The global rise in the aging population presents significant challenges to healthcare systems worldwide, which thus need more efficiency and effectiveness. Healthcare systems deliver services through processes that encode (inter)national regulations and protocols for patient treatment. It follows that efficiency and effectiveness require improvement of healthcare processes. This paper introduces a novel methodology leveraging process mining and simulation to improve healthcare organizational processes through data-driven resource allocation. Traditional qualitative analyses and queuing theory offer limited scope for complex, multi-activity processes. In contrast, process simulation enables process improvement but requires a realistic simulation model; otherwise, the analysis may optimize an assumed process rather than the real one. This paper reports on a data-driven simulation methodology that builds on process mining techniques: process mining puts aside the subjectivity of process actors and focuses on the objectivity of transactional data recorded by information systems. This enables us to discover process simulation models that mimic real behavior. Simulation models support process improvement through the evaluation of alternative what-if scenarios, which can be tested without disrupting live operations. The methodology has been applied to the emergency department of an Italian hospital, focusing on reducing patient waiting times. Simulations involving a careful addition of medical staff demonstrated a potential substantial reduction in waiting times (88%) with a modest cost increase (7%–8%). Furthermore, the improved process exhibited enhanced resilience to surges in patient arrivals, highlighting how improved processes guarantee higher preparedness in front of emergencies. • We propose a simulation-based methodology for health-care process improvement. • The methodology is data-driven, discovering accurate simulation models ensuring the realism. • Simulation experiments based on a real life case study indicate the likely potential of a waiting-time reduction up to 88% with just 7%–8% cost increase. • The methodology addresses healthcare-specific characteristics and challenges. • The improved process exhibits enhanced resilience to surges in patient arrivals.

BACKGROUND: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. RESULTS: We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. CONCLUSIONS: RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models.