INSIGHT

Investigating the potentials of patient generated data for CVD prevention and rehabilitation

What is INSIGHT? 

INSIGHT – INvestigating the potentialS of PatIent Generated data for CVD Prevention and ReHabiliTation – explores the integration of patient-generated health data (PGHD) into clinical practices, focusing on cardiovascular rehabilitation. The project includes three key phases:  

1. Reviewing PGHD Integration Challenge

A comprehensive 10-year review of challenges in integrating PGHD into healthcare systems from the perspectives of both patients and healthcare professionals. This review resulted in the development of a conceptual workflow model.

2. Physical Activity Planning in Cardiac Rehabilitation

Study 1: Healthy participants collected data from wearable devices and discussed the findings with healthcare providers (HCPs) during physical activity planning sessions.

Study 2: A card-sorting workshop with HCPs identified their specific information needs for supporting physical activity consultations.

Key research questions included:

  • What are the challenges and opportunities for integrating patient generated health data
  • How well are available consumer technologies accepted for collecting PGHD and SDM in PAP?
  • What are HCP needs and perspectives on integrating PGHD into clinical pathways for PAP?

3. Enhancing Data Sensemaking with AI

A follow-up study will explore how artificial intelligence (AI) can support healthcare professionals in interpreting PGHD. This research-through-design approach focuses on the following research question:

  • How do HCPs perceive AI integration for supporting patient generated health data sense-making during PA planning in cardiac rehabilitation?

Further collaborators: 

  • Prof. Dr. Albrecht Schmidt, Ludwig Maximilians University of Munich (LMU)
  • Dr. Devender Kumar, University of Southern Denmark (SDU)

Related publications: 

VS Pakianathan, P. (2024). Human Centered Approach for Designing Data Enabled Tools: Exploring the potential of patient generated data for CVD Prevention and Rehabilitation. In Mensch und Computer 2024-Workshopband (pp. 10-18420). Gesellschaft für Informatik eV. https://doi.org/10.18420/muc2024-mci-dc-183  

VS Pakianathan, P., Fatehi, A., & Smeddinck, J. (2024). Towards AI Augmented Personalized Data Sensemaking. In Mensch und Computer 2024-Workshopband (pp. 10-18420). Gesellschaft für Informatik eV. https://doi.org/10.18420/muc2024-mci-ws05-182  

A participant uses a mobile app to share self-tracked health data from wearables with a healthcare provider during a physical activity planning session.

Project Lead

Pavithren V S Pakianathan, M. Eng.

Pre-Doc

cnivguera.cnxvnanguna@quc.yot.np.ng