Predicting onward care needs at admission to reduce discharge delay using machine learning

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Abstract

Early identification of patients who require onward referral for social care can prevent delays to discharge from hospital. We introduce a machine learning (ML) model to identify potential social care needs at the first point of admission. The model performance is comparable to clinician’s predictions of discharge care needs, despite working with only a subset of the information available to the clinician. We find that ML and clinician perform better for identifying different types of care needs, highlighting the added value of a potential system supporting decision making. We also demonstrate the ability for ML to provide automated initial discharge need assessments, in the instance where initial clinical assessment is delayed. Finally, we demonstrate that combining clinician and machine predictions, in a hybrid model, provides even more accurate early predictions of onward social care requirements and demonstrates the potential for human-in-the-loop decision support systems in clinical practice.

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Contributors

Author: Chris Duckworth

Author: Dan Burns

Author: Carlos Lamas-Fernandez

Author: Matthew Stammers

Author: Michael Boniface

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