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Process mining organizatioQn (PMO) is an innovative approach based on artificial intelligence (AI) decision making suitable for designing healthcare processes for human resource (HR) organizations. The proposed work suggests some examples of PMO-based Business Process Modeling and Notation (BPMN) workflows by highlighting the advances in HR management and in risk decrease according to healthcare scenarios. Specifically proposed are different examples of “TO BE” process pipelines related to an upgrade of the organizational healthcare framework, including digital technologies and telemedicine. Important elements are provided to formulate HR management guidelines supporting PMO design. The proposed BPMN workflows are the result of different consulting actions in healthcare institutions based on the preliminary mapping of “AS IS” processes highlighting bottlenecks and needs in HR organization. A pilot experimental dataset is used to show how it is possible to apply AI algorithms providing organization corrective actions. The paper is mainly focused on discussing some validated BPMN models managing HR in the healthcare sector. The methodology is based on the application of the BPMN approach to deploy human resource organizational processes. The results show AI data-driven workflows adopted in healthcare and examples of AI fuzzy c-means outputs addressing organizational actions.

The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.