Predictive Analytics in the Aged Care Sector

3 years ago
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Predictive analytics are used to understand customer decisions and behaviours across a range of industries. They’re also increasingly being used as a powerful method of producing insights and driving improved outcomes for customers across all aspects of a business. For aged care, the use of predictive analytics is still in its infancy.

Some providers are nervous about where to start while others are concerned they don’t have the right data and systems to support it. In reality, most providers have access to a wealth of data, but haven’t yet navigated how best to use it. In an increasingly competitive and complex care environment, predictive analytics can be a powerful way to deliver efficiencies within your organisation, and create better experiences and outcomes for your consumers.

So what is predictive analytics?

As you move up the maturity curve for analytics, you travel from descriptive analytics (what happened?), to diagnostic analytics (why did it happen?) to predictive analytics (what will happen and when?). Predictive analytics allows us to look at a set of data, and after determining the key drivers for the behaviour or area of interest, supports us to predict what might happen in various scenarios. 

For example, if a consumer purchases a particular product through their home care package (e.g. a walking frame), looking at similar purchasing decisions across a wider group of people, we can predict the likelihood of them to purchase additional products or services. In this case, it may be that the consumer is more likely to then purchase physiotherapy, and also require personal care in the next six weeks.

How can it be applied to workforce rostering?

Effective workforce management is one of most important aspects of delivering high quality aged care. Faced with an ageing population and competition by other service-orientated sectors, the aged care sector is grappling with how to attract, retain and utilise its workforce to meet the needs and deliver quality outcomes for older Australians. For providers, workforce issues impact the entire service continuum. Even if an organisation has the right pool of staff, ensuring the right staff are available at the right times to respond to the needs of a consumer can be difficult.

COVID-19 has exacerbated some of these challenges with providers grappling with increased workforce shortages, whilst  responding to a different set of needs and preferences of consumers  and their families during the pandemic. Some of the key issues in scheduling staff include, staff availability due to factors such as capability, illness, leave and care requirements of individuals.

Utilising the existing data that exists within your systems, it is possible to understand if the employee work at any other locations, what is the likelihood of a particular type of staff member being unavailable for reasons such as personal leave or annual leave. When aged care providers understand these patterns, it will inform them whether they need to access supplemental staff with requisite skills.

What if you don’t have the right systems yet?

A recent innovAGEING article by Sanjay Mazumdar discussed the difference between Big Data, Quality Data and Smart Data.  If an organisation is concerned that they don’t have the right systems, or sufficient data or quality of data, the first thing they need to determine is ‘What is the Smart Data’. 

By understanding the problem an aged care provider is trying to solve, for example, workforce rostering, the organisation must understand the insights it needs to generate in order to better address the problem.  This may include specific attributes of when staff aren’t unlikely to be available, and what supplemental skills will be required, languages they speak and qualifications they have. 

An organisation is unlikely to have the ideal data that they need to fully address the problem, but by doing some exploratory analysis of the data that you do have, as you gather more data and improve your systems, you will be able to improve the predictions and further refine the outcomes.

Jade Aitken is a Director in the KPMG Data & Analytics practice, and innovAGEING Expert-in-Residence for Predictive Analytics.