A Simulation-Driven Approach to Decision Support in Process Reorganization: A Case Study in Healthcare

Ilaria Angela Amantea (), Antonio Leva () and Emilio Sulis ()
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Ilaria Angela Amantea: University of Torino
Antonio Leva: University of Torino
Emilio Sulis: University of Torino

A chapter in Exploring Digital Ecosystems, 2020, pp 223-235 from Springer

Abstract: Abstract Amantea, Ilaria Angela Di Leva, Antonio Sulis, EmilioCompanies are currently forced to update their technologies to store and analyze data in order to remain competitive in a rapidly changing market. Digitalization and dematerialization of documents are increasingly needed especially for companies with a large amount of data to manage or store. The efficiency of the process must be balanced to risk management which is a key factor of success for organization as risks are part of every business activity. Compliance is an integral part of risk management with not only economic implications but also on the legal and liability level. This paper proposes a methodological framework to investigate risks and compliance in reorganizations by adopting a Business Process Management perspective that includes modeling and simulation of business processes. We applied our methodology to processes in a Blood Bank department of a large hospital. Our results show that a simulation-driven approach is an effective way to provide a decision support to guide department’s managers to the reorganization and verify, before implementation, the balance between efficiency of the reorganization of activities, risk management and compliance. In addition, digitalization in the health sector would facilitate the self-reporting of errors (methodology encouraged by the Joint Commission for accreditation and certification in Healthcare), that increase transparency. Reporting such incidents can provide a variety of information about successful error management practices as well as weaknesses.

Keywords: Risk management; Business process simulation; Healthcare (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-23665-6_16

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