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The nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.

The authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.

The authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.

The authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

In the healthcare management domain, there is a lack of knowledge concerning the role of resilience practices in improving patient safety. The purpose of this paper is to understand the capabilities that enable healthcare resilience and how digital technologies can support these capabilities.

Within- and cross-case research methodology was used to study resilience mechanisms and capabilities in healthcare and to understand how digital health technologies impact healthcare resilience. The authors analyze data from two Italian hospitals through the lens of the operational failure literature and anchor the findings to the theory of dynamic capabilities.

Five different dynamic capabilities emerged as crucial for managing operational failure. Furthermore, in relation to these capabilities, medical, organizational and patient-related knowledge surfaced as major enablers. Finally, the findings allowed the authors to better explain the role of knowledge in healthcare resilience and how digital technologies boost this role.

When trying to promote a culture of patient safety, the research suggests healthcare managers should focus on promoting and enhancing resilience capabilities. Furthermore, when evaluating the role of digital technologies, healthcare managers should consider their importance in enabling these dynamic capabilities.

Although operations management (OM) research points to resilience as a crucial behavior in the supply chain, this is the first research that investigates the concept of resilience in healthcare systems from an OM perspective, with only a few authors having studied similar concepts, such as “workaround” practices.