Name
Abstract | ||
|---|---|---|
The healthcare sector holds large amounts of semantically rich electronic data generated and used by different sections of the health care community. Data analytic techniques such as data mining and predictive modelling are being used to gain new insights into health care costs, performance and quality of care. In this context, social network analysis (SNA) has the unique ability to play a new role in exploring the context and situations that lead to efficient and effective healthcare. In this paper we describe a specific context of private healthcare in Australia and describe our SNA based approach (applied to health insurance claims) to understand the nature of collaboration among doctors treating hospital inpatients and explore the impact of collaboration on cost and quality of care. In particular, we use network analysis to (a) design collaboration models among surgeons, anaesthetists and assistants who work together while treating patients admitted for specific types of treatments (b) identify and extract specific types of network topologies that indicate the way doctors collaborate while treating patients and (c) analyse the impact of these topologies on cost and quality of care provided to those patients. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ASONAM.2014.6921647 | ASONAM |
Keywords | Field | DocType |
health care,data analytic techniques,sna based approach,social network,insurance,health insurance claims,data analysis,care quality,australia,health care costs,network topologies,data mining,collaboration models,social networking (online),costing,healthcare sector,predictive modelling,social network analysis,collaboration,surgery | Health care,Data mining,Electronic data,Private healthcare,Social network,Computer science,Social network analysis,Knowledge management,Network topology,Predictive modelling,Network analysis | Conference |
ISBN | Citations | PageRank |
978-1-4799-5876-4 | 5 | 0.69 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Wang | 1 | 241 | 51.35 |
| Uma Srinivasan | 2 | 10 | 2.15 |
| Shahadat Uddin | 3 | 192 | 21.38 |
| Sanjay Chawla | 4 | 1372 | 105.09 |