Introducing the DM-P approach for analysing the performances of real-time clinical decision support systems

Abstract

Introducing the DM-P approach for analysing the performances of real-time clinical decision support systems, Knowledge-Based Systems (2020), doi:

Process Mining the Performance of a Real-Time Healthcare 4.0 Systems Using Conditional Survival Models

“…This suggests that even though there were a higher proportion of males in the study, the female patients are the ones who have a higher chance of triggering the alert upon starting mechanical ventilation. An overview of the patient demographics of those involved in our VILIAlert study is provided in our earlier work [6].…”

“…Its design consists of two components (Figure 1): 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 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.…”

“…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.…”

Introducing a Conceptual Framework for Architecting Healthcare 4.0 Systems

“…Although there are many studies, proposing architectural solutions for various use cases, such as mechanical ventilation [5] [6], and neonatal care [7], all of these solutions lack flexibility as they are tightly coupled to existing clinical systems. Furthermore, the systems are very complex in nature, each using a different blend of technologies, and cloud services which makes practical implementation of proposed systems and collaboration between research teams difficult, if not impossible.…”