SeamlessMD’s Machine Learning Efforts Featured in Canadian Healthcare Technology Nov/Dec 2020 Issue

The SeamlessMD team is thrilled to have an article featured in CanHealth’s Nov/Dec 2020 issue entitled, “How machine learning can reduce the backlog of surgical procedures”

Below is the SeamlessMD article in its entirety from page 20 of the Nov/Dec 2020 issue of Canadian Healthcare Technology:

How Machine Learning Can Reduce the Backlog of Surgical Procedures

By Alan Sardana & Dr. Joshua Liu

An analysis published in the September 2020 issue of the Canadian Medical Association Journal estimates that it could take one and a half years to clear the backlog of almost 150,000 surgeries created between March and June 2020, due to the pandemic. It is imperative for our healthcare system to not only help Canadians get surgery but to do so safely and in a manner that minimizes risks associated with hospital care during the pandemic.

Digital patient engagement is a promising virtual care technology that presents an effective way to deliver safer surgery during COVID-19. Digital patient engagement involves empowering patients to be guided through their pre- and post-surgery care via their smartphone, tablet, or computer.

Patients receive electronic reminders about key steps in their care plan (e.g. when to stop eating or drinking before surgery), access interactive education (e.g. exercise videos for rehab), and track symptoms or progress (e.g. pain scores, vital signs, etc.). Healthcare providers at the hospital can get alerts and access dashboards to monitor patient progress (e.g. photos of surgical incisions) and catch problems earlier.

While this may sound futuristic, a Canadian health technology company called SeamlessMD has been pioneering digital patient engagement for several years. Prior to the pandemic, SeamlessMD’s platform was being used by Canadian healthcare organizations to guide patients through surgery, such as at Sunnybrook Health Sciences Centre and The Ottawa Hospital.

In fact, Trillium Health Partners launched the SeamlessMD platform for Cardiac and Orthopedic surgery during the pandemic, and when in-person, presurgery education classes had to be canceled, the hospital innovated quickly with SeamlessMD to deliver prerecorded classes virtually to patients through the platform.

Leveraging Patient-Reported Outcomes (PRO) data collected on platforms such as SeamlessMD (e.g. pain scores, vital signs data, etc.), with A.I. and Machine Learning to better predict adverse health outcomes and deliver safer surgery has always been a vision for the Canadian healthcare system. Now, the COVID-19 pandemic appears to be the turning point needed to not only accelerate the adoption of digital patient engagement platforms, but also the use of AI & Machine Learning to deliver more predictive care.

In August 2020, the Digital Technology Supercluster announced the investment of about $30 million in federal funding for 17 projects to help tackle COVID-19. One of these projects is being led by SeamlessMD to help hospitals use digital patient engagement technology and machine learning to deliver safer surgery during COVID-19, thereby helping to reduce the backlog of surgeries.

A consortium of seven hospitals in Canada, including Trillium Health Partners and Michael Garron Hospital, have already signed up to participate, and this number continues to grow. Besides SeamlessMD, industry partners involved in the initiative include AltaML, Xerus Medical and Excelar Technologies.

Through this project, Canadian hospitals will integrate the SeamlessMD platform with their existing Electronic Medical Record systems, and enable patients to be digitally screened for presurgery risk factors, COVID-19 symptoms, and post-surgery symptoms of complications (e.g. fever, surgical incision photos, etc.). This will allow healthcare providers to remotely assess patient readiness for surgery, avoid unnecessary in-person follow-up visits, and catch problems earlier in the post-surgery recovery process.

In addition, the collaboration will involve training machine learning models with both historical surgery data and real-time symptom data to better predict the risk of cancellations, readmissions, and emergency room visits. This project represents a paradigm shift in healthcare, from a historically paper-based model of patient education to a digital model of intelligent, personalized patient care.

While several of the hospitals involved in the initiative are already using digital patient engagement technology, many will be implementing this type of technology for the first time. While we do not know how long the impacts of the pandemic will last, one thing we know for sure is that the pandemic has accelerated Canadian healthcare into a far more digital future, and there is no turning back.

Click Here for the full November/December 2020 Issue of Canadian Healthcare Technology.

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