Artificial Intelligence in Aged Care – June’s story

Meet June; long time Adelaidean, keen gardener and grandmother of twelve!  At 86 years ‘young’, June moved from her own home into a local aged care facility following a series of falls that saw her hospitalised over the summer.  June was diagnosed with Parkinson’s disease 18 months ago and following an increasing number of falls, June and her family made the decision to move her into residential care.

As symptoms of Parkinson’s disease progress at different rates for different people, getting June’s treatment plan right has been tricky, complicated by the fact that like many aged care residents, she requires several different medications to manage her health.  June and her carers have noticed that her tremors appear to be triggered by stress or emotional experiences and lessen when she is relaxed.  It also appears that regular exercise and engagement in leisure activities aid in keeping June’s tremors at bay.  As tremors often lead to lack of balance, which is likely to result in a fall, June’s care team have put together a robust healthcare plan which includes regular activity and time spent outdoors on top of her medication and occupational therapy.

The aged care facility where June lives recently embarked upon an initiative with the goal of improving the overall response to incidents such as falls, ensuring that responses are timely and that any incidents are attended to by the correct staff.  CCTV cameras have been installed in the corridors on the higher dependency floors, such as the one June lives on.  The CCTV is used to track residents’ movements via location tracking as well as emotions via facial recognition.  Residents of these sections have also been given smart devices to wear that track real-time data such as number of steps taken, standing vs walking rate and heart rate.

When dealing with personal data, it is of paramount importance to ensure its security.  Additional precautionary measures will be taken to ensure the security of June’s personal data so that it will be accessed for authorised purposes only.  Steps need to be taken so that June’s personal data is not shared or used for any commercial gain, for example, as a way to categorise June, possibly affecting her insurance premiums based on her risk as a patient.

Given the knowledge we have around the impact of stress on the incidence of tremors, the data from the CCTV coupled with June’s smart device will trigger an alert to the team lead in charge of her zone, should the variables compute to show an increased likelihood of stress.  The team lead is then able to ensure not only that there are sufficient carers positioned in high risk zones, that they are also equipped to deal with a possible fall. Furthermore, the wearable device shows the care team when June is outside and how much sunlight – linked to positive mental health – June is getting.  The data also enables the team to see links between steps and heart rate.  If it is found, as an example, that steps are going down and heart rate increasing, this could be a sign of a potential health issue, which would enable the appropriate medical intervention to happen proactively.

This scenario illustrates a proactive solution that benefits June and other residents in terms of the level of care they receive, not only through better response to incidents but in helping to prevent incidents happening in the first place.  At an organisational level, management also get insights that assist them in planning and resourcing more effectively as well as the ongoing process improvements brought about by machine learning.

Stay tuned for a follow up instalment as we explore the technical aspects of the business case!

Author: Sophia Siegele; Contributor: Shishir Sarfare

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