Modelling temporal, data-centric medical processes

Abstract

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing medical processes and some interesting workflow management systems are available. Nevertheless, such systems are lacking in an effective management of two key aspects: data dependencies and temporal constraints. In clinical/health context these two aspects are of paramount importance. For example, a surgery intervention could need the results of the concurrent bioptic analysis to be properly concluded; on the other hand, to successfully apply a fibrinolytic therapy to patients with ST-segment Elevation Myocardial Infarction (STEMI ) a maximum delay of 30 minutes must be considered w.r.t. the emergency department admission. In this paper, we propose TNest, a new advanced, structured and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design. All the features of TNest have been considered to model the process related to classical clinical guidelines, i.e.

Incremental execution of temporal graph queries over runtime models with history and its applications

“…Temporal requirements are always present in medical guidelines and their satisfaction is key to the successful completion of procedures [26]. Adding a temporal dimension to the exemplary requirement from Sect.…”

“…The logs are used for simulations in which query answers from InTempo are used while planning adaptations. The case-study is of particular relevance as in healthcare, real-time requirements are key to medical procedures [26] and therefore fast query executions are necessary. The performance of the implementation, i.e., its fulfillment of R3 and R4, is compared to a relevant tool for runtime monitoring as well as a tool from the MDE community.…”

Modern methods of process management at healthcare facilities

“…As a result, a tight integration between data objects and process activities can be achieved. As object-awareness requires a data-driven process modeling and execution approach, based on ob-ject behavior and object interactions, process/activity-centric methodologies are being replaced by data-centric models evolving over time [7]. In the context of a CG, patient's clinical situation (referred to as patient state, scenario, or context [49]) is central and represent the shared knowledge that drives the decision making and evolves as a result of performed actions, made decisions and collected data.…”

A Tractable Generalization of Simple Temporal Networks and Its Relation to Mean Payoff Games

“…Combi et al [12] proposed a method to translate workflow schemata to STNs/ STNUs [36] in order to analyze and validate all temporal aspects in a rigorous way. As already noted in [9] and [10], such translation cannot specifically represent the behavior of an AND join connector, because the kind of constraints in an STN/ STNU is limited. Therefore, in [10], the authors proposed an adjustment of the translation of an AND join connector introducing for each incoming arc of the connector a buffer node connected with some determined new arcs and assuming a reasonable but fixed execution algorithm for the STN.…”