Life Without Barriers is a charitable organisation supporting close to 23,000 people living in over 400 communities across the nation. Their community-based programs assist children, young people, adults, families and communities. They provide family support and out-of-home care, disability services, home and community care and support to refugees and asylum seekers. They also work in the areas of mental health, homelessness and youth justice.
As a provider of support to over thousands of people living, LWB required a solution that optimised resource management efficiency. Forest Grove implemented a critical incident prediction model at Life Without Barriers (LWB) to decrease response time for people with disability and troubled youth.
Based on the precedent of over 2.5 million client progress records and an abundance of unstructured data Forest Grove developed a machine learning model that alerts LWB to clients that are most at risk of experiencing a behavioural or medical incident.
Leveraging KNIME, the ML model carried out the following key processes:
- Automated data Cleansing and missing value imputation
- Event tagging and analysis
- Supervised and unsupervised machine learning processes
- Tiered client ‘at risk’ classification
The predictive model has enabled LWB to determine at-risk clients prior to an incident with a high success rate. A previously unknown correlation between clients who experienced a move (house, facility etc) and a considerable jump in incidents has enabled LWB to focus resources on these at-risk clients.
- A high degree of success in predicting potential at-risk clients prior to a medical or behavioural incident
- Ability to uncover previously unforeseen trends
- Streamlining of resource management & allocation processes
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