ABSTRACT
Patient specific forecasting tools are an area of active research and very much seen as a necessary tool for future improvements in healthcare. In order to succeed with decision making tools, fine-grained data are required to build models relevant and valid at an individual level. Location and assemble of data to build such tools is not trivial. Even then the ability to perform accurate predictions is not guaranteed. This study outlines a method to integrate existing data sources to base predictions on. A key benefit of the method is the minimal extra burden on the patient and the healthcare system. A pilot study is performed to implement the system architecture on data from total knee arthroplasty. Output from the system is presented using web technologies. In doing so, the viability of the method to implement a tool for the prediction of pre-operative and post-operative follow-up is demonstrated. Future steps will include testing and deployment of the system.
