This case study is about the combined use of interviewing, modeling, and simulation methods in healthcare delivery research. For my PhD, I aimed to understand how a real-time Discrete Event Simulation model could help improve the quality of healthcare and the satisfaction of patients and staff. This was motivated by a previous work I had done building a Discrete Event Simulation model for the Accident and Emergency department of a major National Health Service Trust in London for my MSc dissertation. From that work, it emerged that (a) some of the resources (e.g., specialist consultants) that significantly impacted the performance of the Accident and Emergency department were outside the control of the Accident and Emergency department, and (b) due to the dynamic nature of the Accident and Emergency environment, a model of the system based on historical data, as mine was, can very quickly become outdated because the input data do not reflect what is going on in the real system. These raised a number of questions for me: first, what if the Discrete Event Simulation model receives data from the real system in real-time? Second, what if the results from the real-time model could be appropriately shared with patients and staff about the status of the system? And third, what if we could fast-forward the real-time model and predict how many patients of a particular specialty will need a specialist consultant so that we can inform them ahead of time and therefore cut down the time patients spent waiting? This case, however, focuses on methodology for only one element of the work, the Effective Satisfaction Level.