Using Big Data in oncology to prospectively impact clinical patient care: A proof of concept study

Global Aging and Cancer: Advancing Care Through Innovation

The oncology field, like many others, is digitalizing rapidly, a phenomenon that may have been accelerated by the COVID-19 pandemic. This movement is creating opportunities and challenges. Another rapidly developing change is the aging of the global population; because cancer is a disease of aging, there is a need for health systems to adapt to taking care of such patients. In this article, we address how these innovative technologies can be leveraged to improve the care of older patients with cancer beyond academic centers, such as in underserved areas and low- and middle-income countries. We review how digital technologies can be used to enhance the follow-up of patients in low- and middle-income countries. We also tackle the issue of training a global workforce to treat cancer in an aging population and how to leverage innovations in this matter. Finally, we review opportunities to expand the usefulness of big data and machine learning beyond academic centers to support private practices and underserved areas.

Implementation of geriatric assessment in oncology settings: A systematic realist review

Older adults with cancer are more likely to have worse clinical outcomes than their younger counterparts, and shared decision-making can be difficult, due to both complexity from adverse ageing and under-representation in clinical trials. Geriatric assessment (GA) has been increasingly recognised as a predictive and prehabilitative tool for older adults with cancer. However, GA has been notoriously difficult to implement in oncological settings due to workforce, economic, logistical, and practical barriers. We aimed to review the heterogenous literature on implementation of GA in oncology settings to understand the different implementation context configurations of GA and the mechanisms they trigger to enable successful implementation. A systematic realist review was undertaken in two stages: i) systematic searches with structured data extraction combined with iterative key stakeholder consultations to develop programme theories for implementing GA in oncology settings; ii) synthesis to refine programme theories. Medline, Embase, PsycInfo, Cochrane Library, CINAHL, Web of Science, Scopus, ASSIA, Epistemonikos, JBI Database of Systematic Reviews and Implementation Reports, DARE and Health Technology Assessment were searched. Four programme theories were developed from 53 included articles and 20 key stakeholder consultations addressing the major barriers of GA implementation in oncology practice: time (leveraging non-specialists), funding (creating favourable health economics), practicalities (establishing the use of GA in cancer care), and managing limited resources. We demonstrate that a whole system approach is required to improve the implementation of GA in cancer settings. This review will help inform policy decisions regarding implementation of GA and provide a basis for further implementation research.