Project Details
Description
Dr. van Oijen's research group focuses on improving the quality of cancer care by forming and analyzing large datasets, combined with state of the art information technology, including decision support tools and wearable technology. His research can be described in three research topics:
Quality of care
Data from large existing available datasets, such as the Dutch Cancer Registry or the nationwide pathology database PALGA, are studied for treatment patterns, guideline adherence and health outcomes using state of the art statistical methodology. His group is highly involved in linking these datasets and the process of enrichment with patient-reported outcomes and biomaterial for gastrointestinal cancer in the 3P-projects (PACAP, POCOP and PLCRC)
Decision support
This research focuses on translation from results of clinical trials and cohort studies towards implementation in clinical practice. Using Bayesian network meta-analyses, and cost-effectiveness research, input for digital decision support tools is generated. These tools, e.g. IBM Watson for Oncology, are then evaluated in clinical practice in a framework for optimal cancer care.
Wearable technology
Cancer care is mostly focused on in-hospital contacts and services, but most events occur out-of-hospital. Using wearable technology, objective, passively collected, information becomes available about the patient journey. When properly interpreted with user-friendly dashboards, this can result in value-based cancer care.
Quality of care
Data from large existing available datasets, such as the Dutch Cancer Registry or the nationwide pathology database PALGA, are studied for treatment patterns, guideline adherence and health outcomes using state of the art statistical methodology. His group is highly involved in linking these datasets and the process of enrichment with patient-reported outcomes and biomaterial for gastrointestinal cancer in the 3P-projects (PACAP, POCOP and PLCRC)
Decision support
This research focuses on translation from results of clinical trials and cohort studies towards implementation in clinical practice. Using Bayesian network meta-analyses, and cost-effectiveness research, input for digital decision support tools is generated. These tools, e.g. IBM Watson for Oncology, are then evaluated in clinical practice in a framework for optimal cancer care.
Wearable technology
Cancer care is mostly focused on in-hospital contacts and services, but most events occur out-of-hospital. Using wearable technology, objective, passively collected, information becomes available about the patient journey. When properly interpreted with user-friendly dashboards, this can result in value-based cancer care.
| Status | Active |
|---|---|
| Effective start/end date | 31/03/2015 → … |