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Data science

Digital Health

Solving the challenges of utilising healthcare data

Healthcare is increasingly and routinely generating large volumes of data from different sources, which are difficult to handle and integrate. Confidence in data can be established through the knowledge that the data are validated, well-curated and have minimal bias or errors.

NPL has been running an inter-disciplinary project on Digital Health since 2018. The project aims to demonstrate how a 'metrological mindset' can be applied to the curation and analysis of healthcare data and metadata to help solve some of the important and emerging challenges of utilising healthcare data. The project addresses one of the key challenges of the UK's Measurement Strategy, to provide confidence in the intelligent and effective use of data.

Establishing confidence in the data is necessary for establishing trustworthiness and traceability, not only in the data itself, but also in the analysis and interpretation methods. This is essential in the current era where clinical decisions are increasingly being supported by a range of data from different sources. In the first year of the project, a landscaping activity was carried out and a stakeholder workshop was held to decide the areas to work on, resulting in a peer-reviewed publication in the British Journal of Radiology. 

                 120507-Diagram-NMS-N-Smith-Figure-1-01-(1).jpgNPL’s independent and impartial role allows it to act as a facilitator between important stakeholders from the NHS, industry and academia as the current and future challenges in healthcare data curation are outlined and addressed. We are always looking to meet new collaborators and extend our network, so if you wish to learn more about the project or engage with NPL in this area please get in touch..

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Influential publications

The Digital Health work has been published in several peer-reviewed scientific journals:

Building confidence in digital health through metrology
British Journal of Radiology, 2020; 93.
N. Smith, D. Sinden, S. Thomas, M. Romanchikova, J. Talbott, M. Adeogun.

Linkage of the CHHiP randomised controlled trial with primary care data: a study investigating ways of supplementing cancer trials and improving evidence-based practice 
BMC Medical Research Methodology; 2020; 20
Agnieszka Lemanska, Rachel C. Byford, Clare Cruickshank, David P. Dearnaley, Filipa Ferreira, Clare Griffin, Emma Hall, William Hinton, Simon de Lusignan, Julian Sherlock, Sara Faithfull.

Primary care prostate cancer case ascertainment  
Digital Personalized Health and Medicine; 2020; 270
Agnieszka Lemanska, Sara Faithfull, Harshana Liyanage, Sophie Otter, Marina Romanchikova, Julian Sherlock, Nadia A.S. Smith, Spencer A. Thomas, Simon de Lusignan.

Analysis of primary care Computerized Medical Records (CMR) data with deep autoencoders (DAE)
Front. Appl. Math. Stat.; 2019; 5 
Thomas SA, Smith N, Livina V, Yonova I, Webb R, de Lusignan S.

Future work

The Digital Health project is due to continue into 2021/22. The current studies will continue and be expanded. If you wish to learn more about the project or engage with NPL in this area please get in touch.

Please contact us for more information on software and procedures used in the data processing and analysis, descriptions of the data used in our analysis and useful published datasets.

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