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Search: WFRF:(Pufahl Luise)

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1.
  • Kurz, Aaron Friedrich, et al. (author)
  • Reinforcement learning-supported ab testing of business process improvements : an industry perspective
  • 2023
  • In: Enterprise, business-process and information systems modeling. - Cham : Springer Nature. - 9783031342400 - 9783031342417 ; , s. 12-26
  • Conference paper (peer-reviewed)abstract
    • In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.
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2.
  • Munoz-Gama, Jorge, et al. (author)
  • Process mining for healthcare : Characteristics and challenges
  • 2022
  • In: Journal of Biomedical Informatics. - : Elsevier BV. - 1532-0464 .- 1532-0480. ; 127
  • Journal article (peer-reviewed)abstract
    • Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
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