Top

The European Journal of Health Economics

Published in:

01-06-2012 | Original Paper

Socioeconomic analysis of patient-centric networks: effects of patients and hospitals’ characteristics and network structure on hospitalization costs

Authors: Alireza Abbasi, Shahadat Uddin, Liaquat Hossain

Published in: The European Journal of Health Economics | Issue 3/2012

Abstract

Improving operations and delivery of cost-effective healthcare services is considered to be an important area of investigation due to the challenges in allocation of resources in meeting the increasing cost of health care for the twenty-first century. To date, appropriate mechanisms for systematic evaluation of hospital operations and its impact of the delivery of cost-effective healthcare services are lacking. This is, perhaps, the first study, which focuses on using large insurance claims data to develop a social network-based model for exploring the effect of patient–doctor tie strength and patient socio-demographic factors for exploring the social structure of operations and delivery of cost-effective healthcare services. We suggest that delivery of cost-effective healthcare services and operation is embedded within the social structure of hospitals. By exploring the mode of hospital operations in terms of their patient-centric care network, we are able to develop a better understanding of the operation and delivery of cost-effective healthcare services.

National Health Expenditure Data. OECD health data 2010: Statistics and indicators (2010)

Forrest, G., Roque, J., Dawodu, S.: Decreasing length of stay after total joint arthroplasty: effect on referrals to rehabilitation units. Am. Arch. Rehabil. Ther. 80(2), 192–194 (1999)CrossRef

Collins, T., et al.: Risk factors for prolonged length of stay after major elective surgery. Ann. Surg. 230, p. 251 (1999)

Sidiropoulos, A., Katsaros, D., Manolopoulos, Y.: Generalized Hirsch h-index for disclosing latent facts in citation networks. Scientometrics 72(2), 253–280 (2007)CrossRef

Iezzoni, L.: The risks of risk adjustment. JAMA 278(19), 1600–1607 (1997)PubMedCrossRef

Forrest, G., et al.: Factors affecting length of stay and need for rehabilitation after hip and knee arthroplasty* 1. J. Arthroplasty 13(2), 186–190 (1998)PubMedCrossRef

Burns, L., Wholey, D.: The effects of patient, hospital, and physician characteristics on length of stay and mortality. Med. Care 29(3), 251–271 (1991)PubMedCrossRef

O’Brien, S.: An outcome study on average length of stay following total hip and knee replacement. J. Orthop. Nurs. 6(3), 161–169 (2002)CrossRef

Ivory, J., et al.: Total hip replacement. BMJ 3(2), p. 114 (1994)

10.

Lee, A.H., Fung, W.K., Fu, B.: Analyzing hospital length of stay: mean or median regression? Med. Care 41(5), p. 681 (2003)

11.

Chung, KKS., Hossain, L., Davis, J.: Exploring sociocentric and egocentric approaches for social network analysis. In: Proceedings of the international conference on knowledge management in Asia Pacific. Victoria University, Wellington, New Zealand (2005)

12.

Keith, M., Demirkan, H., Goul, M.: How does collaborative group technology influence social network structure?. In: Proceedings of the 41st IEEE Annual Hawaii International Conference on System Sciences, Waikoloa, p. 57 (2008)

13.

Keith, M., Demirkan, H., Goul, M.: Coordination network analysis: a research framework for studying the organizational impacts of service-orientation in business intelligence. In: Proceedings of the 41st IEEE Annual Hawaii International Conference on System Sciences, Waikoloa (2007)

14.

Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, Cambridge (1994)

15.

Mullen, B., Johnson, C., Salas, E.: Effects of communication network structure: Components of positional centrality. Soc. Netw. 13(2), 169–185 (1991)CrossRef

16.

Brandes, U., Fleischer, D.: Centrality measures based on current flow. Springer, Heidelberg (2005)

17.

Chung, K., Hossain, L., Davis, J.: Social networks and ICT correlates to individual performance. In: Applications of Social Network Analysis. University of Zurich, Switzerland (2005)

18.

Everett, M., Borgatti, S.: The centrality of groups and classes. J. Math. Sociol. 23, 181–202 (1999)CrossRef

19.

Granovetter, M.S.: The strength of weak ties. Am. J. sociol. 78(6) p. 1360 (1973)

20.

Krackhardt, D.: The strength of strong ties: the importance of philos in organizations. Networks and organizations: Structure, form, and action, pp. 216–239 (1992)

21.

Levin, D.Z., Cross R.: The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer. Manag. sci., 1477–1490 (2004)

22.

Hansen, M.T.: The search-transfer problem: the role of weak ties in sharing knowledge across organization subunits. Adm. Sci. Q. 44(1), 82–85 (1999)CrossRef

23.

Reagans, R.. Zuckerman, E.: Networks, diversity, and productivity: the social capital of corporate R&D teams. Organ. Sci. 502–517 (2001)

24.

Luke, D., Harris, J.: Network analysis in public health: history, methods, and applications. Pub. Health 28 (2007)

25.

Corteville, L., Sun, M.: An interorganizational social network analysis of the michigan diabetes outreach networks, Michigan department of community health report (2009)

26.

Boyer, L., et al.: A social network analysis of healthcare professional relationships in a French hospital. Int. J. Health Care Qual. Assur. 23(5), 460–469 (2010)PubMedCrossRef

27.

Horwitz, S., Morgenstern, H., Berkman, L.: The impact of social stressors and social networks on pediatric medical care use. Med. Care 23(8), 946–959 (1985)PubMedCrossRef

28.

St Clair, P., et al.: Social network structure and prenatal care utilization. Med. Care, 27(8) p. 823 (1989)

29.

Heckathorn, D., et al.: AIDS and social networks: HIV prevention through network mobilization. Sociol. Focus 32(2), 159–179 (1999)

30.

Anderson, J., Jay, S.: The diffusion of medical technology: social network analysis and policy research. Sociol. Q. 26(1), 49–64 (1985)CrossRef

31.

Uddin, M., Hossain, L.: Social networks enabled coordination model for cost management of patient hospital admissions. J. Healthcare Qual. (in press)

32.

Vincent, H., et al.: Sex and age effects on outcomes of total hip arthroplasty after inpatient rehabilitation. Arch. Phys. Med. Rehabil. 87(4), 461–467 (2006)PubMedCrossRef

33.

Motulsky, H.: Analyzing data with graphpad prism. Graphpad software San Diego, CA (1999)

34.

Leape, L.: Error in medicine. Jama. 272(23), 1851 (1994)

35.

Degenne, A., Forsé, M.: Introducing social networks. Sage Publications Ltd, Thousand Oaks (1999)

36.

Scott, J., Social network analysis: a handbook. Sage, Thousand Oaks (1991)

Title: Socioeconomic analysis of patient-centric networks: effects of patients and hospitals’ characteristics and network structure on hospitalization costs
Authors: Alireza Abbasi
Shahadat Uddin
Liaquat Hossain
Publication date: 01-06-2012
Publisher: Springer-Verlag
Published in: The European Journal of Health Economics / Issue 3/2012
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-011-0303-5

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Socioeconomic analysis of patient-centric networks: effects of patients and hospitals’ characteristics and network structure on hospitalization costs

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2012

Smoking bans and the secondhand smoking problem: an economic analysis

The demand for sports and exercise: results from an illustrative survey

Impact of healthcare reforms on out-of-pocket health expenditures in Turkey for public insurees

Patient socioeconomic determinants for the choice of the cheapest molecule within a cluster: evidence from Belgian prescription data

I feel good! Gender differences and reporting heterogeneity in self-assessed health

Patient socioeconomic determinants of the choice of generic versus brand name drugs in the context of a reference price system: evidence from Belgian prescription data