, calculated for each 15 min interval (TV\({}_{15}\)) exceeds 8 mL/kg for four consecutive intervals. This will initiate the analytics kernel, triggering an alert and sending an SMS to notify the healthcare providers of the change in situation for that patient. In order to ensure that there is no alert fatigue, the configuration of the analytics kernel is designed to put the kernel into \"sleep mode\" once an alert occurs, thus stopping any further alerts to be generated for this patient for the forthcoming 12 h or until the alert has been considered by the clinician and dealt with. It is important to highlight, that to ensure the optimum quality of care for the patient, it is the clinician's responsibility to intervene in a timely manner once they receive the alert. The VILIAlert system was introduced in the Royal Victoria Hospital, Belfast in November 2015 to continually monitor patient ventilation in ICU. For a period of 10 months, the system was allowed to operate, allowing the entire system to be refined through a number of phases of tuning and refinement of the data collection and process of alert generation. The data extracted in the period between 31 August 2016 and 26 October 2017 was used in this paper when operational experience had been gained and the system was viewed as operating in a stable state. We use this data in particular as it allows us to demonstrate the proposed process mining approach to real process data taken from an environment in a healthcare setting where the clinicians decisions are vital importance in preserving life. Survival Analysis Approach for Performance Estimation and Quality of Mechanical Ventilation Evaluation  
Part of the challenge of building a Healthcare 4.0 system and assessing its performance and use of process data is that it relies heavily on the validation of clinicians who do not have time to exhaustively check every instance of an alarm to indicate whether it is genuine or not. This paper overcomes the need for such a laborious process by determining if the generated alerts are genuine or not by focusing on the data for the first six hours of time after the alert generation has occurred. We denote the time window, as APA (**A**nalysed **P**ost **A**lert Time-Window) and slice it into 24 consecutive blocks of 15 min time intervals. As previously mentioned, the alert is raised if TV\({}_{15}\) is greater than 8 mL/kg for four consecutive 15 min blocks. We follow this convention based on the knowledge of when previous studies of this nature were conducted, where the data is obtained at a sampling frequency of 1 tidal volume reading per hour. The sampling frequency for the example in this paper is one reading per minute and thus waiting for four consecutive average 15 min intervals is the same as obtaining one hour's worth of data, hence in keeping with common clinical practice [18].