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DOI: 10.1101/2023.05.17.23290083

Mapping patient interactions in psychiatric presentations to a tertiary emergency department

M. H.McCullough M. Small B. Jayawardena S. Hood
Reliable assessment of suicide and self-harm risk in emergency medicine is critical for effective intervention and treatment of patients affected by mental health disorders. Teams of clinicians are faced with the challenge of rapidly integrating medical history, wide-ranging psychosocial factors, and real-time patient observations to inform diagnosis, treatment and referral decisions. Patient outcomes therefore depend on the reliable flow of information though networks of clinical staff and information systems. We studied information flow at a systems-level in a tertiary hospital emergency department using network models and machine learning. Data were gathered by mapping trajectories and recording clinical interactions for patients at suspected risk of suicide or self-harm. A network model constructed from the data revealed communities closely aligned with underlying clinical team structure. By analysing connectivity patterns in the network model we identified a vulnerability in the system with the potential to adversely impact information flow. We then developed an algorithmic strategy to mitigate this risk by targeted strengthening of links between clinical teams. Finally, we investigated a novel application of machine learning for distinguishing specific interactions along a patient's trajectory which were most likely to precipitate a psychiatric referral. Together, our results demonstrate a new framework for assessing and reinforcing important information pathways that guide clinical decision processes and provide complimentary insights for improving clinical practice and operational models in emergency medicine for patients at risk of suicide or self-harm.