Proceedings of the Annual Meeting of the Cognitive Science Society

Cardiopulmonary resuscitation (CPR) is the single greatest determinant in survival from cardiac arrest and is an essential part of every medical professional’s toolkit. Effectively responding to cardiac arrests requires CPR proficiency, which is best retained through frequent training. One such model is Resuscitation Quality Improvement (RQI) that requires users to exhibit proficient performance in compression and ventilation skills every 3 months. While frequent refresher trainings are superior to the traditional 24-month intervals (Cheng et al., 2020), they still need to consider inter-individual differences. Healthcare environments are rife for personalized, adaptive approaches to tracing users’ proficiency over time. To this end, we explore how users that perform similarly over time can be clustered together. Such performance profiles could ultimately enhance the benefits of frequent training with personalized efficiency for users with different needs. With the long-term goal of building an adaptive scheduling tool in mind, we present some initial explorations in this domain. Using k-means clustering, we show that a small number of clusters seems sufficient to create meaningful performance profiles to make out-of-sample predictions. Furthermore, our simulation study suggests that fuzzy membership to said clusters can be leveraged to enhance predictions. We discuss potential next steps in which these fuzzy performance profiles can be employed by more powerful predictive models to move the field towards personalized, adaptive training schedules that improve learning efficiency with the goal of increasing survival outcomes after cardiac arrest.