Fondazione Bruno Kessler

An emergency department (ED) is a healthcare service that provides the first clinical assessment and treatment to patients with various acute conditions. These departments, however, are often overwhelmed by the large volume of patients. As a consequence, ED crowding has become a global concern and has been correlated to reduced timeliness and effectiveness of care and increased patient mortality. Concerning input, 20% to 30% of patients are brought to the ED by ambulance; the remaining are self-presenting for the vast majority. Notably, non-urgent conditions characterize a high proportion of all ED visits worldwide, and almost all of these visits involve self-presenting patients. Increasing the awareness of these patients about the mandate of EDs and the real-time situation of the neighboring emergency departments has the potential to reduce the self-presentation of patients with minor, non-urgent conditions. Such patient empowerment can be achieved through a dashboard. Concerning throughput, working in the ED requires emergency physicians and nurses to treat many patients at once while maintaining situational awareness of the surroundings. This is especially true for the head of the department, but it also holds for all physicians. It can be crucial, for example, for physicians to know if there is a bottleneck in the flow of the entire patient care process, such as a particularly high average waiting time for radiology reporting or cardiologic consultation. The availability of this information allows countermeasures to be put in place to regain efficiency. All this can be achieved through dedicated dashboards automatically fed from various information system. In addition, appropriate dashboards also enable health policymakers to monitor specific epidemiological phenomena, such as the emergence of certain infectious diseases, in a timely manner.

The peer-to-peer comparison means center-to-center comparison, which requires adjusting for possible differences among centers to be fair and convincing. The first step to reach this goal is to develop a predictive model that accurately estimates each patient's probability of being admitted, starting from clinical conditions and boundary variables. Such a model would make it possible to calculate, for each ED, the expected hospitalization rate; that is, the hospitalization rate that would have been observed if the ED had behaved like the average of the EDs that provided the data to build the model itself. Comparing the observed hospitalization rate in the single ED with the expected rate derived from the model provides a rigorous method of comparing the department with the average performance, taking into account the characteristics of the patients treated and the conditions under which the ED operated. In other words, the predictive model represents the benchmark against which each ED is evaluated.

The goal of this feasibility study is to explore the feasibility of using realistic, customizable virtual reality (VR) environments in response to the onset of Responsive Behaviors in elderly residents with cognitive impairment.
Specifically, the study will assess;
* feasibility through acceptance metrics (including recruitment rate, adherence rate, attrition rate, and tolerance to the intervention);
* safety (monitoring for adverse effects related to VR headset use);
* user engagement, enjoyment, and overall experience.
Participants will be exposed to three VR sessions conducted before, during, or after a situation deemed activating (e.g., personal hygiene activities). Operators will conduct a baseline assessment to identify specific moments when the participant experiences responsive behaviors. Additionally, operators may administer the Oculus VR headset when responsive behaviors occur outside of baseline-identified moments.