International Journal of Clinical Case Reports, 2025, Vol.15, No.2, 90-97 http://medscipublisher.com/index.php/ijccr 92 Figure 1 An overview of the end-to-end machine learning process (Adopted from Deshpande et al., 2023) Image caption: The process starts with automatically segmenting raw imaging scans using the CNN model, followed by extracting morphological features from the vessel tree. These features, along with our labeled cerebrovascular atlas of healthy adults, are used for automatic stroke detection and estimating the collateral index. Lastly, in combination with the patient’s baseline clinical and imaging data, these features are used in a fine-trees decision model to predict 90 days functional outcomes (Adopted from Deshpande et al., 2023) 3.3 Reduction in complications and mortality rates The use of rapid assessment tools has also been associated with a reduction in complications and mortality rates among patients with ACAs. For example, the implementation of systems-based physical assessments not only improved early detection but also resulted in fewer patients being transferred to the intensive care unit (ICU) and a significant reduction in mortality rates. The odds of mortality were reduced by 44% in the group where these assessments were implemented. Furthermore, cerebrovascular reactivity monitoring using the pressure reactivity index (PRx) has been shown to provide additional prognostic information, improving the prediction of mortality and long-term outcomes in patients with traumatic brain injury (Zeiler et al., 2019). The integration of digital sensors for in situ physiological and behavioral monitoring has also been identified as a promising approach to augment current screening and diagnostic processes, potentially leading to better management and outcomes for cerebrovascular disease patients (Zawada et al., 2023). 4 Challenges in Implementing Rapid Assessment Tools in Clinical Settings 4.1 Training and familiarization of nursing staff One of the primary challenges in implementing rapid assessment tools in clinical settings is ensuring that nursing staff are adequately trained and familiarized with the new systems. Studies have shown that a lack of education and training can significantly hinder the effective use of these tools. For instance, the integration of the Australian Nursing Standards Assessment Tool (ANSAT) onto an online platform revealed that insufficient training led to difficulties in using the system effectively, highlighting the need for comprehensive education prior to implementation (Needham et al., 2019). Similarly, the implementation of the interRAI Acute Care assessment system demonstrated that staff engagement and behavioral intention to use the new technology were mixed, indicating that more robust training and support are necessary for successful adoption (Peel et al., 2021). Furthermore, the use of digital health systems in nursing processes has shown that inadequate capture of nursing work and insufficient training can hinder clinical decision-making and limit the visibility of the nursing role in patient care (Hants et al., 2023). 4.2 Integration with existing clinical protocols Integrating rapid assessment tools with existing clinical protocols presents another significant challenge. The complexity of clinical environments and the need for seamless integration with current practices can create barriers to effective implementation. For example, the use of early warning scoring protocols in detecting patient
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