Guidelines for the Mapping of Cancer Registry Data

Roberts, Eric M.; Kumar, Bahar; Collins, Natalie B.; Guo, Liang; King, G. David; Wong, Michelle; Barlow, Janice; Bichler, Joyce; Cady, Linda; Engel, Connie L.; Garrett, Debbie; Harrison, Marie; Morieko, Adriana; Murgai, Neena; Tanjasiri, Sora Park; Pierce, Karen

doi:10.1097/phh.0b013e318268aef1

Cited by 3 publications

(1 citation statement)

References 26 publications

(19 reference statements)

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“…According to the authors, given the absence of specific policies for the provision of medical information and online coaching and the increased patient support needs such an approach may be useful towards the improvement of resource allocation in the healthcare system among others. Other studies have explored protocols for mapping breast cancer registry data [30], use of modelling to optimize cancer screening and predict catchment areas and use of AI for risk stratification of cancer patients [31][32][33]. However, none of these studies included tools designed to be used by policy-makers.…”

Section: Ncds Studiedmentioning

confidence: 99%

Data Analytics to support Policy-making for Non-Communicable Diseases: A Scoping Review (Preprint)

Dritsakis,

Gallos,

Psomiadi

et al. 2024

Preprint

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BACKGROUND There is an emerging need for evidence-based approaches harnessing large amounts of healthcare data and novel technologies (such as Artificial Intelligence, AI) to optimize public health policy-making. OBJECTIVE The aim of the present study was to explore the data analytics tools designed specifically for policy-making in Non-Communicable Diseases (NCDs) and their implementation. METHODS A scoping review was conducted after searching the PubMed database for articles published in the last 10 years. RESULTS Nine articles that presented 7 data analytics tools designed to inform policy-making for NCDs were reviewed. Tools incorporate descriptive and predictive analytics. Some tools were designed to include recommendations for decision support but no pilot studies have been published that apply prescriptive analytics. The tools were piloted with a range of conditions with cancer being the condition least studied. Implementation of tools included use cases, pilots or evaluation workshops were reported that involved policy-makers. However, our findings demonstrate very limited real-world use of analytics by policy-makers, in line with previous studies. CONCLUSIONS Despite the availability of tools designed for different purposes and conditions, data analytics are not widely used to support policy-making for NCDs. However, the review demonstrates the value and potential use of data analytics to support policy-making. Based on the findings we make a number of suggestions for researchers developing digital tools to support public health policy-making. The findings will also serve as input for the EU-funded research project ONCODIR developing a policy analytics dashboard for the prevention of colorectal cancer as part of an integrated platform.

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