Effects of Using Child Personas in the Development of a Digital Peer Support Service for Childhood Cancer Survivors

Background: Peer support services have the potential to support children who survive cancer by handling the physical, mental, and social challenges associated with survival and return to everyday life. Involving the children themselves in the design process allows for adapting services to authentic user behaviors and goals. As there are several challenges that put critical requirements on a user-centered design process, we developed a design method based on personas adapted to the particular needs of children that promotes health and handles a sensitive design context. Objective: The purpose of this study was to evaluate the effects of using child personas in the development of a digital peer support service for childhood cancer survivors. Methods: The user group’s needs and behaviors were characterized based on cohort data and literature, focus group interviews with childhood cancer survivors (n=15, 8-12 years), stakeholder interviews with health care professionals and parents (n=13), user interviews, and observations. Data were interpreted and explained together with childhood cancer survivors (n=5) in three explorative design workshops and a validation workshop with children (n=7). Results: We present findings and insights on how to codesign child personas in the context of developing digital peer support services with childhood cancer survivors. The work resulted in three primary personas that model the behaviors, attitudes, and goals of three user archetypes tailored for developing health-promoting services in this particular use context. Additionally, we also report on the effects of using these personas in the design of a digital peer support service called Give Me a Break. Conclusions: By applying our progressive steps of data collection and analysis, we arrive at authentic child-personas that were successfully used to design and develop health-promoting services for children in vulnerable life stages. The child-personas serve as effective collaboration and communication aids for both internal and external purposes.
Journal of Medical Internet Research

Visita ao Hospital de Câncer de Barretos

Visita ao Hospital de Câncer de Barretos
Image by Dr.Sandro

President Obama’s Cancer Panel Points to SMART On FHIR for Connected Health

President Obama’s Cancer Panel defines connected health as “the use of technology to facilitate the efficient and effective collection, flow, and use of health information.” In their 2016 report to the President, the panel highlights the benefits of using the SMART On FHIR open-access API for development of health applications. “The Precision Cancer Medicine (PCM) … Continue reading “President Obama’s Cancer Panel Points to SMART On FHIR for Connected Health”

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

  Biowebspin, December 19th, 2016

Crowdsourcing for translational research: analysis of biomarker expression using cancer microarrays

by Jonathan Lawson, Rupesh J Robinson-Vyas, Janette P McQuillan, Andy Paterson, Sarah Christie, Matthew Kidza-Griffiths, Leigh-Anne McDuffus, Karwan A Moutasim, Emily C Shaw, Anne E Kiltie, William J Howat, Andrew M Hanby, Gareth J Thomas and Peter Smittenaar in British Journal of Cancer, 2016

Background: Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing – enlisting help from the public – is a sufficiently accurate method to score such samples. Methods: We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers – bladder/ki67, lung/EGFR, and oesophageal/CD8 – to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants’ accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples. Results: We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (40.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/ EGFR and bladder/p53 samples, respectively). Conclusions: These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains.

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