###### Abstract  
As the world moves into the exciting age of Healthcare 4.0, it is essential that patients and clinicians have confidence and reassurance that the real-time clinical decision support systems being used throughout their care guarantee robustness and optimal quality of care. However, current systems involving autonomic behaviour and those with no prior clinical feedback, have generally to date had little focus on demonstrating robustness in the use of data and final output, thus generating a lack of confidence. This paper wishes to address this challenge by introducing a new process mining approach based on a statistically robust methodology that relies on the utilisation of conditional survival models for the purpose of evaluating the performance of Healthcare 4.0 systems and the quality of the care provided. Its effectiveness is demonstrated by analysing the performance of a clinical decision support system operating in an intensive care setting with the goal to monitor ventilated patients in real-time and to notify clinicians if the patient is predicted at risk of receiving injurious mechanical ventilation. Additionally, we will also demonstrate how the same metrics can be used for evaluating the patient quality of care. The proposed methodology can be used to analyse the performance of any Healthcare 4.0 system and the quality of care provided to the patient. Healthcare 4.0; real-time alerts; dynamic prognostics; quality of care; performance analytics; conditional survival analytics +
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in these measurements suggests a change in the patient condition, or could indicate the severity of patient illness and level of urgency for treatment [2]. The vast majority of instruments used for monitoring vital signs and providing organ support in ICU settings, usually have the capability of emitting patients' physiological readings in the format that is compatible with the Health Level 7 (HL7) standards, which define the guidelines for data exchange, the retrieval and the sharing of patient electronic health information, all of which are important to ensure that the various systems integrate seamlessly [2; 3]. The data outputs from these devices are usually stored inside the Electronic Medical Records (EMRs) for the purpose of monitoring and analysing the patients' changing conditions over time [4; 5; 6]. The complexity of having data flowing continuously from a number of data sources in a dynamically changing environment drives the need to have appropriate clinical responses in real-time. However, clinicians in ICUs normally have to make thousands of decisions per day, resulting in between 80 to 200 interventions daily per patient, only one of which is caring about providing adequate quality of mechanical ventilation. Hence, too many data points and types can lead to information overload where healthcare providers are faced with challenges in trying to make sense of the data in high volumes, leading to clinicians' cognitive overload. As a result, clinicians and healthcare providers can have a slow and possibly inconsistent response [7], which in turn can impact negatively on patient outcomes by for example reducing patient survival rates, or extending the period of organ support for the patients. One of the most often used devices for providing organ support are mechanical ventilators, which provide breathing assistance to patients who develop respiratory failures. Preclinical and clinical trials concerning ventilated animal and human subjects respectively provide evidence that if ventilation is not correctly performed it may become a harmful intervention, which can permanently damage a patient's lungs [8]. This injurious ventilation to the lungs is known as ventilator induced lung injury (VILI) and can be experienced not just by patients with respiratory failures, but also by patients with healthy lungs [9].