Can Patient Preferences and Values Be Objectively Incorporated Into the Design and Evaluation of Technologies? A Conceptual Framework and a Proof-Of-Concept Study

Project Summary

Whether or not to start a new treatment is an important decision that patients often take in consultation with their doctors. To help patients and doctors make these decisions, guidelines often recommend “treatment algorithms” that consider the risks and benefits of treatment. These treatment algorithms often classify patients as “high risk” versus “low risk”, and the treatment is recommended only for the “high risk” group.

This classification of a patient into “high” versus “low” risk is often done based on the opinion of the few scientists who develop such guidelines, without consulting patients who may actually take the treatment.
One example is the use of long-term antibiotic therapy for preventing lung attacks in patients with Chronic Obstructive Pulmonary Disease (COPD). Antibiotics reduce the risk of lung attacks, but their long-term use is associated with risk of adverse events, such as hearing loss.

However, we do not know patient “trade-offs” between the risk of lung attacks and the risk of adverse treatment events. For example, we do not know the acceptable risk of hearing loss to patients to avoid one lung attack. Current guidelines establish the trade-offs based on the opinion of clinicians.

To address this problem, this project designed a way to measuring and considering patients’ values and preferences. Using focus groups and follow-up conversations, we designed and tested a Discrete Choice Experiment to measure patient preferences about different aspects of preventative therapies for COPD.

Project Findings

We found that:

  • Patient preferences for different aspects of antibiotic therapy can reliably be measured
  • These differences can be used in creating treatment algorithms that reflects patient preferences


We hope that guideline development committees such as the Canadian Thoracic Society will be motivated by the findings of this study to use a patient-centred approach in developing the next version of their guidelines.

This project is part of the Health Economics and Simulation Modelling Cluster.