In recent years, the widespread adoption of Hospital Information Systems is enabling hospitals to measure and record an ever-growing volume and variety of patient and process-related data. In such context, analytics are emerging as suitable tools and methods for extracting and analyzing such data and for providing useful insights to assist decision-making. The Emergency Department […]
This work aims to improve the accuracy of the waiting times prediction in ED, by incorporating queue variables enabled by process mining that capture the crowding of the activities inside the ED. By undertaking process mining, it is possible to gain insights into the hospital processes extracting the entire patient-flow and the queueing-related information (e.g., […]
This paper aims to investigate the process performances in Emergency Departments with an innovative data-driven approach. This approach permits to gain insights into the Emergency Departments processes showing the entire patient-flow, deploying the performances in term of time and resources on the activities and flows, and identifying process deviations and critical bottlenecks. Moreover, the use […]
This paper aims to investigate the potential impact of Big Data (BD) on Critical Success Factors (CSFs) of Customer Relationship Management (CRM). An extensive and sound literature review about CRM was developed, classifying results within an ad-hoc framework. Evidences were discussed and linked to the existing literature on BD. Then, five propositions linking BD and […]
Purpose – Open innovation (OI) literature suggests that firms can improve their innovation performance by learning from a large set of actors in the innovation process. However, although this premise, the extant literature has rather completely overlooked the ‘who’ question: which partners should be included in the different phases of the innovation funnel? How should […]