# A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes

arXiv: https://arxiv.org/abs/2401.03700 · HTML: https://mlsanity.com/paper/2401.03700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [Heterogeneous Similarity Graph Neural Network on Electronic Health Records](https://mlsanity.com/paper/2101.06800/) (2021)
- [Representation Learning of EHR Data via Graph-Based Medical Entity Embedding](https://mlsanity.com/paper/1910.02574/) (2019)
- [Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction](https://mlsanity.com/paper/2303.09590/) (2023)
- [Network Comparison with Interpretable Contrastive Network Representation Learning](https://mlsanity.com/paper/2005.12419/) (2020)

## Abstract (arXiv)

Communication among healthcare professionals (HCPs) is crucial for the quality of patient treatment. Surrounding each patient's treatment, communication among HCPs can be examined as temporal networks, constructed from Electronic Health Record (EHR) access logs. This paper introduces a visual analytics system designed to study the effectiveness and efficiency of temporal communication networks mediated by the EHR system. We present a method that associates network measures with patient survival outcomes and devises effectiveness metrics based on these associations. To analyze communication efficiency, we extract the latencies and frequencies of EHR accesses. Our visual analytics system is designed to assist in inspecting and understanding the composed communication effectiveness metrics and to enable the exploration of communication efficiency by encoding latencies and frequencies in an information flow diagram. We demonstrate and evaluate our system through multiple case studies and an expert review.