No abstract

No abstract

BackgroundImproving cancer survival in the UK, despite recent significant gains, remains a huge challenge. This can be attributed to, at least in part, patient and diagnostic delays, when patients are unaware they are suffering from a cancerous symptom and therefore do not visit a general practitioner promptly and/or when general practitioners fail to investigate the symptom or refer promptly. To raise awareness of symptoms that may potentially be indicative of underlying cancer among members of the public a symptom-based risk assessment model (developed for medical practitioner use and currently only used by some UK general practitioners) was utilized to develop a risk assessment tool to be offered to the public in community settings. Such a tool could help individuals recognize a symptom, which may potentially indicate cancer, faster and reduce the time taken to visit to their general practitioner. In this paper we report results about the design and development of the REACT (Risk Estimation for Additional Cancer Testing) website, a tool to be used in a community setting allowing users to complete an online questionnaire and obtain personalized cancer symptom-based risk estimation.ObjectiveThe objectives of this study are to evaluate (1) the acceptability of REACT among the public and health care practitioners, (2) the usability of the REACT website, (3) the presentation of personalized cancer risk on the website, and (4) potential approaches to adopt REACT into community health care services in the UK.MethodsOur research consisted of multiple stages involving members of the public (n=39) and health care practitioners (n=20) in the UK. Data were collected between June 2017 and January 2018. User views were collected by (1) the “think-aloud” approach when participants using the website were asked to talk about their perceptions and feelings in relation to the website, and (2) self-reporting of website experiences through open-ended questionnaires. Data collection and data analysis continued simultaneously, allowing for website iterations between different points of data collection.ResultsThe results demonstrate the need for such a tool. Participants suggest the best way to offer REACT is through a guided approach, with a health care practitioner (eg, pharmacist or National Health Service Health Check nurse) present during the process of risk evaluation. User feedback, which was generally consistent across members of public and health care practitioners, has been used to inform the development of the website. The most important aspects were: simplicity, ability to evaluate multiple cancers, content emphasizing an inviting community “feel,” use (when possible) of layperson language in the symptom screening questionnaire, and a robust and positive approach to cancer communication relying on visual risk representation both with affected individuals and the entire population at risk.ConclusionsThis study illustrates the benefits of involving public and stakeholders in developing and implementing a simple cancer sympt...

No abstract

No abstract

No abstract

No abstract

Scientific and clinical research have advanced the ability of healthcare professionals to more precisely define diseases and classify patients into different groups based on their likelihood of responding to a given treatment, and on their future risks. However, a significant gap remains between the delivery of stratified healthcare and personalization. The latter implies solutions that seek to treat each citizen as a truly unique individual, as opposed to a member of a group with whom they share common risks or health-related characteristics. Personalisation also implies an approach that takes into account personal characteristics and conditions of individuals. This paper investigates how these desirable attributes can be developed and introduces a holistic environment, the iHELP, that incorporates big data management and Artificial Intelligence (AI) approaches to enable the realization of datadriven pathways where awareness, care and decision support is provided based on person-centric early risk prediction, prevention and intervention measures.