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Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation

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Artificial Intelligence and Machine Learning for Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 228))

Abstract

Soundfair Australia Ltd collaborated with Swinburne University’s Living Lab to create person-centred care measures that are routinely tracked alongside other success measures in hearing rehabilitation. A Living Lab approach was applied. Living Lab projects at their heart apply co-design in real-life contexts involving multiple stakeholders to achieve innovative products and services for the people involved—in this project, patients as well as staff providing the hearing rehabilitation service. To ensure applied innovative design solutions remain focused on the goal and needs of the end-users it is important to measure their impact and revise and improve both the measures and solutions on a regular basis. To come to relevant conclusions, data needs to be collated and interpreted against the original goals over time. This project aimed to develop a dashboard—an artificial intelligence infrastructure—to bring together the data. Here we report on (i) the process to ensure the right patient data was collected and fed into the system to achieve meaningful measures and (ii) the barriers that we faced when collating and interpreting the measures of the dashboard in practice. We suggest the barriers identified are common in the health sector. We advance the discussion of barriers as consideration points to improve and establish implementation of artificial intelligence in the health sector to improve person-centred care.

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Notes

  1. 1.

    https://idainstitute.com/ida_community/our_partners/person_centered_hearing_network/

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Correspondence to Sonja Pedell .

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Priday, G., Pedell, S., Vitkovic, J., Story, L. (2023). Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation. In: Lim, CP., Vaidya, A., Chen, YW., Jain, T., Jain, L.C. (eds) Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-031-11154-9_10

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