Medical Knowledge and Decision Support

We advance “Medical Knowledge and Decision Support” with data science and artificial intelligence, and qualitatively explore digital transformation in the clinic.

The overarching objectives of the research of the Medical Knowledge and Decision Support group are to implement innovative data science approaches, accelerate discovery, translation and integration of evidence into clinics, and to align technological progress with the needs of clinicians.

Methods

Our research draws methodologically from data science, bioinformatics, computer science, and psychology. We build ontologies and an associated ecosystem for logic-based automation. We apply and develop predictive models using AI/ML, particularly LLMs and neuro-symbolic approaches. We apply mathematical approaches including constraint-based modelling. In addition, we use qualitative approaches to study human perspectives.

Use cases

Use Cases

We address a wide range of application use cases across our projects, including the discovery and prediction of functions and regulatory factors, biomedical and clinical evidence synthesis and automation, pre-trained large language and multi-modal model evaluation, extraction of data from clinical notes for predictive modelling, decision support, medical question answering, and interpretability and explanations for AI applications.

Clinical Areas

Our projects span a range of biomedical and clinical domains, including fundamental metabolism and metabolomics; drug discovery and molecular property prediction; RNA biology and regulatory interactions; oncology and personalised cancer biology; behavioural science and health psychology; mental health including anxiety, depression and psychosis; ophthalmology; infectious diseases; and physiotherapy.

Learn more about our projects

Contact and Collaboration

If you are interested in partnering with us, have an idea you’d like to discuss, or simply want to get in touch, we would love to hear from you! 

Please reach out via email to: janna.hastings@unisg.ch or janna.hastings@uzh.ch

School of Medicine
University of St.Gallen (HSG)
St. Jakob-Strasse 21
9000 St.Gallen, Switzerland

Institut for Implementation Science in Health Care (IfIS)
Universität Zürich
Universitätsstrasse 84
8006 Zürich, Switzerland

New study on the use of digital tools in hospitals

In our recent study conducted in Switzerland, we found that 80% of healthcare professionals were frustrated with the digital tools they are using in hospitals.

Despite decades of technological advancements, usability challenges persist. To investigate this issue and clinicians’ general experiences with digital tools, the chair of Medical Knowledge and Decision Support conducted in-depth qualitative interviews with 52 healthcare professionals from 24 medical specialties across 14 Swiss hospitals. 

Our study uncovered key insights into:

  • the use of digital tools in hospitals
  • workflow integration
  • care delivery experiences
  • the current state of digital transformation

Moreover, we identified 6 intriguing paradoxes that might explain the ongoing challenges in hospital digitalization, including

  1. the promise of efficiency vs. the reality of inefficiency
  2. shift from face-to-face to interface
  3. juggling frustration and dedication
  4. the illusion of information access and trust
  5. the complexity and intersection of workfloMediws and care paths
  6. the opportunities and challenges of shadow IT

🔗 Read the full study here!