Improving Shared Decision-Making with Patients in a Vulnerable Position
Shared decision-making (SDM) can be particularly relevant yet challenging for patients with limited health literacy and multimorbidity who visit their general practitioner (GP). These patients often have difficulty understanding complex medical information or expressing what matters to them. They also face multiple treatment options. Laura Vriese, a CAPHRI PhD candidate at the Department of Family Medicine, explores how to make this process more effective and value-driven.
ZonMw Project
Now in the third year of her PhD, Laura is working on a ZonMw-funded project that aims to develop an intervention that supports SDM with patients in a vulnerable position , by focusing on improving the discussion of what matters to patients (values clarification). Her focus lies on practical strategies that help GPs actively involve patients with limited health literacy and multimorbidity in these discussions.
Two Recent Publications
Laura has recently published two key papers related to her research:
Values Clarification as Part of Shared Decision-Making (Health Literacy and Communication Open): This study showed that the degree of SDM was low within patients with limited health literacy and multimorbidity, and that values clarification did often not take place.
- GP Experiences and Needs (Scandinavian Journal of Primary Health Care): This work revealed that GPs view values clarification important yet challenging with patients with limited health literacy, especially when patients adopted a passive role or when expectations conflicted. GPs expressed a need for communication training and prompts for situations where values clarification is challenging.
Laura’s Tip for Fellow PhDs
Recruiting participants can be one of the biggest hurdles early in a PhD journey. Laura shares this advice: “Speak about your study with genuine enthusiasm—this sometimes made all the difference in encouraging people to take part. Let participants know you value their input, and keep them engaged throughout the process. It makes the journey more rewarding for everyone involved.”
Also read
-
UnveilML: Combining Econometrics and Machine Learning to improve the estimation of factors we cannot see
UnveilML combines econometrics and machine learning to better estimate hidden factors such as financial risk, the business cycle, and global warming. By adding long memory, interpretability, and reliable uncertainty estimates, it aims to deliver more trustworthy insights for high-stakes decisions.
-
Five FHML researchers receive Veni grants
From stimulating myelin repair in human brain cells to studying how tumours release proteins that trigger muscle breakdown: five innovative FHML research projects receive a Veni.
-
NWO awards ten Veni grants to promising UM researchers
As many as ten young UM researchers have been awarded a Veni grant worth up to €320,000 from the Netherlands Organization for Scientific Research (NWO).UM news