Structured data

Electronic Health Record (EHR) integration

Traditionally clinical decision support systems (CDSS) have largely ignored the trustworthiness of guidelines being used, and so incorporate recommendations from guidelines on both end of the scale of trustworthiness, using the same methods and visualizations to display them. The difference between strong and weak guidelines (GRADE) are typically not highlighted, and many decision support systems does not give users access to the full rationale behind the advice given. This can lead to clinicians not getting enough information on when to, or not to apply the recommendation, and potentially lead to wrongful- and over-treatment of patients.

Apart from being extremely time consuming to develop and maintain, this approach to CDSS does not fit well with new standards and definitions for trustworthy guidelines (IOM report 2011).

Screenshot shows the idea behind using the guidelines directly as Lightweight decision support in the Electronic Health Records, developed through the PLUGGED-IN project.


Trustworthy guidelines

If the guideline came from MAGICapp, the CDSS could show the full recommendation as a pop-up in their system using our widgets, or pull the needed data and construct its own display of it.

As most guidelines are static documents, published as simple websites or PDF documents, the advice given in CDSS is commonly copied over from the guidelines, which means that when the original guideline updates the advice given in the CDSS is at risk of being outdated. When a guideline is created in MAGICapp, the CDSS can automatically use the always latest content, without having to manually copy paste data.

The algorithms used to trigger the advice is usually not made by the guideline developers themselves, and so the interpretation of eligible patients might be misunderstood. Thresholds that are set in the algorithms to enable inclusions and exclusions are most often not found in the recommendation description or rationale but added as part of the business-logic of the algorithm and implementation. This can lead to a different perception of the applicable target group, than originally stated in the guideline. It can also mislead clinicians into considering narrower or broader populations than initially purposed in the recommendation.

Standard terminology

In MAGIC Guideline developers can describe suggestions for implementation and encode the target population and the interventions with standard terminology.

We propose that CDSS should be directly linked to online living recommendations (e.g from MAGICapp). Any update in the recommendation should either update the CDSS directly, or send an alert to the CDSS owners.

We have developed a feature where you can state useful clinical elements to show alongside a recommendation that is shown in an EHR. Authors state which elements would be useful, and the HER can show it alongside the recommendation if it has this data. The boundary between recommendation and sensitive EHR is not crossed and the EHR are in full control over what content it shows, and how it is shown.

In this way we have developed MAGICapp to support any CDSS.

Standards that raise the bar

The new standards and definitions for trustworthy guidelines (IOM report 2011) raise the bar also for developers of CDSS and provide a strong rationale for using high quality practice guidelines developed with GRADE methodology. We have therefore developed the PLUGGED-IN study to develop and evaluate novel ways of presenting guidelines developed with GRADE methodology within the Electronic Health Records, linked to patient specific data.

Blue in the screenshot signify information from the recommendation, used by the EHR to contextualize content presentation


Open API, integrate with any system

Our API allow any system developer to connect with our platform, to export data or integrations with other platforms. We are part of a HL7 FHIR group that develops standards for transfer of evidence-related data: EBMonFHIR and CPGonFHIR:

We aim to implement this standard when it is mature enough.

Read more information about our API here.


Use your choice of terminologies

Code your PICO, with a multitude of ontologies. We use the Bioportal service from Stanford which give you a direct search into different ontology databases. The search is  type ahead, to help you find the right concepts.

You code the different parts of the PICO question separately. If you have a need for additional ontologies to support an integration, contact us to discuss its implementation.

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You can add coded EHR elements to your recommendations

These elements are double and triple-coded with terms from various international terminologies, in order to fit as many clinical systems as possible. An EHR system can pick up these codes for a recommendation and display the information available for specific patients.

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Interaction between an EHR and a recommendation

The direct interaction between an EHR and a recommendation that MAGICapp is set up for, relies on the presence of structured information in both the recommendation and the EMR and exchange of this information via APIs. This means that any EHR system can make use of the structured information behind a recommendation, to the degree which it can make use of that type of structured information. The EHR system will stay in total control over how, and what information to display, while the range of relevant items is set by the guideline authors.

An EHR with structured drug information in it’s own system, can use the structured drug information in a recommendation to highlight a specific drug on the patients medication list if there, if it was ever there, or offer it to the clinician as a possible order.

Finding and applying the right information

An EHR without structured drug information, might still use the structured drug information in a recommendation to be able to free text search for the drug name in clinical notes

When activating (clicking to look at more information) a recommendation, the EHR are allowed to pick up all the codes and clinical elements from that recommendation to contextualize the view of patient specific information.

The EHR system will also be allowed to send over search terms to narrow down the list of possible recommendations that will fit the activated patient.

This way the two systems, guideline platform and EHR system, can help each other contextualize their information to aid their clinicians in finding and applying the right information.