Utilization of big data to improve management of the emergency departments. Results of a systematic review

Background. The emphasis on using big data is growing exponentially in several sectors including biomedicine, life sciences and scientific research, mainly due to advances in information technologies and data analysis techniques. Actually, medical sciences can rely on a large amount of biomedical information and Big Data can aggregate information around multiple scales, from the DNA to the ecosystems. Given these premises, we wondered if big data could be useful to analyze complex systems such as the Emergency Departments (EDs) to improve their management and eventually patient outcomes. Methods. We performed a systematic review of the literature to identify the studies that implemented the application of big data in EDs and to describe what have already been done and what are the expectations, issues and challenges in this field. Results. Globally, eight studies met our inclusion criteria concerning three main activities: the management of ED visits, the ED process and activities and, finally, the prediction of the outcome of ED patients. Although the results of the studies show good perspectives regarding the use of big data in the management of emergency departments, there are still some issues that make their use still difficult. Most of the predictive models and algorithms have been applied only in retrospective studies, not considering the challenge and the costs of a real-time use of big data. Only few studies highlight the possible usefulness of the large volume of clinical data stored into electronic health records to generate evidence in real time. Conclusion. The proper use of big data in this field still requires a better management information flow to allow real-time application

Application of Big Data in Decision Making for Emergency Healthcare Management

Application of big data in healthcare has enhanced efficiency and decision making. This is of critical benefit to patients, healthcare professionals and the healthcare institution. Although various research studies have examined the application of big data analytics in healthcare, few studies have explored its application in emergency medicine. This research study explored the application of big data in emergency medicine in facilitating decision making among paramedics and other healthcare practitioners. Appropriate research studies were identified and reviewed systematically to explore the theme of the study. The study found that big data promoted decision making in emergency medicine through the predictor models, which enabled the healthcare practitioners make informed judgments concerning patient care

Enhancing discrete-event simulation with big data analytics: a review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques

Embracing Big Data for Simulation Modelling of Emergency Department Processes and Activities

© 2015 IEEE. Simulation has been demonstrated to be a powerful tool to mimic processes and activities in emergency departments. However, most applications only rely on the data that were manually input by the staff in the departments. First, this practice does not guarantee that the required data to build the simulation models are captured in the computer system, as some information about the processes of emergency departments are not electronically stored. Second, human errors and missing data are also common for manual inputs. A simulation model that is incapable of representing the actual system of the emergency department will deliver wrong conclusions to hospital administrators and may lead to negative consequences if they trust the simulation results. In this paper, we present a case study of developing a simulation model of an emergency department in Hong Kong and discuss the data challenges. Then we propose an RFID-enabled infrastructure to automatically capture large volumes of data regarding the patient activities in the ED in order to build simulation models of more details and a higher accuracy.Link_to_subscribed_fulltex