Completed Public Health & Healthcare Psychology & Behaviour

Cross-cutting theme 1: Big Data and Computing innovation

In plain English

AI plain-English summary

A computing team is building digital systems to handle the vast streams of health data now pouring in from smartphones, wearable sensors, and electronic medical records. This matters because traditional clinical studies are slow and expensive—they struggle to recruit enough participants, collect data reliably, and manage the logistics of large-scale trials. The team’s systems are designed to solve those bottlenecks: they automate participant recruitment, streamline data collection, coordinate study oversight, and manage laboratory analysis and sample storage. If this works, it could dramatically increase the scale and efficiency of randomised trials and epidemiological studies. The practical payoff would be faster, cheaper answers to questions about what keeps people healthy or makes them sick—answers that currently take years and millions of pounds to obtain. This is applied infrastructure work, not fundamental science; its success will be measured by whether future studies run more smoothly, not by a single breakthrough discovery.

View original technical description
The emergence of new digital technologies is allowing huge quantities of information on health exposures and outcomes to be captured, stored, and analysed using advanced statistical techniques. ‘Big Data’ will provide new ways to conduct research and offers the potential of a dramatic increase in the scale and efficiency of clinical studies. This pioneering programme of work within PHRU aims to harness Big Data methodology to facilitate large-scale epidemiological studies and improve the efficiency of randomised trials by identifying barriers to their efficient conduct. This work is supported by a computing team that produces and maintains systems designed to meet the specific requirements of our new and on-going studies, and to ensure that they are sufficiently adaptable to provide a platform for future work. Our systems focus on aspects that are key to study quality: recruitment of large numbers of eligible participants; data collection and study conduct; study management, coordination and oversight; laboratory management including analysis and storage; ascertainment, confirmation and classification of outcome; and analysis, reporting and dissemination.

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Researchers

Martin Landray (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Urban Big Data Centre
MRC-PHE Centre for Environment and Health
MICA: Medical Bioinformatics: Data-Driven Discovery for Personalised Medicine
MRC Centre for Environment and Health
Probabilistic knowledge representation of big data for efficient integration and useful inference in bio-medicine

Original classification

Intramural

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.