People living with obesity suffer from multiple health issues, including diabetes and mental health problems. Misinformation about the complex nature of this condition greatly affects the way one manages obesity. This results in unrealistic expectations by both healthcare providers and patients. Effective obesity management must be individually tailored for each patient. The objective of this project was to improve four communication tools by co-designing them with patients. A co-design approach was used to improve the efficacy and applicability of the tools through a working collaboration between patients, care providers, and researchers. While most articles describe processes to create shared-decision making (SDM) tools which compare alternative diagnosis and treatment options, few papers describe models to create SDM tools which go beyond showing benefits and risks. In this paper, we describe our process and approach to the re-design of four of the 5As obesity tools. We hope this study provides a valuable model for other teams.

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Healthcare systems are under high pressure, from chronic disease, and aging populations with their consequent array of issues. Healthcare professionals are asked to deliver better care with fewer resources, increasing efficiency and efficacy. Simultaneously, patients expect more personalized therapies and physicians are discovering the benefits of making patients more active in their own care. This paper outlines the challenge to arrive at healthcare decisions that are based not only on data but also on the patient's values and preferences. However, helping healthcare to face this challenge requires designers to develop new skills and competences. The paper proposes some general competences that designers need to develop to provide design solutions to satisfy users and healthcare systems' needs.

Improving the quality of patient care, generally referred to as Quality Improvement (QI), is a constant mission of healthcare. Although QI initiatives take many forms, these typically involve collecting data to measure whether changes to procedures have been made as planned, and whether those changes have achieved the expected outcomes. In principle, such data are used to measure the success of a QI initiative and make further changes if needed. In practice, however, many QI data reports provide only limited insight into changes that could improve patient care. Redesigning standard approaches to QI data can help close the gap between current norms and the potential of QI data to improve patient care. This paper describes our study of QI data needs among healthcare providers and managers at Vancouver Coastal Health, a regional health system in Canada. We present an overview of challenges faced by healthcare providers around QI data collection and visualization, and illustrate the advantages and disadvantages of different visualizations. At present, user– centred and evidence–based design is practically unknown in healthcare QI, and thus offers an important new contribution.