Abstract
The adoption of digital tools in the health sector is a need driven by optimizing processes and improving collaboration between different stakeholders. Exploiting data is the principal activity of the eHealth sector, even vital for some patients’ clinical conditions because its monitoring can help prevent adverse events or disease degeneration. However, the actual use of electronic clinical information to evaluate human decisions against predefined protocols or statistically known evolution patterns is still mostly under-exploited. Clinical Pathway is the primary tool for implementing clinical guidelines and evidence-based medicine. It is used to improve the care processes by monitoring changes in clinical practices to reach the best appropriate care more quickly and reduce the health system’s costs. In this work, we present an Edge architecture for Ambient Assisted Living in the context of home hospitalization. Using process mining techniques helps understand patients’ behaviours, assess their compliance with the corresponding clinical path, and support physicians in making decisions broadly and transparently.
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Acknowledgments
This work was partially funded by the European Union, Horizon 2020 research and innovation programme, through the ECHO project (grant agreement no 830943), by the Italian projects PROSIT (PON 2014–2020 FESR, project code F/080028/01-04/X35) and SI-ROBOTICS (PON 2014–2020 FSC, project code ARS\(01\_01120\)).
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Ardito, C. et al. (2022). An Edge Ambient Assisted Living Process for Clinical Pathway. In: Bettelli, A., Monteriù, A., Gamberini, L. (eds) Ambient Assisted Living. ForItAAL 2020. Lecture Notes in Electrical Engineering, vol 884. Springer, Cham. https://doi.org/10.1007/978-3-031-08838-4_26
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DOI: https://doi.org/10.1007/978-3-031-08838-4_26
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