Healthcare Quality & Sustainability
Healthcare quality and accessibility are under increasing pressure and clinical decisions are often not tailored to individuals. Personalizing diagnostics and treatment requires reliable prognostic estimates of expected health outcomes, as well as interpreting these estimates in the context of individual preferences and needs. We enhance decision-making in healthcare, improve patient outcomes, and contribute to future-proof healthcare, while ensuring fairness and ethical responsibility.
Aims
Our research is aimed at enhancing decision-making in Healthcare, improving patient outcomes (Quality) and contributing to future-proof healthcare (Sustainability), while ensuring fairness and ethical responsibility. We improve the quality of healthcare through the evaluation of the benefits and costs of health care interventions. We enable personalized clinical decision-making by developing individualized models to predict disease progression and treatment effects, and perform research to optimize shared decision-making, use of health technologies and the design of care pathways. The application domains of our research encompass, among others, curative interventions, care and treatment for chronic incurable illness, prevention and management of trauma, and AYA (Adolescent and Young Adult) care.
Research priorities
- Health Technology Assessment
- Evaluate health technologies with a comprehensive focus on cost-effectiveness, equity, and system-wide sustainability.
- Patient-Reported Outcomes (PROMs) and Experiences (PREMs)
- Broaden and enhance PROMs and PREMs to more accurately and meaningfully reflect diverse patient outcomes and lived experiences.
- Real-World Outcome Data
- Strengthen the validity and reliability of real-world outcome data by applying advanced causal inference techniques.
- Effective Implementation
- Integrate cutting-edge implementation science to bridge the gap between innovation and practice, ensuring the successful adoption of new models and technologies in real-world healthcare environments.
- Advancing Treatment Effect Prediction
- Develop and validate sophisticated methodologies to accurately forecast individual treatment outcomes, facilitating truly personalized and precision healthcare.
- Shared Decision-Making
- Design and evaluate new models for integrating predictive analytics into patient-clinician dialogues, fostering fair, transparent and patient-centred treatment choices.
- Care Path Design
- Conceptualize and implement innovative care pathway models that proactively address the evolving sustainability and resilience demands of healthcare systems.
- Fairness & Inclusivity
- Systematically integrate considerations of equity, diversity, and cultural competence into the development of predictive models, algorithms, and decision-making tools.
- Appropriate Non-Intervention
- Investigate when refraining from interventions may be an appropriate strategy and how to responsibly integrate this into healthcare decision-making.
Staff
Juanita Haagsma
Associate professor
Juanita Haagsma on LinkedInAgnes van der Heide
Professor
Agnes van der Heide on LinkedInOlga Husson
Associate professor
Olga Husson on LinkedInDavid van Klaveren
Associate professor
David van Klaveren on LinkedInIda Korfage
Associate professor
Ida Korfage on LinkedInMartijn Oude Voshaar
Assistent professor
Martijn Oude Voshaar on LinkedInJudith Rietjens
Professor
Judith Rietjens on LinkedInArianne Stoppelenburg
Assistent professor
Arianne Stoppelenburg on LinkedIn