MAGICapp is an online tool that allows administrators and authors to plan, author and publish guidelines, evidence summaries and decision aids according to the latest international standards for trustworthiness.

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What do we do?

The MAGIC research project is developing a framework, methodology and technological solution for GRADE guidelines. We’re helping facilitate efficient authoring, improved presentation, customized processes for adaptation and dynamic updating of content. We hope to increase shared decision making, expand the use of guidelines throughout the medical field, and greatly improve the flow of data at every stage of the medical knowledge ecosystem.

Current Issues

How we will solve it

Efficient guideline authoring, adaptation and dynamic updating

A guideline authoring and publication platform that facilitate guideline development through methodological and technological assistance, built as a modern, user-friendly collaborative tool. Learn More >

Optimal presentation formats of guideline content

Multilayered guideline content that meet clinicians´information needs at the point of care, but still enables them to go to deeper layers when more information is needed. Learn More >

Efficient guideline dissemination

Structured and tagged content for integration in Web guidelines, EMRs and devices such as applications for tablets and smartphones. Learn More >

Integration of guidelines in Electronic Health Records (EHRs)

Both by using recommendations directly as decision support linked to patient-specific data in the EHR and by giving Computerized decision support systems (CDSS) the opportunity to meet the current standards for trustworthy guidelines (e.g. both strong and weak recommendations). Learn More >

Shared decision-making at the point of care

Electronic decision aids linked to recommendations in guidelines, to be used by patients and clinicians together in a consultation. Learn More >

Research Projects


Use guidelines directly as decision support in electronic medical records. We have developed a new strategy using APIs, to enable presentation of recommendations linked to individual patient data.

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We have developed a Novel Adaptation Process and Taxonomy for modifying individual recommendations, customized for clinical practice guidelines made using the GRADE methodology. The framework is soon to be inbedded within the MAGICapp.

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We seek a) to develop a framework for the production of generic decision aids directly for recommendations in guidelines and systematic reviews using GRADE, b) to design a set of interactive and adaptable presentation formats for these decision aids to facilitate shared decision making in the clinical encounter, and c) to test the feasibility of an automated production of decision aids from electronically published guidelines using the MAGIC app.

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Dynamic updating of trustworthy guidelines by monitoring the constant stream of new evidence being published.

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