Student Track

What is the Student Track?

HL7 FHIR DevDays is all about healthcare interoperability, a place where hundreds of like-minded people come together to talk about FHIR and how it can improve healthcare. The Student Track provides a unique opportunity for students to not only join and interact with the FHIR community, but to also go hands-on with the standard and learn from fellow students and FHIR experts. 

Students from universities around the world will join us in Amsterdam in June 2023. There is a competitive element to the track.
The members of the jury are:

• James Agnew, Smile Digital Health
• Lilian Minne, Nictiz
• Ronald Cornet, Amsterdam University Medical Center

Seven universities will participate:

• University of Amsterdam, The Netherlands
• University of Applied Sciences Upper Austria, Austria
• University of Applied Sciences Vienna, Austria
• University of Applied Sciences Joanneum and Vienna, Austria
• Heilbronn University of Applied Sciences, Germany (3 teams)

The following projects will be pitched

Team 1: University of Amsterdam, the Netherlands
Title: MedDash
MedDash project utilizes FHIR standard to transfer patient data from Firely server to a web-based dashboard interface designed with Flask framework along with the Ploty library.

By leveraging the standard resources within FHIR, this dashboard can easily display graphs that highlight trends, patterns, and insights that may be difficult to discern from raw data alone. This enables healthcare providers to quickly identify potential issues, such as spikes in patient activity, and take action as needed.
The dashboard can be easily customized to meet the needs of different hospital departments, with options to select which patient data sources to display, how the data is displayed, and other features.

Team 2: University of Applied Sciences Upper Austria, Austria
Title: Dynamic Clinical Trial Cohort Definition and Evaluation with CQL
Our project aims to develop a tool that streamlines the patient selection process in clinical trials. The tool consists of a user interface for the dynamic selection of inclusion and exclusion criteria for creating cohorts utilizing medical terminology systems. By leveraging CQL’s expressive power, the tool supports the criteria definition and evaluation of FHIR patients’ eligibility in real-time.

Team 3: University of Applied Sciences Vienna, Austria
Title: Mobile access to one’s medication data
Inspired by the Austrian Electronic Health Record (ELGA) and the IHE trial-profile [MMA] Mobile Medication Administration, we are implementing an Android App, which queries MedicationRequests for a patient from a FHIR server. In consequence, we provide the patient with a clear overview of their current medication and corresponding dosage. This app shall help to reduce medication administration errors for outpatients.

Team 4: University of Applied Sciences Joanneum and Vienna, Austria
Title: PhysioWeb
We propose to develop PhysioWeb as part of the TherapEase concept, a FHIR-based web application that enables standardised, continuous communication between physiotherapists and patients. Within this application, patients complete a general health questionnaire at the start of treatment and another after each exercise session. This provides the physiotherapist with continuous feedback on the progress and allows for more personalised treatment.

To model the project’s data requirements, we will develop our own implementation guide using FHIR Shorthand (FSH), and the web-based application will be implemented using Python/Django.

Team 5: Heilbronn University of Applied Sciences, Germany
Title: FHIR Consent – Analyzing the resources in a research context
We used the FHIR R5 Consent resource to model an interoperable solution for different research contexts. We focussed on German research study consents, the broad consent for secondary use of clinical data and consents for cancer registries defined by law. Our lessons learned and challenges in the use of the R5 Consent resource will be presented.

Team 6: Heilbronn University of Applied Sciences, Germany
Title: Modeling AI Inference
We used FHIR to model and implement on how to use AI Inference on remote systems for medical imaging. To implement this, we modeled not only a catalog of AI services, but also structured queries, responses, and consider the response in case of an error.

Team 7 Heilbronn University of Applied Sciences, Germany
Title: A FHIR-based Clinical Trial Recruitment Support System for Study Inclusion in Oncologic Follow-up
Our project aims to facilitate clinical trial recruitment for patients in follow-up using Subscriptions for available clinical trials. It includes a service that filters trials based on CQL, matches them with patients, and subsequently informs physicians. The project also contemplates Conversational Artificial Intelligence as user-interface for complementary trial information.

Student Track Lead
Philip van Damme, Amsterdam University Medical Center