Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Joao H. Bettencourt-Silva, Gurdeep S. Mannu, Beatriz de la Iglesia

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)
38 Downloads (Pure)


Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain.

This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.
Original languageEnglish
Title of host publicationMachine Learning for Health Informatics
Subtitle of host publicationState-of-the-Art and Future Challenges
EditorsAndreas Holzinger
Number of pages26
ISBN (Electronic)978-3-319-50478-0
ISBN (Print)978-3-319-50477-3
Publication statusPublished - 10 Dec 2016

Publication series

NameMachine Learning for Health Informatics
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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