Contribution of the Study

This thesis explores a research problem that covers different fields, primarily - data science, network science and healthcare. To address the research problem, we develop a framework that has a theoretical and methodological contribution towards its related fields. Also, the outcome of the research can be potentially implemented into a predictive tool that can be used by different stakeholders in the healthcare industry. Considering these, we describe below the possible contribution of this thesis in three different contexts.

1.5.1 Theoretical contribution

This thesis proposes a framework that utilises graph theory and social network measures to predict the likelihood of chronic diseases developing in a patient. The type of healthcare data that we are using in this research is administrative data, which has rarely been used in previous research on chronic disease risk prediction. Therefore, this research is likely to contribute to the scientific community by showing the potential for the use of administrative data in chronic risk prediction.

The theoretical contribution of the research is likely to come from the methods and network based measures that we have proposed in the framework. These methods and measures can give insights about chronic disease risk and progression from health data.

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For example, in the first part of the framework—‘understanding the progression of chronic disease using administrative data’—we have proposed a network formulation to representation of chronic disease progression. The framework shows that the diseases that occur as comorbidities of chronic disease (i.e., diabetes) have a networked relation that differs from the network of diseases of non-chronic patients. In the second part of the framework, we have proposed mathematical formulation of several new scoring measures (i.e., risk factors) derived from network theory and social network analysis (SNA). These new measures quantify the risk of chronic disease by looking at network- based similarities between a test patient’s health trajectory and the baseline network of the chronic disease. The implementation of the framework in the type 2 diabetes context has shown that some of these network-based measures are significant in predicting the risk for test patients. Therefore, the framework as a whole uniquely shows the networked structure of chronic disease comorbidities and the potential to use this structure for prediction using graph theory. These sets of concepts that the research has revealed are the primary theoretical contributions from the research.

1.5.2 Methodological contribution

The present research framework is more inclined to exploratory and analytical methods than to generating theories. Therefore, the significant contribution of this research will be its methodological contribution. The full methodological framework is divided into two steps, each of which introduces new methods required for analysis. For some parts, the framework proposes alternate methods (i.e., more than one), which are run in turns as part of the exploratory analysis. Later on, the prediction performances of these different combinations of methods are compared to understand which framework workflow, in terms of choice of methods, yields the maximum accuracy. This design is adopted to keep the framework as flexible and generic as possible, because administrative datasets may differ significantly in their properties based on the source organisation and country. Also, the context relevant to the chronic disease under investigation may vary. Therefore, the framework and underlying components should be adaptable to different contexts.

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1.5.3 Knowledge translation for the health policy maker

The outcomes of this research should be of interest to several stakeholder groups connected to the healthcare industry. The research likely will offer little to physicians in terms of clinical diagnosis, as the input data for this research are generated once the physicians have completed the diagnostic procedures and evaluated the health condition. Rather, the contribution of this research is more targeted at policy makers working in government health organisations such as the AIHW, international organisations such as the WHO, or in public or private health funds.

The outcomes of the research and the knowledge generated from them can be directly translated into creating suitable risk assessment tools for the healthcare industries. One of the major aims of stakeholders and policy makers in these industries is to formulate health policies that can deliver the best possible health outcomes in addressing the burden of chronic disease and an ageing population. This aim is aligned with our research aim of understanding chronic disease progression. Accordingly, the network analysis methods presented in this research for analysing and visualising administrative health data have the potential to facilitate evidence-based decision making for stakeholders in the healthcare sector. The analytics can reveal trends among various comorbidities associated with a particular chronic disease for a targeted population (e.g., members of a health fund); for example, they can show which diseases occur more commonly with type 2 diabetes and their relative prevalence, and they can reveal patterns of disease progression that can help the analyst to forecast the burden of diseases that may ensue in the near future. These insights should help tremendously in formulating policies for resource allocation and budget planning. The first part of our research framework— ‘understanding the health trajectory of chronic disease patients’—is focused on revealing these insights.

The most important and directly translatable contribution for policy makers should come from the second part of our research framework. This part of the framework is aimed at predicting the likelihood of chronic disease for a given patient. As per our direct

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discussions with the private and public health funds and other interest groups before undertaking the research and receiving administrative data, it is evident that policy makers always face challenges in tailoring effective policy to different populations based on age, vulnerability, demographic condition, health status and other factors. As they formulate policies for the delivery of cost-effective and high quality care, they face several conflicting goals that need to be balanced. For example, to deliver high quality healthcare to consumers, they must allocate sufficient financial, medical and human resources and services. These resources and services are also scarce and exhaustive as the development and supply of health workforce and resources takes a lot of time, budget and pre- planning. Thus, it is a big challenge to plan, supply and distribute limited health resources in a comprehensive, efficient and transparent manner (Armstrong, et al., 2007). Further, market competition and financial models play a role in minimising resource allocation while achieving a balance between ensuring maximum turn-over for the stakeholders and providing sufficient quality of care to the consumers. One potential way to ensure this is to identify high-risk chronic disease patients in advance and plan preventive health management strategies for those high-risk groups. These can potentially reduce the future cost of accessing providers and improve quality of life for the patients, thus achieving a win-win situation for all. This is exactly the aim of the second part of our research.