Daniel McIsaac" class="rev-slidebg tp-rs-img" data-no-retina> INNOVATION FUND Technology and AI in Healthcare Quality and Safety SHOWCASE 2019

A patient-oriented risk communication tool to improve patient experience, knowledge and outcomes after elective surgery

Quality and Safety

Daniel McIsaac


613-798-5555 x 18629



The Ottawa Hospital, The University of Ottawa


TOH-18-015 We have collaborated with the TOH mHealth lab and our patient advisors to develop, refine and test our PREDICT tool in an iterative process. The feedback from our patient advisors contributed greatly to the revisions and updates made to our technology. The PREDICT tool has been developed and designed to ensure inclusivity (large buttons, simple, well-lit) and utility (patient health data+NSQIP algorithm+locally calibrated to TOH). Personalized risks of surgery can be generated and can inform patients. Initial analyses demonstrate that people’s knowledge of their surgical risk increase by 17% after using the app, with no increase in anxiety levels.


TOH-18-015 Background: One to two percent of people die after major surgery, and 15-20% experience a serious complication. Patients want to receive more information about their personal risks, yet, personalized risk estimates are provided to fewer than 1 in 5 patients. When patients lack information about risks and benefits, their decision quality is lower. Our objectives were to develop a user-friendly, patient-oriented, personalized preoperative risk communication application (PREDICT tool) and to evaluate whether the tool improves patients’ knowledge of their personalized risks. Methods: In collaboration with patient partners and the TOH mHealth Lab, we developed a mobile application using principles of patient-oriented research and inclusive design. Patients self-report their health history to populate the National Surgical Quality Improvement Program Universal Risk Calculator (calibrated to local data) to generate personalized risks of mortality, serious complications, and hospital length of stay. Patients are also asked to describe the benefits they hope to derive from surgery. These personalized risk estimates are communicated directly to the patient using pictograms, balanced with perceived benefits. We have evaluated the application using a controlled before-and-after study design, powered to detect a net 5% increase in knowledge of personalized risks pre- to post- implementation. Secondary outcomes include experience, anxiety, and acceptability. Results: Ninety-five people were enrolled pre- and post-implementation. Initial analyses demonstrate that after using the application, post-implementation participants had a 17% increase in knowledge (P<0.001) with a no increase in anxiety (P=0.85); 95% of participants were willing or extremely willing to use the application prior to another surgery.

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