Building and implementing a predictive decision support system based on a proactive full capacity protocol to mitigate emergency overcrowding problem Project Summary Emergency departments (EDs) face a major problem of overcrowding that poses a significant patient safety risk and leads to poor healthcare service quality and high mortality rates. ED overcrowding is a patient flow problem, which can be solved by improving patient flow from arrival to admission or discharge. According to the American College of Emergency Physicians, a full capacity protocol (FCP) is a key approach for improving patient flow and consequently mitigating ED overcrowding. FCP has different levels that are triggered by different criteria, which are based on patient flow measures (PFMs). The current practice of FCP uses real-time (i.e., reactive) information to decide FCP criteria. However, when it comes to implementing FCP interventions, using real-time information is not efficient because in many cases FCP levels are activated too late when ED is already overcrowded. This project improves the reactive FCP to make it proactive through using Artificial Intelligence and predictive analytics. The PFMs will be predicted using deep learning models and then integrated with reactive FCP. A decision support system will be developed to implement the proposed proactive FCP. The overall objective of this project is to develop a framework to mitigate ED overcrowding. There are four aims (Aims 1& 2 under R21; Aims 3& 4 under R33): Aim 1: Develop deep learning models to predict different PFM values and incorporate them in a proactive FCP. Many PFM values represent the patient flow from arrival to admission. We will build multiple deep learning models to predict PFM values (e.g., numbers of boarding). Then, we will update the reactive FCP to include the predicted PFM values. Aim 2: Develop a DES model to evaluate the effectiveness of proactive FCP. Before running the proactive FCP in production, we will compare reactive and proactive FCPs on the outcomes they generate such as average length of stay (LOS), waiting time and staff satisfaction. Aim 3: Design, evaluate, and implement a decision support system (DSS) based on the proactive FCP. We will design user-centric DSS to aid clinicians and the PFCT in implementing the proactive FCP. We will use the proactive FCP criteria as input for the DSS to automate key parts of the proactive FCP interventions. Aim 4: Expand and generalize the DSS by standardizing data input and output interfaces. We will create a FHIR-based application programming interface (API) to allow site-specific configuration, model training, evaluation, and streamlining of implementation processes. Successful completion of this project delivers a state-of-the-art interoperable DSS for the implementation of a proactive FCP based on early, accurate predictions of PFM values to allow proper planning and execution of patient flow processes, thereby mitigating ED overcrowding....