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| Title | Enhancing the SMART on FHIR Backend for Patient-Controlled Data Sharing |
| Description | The goal of this project is to improve and extend the backend of a SMART on FHIR application that empowers patients to autonomously access, share, and manage their electronic health records (EHR). The application serves as a data interoperability layer, allowing users to seamlessly transfer their healthcare data to meet various needs, such as research participation, second opinions, or personal health tracking. |
| Expected Outcomes | One or both of the following: Improving the integration with diverse FHIR servers and refining data transformation capabilities. Building APIs or plugins to support additional healthcare applications and third-party services. |
| Skills | Java, python |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Enhancing the SMART on FHIR Frontend for Patient-Controlled Data Sharing |
| Description | This project aims to improve the frontend user interface for a SMART on FHIR application that enables patients to autonomously access, manage, and share their electronic health records (EHR). The focus is on making the UI more intuitive, accessible, and user-friendly, ensuring a seamless experience for users connecting to the backend service. |
| Expected Outcomes | Improved User Experience (UX) |
| Skills | Javascript, Node |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Developing a FHIR Resource Tabular Viewer for Efficient Data Exploration |
| Description | This project aims to build a FHIR Resource Tabular Viewer, an application that transforms complex, nested FHIR data structures into an easy-to-navigate tabular format. This tool will allow users—such as researchers, clinicians, and developers—to efficiently search, filter, and analyze FHIR resources, improving accessibility and usability of healthcare data. |
| Expected Outcomes | Tabular Representation of FHIR Data and Search & Filtering Capabilities. |
| Skills | Python, Javascript |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Developing Custom Jupyter Notebooks for AVRO File Processing and QA/QC Analysis |
| Description | This project aims to create custom Jupyter notebooks that help users efficiently unpack AVRO files, perform quality assurance (QA) and quality control (QC) checks, and run basic data analyses. The goal is to provide a user-friendly, interactive environment where users can explore, validate, and analyze AVRO-formatted data without requiring deep expertise in data engineering. |
| Expected Outcomes | AVRO File Handling on startup, QA/QC Checks, Basic Data Analysis. |
| Skills | Python, networking |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Extending the HAPI FHIR Server for Enhanced Functionality and Interoperability |
| Description | This project aims to extend the HAPI FHIR Server, a leading open-source implementation of the FHIR standard, to improve its functionality, scalability, and interoperability. The enhancements will support advanced healthcare use cases, making it easier for developers and organizations to manage and exchange FHIR-compliant health data efficiently. |
| Expected Outcomes | Custom FHIR Operations & Extensions |
| Skills | Java, FHIR |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Developing a Translation Service to Connect GEARBOx API with mCODE Trial Matching Service |
| Description | This project aims to build a translation service that connects the GEARBOx API with the mCODE (Minimal Common Oncology Data Elements) trial matching service. The goal is to enable seamless translation of oncology data between GEARBOx and mCODE, allowing healthcare providers, researchers, and clinical trial platforms to effectively match patients to relevant clinical trials based on their mCODE-compliant health data. |
| Expected Outcomes | Data Mapping & Transformation, Interoperability & Validation |
| Skills | Python, Typescript |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | medium |
| Title | Building a Chatbot for Generating GraphQL and Custom Queries for Cohort Descriptions |
| Description | This project aims to develop a chatbot powered by ChatGPT or another large language model (LLM) that allows users to describe a cohort of patients and automatically generates GraphQL queries or custom queries based on the input for the PCDC. The goal is to simplify the process of building complex queries for patient data by allowing users to interact with the chatbot in natural language, rather than navigating through a UI or manually searching for filters. |
| Expected Outcomes | GraphQL Query Generation |
| Skills | LLM, Javascript |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | hard |
| Title | Developing a Cross-Platform App for User Consent and Data Sharing from Apple Health and CommonHealth Using React Native |
| Description | This project focuses on creating a cross-platform mobile app (iOS and Android) using React Native that allows users to consent and share their health data from both Apple Health and CommonHealth. The app will enable users to manage their data sharing preferences, securely transmit health information, and empower them to participate in research or share data with healthcare providers. |
| Expected Outcomes | Initial App version |
| Skills | React Native, Android, iOS |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | hard |
| Title | Enhancing the Cohort Discovery Chatbot |
| Description | This project aims to enhance a cohort discovery chatbot by improving its accuracy, usability, and query generation capabilities. Enhancements will focus on refining natural language understanding (NLU), improving query accuracy, supporting more complex filters, and integrating feedback mechanisms to learn from user interactions. |
| Expected Outcomes | Improved chatbot accuracy in understanding and generating cohort queries |
| Skills | LLM, Javascript |
| Mentors | TBD - One of the Senior Developer in the team. |
| Project Size | 350 hours |
| Rating | hard |