Case Study 1:
In a care home setting, AI could be used to predict when a resident might become dehydrated based on monitoring their fluid intake. If the AI is trained on accurate data, it can alert staff to act before a health crisis occurs. But if the data feeding the system is incomplete or inaccurate, the AI might miss key warning signs. Ensuring the truth of data means regularly reviewing and updating information to reflect current circumstances and needs.
Case Study 2:
A social care provider in Scotland implemented an AI tool to assess care needs more efficiently. When families raised concerns about how decisions were made, the provider offered workshops to explain the AI’s role and limitations. This transparency built trust and gave families confidence that their loved ones’ needs were being addressed thoughtfully, not just through an impersonal algorithm.
Case Study 3:
An AI-based scheduling tool was designed to allocate home care visits. Initially, the system favoured urban areas because the data suggested that care workers could cover more clients in densely populated areas. However, this created a disparity for rural clients, who were left waiting for care. Once this was identified, the system was adjusted to factor in equity, ensuring rural clients received timely care as well.
Case Study 4:
In one local authority, AI was used to monitor supported people who lived independently, tracking patterns in behaviour that might signal a decline in health. Residents were initially sceptical about this “surveillance,” but through open dialogue and reassurance that the data was private and solely for their safety, trust was built. Regular check-ins allowed for adjustments based on residents’ concerns, further deepening trust.
People must have confidence that the systems we put in place will not only work as intended but can be held accountable if they fall short.
Case Study 5:
In a care setting for people with disabilities, AI-driven speech-to-text tools were introduced to help those with limited mobility communicate more easily. However, initial versions of the software didn’t account for regional accents or speech impairments. By involving users in the design process and making the tool more adaptive, the care provider ensured that the AI was truly accessible to those it was meant to help.
Case Study 6:
In a residential care home, an AI system was introduced to handle scheduling and routine care documentation. By taking over these tasks, the system freed up staff time to focus on what really mattered- spending quality time with residents, engaging in meaningful conversations, and providing the kind of personal care that no machine can replicate. This allowed AI to support humanity, not overshadow it.
