Guideline-based careflow systems

  • Authors:
  • S Quaglini;M Stefanelli;A Cavallini;G Micieli;C Fassino;C Mossa

  • Affiliations:
  • Dipartimento di Informatica e Sistemistica, Universití di Pavia, Via Ferrata 1, I-27100 Pavia, Italy;Dipartimento di Informatica e Sistemistica, Universití di Pavia, Via Ferrata 1, I-27100 Pavia, Italy;Stroke Unit, IRCCS Istituto Neurologico 'C. Mondino', Via Palestro, 11, I-27100 Pavia, Italy;Stroke Unit, IRCCS Istituto Neurologico 'C. Mondino', Via Palestro, 11, I-27100 Pavia, Italy;Dipartimento di Informatica e Sistemistica, Universití di Pavia, Via Ferrata 1, I-27100 Pavia, Italy;Consorzio di Bioingegneria e Informatica Medica, Via Ferrata 1, I-27100 Pavia, Italy

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2000

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Abstract

This paper describes a methodology for achieving an efficient implementation of clinical practice guidelines. Three main steps are illustrated: knowledge representation, model simulation and implementation within a health care organisation. The resulting system can be classified as a 'guideline-based careflow management system'. It is based on computational formalisms representing both medical and health care organisational knowledge. This aggregation allows the implementation of a guideline, not only as a simple reminder, but also as an 'organiser' that facilitates health care processes. As a matter of fact, the system not only suggests the tasks to be performed, but also the resource allocation. The methodology initially comprehends a graphical editor, that allows an unambiguous representation of the guideline. Then the guideline is translated into a high-level Petri net. The resources, both human and technological necessary for performing guideline-based activities, are also represented by means of an organisational model. This allows the running of the Petri net for simulating the implementation of the guideline in the clinical setting. The purpose of the simulation is to validate the careflow model and to suggest the optimal resource allocation before the careflow system is installed. The final step is the careflow implementation. In this phase, we show that the 'workflow management' technology, widely used in business process automation, may be transferred to the health care setting. This requires augmenting the typical workflow management systems with the flexibility and the uncertainty management, typical of the health care processes. For illustrating the proposed methodology, we consider a guideline for the management of patients with acute ischemic stroke.