AI is having a transformative impact on social care. Leaders are already seeing the benefits of AI services, with significant expectations for the future.
As one example, the NHS Transformation Directorate, responsible for driving innovation in the NHS, has committed £123 million to support AI through The Artificial Intelligence in Health and Care Award.
In this article, we’re going to look at how leaders are driving change through AI in social care. We’ll also explore how you can implement cutting-edge technology in your organisation.
Current challenges in social care
Both private and public care organisations face a range of serious challenges. According to a report by The Health Foundation published in partnership with innovation foundation Nesta, “Whoever is in government after the next general election will inherit a health and care system in crisis. The NHS is under extreme strain and many people are going without the care they need.”
Here is an overview of the main challenges the UK social care sector is facing:
- Workforce shortages: There is a significant gap in the number of care professionals compared to the demand for services. This problem is exacerbated by high turnover rates and burnout among staff.
- Funding constraints: Insufficient funding cannot meet growing demand for care services. This impacts both quality and accessibility.
- Increasing demand: The UK, like many European countries, is seeing a rising number of elderly people and individuals with chronic conditions or disabilities. All of whom require long-term, often highly personalised care.
- Technological integration: Social care organisations are often slow to adopt new technologies. In addition, legacy systems create implementation challenges.
- Regulatory and policy challenges: Navigating complex regulation can delay (and sometimes even completely obstruct) the development and adoption of innovations.
- Mental health needs: Meeting mental health needs represents a significant financial burden. Many illnesses require multidisciplinary, integrated services within social care frameworks.
- Operational inefficiency: Outdated systems lead to delays, poor allocation of resources and increased administrative work. This hinders the delivery of timely and effective care.
The role of AI in addressing these challenges
AI is already seeing real-world applications, whether in testing environments or as part of full rollouts in the NHS and private sector.
While artificial intelligence in social care has the potential to impact most areas experiencing challenges, three in particular stand out.
- Enhancing efficiency through automation: AI automation has a whole host of operational applications. It can help reduce wait times, speed up triaging, provide training, shorten time-consuming manual tasks and reduce administrative overheads. An example includes the use of AI avatars (developed by Cera) to train staff in new skills, such as spotting the signs of stroke.
- Predictive analytics for proactive care: AI can predict patients' needs and prevent crises, allowing for proactive care and prevention. For example, AI has been used at Breckside Park Residential Home and Rowan Garth Care Village, both in Liverpool, to reduce falls by tracking movement patterns.
- Customisation of care through AI: The NHS is already using AI to process vast amounts of patient data to create customised care plans. One application includes analysing individual genetic data to generate tailored drug combinations for treating lung cancer.
Ethical considerations and implementation
AI presents several deeply complex ethical questions. These typically centre on accessibility, privacy rights and consent, and accountability.
How can social leaders implement these new technologies in an ethical way that respects the needs of the patients and the community?
- Accessibility and inclusivity: AI technologies should be available to all individuals, regardless of socioeconomic status or geographic location. Leaders should pay particular attention to factors that can lead to marginalisation, including cultural biases, digital literacy and affordability.
- Privacy and consent: Because AI systems in social care often process sensitive personal data, strict mechanisms for obtaining informed consent are crucial. This includes transparently communicating what data is used for, how it is used and who has access to it.
- Professional accountability: It is vital that professional oversight and input occur at all stages of AI use, especially where complex and life-changing decisions are involved. Ongoing staff training facilitates this human involvement.
Conclusion
AI will dramatically increase the quality, efficiency and availability of social care services. Few can predict exactly what a social care landscape intertwined will look like.
Yet one thing is for certain. Leaders that embrace digital evolution are set to benefit significantly in the coming years and decades.
A phased, gradual implementation is most likely to provide social care organisations with most of the benefits of AI while mitigating the risks of this fast-changing space.
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