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Pilot Sensor Randomized Controlled Trial: Setting Expectations to Increase Satisfactory Outcomes After Total Knee Replacement

London Health Sciences Centre Research Institute OR Lawson Research Institute of St. Joseph's · 2021
ClinicalTrials.gov · Datasets · License: Public Domain · 2021
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machine-learningnapatient-satisfactionsatisfactiontotal-knee-arthroplastywearable-sensors
More than 70,000 total knee replacement procedures are performed annually in Canada, representing a growth of 17% over the past 5 years, with further increases anticipated due to an aging population. While total knee replacement offers improved quality of life for patients and is cost effective for the healthcare system, 20% of patients routinely report dissatisfaction with the procedure. Patient dissatisfaction has been strongly linked to unmet expectations of outcomes after the surgery, especially with respect to physical activity. Counselling patients on appropriate expectations has been suggested as a means to improve satisfaction. Recently, our group has developed a tool to predict the functional ability of an individual patient after total knee replacement. This tool employs machine learning to classify patients as more likely to maintain or improve function, based on a functional test performed in clinic while wearing a sensor system around each knee. Implementing this tool in clinic pre-operatively could assist in setting appropriate expectations for each patient. Our primary objective is to compare patient satisfaction scores at one year after total knee replacement in patients who were informed of their specific expected functional outcome compared to patients who were not informed of their predicted functional outcome. We hypothesize that patients who are given an informed expectation will have higher satisfaction scores. This in turn may decrease health system costs associated with additional clinic visits from dissatisfied patients.
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