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Data and information have been common themes throughout the Royal Commission into Aged Care Quality and Safety. Providers and other healthcare organisations have not been able to access the right data in a timely manner to support and inform quality care, while consumers are unable to find relevant information to support their access to care services.

Aged care, like most care provider sectors, suffers from a surplus of data and a lack of ability to derive insights from that data. The majority of aged care providers using digital technologies use a combination of siloed systems that don’t support the use of data to enhance quality of care.

This is compounded by the challenge that older Australians often have a primary care provider who is an independent general practitioner, and will maintain their own clinical records for their patients, which are integrated with the aged care facility’s records through a combination of photocopier, fax machine and/or copy/paste of information into the facility’s records.


Improving data siloes

Delivering integration through the use of advanced data science and AI techniques.

As highlighted in the KPMG thought leadership paper Next Generation Insight Architecture, the challenge of data silos can be addressed through the use of concepts like data mesh and federated search.

Using these concepts, the data sits in a network of individual data stores but the 'smarts' are in how a specific search or query is answered by intelligently bringing together data from across this network.

A further challenge is that the data may be PDFs of handwritten clinical notes, faxes, emails etc. This is what is often referred to as unstructured data. 

As shown in the article, Natural language processing: A more fluent audit, AI techniques such as natural language processing (NLP) are being used to analyse large volumes of unstructured data to extract key facts and information in fields such as financial auditing.

Moreover, this can be done in an automated way using robotic process automation (RPA) and combined with information derived from other sources (e.g. eHealth records, client records, ACAT assessment notes) to provide a more complete picture of a client.


Supporting greater consumer-centricity

A key theme in the Royal Commission findings is the need to achieve greater consumer-centricity in aged care.

This requires developing a deep understanding of each consumer and their respective needs, wants and expectations, and then matching the provision of services to their needs.

Advanced data science and AI techniques can be used to develop an accurate profile of each customer and aged care workers to ensure appropriate matching of needs to services.

These techniques have been used successfully in the retail and financial services sectors, where products and services are recommended to customer based on their buying habits, personal circumstances and risk profile.

There are many examples of the type of insights that could support this service matching.

  • What are the services that help provide meaning and value in the older Australian’s life? This is a key aspect of ‘goal directed care planning'.
  • What characteristics, background, experiences in an aged care support worker will best match the consumer?
  • What qualifications, registrations or certifications does a care worker require for a service?
  • When can the service be optimally delivered?

Key to delivering these insights will be sourcing appropriate data. This could include a combination of internal and external data, structured data (e.g. entries in a database) or unstructured data (e.g. PDF of handwritten clinical notes).


Potential data sources

Building a profile of older Australians and aged care workers to enable service matching to occur.

  • Clinical and consumer records – these could be used to build a profile of a consumer (ethnicity, languages, likes, dislikes) to better match support workers to each consumer.
  • Support worker records – these could be used to build a profile of a support worker (ethnicity, languages, likes, dislikes) to better match them to the consumer.
  • Registry of qualifications, skills and accreditations for support workers – this could be used to match the appropriate worker to the relevant services.
  • ACAT assessment interview notes – specific keywords could be mined to develop a profile of what characterises value and meaning for the older Australian
  • Weather – this could be used to determine whether a service (e.g. gardening) should be scheduled or not.
  • Traffic reports – this could be used to determine whether a s required service (e.g. transporting a client to a community event) is likely to be delayed.

Trust, governance and policy reform

The human considerations at the centre of AI and policy.

One factor that needs to be front of mind when applying AI techniques in the aged care sector is the sensitivity of the data and insights that will be derived. It is essential that any AI system used in this sector is both trusted and trustworthy.

As highlighted in the paper Achieving Trustworthy AI, factors such as ethics, data security, data privacy and the impact on the client must be at the heart of the AI system. The concept of ensuring trust in AI systems is very much at the fore of people in this field and will a critical factor in its acceptance in aged care.

In addition to ensuring that the AI systems used are trusted, it is also vitally important that the 'human is in the loop'. AI systems are very effective in processing large volumes of data to derive insights that would be very difficult for a human to uncover, but in the sensitive sectors such as aged care, the ultimate decision maker should be a person using the insights provided by the AI as one factor in their decision making.

In this context, AI is really augmented intelligence, where the machine augments human decision making - human judgment and experience are critical elements in ensuring empathetic and fair decisions are made.

The Royal Commission made several recommendations regarding improvements to governance, regulatory compliance and risk management. These recommendations will place additional requirements on aged care providers many of which will result in the need to improve data quality and accuracy of reporting.

A similar trend is occurring in other highly regulated sectors such as financial services and utilities. As highlighted in the paper, There’s a revolution coming: embracing the challenge of a new RegTech era, in these sectors, AI systems, referred to as RegTech, are being used to automate complex reporting, proactively identify compliance risk areas and deliver greater confidence in compliance reporting.

In practical use, RegTech systems could be used to automate the process of reporting into the national aged care mandatory quality indicator program (QI Program) and proactively identifying where there may be compliance risks.


High-quality care and support

Applying learning and approaches from other sectors.

Many of the recommendations from the Royal Commission into Aged Care Quality and Safety rely on bringing together the ‘right data at the right time’ from various sources to generate meaningful insights on aged care clients, providers and systems.

These insights will be critical in ensuring the delivery of consumer-centric, high quality care and supports.

To deliver on these recommendations, the sector can draw upon approaches being used successfully in other sectors, including data mesh and federated search to deal with data silos, AI techniques like natural language processing to deal with unstructured data, robotic process automation to help automate repeatable processes and AI-enabled RegTech solutions to deal with increased regulatory compliance reporting. Advanced data and AI technologies are integral for the aged care sector to drive better outcomes for older Australians.


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KPMG aged care insights

Insights related to aged care in Australia and the Aged Care Royal Commission.