Patient portal is an emerging healthcare technology that has shown promising effects in enhancing patient care experience and promoting patient health outcomes. Opal, a digital patient-centred portal, is currently available to patients at the Cedars Cancer Center at McGill University Health Centre (MUHC), providing real-time access to personal health information (upcoming appointments, clinical notes, lab results, etc.) in conjunction with the disease- and treatment-specific education materials. This project aims to quantify the initial impact of the Opal patient portal on operational and health-economic outcomes at the Cedars Cancer Centre. We leverage patient-level data from the Opal patient portal and MUHC. The primary method is to use propensity score matching to construct a matched cohort that compares operational outcomes and resource utilization between Opal users to non-users.

The planned outcomes of interest include missed appointments, emergency room visits, hospitalization, medical record requests, and fertility clinic appointments

Cancer patients go through a lot during treatment. Many patients need support from other patients who know what they are going through because family and friends, no matter how supportive they try to be, don’t always understand their struggles.
The Opal Health Informatics Group is developing two programs for supporting cancer patients in the portal Opal: OncoBuddy and OncoConseil. OncoBuddy is a support system that matches cancer patients with volunteer patients (we call them buddies) based on a matching algorithm that considers criteria selected by the patients themselves. OncoConseil, on the other hand, matches patients with threads of information that might interest them, such as tips and tricks to get a smoother experience during the treatment.
This project focuses on finding selection criteria for building the matching algorithm. Using semi-structured interviews with cancer patients, we will extract a panel of selection criteria that are the most valued by patients, such as type of cancer, age and gender of the buddy, stage of cancer, etc. 
 

Cancer patient peer support is a beneficial tool for current patients and previous patients who can share lived experiences. However, current peer support is inefficient as it is conducted manually and relies on a coordinator to match patients based on a few known factors.

Therefore, this research study will examine ways to develop AI-powered matching algorithms that will more efficiently and effectively match cancer patients according to a wider and more complex set of factors than can be done manually. In this research project, we are designing and developing an AI-matching algorithm for the OncoBuddy/OncoConseil project and evaluating its effectiveness to ensure recommended matches will result in appropriate peer support. We are comparing multiple existing AI models, (i.e. the Deferred Acceptance Algorithm and the Genetic Algorithm) and testing the models on synthetic patient data that we have generated with statistical inferences from the existing Opal database and Statistics Canada. A fitness function derived from previous research will determine the efficacy of the matching algorithms.

As a result, we have generated a synthetic dataset of 1770 patients to be used for training and testing purposes, implemented multiple AI algorithms, and deployed a live dashboard prototype.

The cancer experience and the uncertainty surrounding it is anxiety provoking. One way in which the non-clinical uncertainty of the cancer experience can be reduced is through peer support. The Opal Health Informatics Group seeks to evaluate the efficacy of an artificial intelligence-based peer support matching algorithm in the pre-existing patient portal Opal in the hopes of facilitating peer support programs (for cancer patients and their caregivers) in Quebec.

Our work focused on the architectural design, development, and demonstration of a proof of concept stand-alone web application used to demonstrate the results of several varieties of AI-powered matching algorithms on test patient data.