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Article: Mapping and modeling of physician collaboration network

TitleMapping and modeling of physician collaboration network
Authors
Issue Date2013
Citation
Statistics in Medicine, 2013, v. 32 n. 20, p. 3539-3551 How to Cite?
AbstractEffective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to the rest 2 nodes), alternative k-star (subset of k nodes in which 1 node is connected to each of other k-1 nodes), and alternative k-2 path (subset of k nodes in which, between a specific pair of nodes, there exists k-2 paths of length 2) parameters of ERG have impact on the hospital readmission rate. Our findings can have implications for healthcare administrators or managers who could potentially improve the practice cultures in their organizations by following these outcomes. © 2013 John Wiley & Sons, Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/194441
ISSN
2015 Impact Factor: 1.533
2015 SCImago Journal Rankings: 1.811
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorUddin, S-
dc.contributor.authorHamra, J-
dc.contributor.authorHossain, L-
dc.date.accessioned2014-01-30T03:32:35Z-
dc.date.available2014-01-30T03:32:35Z-
dc.date.issued2013-
dc.identifier.citationStatistics in Medicine, 2013, v. 32 n. 20, p. 3539-3551-
dc.identifier.issn0277-6715-
dc.identifier.urihttp://hdl.handle.net/10722/194441-
dc.description.abstractEffective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to the rest 2 nodes), alternative k-star (subset of k nodes in which 1 node is connected to each of other k-1 nodes), and alternative k-2 path (subset of k nodes in which, between a specific pair of nodes, there exists k-2 paths of length 2) parameters of ERG have impact on the hospital readmission rate. Our findings can have implications for healthcare administrators or managers who could potentially improve the practice cultures in their organizations by following these outcomes. © 2013 John Wiley & Sons, Ltd.-
dc.languageeng-
dc.relation.ispartofStatistics in Medicine-
dc.titleMapping and modeling of physician collaboration network-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/sim.5770-
dc.identifier.pmid23468249-
dc.identifier.scopuseid_2-s2.0-84882258354-
dc.identifier.volume32-
dc.identifier.issue20-
dc.identifier.spage3539-
dc.identifier.epage3551-
dc.identifier.isiWOS:000323049700011-

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