July 23, 2024

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Knowledge exchange on healthcare big data analytics

4 min read

Utilising existing healthcare databases has huge potential for improving healthcare services. However, research methods used in existing studies on analyzing healthcare databases for preventive services are limited to parametric methods and the availability of recent non-parametric methods including big-data analytics provide new opportunities for healthcare management researchers to understand diverse social and biographical factors that causes diseases through explorative approaches.

The objective of this project is to facilitate an inter-disciplinary research collaboration through knowledge exchange on the application of big-data analytics to the healthcare management between Brunel University London and Seoul National University (SNU).

More specifically, the collaboration will primarily focus on cancer survivorship and comprehensive community care based on the ongoing projects in each counterpart. SNU team have long been working on social and biological factors of diseases using healthcare databases in Republic of Korea.

On the other hand, having an MBA programme on Healthcare Management in its Business School, Brunel team have been working on healthcare management applying various data analytics methods including big data mining and process mining. Through the knowledge exchange collaboration, the SNU team is expected to have access to NHS data in the UK and innovative data analytics methods while the Brunel team to Healthcare Database in Republic of Korea as well as cases developed by SNU team to be used in their MBA and research group seminars.

For the sustainable collaboration between two parties, the proposed collaboration will develop a joint funding proposal that exploit innovative data analytics for healthcare management through the joint seminars. The specific objectives of the collaboration program include

  • to broaden knowledge of scientists of both parties including early stage researchers on non-parametric big data analytics methods for healthcare management through at least 10 joint seminars based on conference calls and two onsite symposia;
  • to secure access to healthcare related databases for partners in hosting countries as a part of knowledge exchange seminars; and
  • to ensure long-term collaboration by developing a joint funding proposal as the outcomes of the joint seminars.

The proposed collaboration is expected to make following scientific impacts. Firstly, it has methodological contribution in terms of Big data analytics on healthcare data. The development of electronic medical records and healthcare claim data warehouse requires new data mining approaches over traditional statistical approaches to incorporate multidimensional complex data and identifying patterns in sequential events during treatment. Due to limited ability to deal with a prior knowledge, popular approaches such as machine learning and Bayesian approaches cannot answer a causal question. Big data analytics approaches used on social sciences can provide an insight into how we can deliver meaningful results from healthcare big data.

Secondly, it has contribution to improving community care in terms of domain knowledge. Community-based care focusing on improving access and uptake of healthcare service to specific groups (e.g. ethnic minority, elderly, deprived) requires different approaches than interventions conducted in a hospital setting in terms of the nature of the study hypothesis, eligibility, compliance of the participants, etc.

Healthcare systems of the UK (National Health Service) and Korea (Korea National Health Insurance Services) shares common interests including expanding prevention services, improving the accessibility of care, and developing effective healthcare delivery setting. This collaboration will provide an opportunity to share experiences of each country and to develop new strategies for improving community-based care.

Meet the Principal Investigator(s) for the project

Professor Habin Lee

Professor Habin Lee – I am Chair in Digital Business Analytics at Brunel Business School and have received a PhD in Management Engineering and MEng in Management Science from KAIST (Korea Advanced Institute of Science and Technology). I am a Divisional Lead, Innovation & Sustainability that leads MSc in Busines Intelligence & Digital Marketing, MSc in Global Supply Chain Management, and BSc in Business and Management programmes. I am sitting at the Executive Board as the representative of Divisional Leads within the school. Previously, I gained industrial experience from BT Group CTO for 6 years before joining Brunel. I secured more than £3 millions of research grants from MRC, ESRC, EU FP7, H2020 and other international funding bodies to Brunel University London. I coordinated international research consortia including UbiPOL, CEES, MINI-CHIP, and GREENDC. My research interests include governance mechanisms in online communities and supply chain networks in public and private sectors applying computational big data analytics and process theories. The excellence of my research has been awarded by international institutes such as  AIS (Association for Information Systems), WfMC (Workflow Management Coalition) and IET (The Institutes of Engineering and Technology) as well as BT Group. I published articles on international journals including Management Science, Journal of AIS, European Journal of Operational Research, IEEE Tr Mobile Computing, IEEE Pervasive Computing, Information Systems Management, and Government Information Quarterly among others. I have strong connections within the industry, providing paid consulting services to several companies including BT Group. 

Related Research Group(s)


Operations and Information Systems Management – We specialize in responsible and sustainable operation management exploring information systems, operations research, management science, and general management and strategic management knowledge and approaches across public and private industry sectors

Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.

Project last modified 22/11/2023


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