The term “process intelligence” summarizes all aspects of the smart digitization and automation of discrete processes in medicine and biotechnology, i.e., administrative, technical and scientific processes that consist of individual steps. Such processes are provided as flow charts, source code, protocols, standard operating procedures or clinical guidelines.
From a technical point of view, “process intelligence” includes the digitization of processes, i.e., the manual translation of process descriptions from analog, often paper-based formats into machine-readable, logical and semantically consistent format based on common formal notations. Such processes are implemented, executed, controlled, documented and subsequently evaluated in software―either directly in a programming language as an executable program (e.g., for controlling a device) or indirectly through a process management system.
From an analytical point of view, process intelligence includes the development of automatable procedures for the evaluation of process data and the implementation of these procedures in software. Such procedures are used to retrospectively document and track errors, derive error dependencies, error causes, and error costs. Furthermore, process mining can automatically compare real processes with their original definition and filter out differences and deviations in order to identify changes to processes and derive improvements.
From a cognitive point of view, we use the term “process intelligence” to describe approaches that make it possible to automatically translate process descriptions from natural language texts into technical process descriptions (natural language processing), to plan processes automatically (AI planning), and to develop time predictions about process sequences under consideration of complex boundary conditions (scheduling). In doing so, we draw on the state-of-the-art in science and technology of machine learning and artificial intelligence.
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