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Background Methods Results Conclusion Climate change is a critical global issue, impacting ecosystems, economies and communities worldwide. The shift from paper‐based to digital systems is becoming increasingly prevalent across industries, with downstream positive impacts on sustainability. In 2020, Austin Health, a public tertiary hospital in Victoria, Australia, adopted a web‐based outpatient booking application, which succeeded the prior paper‐based system. The application served as an integrated platform for administrative staff to access various Austin Information Technology platforms and replaced previous mail‐based outpatient appointment notifications with Short Message Service‐based notifications. This study aimed to assess the environmental impact and organisation‐wide economic cost of a web‐based outpatient booking application compared to the prior paper‐based system across the same time period.A retrospective environmental and economic assessment was conducted for both the web‐based booking application and the paper‐based system. The evaluation covered 36,925 outpatient diagnostic imaging studies – including CT, MRI, ultrasound and mammography – performed at Austin Health from 1st July 2023 until 30th June 2024. The environmental impact was assessed by calculating the expected carbon dioxide equivalent (CO2e) emissions produced by each system. The economic cost analysis was conducted from the perspective of the hospital and included the direct costs of labour and materials/consumables.CO2e emissions were significantly reduced using the web‐based outpatient booking application compared to the prior paper‐based system (38.5 tonnes compared to 0.002 tonnes), predominantly attributable to the elimination of postage‐related fuel emissions (27.7 tonnes). The estimated net operating cost savings across the year was at least AUD 175,000 (in 2024 Australian Dollars, adjusted for inflation). This was primarily due to labour savings from the elimination of workflow inefficiencies (at least 2342 h saved) amounting to at least AUD 85,272 in salary, followed by savings from eliminated pathology tests (AUD 57,422) and postage costs (AUD 55,193).The adoption of the web‐based outpatient booking application led to a substantial reduction in carbon emissions and operating costs, alongside enhanced operational efficiency and productivity. These benefits are anticipated to be enduring, especially in the face of an ever‐increasing demand for medical imaging services. [ABSTRACT FROM AUTHOR]

This study aimed at investigating the impact of entering high-ability classes on the academic self-concept of high-ability primary girls in Singapore. Participants in this study are 91 Primary 4 girls, 30 high-ability pupils, and 61 pupils from classes that include high-, middle-, and low-ability pupils. This study utilized a mixed-method approach. The quantitative part of the study used the Academic Self-Concept Questionnaire (ASCQ) to measure the pupils' academic self-concept before and after they were streamed into high-ability classes. Findings indicate that the high-ability learners had a statistically lower academic self-concept score after streaming, whereas no significant difference could be found between the academic self- concept scores for the pupils from the mixed-ability classes before and after streaming had taken place. Narrative interviews revealed that high-ability students experienced greater pressure because of heightened competition in class. Implications of the study for educators are discussed.

Background Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. Methods We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43xx1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. Results ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. Conclusions ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls. [ABSTRACT FROM AUTHOR]

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