Learning health systems need to bridge the 'two cultures' of clinical informatics and data science. Issue 2 (1st April 2018)

• Record Type: Journal Article
• Authors: Scott, Philip J.; Dunscombe, Rachel; Evans, David; Mukherjee, Mome; Wyatt, Jeremy C.
• Is Part Of: BMJ health & care informatics. Volume 25:Issue 2(2018)
• Page Start: 126
• Page End: 131
• Publication Date: 2018-04-01
• Subjects: Big Data -- health informatics -- bioinformatics -- evidence-based practice -- health policy -- programme evaluation -- education -- learning health systems; Medical informatics -- Great Britain -- Periodicals; Information storage and retrieval systems -- Medical care -- Periodicals; Primary care (Medicine) -- Great Britain -- Data processing -- Periodicals; 362.10285
• DOI: 10.14236/jhi.v25i2.1062
• Languages: English
• ISSNs: 2632-1009

Abstract

Abstract : Background: UK health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational 'Big Data'. Learning health systems require not only data, but feedback loops of knowledge into changed practice. This depends on knowledge management and application, which in turn depends upon effective system design and implementation. Biomedical informatics is the interdisciplinary field at the intersection of health science, social science and information science and technology that spans this entire scope. Issues: In the UK, the separate worlds of health data science (bioinformatics, 'Big Data') and effective healthcare system design and implementation (clinical informatics, 'Digital Health') have operated as 'two cultures'. Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on 'data cleansing' or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. Recommendation: The UK needs increased clinical informatics research and education capacity and capability at much greater scale and ambition to be able to meet policy expectations, address the fundamental gaps in the discipline's evidence base and mitigate the absence of regulation. Independent evaluation of digital health interventions should be the norm, not the exception. Conclusions: Policy makers and research funders need to acknowledge the existing gap between the 'two cultures' and recognise that the full social and economic benefits of digital health and data science can only be realised by accepting the interdisciplinary nature of biomedical informatics and supporting a significant expansion of clinical informatics capacity and capability.