Automated conflict resolution between multiple clinical pathways: a technology report. Issue 3 (1st July 2018)

• Record Type: Journal Article
• Authors: Litchfield, Ian; Turner, Alice; Backman, Ruth; Filho, João Bosco Ferreira; Weber, Phil; Lee, Mark
• Is Part Of: BMJ health & care informatics. Volume 25:Issue 3(2018)
• Page Start: 142
• Page End: 148
• Publication Date: 2018-07-01
• Subjects: multimorbidity -- clinical guidance -- conflict identification -- decision support -- patient pathways; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.14236/jhi.v25i3.986
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : Background: The number of people in the UK with three or more long-term conditions continues to grow and the management of patients with co-morbidities is complex. In treating patients with multimorbidities, a fundamental problem is understanding and detecting points of conflict between different guidelines which to date has relied on individual clinicians collating disparate information. Objective: We will develop a framework for modelling a diverse set of care pathways, and investigate how conflicts can be detected and resolved automatically. We will use this knowledge to develop a software tool for use by clinicians that can map guidelines, highlight root causes of conflict between these guidelines and suggest ways they might be resolved. Method: Our work consists of three phases. First, we will accurately model clinical pathways for six of the most common chronic diseases; second, we will automatically identify and detect sources of conflict across the pathways and how they might be resolved. Third, we will present a case study to prove the validity of our approach using a team of clinicians to detect and resolve the conflicts in the treatment of a fictional patient with multiple common morbidities and compare their findings and recommendations with those derived automatically using our novel software. Discussion: This paper describes the development of an important software-based method for identifying a conflict between clinical guidelines. Our findings will support clinicians treating patients with multimorbidity in both primary and secondary care settings.