From Registry data to patient-centred information tools: supporting patients facing primary total hip replacement at HUG

What if patients who are about to have a total joint replacement could have information about how, not everyone but, only people who are most like them did when they had their surgery?

We will discuss how we created an information tool that does just that using data from HUG patients. We will unpick how we applied conditional inference tree analysis to data from the Geneva Hip Registry to create classification algorithms based on pre-operative predictors to produce clusters of patients with homogeneous outcomes of pain, activity, complications, and the risk of having a contralateral hip replacement.