Guidelines and workflow models

Mor Peleg, Peter Haug

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

A heterogeneous community is developing computerized methods and systems for supporting health care processes. One approach focuses on assuring the quality of care by delivering decision support to care providers based on clinical knowledge, and the other focuses on supporting management of routine medical actions, such as ordering laboratory tests and medications or scheduling patient visits. The first community is more academic, and studies classical clinical decision support, while the second originated in industry, and focuses on application and adaptation of business process management and mining techniques to the healthcare domain. Both of these approaches have made important achievements, yet their integration is far from being realized. In this chapter, we follow a life-cycle approach, reviewing the contributions of the two communities mentioned, by considering the stage of the life cycle that each achievement addresses: from knowledge acquisition to knowledge (process) modeling and specification, validation and verification, enactment, process mining and improvement, and sharing of executable guideline knowledge. We stress the potential and achievements made in integration of guideline and workflow knowledge, and discuss the success stories and limitations, and the opportunities for future research.

Original languageEnglish
Title of host publicationClinical Decision Support and beyond
Subtitle of host publicationProgress and Opportunities in Knowledge-Enhanced Health and Healthcare
PublisherElsevier
Pages309-348
Number of pages40
ISBN (Electronic)9780323912006
ISBN (Print)9780323995771
DOIs
StatePublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc. All rights reserved.

Keywords

  • Clinical decision support
  • Computer-interpretable guidelines
  • Decision support systems
  • Evidence-based medicine
  • Workflow

ASJC Scopus subject areas

  • General Computer Science

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