Jafar Hamra

A study of physician collaborations through social network and exponential random graph

BackgroundPhysician collaboration, which evolves among physicians during the course of providing healthcare services to hospitalised patients, has been seen crucial to effective patient outcomes in healthcare organisations and hospitals. This study aims to explore physician collaborations using measures of social network analysis (SNA) and exponential random graph (ERG) model.MethodsBased on the underlying assumption that collaborations evolve among physicians when they visit a common hospitalised patient, this study first proposes an approach to map collaboration network among physicians from the details of their visits to patients. This paper terms this network as physician collaboration network (PCN). Second, SNA measures of degree centralisation, betweenness centralisation and density are used to examine the impact of SNA measures on hospitalisation cost and readmission rate. As a control variable, the impact of patient age on the relation between network measures (i.e. degree centralisation, betweenness centralisation and density) and hospital outcome variables (i.e. hospitalisation cost and readmission rate) are also explored. Finally, ERG models are developed to identify micro-level structural properties of (i) high-cost versus low-cost PCN; and (ii) high-readmission rate versus low-readmission rate PCN. An electronic health insurance claim dataset of a very large Australian health insurance organisation is utilised to construct and explore PCN in this study.ResultsIt is revealed that the density of PCN is positively correlated with hospitalisation cost and readmission rate. In contrast, betweenness centralisation is found negatively correlated with hospitalisation cost and readmission rate. Degree centralisation shows a negative correlation with readmission rate, but does not show any correlation with hospitalisation cost. Patient age does not have any impact for the relation of SNA measures with hospitalisation cost and hospital readmission rate. The 2-star parameter of ERG model has significant impact on hospitalisation cost. Furthermore, it is found that alternative-k-star and alternative-k-two-path parameters of ERG model have impact on readmission rate.ConclusionsCollaboration structures among physicians affect hospitalisation cost and hospital readmission rate. The implications of the findings of this study in terms of their potentiality in developing guidelines to improve the performance of collaborative environments among healthcare professionals within healthcare organisations are discussed in this paper.

Exploring communication networks to understand organizational crisis using exponential random graph models

In recent social network studies, exponential random graph (ERG) models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the ERG models and demonstrate its use in modelling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modelling on communication networks, and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of ERG models, we find significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. We also notice that the attribute of hierarchical positions of actors (i.e., high rank versus low rank staff) have impact on the evolution process of networks during crisis. These findings could be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis.

Exponential random graph modeling of communication networks to understand organizational crisis

In recent social network studies, exponential random graph models have been used comprehensively to model global social network structure as a function of their local features. In this study, we describe the exponential random graph models and demonstrate its use in modeling the changing communication network structure at Enron Corporation during the period of its disintegration. We illustrate the modeling on communication networks and provide a new way of classifying networks and their performance based on the occurrence of their local features. Among several micro-level structures of exponential random graph models, we found significant variation in the appearance of A2P (Alternating k-two-paths) network structure in the communication network during crisis period and non-crisis period. This finding could also be used in analyzing communication networks of dynamic project groups and their adaptation process during crisis which could lead to an improved understanding how communications network evolve and adapt during crisis.

Effects of networks on learning during emergency events

Purpose – This paper aims to explore the relationship between learning and the social networks employed within the context of emergency management. It hypothesises, using social network theory as a framework for analysis, that changes to interconnectedness between actors are implicated in the potential for those actors to learn and improvise in dynamically changing and emergent conditions.Design/methodology/approach – To test the hypotheses, survey data were investigated which were collected as part of a research study with the support of the Australian Bushfire Co‐operative Research Centre (CRC). This survey was completed by experienced personnel reflecting on a number of indicators in an emergency event.Findings – Results show that increases in actors’ involvement within the social emergency management network influences the ability of those actors to engage in learning‐related work activity. The paper infers that by developing learning related resources within the context of their social interactions t...

Network effects on learning during emergency events

Understanding the factors that enhance or impede learning of individuals is instrumental in achieving organizational performance goals. In this study, the effect of social network structures on the learning attitudes of emergency personnel during an emergency event was investigated. On the basis of a social influence model of learning, a theoretical framework has been proposed to investigate the effects of network structure on learning outcomes of bushfire incident management teams. To test our framework, we investigated social network data, which were extracted from the transcripts of the 2009 Victorian Bushfires Royal Commission report. Empirical results suggest that a network structure of emergency personnel can be identified, which plays a key role in the ability of those actors to engage in learning-related work activity, allowing them to adapt and improvise in complex emergency events. By presenting a model of learning-related work activity, based on a social network analysis of its structure, emergency staff members can strengthen their capacity to be flexible and adaptable.

Exponential random graph modeling of emergency collaboration networks

Effective response to bushfires requires collaboration involving a set of interdependent complex tasks that need to be carried out in a synergistic manner. Improved response to bushfires has been attributed to how effective different emergency management agencies carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among emergency management personnel on the effective outcome in delivering improved bushfire response. There are, however, very few systematic empirical studies with a focus on the effect of collaboration networks among emergency management personnel and bushfire response. Given that collaboration evolves among emergency management personnel when they communicate, in this study, we first propose an approach to map the collaboration network among emergency management personnel. Then, we use Exponential Random Graph (ERG) models to explore the micro-level network structures of emergency management networks and their impact on performance. ERG Models are probabilistic models presented by locally determined explanatory variables and that can effectively identify structural properties of networks. 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 2009 Royal Commission Report dataset, we construct and model emergency management response networks. We notice that alternative-k-star, and alternative-k-two-path parameters of ERG have impact on bushfire response. The findings of this study may be utilized by emergency managers or administrators for developing an emergency practice culture to optimize response within an emergency management context.

Social network analysis of learning teams during emergency events

Understanding factors that enhance or diminish learning and adaptability levels of individuals is instrumental in achieving individual and organizational performance goals. In this study, the effect of social network structure on learning attitudes of emergency personnel during an emergency event is investigated. Based on social network theories, and the social influence model of learning, a theoretical framework is proposed to investigate the effects of network structure on learning outcome of bushfire coordinating teams. To test our framework, we investigate social network data which has been extracted from the transcripts of the 2009 Victorian Bushfires Royal Commission report. Empirical results suggest that network structure of emergency personnel play a crucial role in the ability of those actors to engage in learning-related work activity. We infer that this will mean that these actors are better able to adapt and improvise in complex emergency events. We suggest that social network analysis may have a valuable part to play in the study of emergency events. By presenting a model of learning-related work activity, based on network structure, personnel within emergency services organizations can strengthen their capacity to be flexible and adaptable.

Coordination Challenges for Global Disease Outbreaks Security

We are beginning to observe the management challenges for global security dealing with disease outbreaks. The rapid movement and migration of the world population as well as the transfer of resources across different regions provides immense challenges for developing improved surveillance systems for effectively coordinating our efforts to global security challenges faced by disease outbreaks. There are considerable efforts to collect case data during disease outbreaks (DO) from the spread of infections perspective. However, research for developing reliable framework for the collection of inter-organizational coordination (IOC) response data is lacking to date. Our objective here is to introduce a corpus that can be used in designing and capturing IOC data for facilitating empirical analysis of evolution of networks in a coordinated effort to DO. Since this is new domain, we introduce the main qualitative questions that will discover the characteristics of such coordination and propose a corpus or schema that can be used to classify the quantitative data collection and preparation for further empirical analysis.