Opal: A patient portal and an opportunity for teaching and research

Prof. John Kildea, Prof. Laurie Hendren and Prof. Tarek Hijal - McGill University

Jan. 11, 2019, 2:30 p.m. - Jan. 11, 2019, 3:30 p.m.

Trottier 2120


Opal is a patient portal app designed to empower patients with their medical data at the McGill University Health Centre. Using Opal, patients have access to appointment information (calendar, reminders, maps, preparation advice, check-in, call-in), immediate laboratory results, clinical notes, messages from their care team, symptom questionnaires, and a personalized education library. The Opal Health Informatics Group is the research team behind Opal. Based at the RI-MUHC, the team is led by Laurie Hendren (patient and computer scientist), Tarek Hijal (radiation oncologist) and John Kildea (medical physicist). The mission of the Opal Health Informatics Group is to partner with patients, clinicians and the next generation of healthcare professionals (ie students) to drive excellence in medical research and innovation. The vision of the Opal Health Informatics Group is to make Opal the go-to electronic partner in care that patients know they can rely on. Opal links siloed data by putting them together in the hands of patients. It allows patients to report their symptoms as they feel them (“patient-reported outcomes”), enabling improved care and facilitating real-world evidence research. Opal will also allow patients to consent to clinical trials, donate their data for research studies, and actively participate in research.

Speaker Bio(s):

John Kildea, PhD
Investigator, RI-MUHC, Glen site, Cancer Research ProgramAssistant
Professor, Department of Oncology, Medical Physics Unit, Faculty of Medicine, McGill University

John Kildea's research focuses on the application of physics and informatics in cancer research.He leads two distinct research groups. The first, Neutron-Induced Carcinogenic Effects (NICE), studies the production and toxicity of non-therapeutic neutrons in radiotherapy. This fundamental physics and radiobiology research involves modelling and radiation detection. The second group, Radiation Oncology Knowledge Sharing (ROKS), is a translational research initiative that aims to improve the experiences and outcomes of radiation oncology patients through the use of electronic data. The ROKS group analyses electronic medical record data and develops software to share knowledge between healthcare professionals and between professionals and patients. This research is carried out with collaborators in the School of Computer Science at McGill. the group's research has lead to development of Opal, the Oncology portal and application (depdocs.com/opal) for cancer patients. https://www.mcgill.ca/medphys/staff/john-kildea

Laurie Hendren, PhD, FRSC
Professor of Computer Science at McGill University,
Associate Investigator at the Research Institute of the MUHCH
Canada Research Chair in Compiler Tools and Techniques.

Laurie Hendren's research focus on designing and implementing new compiler tools and techniques to support new programming languages, compiler transformations and optimizations, and novel program understanding and refactoring tools. She became interested in developing computer-based tools for patients while being treated for breast cancer in 2014, and continues to use her experiences as an active cancer patient to guide her research. She is a member of the MUHC Cancer Mission Patients’ Committee. http://www.sable.mcgill.ca/~hendren/

Tarek Hijal, MD, C.M., M.Sc., FRCPC
Associate Professor, Department of Oncology, Division of Radiation Oncology, Faculty of Medicine, McGill University
Associate Investigator, RI-MUHC, Glen site Cancer Research Program

Dr. Hijal is a radiation oncologist specializing in the treatment of breast cancer, digestive and hematologic malignancies. H's research focuses on assessing new radiation treatments for breast and rectal cancers, such as preoperative one-day radiation treatments for breast cancer. He is also involved in improving radiotherapy care through the use of databases and machine learning.