Healthcare data analytics, financing and risk management

The VheP team has expertise in healthcare data analytics and risk management. Examples of the team’s work in this sphere include understanding patterns of prevalence of chronic conditions such as diabetes or hypertension, optimizing operating room use, calculating insurance premiums, or budgeting more accurate government subsidies, among others.

Overview

Health sectors across the globe are facing increasing challenges, such as aging, increases in the prevalence of chronic conditions, and mounting use and preferences towards new technologies. Governments, insurers and providers are being pressured to make better use of their resources while meeting their populations needs. Data analytics and risk management has immense potential in addressing these challenges. Examples of the VheP team’s work in this sphere include understanding patterns of prevalence of chronic conditions such as diabetes or hypertension, optimizing operating room use, calculating insurance premiums, or budgeting more accurate government subsidies, among others.

Research Focus

There are many major industries which use data analytics in their daily operations to conduct their business. The health sector, however, is behind and fails to make the best use of the vast data collected both routinely and through research. VheP aims to join the pioneers within healthcare data analytics and combine the expertise of its data scientists and heath researchers to use advanced statistics, machine learning and big data to bring the healthcare sector to the front of this space and ensure efficient use of resources and optimal patient outcomes. It will achieve this through the following streams of research:

Data Analytics

There is a huge amount of data collected both routinely and through research practices within the healthcare sector. However this data is often very complex; covering multiple large databases with redundant and missing data. Using this data effectively requires sophisticated analyses such as machine learning, data linking and complex modelling to extract the key information. When successful these techniques allow improved optimisation, efficiency and patient outcomes across both the entire health system and its individual components.

Regulatory and institutional frameworks

As healthcare systems and financing moves towards a more value based approach, and as the use of data analytics and shared big data become more prevalent, proper risk management within healthcare becomes increasingly important. Changes to institutional frameworks, hospital expansions or mergers, more integrated care across the community and so on all create risks, both financially and to patient care. Careful consideration and analysis of these risks is crucial to ensuring healthcare and the system continue to function optimally and efficiently.