5,401 research outputs found
Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice
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
Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer
In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized
Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining
Personalized head and neck cancer therapeutics have greatly improved survival
rates for patients, but are often leading to understudied long-lasting symptoms
which affect quality of life. Sequential rule mining (SRM) is a promising
unsupervised machine learning method for predicting longitudinal patterns in
temporal data which, however, can output many repetitive patterns that are
difficult to interpret without the assistance of visual analytics. We present a
data-driven, human-machine analysis visual system developed in collaboration
with SRM model builders in cancer symptom research, which facilitates
mechanistic knowledge discovery in large scale, multivariate cohort symptom
data. Our system supports multivariate predictive modeling of post-treatment
symptoms based on during-treatment symptoms. It supports this goal through an
SRM, clustering, and aggregation back end, and a custom front end to help
develop and tune the predictive models. The system also explains the resulting
predictions in the context of therapeutic decisions typical in personalized
care delivery. We evaluate the resulting models and system with an
interdisciplinary group of modelers and head and neck oncology researchers. The
results demonstrate that our system effectively supports clinical and symptom
research
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