aceAnticipating health and care trajectories from routinely collected social care records
Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed models to predict three outcomes of interest: future care plan needs, hospital admissions, and all-cause mortality, ev…
Models trained on routinely collected social care records predicted future hospital admissions and all-cause mortality with meaningful discriminative performance reaching AUROC 0.893.
Findings are based on a single region's social care system and care plan needs prediction remained variable, limiting generalizability and reliability for that outcome.
Evidence
- Peer-reviewednpj Health Systems2026-08-29
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Truvace Impact Record TRV-2026-0935, v1: “Anticipating health and care trajectories from routinely collected social care records.” Truvace, 2026-08-31. /record/TRV-2026-0935 (accessed at citation time). sha256 2aa73d0e295646b3…
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