Computing and Information Systems - Research Publications
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ItemProcess Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients( 2021-12-08)Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, known as event logs. Although process mining was originally conceived to facilitate business process management activities, research studies have shown the benefit of leveraging process mining tools in different contexts, including healthcare. However, applying process mining tools to analyse healthcare process execution data is not straightforward. In this paper, we report the analysis of an event log recording more than 30 million events capturing the general practice healthcare processes of more than one million patients in Victoria–Australia–over five years. Our analysis allowed us to understand benefits and limitations of the state-of-the-art process mining techniques when dealing with highly variable processes and large data-sets. While we provide solutions to the identified limitations, the overarching goal of this study was to detect differences between the patients‘ health services utilization pattern observed in 2020– during the COVID-19 pandemic and mandatory lock-downs –and the one observed in the prior four years, 2016 to 2019. By using a combination of process mining techniques and traditional data mining, we were able to demonstrate that vaccinations in Victoria did not drop drastically–as other interactions did. On the contrary, we observed a surge of influenza and pneumococcus vaccinations in 2020, contradicting research findings of similar studies conducted in different geographical areas.
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ItemMeasuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation (Preprint)(JMIR Publications, 2020)Background: The use of location-based data in clinical settings is often limited to real-time monitoring. Here we develop a proximity-based localisation system, and show how its longitudinal analysis can provide operational insights relating to mobility and occupancy in clinical settings. Objective: We measure the accuracy of the system, and algorithmically calculate measures of mobility and occupancy. Methods: We developed a Bluetooth Low Energy proximity-based localisation system, and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. We additionally collected ground-truth data, and used it to validate system performance and accuracy. We conducted a number of analyses to estimate how people move in the hospital, and where they spend their time. Results: Using ground truth data, we estimated our system’s accuracy to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms of the hospital, by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering we showed that the system can differentiate between staff and patients without training. Conclusions: Analysis of longitudinal location-based data can offer rich operational insights to hospitals. Pri- marily, they allow for quick and consistent assessment of new strategies and protocols, and provide a quantitative way to measure their effectiveness.