Patientcentered decision support and research using big data

Important results from the project

The project has largely met the goals as planned. The constellation and draft project agreement for step 2 are in place as are first drafts of requirements for the data entry and clinical decision support applications. A demonstrator for the structured data entry by patient as well as a machine learning prototype for clinical decision support are developed and evaluated. Test application for feeding of a database is done. The project has done a business modelling workshop with output documented in the form of a business model canvas.

Expected long term effects

An iPad app for patient entered information has been developed and evaluated, a smaller paper pilot has been run on real patients, 180 analogue discomfort drawings from actual patients have been digitized and diagnosed by the project developed machine learning algorithms. Users (patients, health care providers, researchers), principals and system suppliers have commented positively on the project, which has strengthened the evidence of the need and that the solution approach leads to higher quality and efficiency in the management of patients with pain in the locomotion system.

Approach and implementation

Good mix of physical and virtual project meetings. There were 4 physical and many virtual project meetings with all project parties as well as multiple meetings with two or more parties to work on the respective packages. Structure and tools around document management worked ok but should be improved for step 2. Great to have external support with app development and e-health strategy as well as collaboration with students for design och evaluation. In general, more time consumed than planned. The formal constellation building started too late.

External links

Projektet införskaffade URL och byggde en enkel hemsida i wordpress.