Decision support for personalized chronic pain care
Current shared decision making tools are generic, without possibilities for personalization. In this project, we address the problem of how to provide the patient and his physician with relevant, valid and patient adapted decision alternatives with realistic probabilities for outcomes, side effects and adverse events.
Prepare a solution that provide the chronic pain patient and his physician personalized decision alternatives, with evidence-based probabilities for outcomes and side effects.
The solution will allow real-time construction of decision models exploiting personalized virtual registries obtained from electronic health records (EHRs).
To demonstrate deliverability, we provide a preliminary proof-of-concept, demonstrating feasibility of this technique using real EHR data from primary care.
The method we will use to achieve the goal of this work package is to iteratively performing the steps of;
- including new EHR data elements (both specialist and primary care) that can be used for selection of patient into the virtual dataset
- including outcome data (beneficial and adverse effects)
- implementing distributed matching methods
- evaluation of performance and bias.
Patient representatives from Troms Fibromyalgiforening will be involved in the decision on which measures to include, ensuring that the outcome data is relevant to the experience of individuals suffering from chronic pain.