Clinician perspectives on the utility and acceptability of OxSATS: a qualitative study of a novel structured suicide risk assessment tool
Omar Ouaret Sorr, Howard RylandBackground
Suicide remains a leading preventable cause of death, and patients presenting with self-harm constitute a high-risk group. The Oxford Suicide Assessment Tool for Self-Harm (OxSATS) is a validated model estimating suicide risk. While its statistical performance is established, its clinical utility remains unexplored.
Objective
To understand clinicians’ views on the feasibility, acceptability and potential workflow integration of OxSATS.
Methods
Semistructured interviews with 15 multidisciplinary National Health Service clinicians explored current suicide risk assessment practice, experience with structured tools and views on OxSATS. Participants applied OxSATS to standardised vignettes to simulate real-world decision-making. Interviews were analysed using reflexive thematic analysis.
Findings
Clinicians valued OxSATS for its simplicity, objectivity and percentage-based outputs. OxSATS was viewed as promoting a shared language around suicide risk and supporting assessment consistency. Alignment between OxSATS estimates and clinical judgement was good. However, some clinicians perceived the tool as underestimating risk associated with violent methods. Key implementation barriers included limited scope for wider psychosocial context and potential over-reliance on the tool by inexperienced clinicians. Participants emphasised that OxSATS should complement, not replace, clinical formulation and highlighted the need for clear guidance to support adoption.
Conclusion
OxSATS was viewed as a promising adjunct to suicide risk assessment following self-harm. Addressing concerns around scope, interpretation of percentage risk and integration into wider suicide risk training will be key to successful implementation.
Clinical implications
This study highlights clinicians’ interest in evidence-based adjuncts to suicide risk formulation and supports the use of probability-based models to enhance decision-making.