Literature DB >> 30398454

Learning health systems need to bridge the 'two cultures' of clinical informatics and data science.

Philip Scott1, Rachel Dunscombe2, David Evans3, Mome Mukherjee4, Jeremy Wyatt5.   

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 depend 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 NHS and social care data is of unusably 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.

Entities:  

Keywords:  Big Data; bioinformatics; biomedical informatics; education; evidence-based practice; health informatics; health policy; learning health systems; programme evaluation

Mesh:

Year:  2018        PMID: 30398454     DOI: 10.14236/jhi.v25i2.1062

Source DB:  PubMed          Journal:  J Innov Health Inform        ISSN: 2058-4555


  5 in total

1.  Why Does Current Clinical Decision Support Frequently Fail to Support Clinical Decisions?

Authors:  Matthew Molloy; Philip Hagedorn; Maya Dewan
Journal:  Pediatr Crit Care Med       Date:  2022-08-01       Impact factor: 3.971

2.  Modeling Data Journeys to Inform the Digital Transformation of Kidney Transplant Services: Observational Study.

Authors:  Videha Sharma; Iliada Eleftheriou; Sabine N van der Veer; Andrew Brass; Titus Augustine; John Ainsworth
Journal:  J Med Internet Res       Date:  2022-04-21       Impact factor: 7.076

Review 3.  Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.

Authors:  Jeffrey A Cohen; Maria Trojano; Ellen M Mowry; Bernard Mj Uitdehaag; Stephen C Reingold; Ruth Ann Marrie
Journal:  Mult Scler       Date:  2019-11-28       Impact factor: 6.312

Review 4.  Data Integration Challenges for Machine Learning in Precision Medicine.

Authors:  Mireya Martínez-García; Enrique Hernández-Lemus
Journal:  Front Med (Lausanne)       Date:  2022-01-25

5.  Deriving a Standardised Recommended Respiratory Disease Codelist Repository for Future Research.

Authors:  Clare MacRae; Hannah Whittaker; Mome Mukherjee; Luke Daines; Ann Morgan; Chukwuma Iwundu; Mohammed Alsallakh; Eleftheria Vasileiou; Eimear O'Rourke; Alexander T Williams; Philip W Stone; Aziz Sheikh; Jennifer K Quint
Journal:  Pragmat Obs Res       Date:  2022-02-16
  5 in total

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