A highly rated care provider knew that there is nothing quite like being in your own space, and that given the option, people would choose to stay in their homes and communities for as long as possible in later life. To make that a possibility, they need the perfect level of support to ensure their wellbeing and give peace of mind to their families. For those providing the care services, the only way to achieve this is to put those you are caring for at the centre of everything you do.
In order to ensure that each client had a care plan that was tailored to their evolving needs, the team of caregivers turned to technology and data to keep everyone involved up to date at all times – from the carers and customer service agents, to the families and the patients themselves. They knew that they wanted to derive more value from the data that they were collecting, but they needed help – that’s where Endava stepped in.
In order to gain insight from data, first you need to know exactly what you have and get it all into one place. After each visit, carers would complete a report and store it in a centralised folder. But the synergy stopped there. As a result of differing personal styles and preferences, the dataset was varied and sometimes unstructured.
Endava began to make sense of the data by centralising it, analysing all the content and finding the patterns. Using live data, the Endava team built a database in AWS using Redshift and applied Quicksight for virtualisation. They then began the process of building a robust platform based on a machine learning model that is trained to recognise various medical classifications and generate an appropriate risk rating per patient, that is updated in real-time as their needs change.
By making the most of the data available to them, the care provider can ensure that the right staff are assigned to each patient, based on a range of factors including their personal preferences, their ailments and their risk rating.
Trained customer service agents have access to the rating system which updates immediately after each visit, and they are empowered to take action based on their training, experience and the fact that they can trust the insight that is being derived from the data in real-time. Follow up questions are also added to subsequent reports for the patient, based on their score and their condition, to track improvement or deterioration more accurately.
Further applications of the data use predictive analytics to help anticipate the evolution of each patient over time, by reviewing their visit history and comparing it to trends.
The scope of the platform is impressive. As more information can be fed into it, from new sources such as family members or wearable devices, it will continue to evolve along with the needs of the patients and carers, highlighting needs for carer training and development, patient care plan adjustments, and additional support for family members. It will also allow the business to scale to meet an increasing demand for their excellent services. it will continue to evolve along with the needs of the patients and carers, highlighting needs for carer training and development, patient care plan adjustments, and additional support for family members. It will also allow the business to scale to meet an increasing demand for their excellent services.
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