Transitioning towards a proactive practice: a longitudinal field study on the implementation of a ML system in adult social care

Politicians and care associations advocate for the use of machine learning (ML) systems to improve the delivery of adult social services. Yet, guidance on how to implement ML systems remains limited and research indicates that future implementation efforts are likely to encounter difficulties. We aim to enhance the understanding of ML system implementations by conducting a longitudinal field study with a team responsible for deploying a ML system within an adult social services department. The ML system implementation represented a cross-organisational effort to facilitate the department’s transition to a proactive practice. Throughout this process, stakeholders adapted to numerous challenges in real-time. This study makes three contributions. First, we provide a description of how ML systems are implemented and highlight practical challenges. Second, we illustrate the utility of HCI knowledge in designing workflows for ML-assisted preventative care programmes. Finally, we provide recommendations for future deployments of ML systems in social care.