Systematic review of the use of big data to improve surgery in low- and middle-income countries. Issue 2 (8th January 2019) - Knight, S R; Ots, R; Maimbo, M; Drake, T M; Fairfield, C J; Harrison, E M
Abstract: Background: Technological advances have led to the generation of large amounts of data, both in surgical research and practice. Despite this, it is unclear how much originates in low- and middle-income countries (LMICs) and what barriers exist to the use of such data in improving surgical care. The aim of this review was to capture the extent and impact of programmes that use large volumes of patient data on surgical care in LMICs. Methods: A PRISMA-compliant systematic literature review of PubMed, Embase and Google Scholar was performed in August 2018. Prospective studies collecting large volumes of patient-level data within LMIC settings were included and evaluated qualitatively. Results: A total of 68 studies were included from 71 LMICs, involving 708 032 patients. The number of patients in included studies varied widely (from 335 to 428 346), with 25 reporting data on 3000 or more LMIC patients. Patient inclusion in large-data studies in LMICs has increased dramatically since 2015. Studies predominantly involved Brazil, China, India and Thailand, with low patient numbers from Africa and Latin America. Outcomes after surgery were commonly the focus (33 studies); very few large studies looked at access to surgical care or patient expenditure. The use of large data sets specifically to improve surgical outcomes in LMICs is currently limited. Conclusion: Large volumes of data are becoming more common and provide a strong foundation for continuing investigation. Future studies should address questions more specific to surgery. Graphical Abstract: The use of 'big data' is becoming increasingly common in surgical research and practice. This study shows that patient inclusion in large-data studies in low- and middle-income countries (LMICs) has increased dramatically since 2015; however, future studies would benefit from asking research questions that are more specific to surgery in LMIC settings. Growing access to big data