Health Analytics (HA)

"Improving your health through data analytics"

Scope

The health services industry is going through a big transformation, owing to the advent of digitization and new information collection systems (e.g. patient portals, IoT, cloud computing, big data, and wearables). While advances in medical knowledge result in better diagnosis and more and better treatment solutions, patient-centricity, self-care and integrated care delivery are appearing as new trends. The way health services are delivered is being revolutionized by sharing and integrating large volumes of health data. Health analytics is at the basis of this revolution, whereby health innovation is driven by analyzing, processing and acting on health data.

Vision

In the future, the availability of large volumes of health data is an important asset for health service organizations. Data, controlled by the citizens, are collected ubiquitously throughout the care continuum, connected seamlessly and interpreted within the right context. Data are used to improve health service delivery and to advance medical knowledge, leading to better outcomes while increasing efficiency. For instance data are continuously used to analyze and improve workflows and medical guidelines, thereby providing stronger evidence for best practice solutions. For this purpose, health analytics enables personalized care delivery throughout the care continuum. Using personalized health records citizens are empowered to control their own health better, such that the collected data is of service to the citizens and to the society at large.

Research challenges

  • Collect and integrate health data at the broad scale.
    Semantic interoperability is an important aspect to be considered to integrate health data.
  • Develop data-driven and consistent multi-scale decision-support models.
    Data-driven and consistent multi-scale decision-support models need to be developed to support the whole care continuum, and both citizens and health professionals.
  • Creation of personalization models.
    Personalization models need to be created based on the data in order to personalize and/or customize treatments
  • Develop data analytics solutions to optimize and personalize care workflows.
  • Integrate data analytics into continuous medical improvements for value based healthcare.

Project examples

  • Philips Data Science Flagship - Philips & TU/e
    Continuous personal health – Develop data-driven, predictive solutions for the whole care continuum.
  • BrainBridge Program - Philips, TU/e and Zhejiang University
    Clinical Pathway Analysis – Develop tools to analyze and study the performance of clinical pathways and clinical workflows. Cardiovascular Risk Assessment – Develop an intelligent system for long-term cardiovascular risk assessment and prediction.
  • Gamebus, EIT Digital
    Valorization focused project to stimulate physical, cognitive and social healthy behavior across communities and generations of people.