Terminologies, API and structured data
Traditional decision support systems have largely ignored the trustworthiness of guidelines being used and still typically develop rules and algorithms. 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). These new standards 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.
Screenshots below shows the idea behind using the guidelines directly as Lightweight decision support in the Electronic Health Records, developed through the PLUGGED-IN project.
(blue in the screenshots below signify information from the recommendation, used by the EMR to contextualize content presentation)
Our API allow any system developer to connect with our platform, to export data or integrations with other platforms.
Do you have suggestions for add-on features or want to collaborate on integrations? We would be thrilled to hear from you.
Read more information about our API here
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. It’s type ahead to help you find the right codes.
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.
You can also 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.
Our electronic authoring and publication platform (MAGICapp) is structured to facilitate Decision support and EHR integration through functions that allow connecting structured clinical questions (PICOs) to ontologies and taxonomies (e.g. ICD-10, SNOMED-CT and MeSH). These clinical questions are again connected to the recommendations they support.
Behind each recommendation you will find one or more or PICO questions, and therefor also find all the structured terminology the authors have added to these PICOs. The authors can also add relevant clinical variables to a recommendation directly, like laboratory tests, observations, disease groups, medications as supporting information. These clinical variables are not to be seen as exclusion and inclusion criteria for the recommendation, but rather as useful supporting information for the clinicians when making a clinical judgment.
This approach will allow a correct use of weak recommendations where rules and algorithms are of little use and clinicians need access to information underlying recommendations to facilitate well informed decisions with patients.
For some recommendations the traditional approach in CDSS with alerts and reminders are of course still of relevance, but we encourage developers to think through the implications of using especially weak recommendations for this purpose.
Having the guidelines and recommendations in a more structured format will also greatly facilitate the development of Decision support, including alerts/reminders, order sets and suggestions . Through the PLUGGED-IN project and our work with Flow of data we will investigate both the use of Guidelines directly in the Electronic Health Records and how the single recommendations and the knowledge within can be used in Decision support systems.
Screenshots below shows the idea behind using the guidelines directly as Lightweight decision support in the Electronic Health Records, developed through the PLUGGED-INproject.
The interaction between an EMR and a recommendation relies on the presence of structured information in both the recommendations and the EMR and exchange of this information via APIs. This means that any EMR system can make use of the structured information behind a recommendation, to the degree which it can make use of the information. The EMR 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 EMR with structured drug information in it’s own system, can use the structured drug information in a recommendation to highlight this drug on the patients medication list if there, if it was ever there or offer it to the clinician as a possible order.
An EMR without structured drug information in it’s own system, 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 EMR are allowed to pick up all the codes and clinical elements from that recommendation to contextualize the view of patient specific information.
The EMR 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 EMR system, can help each other contextualize their information to help their clinicians find the information they need.