Projects

Top Sector Life Sciences & Health (LSH) entails a broad scope of disciplines, from pharmaceuticals to medical technology and from healthcare infrastructure to vaccination. To realise its mission – vital citizens in a healthy economy - the Top Sector builds on the strengths of the Dutch LSH sector to address the biggest societal challenges in prevention, cure and care. By funding multidisciplinary public-private partnerships (PPPs) the Top Sector aims to facilitate innovation. Here we give an overview of  a number of funded R&D projects by Top Sector LSH. The page is updated continuously.

Personalised decision support tool in breast cancer care

Personalised decision support tool in breast cancer care

A personalised tool supporting Shared Decision Making (SDM) about adjuvant therapy for breast cancer will be developed. This will be done in a new consortium, connecting the latest advances in prediction modeling and machine learning (Pacmed/IKNL) with the actual healthcare setting in which doctors and patients make decisions (PATIENT+). Cross-over, all activities will be driven by knowledge from the scientific field of decision psychology (VUmc).

Often cited barriers of current tools are: (a) a lack of personalised information (“this does not apply for my patient”); (b) difficulties in risk communication to patients with inadequate health literacy. Recent developments in prediction modelling have created novel opportunities to develop better and more tailored tools. It does, however, raise new questions regarding presentation of information and risk communication, especially to lower health literate patients (one third of Dutch population). It is thus vital for the success of tools that information is presented in user-centered presentation formats. Better informed patients who engage in SDM is known to result in better value care (safer and more cost-effective). 

It is the combination of data science, healthcare practice and decision psychology that makes this project unique. The project consists of five work packages: (1) development of an algorithm predicting personalised 5- and 10-year survival; (2) development of an Application Programming Interface opening up this algorithm and related clinical data/information; (3) Development of a personalised tool; (4) Integration of this tool in three hospitals; (5) Cross-over work package addressing (fundamental) research questions about tailoring/presenting information. Central in all work packages are data from the Netherlands Cancer Registry.

A framework will be developed for supporting next-level personalised SDM, as well as a proof of concept by integrating a self-learning decision support algorithm into an actual tool. In addition, several scientific papers will be written for international peer-reviewed journals.