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Healthcare organizations are under increasing pressure to improve productivity, gain competitive advantage and reduce costs. In many cases, despite management already gained some kind of qualitative intuition about inefficiencies and possible bottlenecks related to the enactment of patients' careflows, it does not have the right tools to extract knowledge from available data and make decisions based on a quantitative analysis. To tackle this issue, starting from a real case study conducted in San Carlo di Nancy hospital in Rome (Italy), this article presents the results of a process mining project in the healthcare domain. Process mining techniques are here used to infer meaningful knowledge about the patient careflows from raw event logs consisting of clinical data stored by the hospital information systems. These event logs are analyzed using the ProM framework from three different perspectives: the control flow perspective, the organizational perspective and the performance perspective. The results on the proposed case study show that process mining provided useful insights for the governance of the hospital. In particular, we were able to provide answers to the management of the hospital concerning the value of last investments, and the temporal distribution of abandonments from emergency room and exams without reservation.

Robotic Process Automation (RPA) is an emerging technology that automates intensive routine tasks (or simplyroutines) previously performed by a human user on the User Interface (UI) of a computer system, by means of a software (SW) robot. To date, RPA tools available in the market strongly relies on the ability of human experts to manually implement the routines to automate. Being the current practice time-consuming and error-prone, in this paper we present SmartRPA, a cross-platform software tool that tackles such issues by exploiting UI logs keeping track of many routine executions to generate executable RPA scripts that automate the routines enactment by SW robots.

Robotic Process Automation (RPA) is an emerging technology that allows organizations to automate intensive repetitive tasks (or simply routines) previously performed by a human user on the User Interface (UI) of web or desktop applications. RPA tools are able to capture in dedicated UI logs the execution of several routines and then emulate their enactment in place of the user by means of a software (SW) robot. A UI log can record information about many routines, whose actions are mixed in some order that reflects the particular order of their execution by the user, making their automated identification far from being trivial. The issue to automatically understand which user actions contribute to a specific routine inside the UI log is also known as segmentation. In this paper, we leverage a concrete use case to explore the issue of segmentation of UI logs, identifying all its potential variants and presenting an up-to-date overview that discusses to what extent such variants are supported by existing literature approaches. Moreover, we offer points of reference for future research based on the findings of this paper.

Robotic Process Automation (RPA) operates on the user interface (UI) of software applications and automates - by means of a software (SW) robot - mouse and keyboard interactions to remove intensive routine tasks (or simply routines). With the recent advances in Artificial Intelligence, the automation of routines is expected to undergo a radical transformation. Nonetheless, to date, the RPA tools available in the market are not able to automatically learn to automate such routines, thus requiring the support of skilled human experts that observe and interpret how routines are executed on the UIs of the applications. Being the current practice time-consuming and error-prone, in this paper we present SmartRPA, a cross-platform tool that tackles such issues by exploiting UI logs to automatically generate executable RPA scripts that automate the routines enactment by SW robots.

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real life event log extracted from an international software engineering company and four quality metrics: understandability, correctness, precision, and usefulness. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.

Robotic Process Automation (RPA) is a fast-emerging automation technology in the field of Artificial Intelligence that allows organizations to automate high volume routines. RPA tools are able to capture the execution of such routines previously performed by a human user on the interface of a computer system, and then emulate their enactment in place of the user. In this paper, after an in-depth experimentation of the RPA tools available in the market, we developed a classification framework to categorize them on the basis of some key dimensions. Then, starting from this analysis, we derived four research challenges necessary to inject intelligence into current RPA technology.

The identification of control-flow errors in business processes (BPs) at design time is one of the most relevant tasks of the early phases of the Business Process Management (BPM) lifecycle, named BP verification. In this paper, starting from a Petri net-based representation of a BP model, we show how instances of the verification problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If verification problems are converted into planning problems, one can seamlessly leverage the best performing automated planners, with advantages in terms of versatility and customization.