AI in aged care: A forward-looking perspective

David Waldie, CEO and Founder, eevi

In this guest post, David Waldie, CEO & Founder of eevi, looks at the significant opportunities for implementing AI in aged care, including the types of outcomes that can help mitigate the ongoing workforce challenges impacting the sector.

With growing discussion around AI in aged care, we take a look at the opportunities & considerations in the adoption of this advanced technology.

The conversation around AI in aged care continues to evolve. The opportunity it presents in Australia against a backdrop of workforce challenges and an ageing population is immense.

Industry leaders face the challenge of optimising operations with the right mix of artificial and human intelligence oversight.

Here, we review discussions around what will arguably be one of the most significant technologies changes our industry has seen.

How can AI be integrated into nurse call systems to improve response times and efficiency in Aged Care homes?

By predicting peak call times, optimising staff allocation, filtering and prioritising alerts, bringing knowledge of resident conditions and routines, and assisting decision-making, AI significantly improves existing response times and efficiency.

Voice-activated systems and sentiment analysis using natural language processing (NLP) allow residents to communicate their needs. Sensors provide situational awareness of the room. Machine learning algorithms analyse this data with data on conditions, routines, rosters, availability, and past calls to streamline operations, ensuring faster response times.

AI can provide real-time awareness, assisted decision-making, and automated task assignments, helping caregivers manage their workload more effectively and improving overall response efficiency.

What specific AI technologies are most effective for enhancing nurse call systems in Aged Care settings?

Best guess, we see machine learning, natural language processing (NLP), computer vision, predictive analytics, and AI chatbots as all having a role. Machine learning helps predict volumes and optimise staffing. NLP improves voice recognition for better resident communication.

Computer vision monitors residents to detect falls or unusual behaviour. Predictive analytics identify patterns indicating health deterioration. AI chatbots manage non-critical requests, providing timely assistance and ensuring residents feel heard, even during busy periods. These are just examples today, but the landscape of capabilities and applications of AI is changing rapidly.

How does AI contribute to personalised care and better resident outcomes within Aged Care facilities through nurse call systems?

Simply put, better care decisions, faster responses, and more automation, result in more time with carers. AI learns individual preferences, health conditions, and behaviour patterns to provide more relevant assistance. Predictive analytics can detect early signs of health issues, allowing for timely interventions that improve outcomes.

AI systems continuously adapt based on interactions and outcomes, ensuring increasingly personalised care. This leads to higher resident satisfaction and better health outcomes by addressing specific needs and preventing issues before they become critical. Add to this automating compliance, investigations and reporting, enabling carers to spend more time with residents.

What are some examples of AI-powered features or functionalities that can be integrated into nurse call systems to assist caregivers in Aged Care homes?

Replace the traditional call bell with AI-power, to understand the resident’s need as if a carer were in the room. NLP-enabled voice recognition, fall detection using computer vision, sensor data with predictive health monitoring, and behavioural analysis offer this prospect.

Move beyond traditional call prioritisation, to intelligent triage and routing, applying machine learning with situational awareness to the array of inputs like carer rosters and availability, resident conditions and routines and historical data to assist decision-making.

Overlay this with the ability of predictive health monitoring to identify potential issues early, prompting scheduled look-ins. Automate reporting and behavioural analysis to provide insights for care planners to make continuous improvement to the care plan.

What are the key considerations or challenges when implementing AI in nurse call systems for Aged Care, particularly regarding data privacy and security?

It is a long list, but it includes achieving interoperability with existing systems, addressing ethical concerns, training staff, and obtaining resident consent. Data privacy must comply with regulations like GDPR, and robust encryption is essential for security.

Systems need to integrate seamlessly with existing infrastructure. Ethical concerns include AI decision-making and care prioritisation. Staff training is crucial for effective use, and resident consent is necessary for data collection and AI utilisation, balancing innovation with respect for resident rights.

How does AI in nurse call systems enhance communication and coordination among staff members in Aged Care homes?

AI enhances communication and coordination among staff by providing real-time updates and notifications, ensuring timely awareness of critical situations. At its simplest, AI can automatically assign tasks based on caregivers’ location, availability, and expertise, optimising workload distribution and reducing response times.

Comprehensive handover reports generated by AI summarise key activities and incidents, facilitating seamless shift transitions. These features ensure that staff members are well-informed and coordinated, improving overall team efficiency and care quality.

Can AI-powered nurse call systems help predict and prevent emergencies or health deterioration among Aged Care facility residents?

Yes, AI-powered nurse call systems promise to predict and prevent emergencies by continuously analysing data from sensors and health records. Predictive analytics identify patterns and trends indicating potential health issues, enabling early interventions.

For example, AI can detect subtle changes in mobility, vital signs, or behaviour that are early indicators of falls or medical crises, alerting caregivers to take preventive action. This proactive approach promises to help prevent emergencies, enhance resident safety, and improve health outcomes by addressing issues before they escalate.

How do AI-driven analytics derived from nurse call systems data contribute to continuous improvement in aged care service delivery?

AI-driven analytics can provide deeper insights into staffing, response times, call patterns, and resident needs, helping aged care facilities identify areas for improvement.

This can include optimising staffing and addressing recurrent issues, prompting adjustments in scheduling or interventions. Continuous feedback from AI-driven analytics helps refine care processes and strategies, leading to ongoing improvements in service delivery and better overall care quality for residents.

What future advancements do you foresee in AI and nurse call systems for Aged Care, and how might they further transform the industry?

You would be brave to predict how AI will impact Aged Care. Future advancements in AI and nurse call systems may include advanced predictive analytics for more accurate health deterioration predictions, integration with wearable technology for real-time health monitoring, and augmented reality (AR) tools to assist caregivers.

Sophisticated AI assistants could handle a broader range of resident needs autonomously, with AI-assisted care planning and decision-making. Improved interoperability with other healthcare systems and enhanced privacy measures will be critical enablers. These advancements promise to transform the industry. The beneficiaries will be both residents and operators, by improving care efficiency, enhancing personalised care, and ensuring better health outcomes for residents in Aged Care facilities.

How do we ensure AI delivers on the social good it promises in care?

Human interaction is at the centre of care. That is not going to change. Interestingly, one study* on the impact of monitoring technologies in elder care observed that an important benefit was the increased communications from care providers.

The study reported that calls and conversations as part of the study broke up isolation and were appreciated at a deeper level than just for care delivery. That is, technology is no good without the care conversation. The same goes for AI in nurse calls. We would see AI freeing up carers to spend more time with residents. Or delivering the right care to a resident at the right time. Or enabling better care planning for future needs. In these cases, a social good.

Technologists are grappling with what guardrails should apply to using AI**. Perhaps one is understanding resident preferences and implementing this as the goal of technology, AI or otherwise.

For example, make wandering okay, by understanding the resident preference for routines, risk and freedom and implementing this as the goal, as opposed to a goal of accurately geofencing a resident. Another is care decisions. For example, analysis of sensor data and natural language may assist with identifying an emergency, but the decision of an appropriate response rests with the care team. There is a lot to unpack in terms of ethics and the role of AI in the care environment, but there is a lot to gain.

AI will be topic of discussion at the 2024 Future of Ageing Conference & Awards evening to be held in Sydney on August 29, 2024. View the preliminary program here.

*CSIRO DACS: Smarter Safer Homes to Support Older Persons in Their Own Homes Through Enhanced Care Models (2022)
** McKinsey on AI: Why we Need to Rethink the Purpose fo AI: A Conversation with
Stuart Russell. (2020)

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