Data Visualisation in Healthcare Analytics

Currently Analysing Data
Investigation of the Influence of Data Visualisation and Health Analytics on Executive Decision Processes in Healthcare

Background and motivation: The growing interest in Big Data, business analytics and the increased adoption of health information systems and electronic medical records in healthcare lead to an emerging trend of Healthcare analytics, also called health analytics. Health analytics is defined by Healthcare Information and Management Systems Society (HIMSS) as “systematic use of data and related clinical and business (C&B) insights developed through applied analytical disciplines such as statistical, contextual, quantitative, predictive, and cognitive spectrums to drive fact-based decision making for planning, management, measurement and learning”.

Critical building blocks of healthcare analytics are data analytics and visual analytics. Both consist of systematic approaches, techniques and tools, clinical and business resources, used to interpret and analyse high volumes of dynamic patient data, clinical records, financial and other business data, and present findings to various audiences from the clinicians, hospital administration and management, to government and the public. Visual analysis refers to “the science of analytical reasoning facilitated by interactive visual interfaces” (Thomas and Cook, 2006). Healthcare providers increasingly explore various visual and interactive techniques in generating and examining large graphs and charts, interactive visualisations, and 2D/3D visualisation of discrete event simulation (DES) to understand complex and large datasets, identify and connections and trends, model and simulate healthcare events, and communicate and interpret the findings. Expected outcomes include more efficient and effective clinical performance monitoring and improvement, patient flow modelling and management, better patient care quality, safety and efficiency, better support for clinical costing and resource coordination, better planned growth and competitive advantage.

Objective: This project aims to explore the current use and impact of health analytics in general, and the role of data visualisation in particular, on providing support for healthcare decision-making processes.

Approach: Mixed methods, to include a Systematic Literature Review and Expert Interviews.

Expected outcomes:

  • An up-to-date accumulated and aggregated understanding of current approaches to healthcare data analytics and evidence-based impacts of health analytics.
  • An up-to-date accumulated and aggregated understanding of current use and impact of visual analytics

Contributions: The project will examine and synthesise key concepts and findings reported in the literature and voiced by the interviewed experts. It will make use of and contribute to refining the Visual Analytics model presented in Cybulski et al. (2013)

Key references:

  • Chen, H., Chiang, R. H. L. and Storey, V. C. (2012). “Business intelligence and analytics: from big data to big impact.” MIS Q. 36(4): 1165-1188.
  • Mettler, T. and Vimarlund, V. (2009). “Understanding business intelligence in the context of healthcare.” Health Informatics Journal 15(3): 254-264.
  • Fitzgerald, J. A. and Dadich, A. (2009). “Using visual analytics to improve hospital scheduling and patient flow.” J. Theor. Appl. Electron. Commer. Res. 4(2): 20-30.
  • Gunal, M. and Pidd, M. (2010). “Discrete event simulation for performance modelling in health care: a review of the literature.” Journal of Simulation 4: 42 – 51.
  • Cybulski, J., Keller, S., Nguyen, L., Saundage, D. (2013) Creative problem solving in digital space using visual analytics, to appear in Journal of Computers in Human Behaviour.
Team Members:
Emilia Bellucci
Yee Ling Boo
Lemai Nguyyen

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