Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Visual Analytics Design for Connecting Healthcare Team Communication to Patient Outcomes (2401.03700v1)

Published 8 Jan 2024 in cs.SI, cs.HC, and cs.LG

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. A. P. Gurses and Y. Xiao, “A systematic review of the literature on multidisciplinary rounds to design information technology,” Journal of the American Medical Informatics Association, vol. 13, no. 3, pp. 267–276, 2006.
  2. M. Smits, M. Zegers, P. Groenewegen, D. Timmermans, L. Zwaan, G. Van der Wal, and C. Wagner, “Exploring the causes of adverse events in hospitals and potential prevention strategies,” Quality and Safety in Health Care, vol. 19, no. 5, pp. e5–e5, 2010.
  3. A. Bagnasco, B. Tubino, E. Piccotti, F. Rosa, G. Aleo, P. Di Pietro, L. Sasso, D. Passalacqua, L. Gambino et al., “Identifying and correcting communication failures among health professionals working in the emergency department,” International emergency nursing, vol. 21, no. 3, pp. 168–172, 2013.
  4. K. J. Verhaegh, A. Seller-Boersma, R. Simons, J. Steenbruggen, S. E. Geerlings, S. E. de Rooij, and B. M. Buurman, “An exploratory study of healthcare professionals’ perceptions of interprofessional communication and collaboration,” Journal of interprofessional care, vol. 31, no. 3, pp. 397–400, 2017.
  5. M. Kilduff and D. J. Brass, “Organizational social network research: Core ideas and key debates,” Academy of Management Annals, vol. 4, no. 1, pp. 317–357, 2010. [Online]. Available: <GotoISI>://WOS:000286416100005http://www.tandfonline.com/doi/abs/10.1080/19416520.2010.494827
  6. N. Katz, D. Lazer, H. Arrow, and N. Contractor, “Network theory and small groups,” Small Group Research, vol. 35, no. 3, pp. 307–332, 2004. [Online]. Available: http://sgr.sagepub.com/content/35/3/307.abstracthttp://sgr.sagepub.com/content/35/3/307.full.pdf
  7. P. Butow and E. Hoque, “Using artificial intelligence to analyse and teach communication in healthcare,” The breast, vol. 50, pp. 49–55, 2020.
  8. C. M. Pires and A. M. Cavaco, “Communication between health professionals and patients: review of studies using the rias (roter interaction analysis system) method,” Revista da Associação Médica Brasileira, vol. 60, pp. 156–172, 2014.
  9. S. Atkins, “Assessing health professionals’ communication through role-play: An interactional analysis of simulated versus actual general practice consultations,” Discourse Studies, vol. 21, no. 2, pp. 109–134, 2019.
  10. C. V. de Almeida and C. Belim, “Health professionals’ communication competences decide patients’ well-being: Proposal for a communication model,” in Joy.   Emerald Publishing Limited, 2020, vol. 5, pp. 201–222.
  11. T. Wu, Y. Wang, Y. Wang, E. Zhao, Y. Yuan, and Z. Yang, “Representation learning of ehr data via graph-based medical entity embedding,” arXiv preprint arXiv:1910.02574, 2019.
  12. Z. Liu, X. Li, H. Peng, L. He, and S. Y. Philip, “Heterogeneous similarity graph neural network on electronic health records,” in 2020 IEEE International Conference on Big Data (Big Data), pp. 1196–1205.
  13. D. Cai, C. Sun, M. Song, B. Zhang, S. Hong, and H. Li, “Hypergraph contrastive learning for electronic health records,” in Proceedings of the 2022 SIAM International Conference on Data Mining (SDM).   SIAM, 2022, pp. 127–135.
  14. J. Tang, M. Musolesi, C. Mascolo, and V. Latora, “Temporal distance metrics for social network analysis,” in Proceedings of the 2nd ACM workshop on Online social networks, 2009, pp. 31–36.
  15. N. Santoro, W. Quattrociocchi, P. Flocchini, A. Casteigts, and F. Amblard, “Time-varying graphs and social network analysis: Temporal indicators and metrics,” arXiv preprint arXiv:1102.0629, 2011.
  16. G. Kossinets, J. Kleinberg, and D. Watts, “The structure of information pathways in a social communication network,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008, pp. 435–443.
  17. P. Holme, “Network reachability of real-world contact sequences,” Physical Review E, vol. 71, no. 4, p. 046119, 2005.
  18. F. Beck, M. Burch, S. Diehl, and D. Weiskopf, “A taxonomy and survey of dynamic graph visualization,” in Computer graphics forum, vol. 36, no. 1.   Wiley Online Library, 2017, pp. 133–159.
  19. M. Gleicher, D. Albers, R. Walker, I. Jusufi, C. D. Hansen, and J. C. Roberts, “Visual comparison for information visualization,” Information Visualization, vol. 10, no. 4, pp. 289–309, 2011.
  20. Y. H. Kidane and P. A. Gloor, “Correlating temporal communication patterns of the eclipse open source community with performance and creativity,” Computational and mathematical organization theory, vol. 13, pp. 17–27, 2007.
  21. T. Wolf, A. Schroter, D. Damian, and T. Nguyen, “Predicting build failures using social network analysis on developer communication,” in 2009 IEEE 31st International Conference on Software Engineering.   IEEE, 2009, pp. 1–11.
  22. N. Creswick and J. I. Westbrook, “Social network analysis of medication advice-seeking interactions among staff in an australian hospital,” Intl. journal of medical informatics, vol. 79, no. 6, pp. e116–e125, 2010.
  23. X. Zhu, S.-P. Tu, D. Sewell, N. A. Yao, V. Mishra, A. Dow, and C. Banas, “Measuring electronic communication networks in virtual care teams using electronic health records access-log data,” International Journal of Medical Informatics, vol. 128, pp. 46–52, 2019.
  24. A. G. Dunn and J. I. Westbrook, “Interpreting social network metrics in healthcare organisations: a review and guide to validating small networks,” Social science & medicine, vol. 72, no. 7, pp. 1064–1068, 2011.
  25. V. Latora and M. Marchiori, “Efficient behavior of small-world networks,” Physical review letters, vol. 87, no. 19, p. 198701, 2001.
  26. D. S. Callaway, M. E. Newman, S. H. Strogatz, and D. J. Watts, “Network robustness and fragility: Percolation on random graphs,” Physical review letters, vol. 85, no. 25, p. 5468, 2000.
  27. S. P. Borgatti, A. Mehra, D. J. Brass, and G. Labianca, “Network analysis in the social sciences,” science, vol. 323, no. 5916, pp. 892–895, 2009.
  28. S. P. Borgatti, “2-mode concepts in social network analysis,” Encyclopedia of complexity and system science, vol. 6, pp. 8279–8291, 2009.
  29. T. Fujiwara, J. Zhao, F. Chen, Y. Yu, and K.-L. Ma, “Network comparison with interpretable contrastive network representation learning,” J Data Science, Statistics, and Visualisation, vol. 2, no. 5, 2022.
  30. J. Y. Zou, D. J. Hsu, D. C. Parkes, and R. P. Adams, “Contrastive learning using spectral methods,” Proc. NIPS, vol. 26, 2013.
  31. H.-Y. Lu, T. Fujiwara, M.-Y. Chang, Y.-c. Fu, A. Ynnerman, and K.-L. Ma, “Visual analytics of multivariate networks with representation learning and composite variable construction,” arXiv:2303.09590, 2023.
  32. D. Kempe, J. Kleinberg, and A. Kumar, “Connectivity and inference problems for temporal networks,” in Proceedings of the thirty-second annual ACM symposium on Theory of computing, 2000, pp. 504–513.
  33. K. L. Cooke and E. Halsey, “The shortest route through a network with time-dependent internodal transit times,” Journal of mathematical analysis and applications, vol. 14, no. 3, pp. 493–498, 1966.
  34. F. Reitz, “A framework for an ego-centered and time-aware visualization of relations in arbitrary data repositories,” arXiv:1009.5183, 2010.
  35. L. Shi, C. Wang, Z. Wen, H. Qu, C. Lin, and Q. Liao, “1.5 d egocentric dynamic network visualization,” IEEE transactions on visualization and computer graphics, vol. 21, no. 5, pp. 624–637, 2014.
  36. Q. Liu, Y. Hu, L. Shi, X. Mu, Y. Zhang, and J. Tang, “Egonetcloud: Event-based egocentric dynamic network visualization,” in 2015 IEEE Conference on Visual Analytics Science and Technology (VAST).   IEEE, 2015, pp. 65–72.
  37. E. Loubier and B. Dousset, “Temporal and relational data representation by graph morphing,” Safety and Reliability for managing Risk (ESREL 2008), Hammamet, vol. 14, no. 02, pp. 2008–16, 2008.
  38. S. Diehl and C. Görg, “Graphs, they are changing: dynamic graph drawing for a sequence of graphs,” in International Symposium on Graph Drawing.   Springer, 2002, pp. 23–31.
  39. P. A. Gloor and Y. Zhao, “Tecflow-a temporal communication flow visualizer for social networks analysis,” in ACM CSCW Workshop on Social Networks, vol. 6, 2004, p. 5.
  40. L. C. Freeman et al., “Centrality in social networks: Conceptual clarification,” Social network: critical concepts in sociology. Londres: Routledge, vol. 1, pp. 238–263, 2002.
  41. M. E. Charlson, P. Pompei, K. L. Ales, and C. R. MacKenzie, “A new method of classifying prognostic comorbidity in longitudinal studies: development and validation,” Journal of chronic diseases, vol. 40, no. 5, pp. 373–383, 1987.
  42. A. Elixhauser, C. Steiner, D. R. Harris, and R. M. Coffey, “Comorbidity measures for use with administrative data,” Medical care, pp. 8–27, 1998.
  43. Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec, “Gnnexplainer: Generating explanations for graph neural networks,” Advances in neural information processing systems, vol. 32, 2019.
  44. M. Vu and M. T. Thai, “Pgm-explainer: Probabilistic graphical model explanations for graph neural networks,” Advances in neural information processing systems, vol. 33, pp. 12 225–12 235, 2020.
  45. S. P. Borgatti and D. S. Halgin, “Analyzing affiliation networks,” The Sage handbook of social network analysis, vol. 1, pp. 417–433, 2011.
  46. L. C. Freeman, “A set of measures of centrality based on betweenness,” Sociometry, pp. 35–41, 1977.

Summary

We haven't generated a summary for this paper yet.