# Welcome to CTIMS CTIMS open source software is used for creating CTML files for representing clinical trials in a computer readable format called Clinical Trial Markup Language. CTIMS project is the winner of the 2022-23 Princess Margaret Grand Challenge. ## Overview Nearly 25% of patients diagnosed with cancer at Princess Margaret Cancer Center are matched to clinical trials and play an important role in scientific discovery. The process to match patients to clinical trials is highly manual, enormously time consuming, and prone to error. Matching patients to clinical trials is a highly manual and time consuming process – taking anywhere between 2 hours and 4 weeks to complete. This is often an iterative process and can lead to further delays. **CTIMS aims to**: * Match eligible patients to clinical trials using automation * Decrease the time and resources required to find eligible study patients​ * Support complex matching criteria using all aspects of a patient’s digital fingerprint ​ * Create a scalable and responsive system to support clinical trials​ ## CTIMS Team The CTIMS team is a collaboration between member of the [Pugh Lab](https://pughlab.org/) and the [Cancer Digital Intelligence](https://pmcdi.ca/) (CDI) team. * **Principal Investigator:** Trevor Pugh * **CDI Leadership**: Kelly Lane, Tran Truong, Benjamin Haibe-Kains * **CTIMS Team**: (Jag) Prasanna K Jagannathan, Marian Tang, Sharon Narine, Benjamin Grant, Anton Sukhovatkin, Mickey Ng, Pietro Andreolis, Srimathi Jayasimman, and Adam Badzynksi ## CONTACT US For questions, technical support, feedback, or to register your account please contact the CTIMS team at ## --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://pughlab.gitbook.io/ctims/welcome-to-ctims.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.