A Study of Healthcare Team Communication Networks using Visual Analytics

Hsiao-Ying Lu, Yiran Li, Brittany Garcia, Shin-Ping Tu, and Kwan-Liu Ma

ICMHI: International Conference on Medical and Health Informatics, 2023
image
The visual interface for facilitating interactive analysis using the workflow in Figure 3. (a) Relating to Step 4, this view visualizes the input attributes’ contributions to the 1D network representation. (b) The composite variables generated in Step 5 are shown as a list of scatterplots. (c) Other auxiliary information is displayed, including the prediction accuracy of NNs trained for Step 2 and legends used across the views.

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Abstract

Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients’ survival outcome. However, it is challenging to uncover and understand such factors in the complex Multiteam System (MTS) communication networks representing daily HCP cooperation. In this paper, we present a study on MTS communication networks constructed with real-world cancer patients’ Electronic Health Record (EHR) access logs. We adopt a visual analytics workflow to extract associations between semantic characteristics of MTS communication networks and the patients’ survival outcomes. The workflow consists of a neural network learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. We provide the insights found using this workflow with two case studies and an expert interview.

BibTex

@inproceedings{lu2023study,
    title={A Study of Healthcare Team Communication Networks using Visual Analytics},
    author={Lu, Hsiao-Ying and Li, Yiran and Garcia, Brittany and Tu, Shin-Ping and Ma, Kwan-Liu},
    booktitle={Proceedings of the 2023 7th International Conference on Medical and Health Informatics},
    pages={104--111},
    year={2023}
}