Clinical Decision Support Tool

There is research published on new prognostic markers on a monthly basis, but few are ever taken into clinical practice, as it is difficult for clinicians to know how to use several prognostic markers together. The DoMore! project will develop a support tool for prognostication in prostate-, colorectal- and lung cancer.

Every cancer type has an established set of specific parameters that aid in the decision-making of a patient’s prognosis based on the treatment regimen options. The number of influential factors is already high, and the clinical decision is exceedingly complex. In addition, the field of molecular biology continues to contribute new markers. The DoMore! project will establish six new digital markers for cancer prognostication in patients with prostate, colorectal and lung cancer. Since we will be adding to the rising number of prognostic markers, it is necessary to inform clinicians on how to weigh the various results. This project will develop a support tool to guide the clinician in his or her decision-making, but will be limited only to prognostication and for use on the three cancer types we will work with in the project: prostate, colorectal and lung. For this, we already have a strong footing having created MedInsight more than a decade ago.

We developed MedInsight to fulfill the need for a platform to safely store and retrieve structured data about patients. It is now home to over 200 unique medical registers, containing over 500,000 unique patients and their medical data, with about 1,000 unique users. Several of these registers contain structured data dating back 25 years and up until today and some even contain the complete history for their specific patient group in Norway. Click here to read more about Medinsight

Additionally, the academic partners and other research groups involved have other ongoing studies on most of the patient cohorts included in this project. Hence, we will get access to a large amount of other data (especially genome analysis) that we will use to paint a more complete picture of each patient and include in our multivariate analysis for each cohort.


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