Objectives To 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. Method We 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. Results The 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. Conclusions For 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.

ABSTRACTIntroductionPredict Prostate is a freely-available online personalised risk communication tool for men newly diagnosed with non-metastatic prostate cancer. Its accuracy has been assessed in multiple validation studies but the clinical impact of the tool on patient decision-making had not previously been evaluated.MethodsA multi-centre randomised controlled trial was performed across 8 UK centres, wherein newly diagnosed men considering either active surveillance or radical treatment, were randomised to either standard of care (SOC) information or SOC and presentation of Predict Prostate. Validated questionnaires were completed assessing impact of the tool on decisional conflict, uncertainty, anxiety and understanding of survival.Results156 patients were included; mean age 67 years (range 44-80) and PSA of 6.9ng/ml (range 0.5-59.8). 81 were randomised to the Predict Prostate arm, and 75 to SOC information only. Mean decisional conflict scores were 26% lower in the Predict Prostate group (mean = 15.9) than in the SOC group (mean = 21.5) (p=0.01). Scores on the ‘effective decision’, ‘uncertainty’ and ‘value clarity’ subscales all indicated that the Predict Prostate group felt more informed and clear about their decision (all p<0.05). There was no significant difference in anxiety between the two groups.Patient perceptions of 15-year prostate cancer specific mortality (PCSM) and overall survival benefit from radical treatment were considerably lower among men in the Predict Prostate group (p<0.0001). 58% of men reported the Predict Prostate estimates for PCSM were lower than expected, and 35% reported being less likely to select radical treatment. Over 90% of patients in the Predict Prostate group found it useful and 94% would recommend it to others.ConclusionPredict Prostate reduces decisional conflict and uncertainty in non-metastatic prostate cancer and shifts patient perceptions around prognosis to be more realistic. This is the first randomised study of such a tool in this context; it demonstrates Predict Prostate can directly inform the complex decision-making process in prostate cancer.

Objectives Papers, press releases and headlines very commonly still cite relative risks only, leading to headlines such as a drug or behaviour 'halves your risk of' or 'doubles your risk' of an effect. These headlines sound dramatic, but leave the reader none the wiser of the actual magnitude of either the risk or the effect. This is despite the fact that many journals now require authors to give the numbers for absolute as well as relative risks (or benefits). The reasons for this failure to use absolute risks are often that they are not available, or that journalists and press officers lack the confidence to convert the numbers they have been given to the numbers they wish to report. We are designing and currently user-testing an App to represent the findings of research clearly and accurately, for use by journalists and publishers. Method Registered press officers will have password-protected access to a web front-end allowing them to create a press alert via the App. The interface will help them input correct absolute risk data from the research paper (or, with the authors, source it externally). They will also be include a link to their full press release. When a press alert has been completed, the App will push a notification to the phones of all registered journalists who have it (pre-embargo). The App will display the results of the study in terms of absolute and relative risks as well as 'number needed to treat' in various graphical, numerical and verbal formats. Journalists can use these directly in their publications and stories, and follow the link to the full press release. We aim to evaluate the App's effects, monitoring story uptake volume, reporting of absolute risks and proportion of stories including caveats about interpretation. Results N/A. Conclusions This App could form an efficient conduit between academics (and their press officers) and journalists, carrying accurate and automated graphics/phrases designed to clearly represent their results. The advantages to press officers and authors is instant access to the phones of relevant journalists. The advantages to journalists is instant push-notification of new press releases alongside easily re-used phrases, numbers and graphics for their stories. Our evaluation will determine how being explicit about absolute risks changes reporting emphasis. Other work we are currently undertaking on the effect of absolute risks on public perception of a story will be complementary to this.

Risk-adjusted survival statistics after children's heart surgery are published annually in the United Kingdom. Interpreting these statistics is difficult, and better resources about how to interpret survival data are needed. Here we describe how a multidisciplinary team of mathematicians, psychologists, and a charity worked with parents of heart surgery children and other users to codevelop online resources to present survival outcomes. Early and ongoing involvement of users was crucial and considerably changed the content, scope, and look of the website, and the formal psychology experiments provided deeper insight. The website http://childrensheartsurgery.info/ was launched in June 2016 to very positive reviews. (C) 2017 The Authors. Published by Elsevier Inc. on behalf of The Society of Thoracic Surgeons.

BackgroundIn 2011, we developed a risk model for 30-day mortality after children’s heart surgery. The PRAiS (Partial Risk Adjustment in Surgery) model uses data on the procedure performed, diagnosis, age, weight and comorbidity. Our treatment of comorbidity was simplistic because of data quality. Software that implements PRAiS is used by the National Congenital Heart Disease Audit (NCHDA) in its audit work. The use of PRAiS triggered the temporary suspension of surgery at one unit in 2013. The public anger that surrounded this illustrated the need for public resources around outcomes monitoring.Objectives(1) To improve the PRAiS risk model by incorporating more information about comorbidities. (2) To develop online resources for the public to help them to understand published mortality data.DesignObjective 1 The outcome measure was death within 30 days of the start of each surgical episode of care. The analysts worked with an expert panel of clinical and data management representatives. Model development followed an iterative process of clinical discussion of risk factors, development of regression models and assessment of model performance under cross-validation. Performance was measured using the area under the receiving operator characteristic (AUROC) curve and calibration in the cross-validation test sets. The final model was further assessed in a 2014–15 validation data set.Objective 2 We developed draft website material that we iteratively tested through four sets of two workshops (one workshop for parents of children who had undergone heart surgery and one workshop for other interested users). Each workshop recruited new participants. The academic psychologists ran two sets of three experiments to explore further understanding of the web content.DataWe used pseudonymised NCHDA data from April 2009 to April 2014. We later unexpectedly received a further year of data (2014–15), which became a prospective validation set.ResultsObjective 1The cleaned 2009–14 data comprised 21,838 30-day surgical episodes, with 539 deaths. The 2014–15 data contained 4207 episodes, with 97 deaths. The final regression model included four new comorbidity groupings. Under cross-validation, the model had a median AUROC curve of 0.83 (total range 0.82 to 0.83), a median calibration slope of 0.92 (total range 0.64 to 1.25) and a median intercept of –0.23 (range –1.08 to 0.85). In the validation set, the AUROC curve was 0.86 [95% confidence interval (CI) 0.83 to 0.89], and its calibration slope and intercept were 1.01 (95% CI 0.83 to 1.18) and 0.11 (95% CI –0.45 to 0.67), respectively. We recalibrated the final model on 2009–15 data and updated the PRAiS software.Objective 2We coproduced a website (http://childrensheartsurgery.info/) that provides interactive exploration of the data, two animations and background information. It was launched in June 2016 and was very well received.LimitationsWe needed to use discharge status as a proxy for 30-day life status for the 14% of overseas patients without a NHS number. We did not have sufficient time or resources to extensively test the usability and take-up of the website following its launch.ConclusionsThe project successfully achieved its stated aims. A key theme throughout has been the importance of collaboration and coproduction. In particular for aim 2, we generated a great deal of generalisable learning about how to communicate complex clinical and mathematical information.Further workExtending our codevelopment approach to cover many other aspects of quality measurement across congenital heart disease and other specialised NHS services.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Background Use of risk calculators for specific diseases is increasing, with an underlying assumption that they promote risk reduction as users become better informed and motivated to take preventive action. Empirical data to support this are, however, sparse and contradictory. Aim To explore user reactions to a cardiovascular risk calculator for people with type 2 diabetes. Objectives were to identify cognitive and emotional reactions to the presentation of risk, with a view to understanding whether and how such a calculator could help motivate users to adopt healthier behaviours and/or improve adherence to medication. Design and setting Qualitative study combining data from focus groups and individual user experience. Adults with type 2 diabetes were recruited through website advertisements and posters displayed at local GP practices and diabetes groups. Method Participants used a risk calculator that provided individualised estimates of cardiovascular risk. Estimates were based on UK Prospective Diabetes Study (UKPDS) data, supplemented with data from trials and systematic reviews. Risk information was presented using natural frequencies, visual displays, and a range of formats. Data were recorded and transcribed, then analysed by a multidisciplinary group. Results Thirty-six participants contributed data. Users demonstrated a range of complex cognitive and emotional responses, which might explain the lack of change in health behaviours demonstrated in the literature. Conclusion Cardiovascular risk calculators for people with diabetes may best be used in conjunction with health professionals who can guide the user through the calculator and help them use the resulting risk information as a source of motivation and encouragement.

We are all faced with uncertainty about the future, but we can get the measure of some uncertainties in terms of probabilities. Probabilities are notoriously difficult to communicate effectively to lay audiences, and in this review we examine current practice for communicating uncertainties visually, using examples drawn from sport, weather, climate, health, economics, and politics. Despite the burgeoning interest in infographics, there is limited experimental evidence on how different types of visualizations are processed and understood, although the effectiveness of some graphics clearly depends on the relative numeracy of an audience. Fortunately, it is increasingly easy to present data in the form of interactive visualizations and in multiple types of representation that can be adjusted to user needs and capabilities. Nonetheless, communicating deeper uncertainties resulting from incomplete or disputed knowledge--or from essential indeterminacy about the future--remains a challenge.

This copy is for your personal, non-commercial use only. clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles ): November 20, 2011 www.sciencemag.org (this infomation is current as of The following resources related to this article are available online at http://www.sciencemag.org/content/333/6048/1393.full.html version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/content/suppl/2011/09/08/333.6048.1393.DC1.html can be found at: Supporting Online Material http://www.sciencemag.org/content/333/6048/1393.full.html#ref-list-1 , 14 of which can be accessed free: cites 40 articles This article

Certain numeric puzzles, known as ‘magic letters’, each have a finite permutation group associated with them in a natural manner. We describe how the isomorphism type of these permutation groups relates to the structure of the magic letters.