In order to prompt interventions to improve birth care, quality indicators should be accurately and continuously available. This project will initiate a quality cycle in integral birth care, centered around a maternity dashboard (MDB) that provides clinical quality indicators (e.g. percentage of caesarean sections). The MDB can be used to identify where birth care can be improved. In this proposed research, the technical aspects, such as data validity, analysis and visualization, need to be addressed in a collaboration between healthcare professionals and data scientists. The PhD candidate is required to
- Collect data from users (questionnaires, focus groups), and from the Perined national registration, which will also be used for performing the MDB audits.
- Perform Data analysis in close collaboration with Perined using both advanced statistical models and data mining algorithms.
- Conduct retrospective study to investigate electronic medical record data (Máxima MC) to integrate CTG (FM30) data in the MDB and use state-of-art AI techniques to improve interpretation of CTG for perinatal care providers.
- Improve data quality and aggregation from multiple sources. Implement a reliable data infrastructure using Perined data and PROMs, including providing tools to facilitate correcting errors faster.
- Develop an MDB that provide continuous availability of quality indicators: analyze and display the data; visual information on perinatal outcomes for the total population as well as subgroups of women or babies (e.g. depending on care path and 'vulnerable' groups of women).
You will contribute to the PICASSO project of Eindhoven MedTech Innovation Center (e/MTIC), in collaboration with Máxima MC and Philips Research and will work in a team of engineers and clinicians at all partner locations in Eindhoven/Veldhoven.