Clique and Clan Analysis of Patient-Sharing Physician Collaborations

Authors: Shahadat Uddin, Md Ekramul Hossain, Arif Khan

Abstract:

The collaboration among physicians during episodes of care for a hospitalised patient has a significant contribution towards effective health outcome. This research aims at improving this health outcome by analysing the attributes of patient-sharing physician collaboration network (PCN) on hospital data. To accomplish this goal, we present a research framework that explores the impact of several types of attributes (such as clique and clan) of PCN on hospitalisation cost and hospital length of stay. We use electronic health insurance claim dataset to construct and explore PCNs. Each PCN is categorised as ‘low’ and ‘high’ in terms of hospitalisation cost and length of stay. The results from the proposed model show that the clique and clan of PCNs affect the hospitalisation cost and length of stay. The clique and clan of PCNs show the difference between ‘low’ and ‘high’ PCNs in terms of hospitalisation cost and length of stay. The findings and insights from this research can potentially help the healthcare stakeholders to better formulate the policy in order to improve quality of care while reducing cost.

Keywords: Clique, clan, electronic health records, physician collaboration.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2643617

References:

[1] G.-J. De Vreede and R. O. Briggs, "Collaboration engineering: designing repeatable processes for high-value collaborative tasks," in System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005, pp. 17c-17c.
[2] J. Knoben and L. A. Oerlemans, "Proximity and inter‐organizational collaboration: A literature review," International Journal of Management Reviews, vol. 8, pp. 71-89, 2006.
[3] J. Huang, Z. Zhuang, J. Li, and C. L. Giles, "Collaboration over time: characterizing and modeling network evolution," in Proceedings of the 2008 international conference on web search and data mining, 2008, pp. 107-116.
[4] A. Khan, N. Choudhury, S. Uddin, L. Hossain, and L. Baur, "Longitudinal trends in global obesity research and collaboration: a review using bibliometric metadata," Obesity Reviews, vol. 17, pp. 377-385, 2016.
[5] M. K. Ahuja, D. F. Galletta, and K. M. Carley, "Individual centrality and performance in virtual R&D groups: An empirical study," Management science, vol. 49, pp. 21-38, 2003.
[6] X. Liu, J. Bollen, M. L. Nelson, and H. Van de Sompel, "Co-authorship networks in the digital library research community," Information processing & management, vol. 41, pp. 1462-1480, 2005.
[7] M. E. Newman, "The structure of scientific collaboration networks," Proceedings of the national academy of sciences, vol. 98, pp. 404-409, 2001.
[8] M. Sawyer, K. Weeks, C. A. Goeschel, D. A. Thompson, S. M. Berenholtz, J. A. Marsteller, et al., "Using evidence, rigorous measurement, and collaboration to eliminate central catheter-associated bloodstream infections," Critical care medicine, vol. 38, pp. S292-S298, 2010.
[9] S. Uddin, L. Hossain, and M. Kelaher, "Effect of physician collaboration network on hospitalization cost and readmission rate," The European Journal of Public Health, vol. 22, pp. 629-633, 2011.
[10] S. Uddin, A. Khan, and M. Piraveenan, "Administrative claim data to learn about effective healthcare collaboration and coordination through social network," in System Sciences (HICSS), 2015 48th Hawaii International Conference on, 2015, pp. 3105-3114.
[11] S. Uddin and L. Hossain, "Dyad and triad census analysis of crisis communication network," Social Networking, 2013.
[12] S. N. Kalkanis, E. N. Eskandar, B. S. Carter, and F. G. Barker, "Microvascular decompression surgery in the United States, 1996 to 2000: mortality rates, morbidity rates, and the effects of hospital and surgeon volumes," Neurosurgery, vol. 52, pp. 1251-1262, 2003.
[13] M. L. Sylvia, M. Griswold, L. Dunbar, C. M. Boyd, M. Park, and C. Boult, "Guided care: cost and utilization outcomes in a pilot study," Disease Management, vol. 11, pp. 29-36, 2008.
[14] A. Bavelas, "Communication patterns in task‐oriented groups," The Journal of the Acoustical Society of America, vol. 22, pp. 725-730, 1950.
[15] H. Guetzkow and H. A. Simon, "The impact of certain communication nets upon organization and performance in task-oriented groups," Management science, vol. 1, pp. 233-250, 1955.
[16] M. E. Shaw, "Random versus systematic distribution of information in communication nets," Journal of personality, vol. 25, pp. 59-69, 1956.
[17] R. J. Mokken, "Cliques, clubs and clans," Quality & Quantity, vol. 13, pp. 161-173, 1979.
[18] J. Scott, Social network analysis: A handbook. London: Sage Publications Ltd, 2005.
[19] S. Wasserman and K. Faust, Social network analysis: Methods and applications. Cambridge: Cambridge University Press, 2003.
[20] R. D. Luce, "Connectivity and generalized cliques in sociometric group structure," Psychometrika, vol. 15, pp. 169-190, 1950.
[21] R. D. Alba, "A graph‐theoretic definition of a sociometric clique," Journal of Mathematical Sociology, vol. 3, pp. 113-126, 1973.
[22] (2018). MBS Online: Medicare Benefits Schedule. Available: http://www.health.gov.au/mbsonline.
[23] S. P. Borgatti, M. G. Everett, and L. C. Freeman, "Ucinet for Windows: Software for social network analysis," 2002.
[24] T. A. Snijders, G. G. Van de Bunt, and C. E. Steglich, "Introduction to stochastic actor-based models for network dynamics," Social networks, vol. 32, pp. 44-60, 2010.
[25] S. Uddin, J. Hamra, and L. Hossain, "Exploring communication networks to understand organizational crisis using exponential random graph models," Computational and Mathematical Organization Theory, vol. 19, pp. 25-41, 2013.
[26] S. Uddin, L. Hossain, J. Hamra, and A. Alam, "A study of physician collaborations through social network and exponential random graph," BMC health services research, vol. 13, pp. 234-247, 2013.