Abstract
ObjectivesTo develop a new interface for the widely used prognostic breast cancer tool: Predict: Breast Cancer. To facilitate decision‐making around post‐surgery breast cancer treatments. To derive recommendations for communicating the outputs of prognostic models to patients and their clinicians.MethodWe employed a user‐centred design process comprised of background research and iterative testing of prototypes with clinicians and patients. Methods included surveys, focus groups and usability testing.ResultsThe updated interface now caters to the needs of a wider audience through the addition of new visualisations, instantaneous updating of results, enhanced explanatory information and the addition of new predictors and outputs. A programme of future research was identified and is now underway, including the provision of quantitative data on the adverse effects of adjuvant breast cancer treatments.Based on our user‐centred design process, we identify six recommendations for communicating the outputs of prognostic models including the need to contextualise statistics, identify and address gaps in knowledge, and the critical importance of engaging with prospective users when designing communications.ConclusionsFor prognostic algorithms to fulfil their potential to assist with decision‐making they need carefully designed interfaces. User‐centred design puts patients and clinicians needs at the forefront, allowing them to derive the maximum benefit from prognostic models.
Highlights
Around 55,000 women are diagnosed with invasive breast cancer each year in the United Kingdom,[1] with an estimated 2 million new cases each year worldwide.[2]
There were not sufficient data for a detailed analysis the comments found provided a valuable insight into patients' unmediated experience of the Predict: Breast Cancer website
We developed prototypes informed by the background research
Summary
Around 55,000 women are diagnosed with invasive breast cancer each year in the United Kingdom,[1] with an estimated 2 million new cases each year worldwide.[2]. There were not sufficient data for a detailed analysis the comments found provided a valuable insight into patients' unmediated experience of the Predict: Breast Cancer website. These were derived by grouping forum comments and focus group notes into broad themes. The public survey data resulted in 249 free-text comments. These were given an initial code, for example, the comments: 'provides statistical info but fees very clinical, [...]at a time when you are dealing with cancer you might appreciate a more personal feel'. Participants' Likert ratings were largely positive about the site with the majority selecting a better than neutral rating for each of the questions (see Table S1 )
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