Year: 2023
Type: review
Abstract: Researchers and research funders aiming to improve diagnosis seek to identify if, when, where, and how earlier diagnosis is possible. This has led to the propagation of research studies using a wide range of methodologies and data sources to explore diagnostic processes. Many such studies use electronic health record data and focus on cancer diagnosis. Based on this literature, we propose a taxonomy to guide the design and support the synthesis of early diagnosis research, focusing on five key questions: Do healthcare use patterns suggest earlier diagnosis could be possible? How does the diagnostic process begin? How do patients progress from presentation to diagnosis? How long does the diagnostic process take? Could anything have been done differently to reach the correct diagnosis sooner? We define families of diagnostic research study designs addressing each of these questions and appraise their unique or complementary contributions and limitations. We identify three further questions on relationships between the families and their relevance for examining patient group inequalities, supported with examples from the cancer literature. Although exemplified through cancer as a disease model, we recognise the framework is also applicable to non-neoplastic disease. The proposed framework can guide future study design and research funding prioritisation.
Source: British Journal of Cancer
Authors: Emma Whitfield, Becky White, Spiros Denaxas, Matthew E. Barclay, Cristina Renzi, Georgios Lyratzopoulos
Language: English
Subfield: Family Practice
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,690
Funders: Oregon Health and Science University, Wellcome Trust, Scottish Government, UK Research and Innovation, Cancer Research UK, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, University College London, Public Health Agency, University College London Hospitals NHS Foundation Trust, Chief Scientist Office, Scottish Government Health and Social Care Directorate, Knight Cancer Institute, Oregon Health and Science University, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, Health Data Research UK
Awards: G1240744640, G1543817132, G157493538, G1633672591, Infrastructure and Services - Useable Data, G1898492273, G2045002217, G2123105393, The HDRUK/Turing Wellcome PhD Programme in Health Data Science, G2393280133, G25914223, G2881790564, G3238571100, G3248970876, G3789573264, G4069887101, G444630160, The Alan Turing Institute, G4782868754, G4906577935, G4957395377, G5023556647, G5153958305, Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care services, G5304384294, G5335951994, G5342683040, G5509661070, Infrastructure and Services - Capacity Building, G5697004987, G5918494183, G6157846266, G6226316871, G6382444801, G6471925529, G6582518128, G6688365311, G6781659469, G7423961978, G7631291304, BHF Data Science Centre, G8581904048, G8795210754, G920190840
OpenAlex ID: W4387330968