Next Generation Technology on Falls Prevention

Steven Strange

5 things the audience will learn

  1. Using data in the context of ‘falls prevention’
  2. How advanced analytics can be used to ‘predict falls’
  3. Why all staff should be empowered to access data
  4. How technology needs to adapt with the aging population
  5. Potential cost savings via smart technologies

Presentation Abstract

There are now social and (importantly) financial incentives for the elderly to stay in their homes longer (Home Care) by receiving the required services they may once have only been able to get from a Residential Aged Care (RAC) facility.

There is no doubt that there are positive financial outcomes for the tax payer that come via the government’s policy framework (to incentivise the elderly to stay in their homes longer). That notwithstanding, as with any policy driven approach, there can be unintended consequences.

One of these is that citizens are entering RAC at higher levels of acuity. That is, they are staying in their homes longer, much longer than they would have twenty or more years ago. Add to that, people in Australia are, on average, living longer. Which means that post 80, they have a longer stretch of frailty, on the assumption that they are living longer than previous averages.

Thus, the context of people entering RAC at an older age and further, being frailer (logically) at the same time, means that this in itself increases the frequency or likelihood of increased risk of falls and some of which (a subset), will be preventable.

Resident falls within Residential Aged Care (RAC) are a significant problem. “Avoidable falls” is a problem worth solving. Any reduction in avoidable falls will have a large and positive societal outcome. The families win, the resident wins, the provider (owner of the RAC Facility) wins and importantly, the tax payer wins (reduced number of hospital visits, etc.).

Technologies need to conceptualise the development of product/systems/processes that target the prevention of avoidable falls. That is, beyond predictive reporting/systems to preventative analytics.

Advance analytics delivered via new technologies, could be designed in such a way that the industry (context) would have an opportunity to benefit from predictive analytics (data sets) that can be “morphed” into a set of prevention strategies.

That is, prevention of a large percentage of avoidable falls. This technology may take the form of “apps” or other delivery vehicles.

Next generation technology not only provides new opportunities in regards to mitigating risk factors but also can present high risk resident alerts in real time (on line) to care workers and other stakeholders. In order to present credible risk (real time prevention), one has to also account for data feeds from the “care floor”. That is, operational inputs/data feeds.

Care staff and executives should be able to manage aggregated/consolidated events in real time, whilst ensuring policy and compliance are met.

In addition, care staff could see – in real time – the policy impact of a specific resident who, for example, has had more than a certain number of falls within a given period of time. The policy impact manifests as a generated clinical pathway which provides appropriate care tasks and accountabilities for the resident.

The potential cost savings for aged care providers who appropriately use data are enormous, as more effective preventative strategies will help mitigate the cost of care and services associated with fall related injury in older people (as one example).

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