120 Improving Delirium Recognition and Management Through In-Situ Simulation. (16th March 2021) - Cheston, H; Miller, E; Mufti, S
Abstract: Introduction: Delirium is a common condition that is often associated with increased morbidity and mortality, longer hospital admission, and discharge to a residential or nursing home. By improving our ability to recognise and manage these patients we can intervene early to help reduce the likelihood of these outcomes. Method: We organised several in-situ simulation scenarios with junior staff working on the Elderly Care Unit. The research team designed a scenario to re-create a typical delirious patient on the ward. Participants had to recognise the patient was delirious and instigate a management plan. Participants completed a pre and post-intervention questionnaire to ascertain whether they felt the simulation had improved their confidence. Additionally we performed an audit to investigate whether our intervention led to an improvement in the recognition and management of delirium in patients on the ward. Results: The questionnaires showed an increase in participants' confidence and knowledge when managing a delirious patient on the ward. From reviewing patient notes pre-intervention we identified that 24 patients were delirious during admission, 14 of which were accurately diagnosed with delirium. The remaining 10 patients were diagnosed with "Acute Confusion". On reviewing these 10 patients' notes, they were all likely to have a diagnosis of delirium. Post-intervention there were 14 patients identified as delirious during their admission. All these patients were correctly documented as having delirium with no inaccurate use of terminology. The data also showed increasing use of tools such as AMTS and 4AT to diagnose delirium. Conclusions: From the data gathered, we can see participants are better at recognising and diagnosing delirium. However, our sample sizes are too small to test statistical significance between data points. To improve the project we would include a larger sample size to determine whether the simulation produces a statistically significant improvement in confidence levels.