Anonymised Clinical Data Key to Understanding Traumatic Brain Injuries
News Dec 11, 2014
Funding for a two-year collaborative project involving health informatics company Aridhia, NHS Greater Glasgow and Clyde, Philips Medical Systems UK and the University of Glasgow was announced today. The funding comes from Innovate UK (formerly TSB) for Digital Health in a Connected Hospital.
The project brings recent advances in big data modelling directly into clinical practice and could lead to improved detection and prediction in the early management of traumatic brain injuries (TBI).
By exploiting big data in a real healthcare setting, it is hoped that clinicians will be able to make treatment decisions earlier, leading, through reduced length of hospital stay and in-hospital mortality, to more cost-effective healthcare delivery.
Aridhia will deploy its data science platform and data safe haven, AnalytiXagility, to securely store and analyse high frequency, anonymised data collected from patients through bedside monitoring systems in the neurointensive care unit at the Southern General Hospital.
The platform will implement an ‘app’ which clinicians will use at the bedside to select clinical analysis algorithms to inform the best course of care for the patient
David Sibbald, CEO at Aridhia, explains: “With few proven effective medical therapies for brain injury, traumatic brain injury is devastating, not only to the victim but also to their carers and to the society that supports their long term recovery, often lasting many years.”
The extension of Aridhia’s platform into a clinical setting will be made possible through a collaborative effort from the team at Aridhia and the expertise provided by the two non-industry partners – NHS Greater Glasgow and Clyde and the University of Glasgow – who will ensure the technology is meeting clinical needs.
NHS Greater Glasgow and Clyde’s Department of Clinical Physics and Bioengineering will provide expertise in managing and analysing the data collected from patient monitoring equipment in the unit. This team from within the Diagnostics Directorate at NHS Greater Glasgow and Clyde, led by Dr Laura Moss, will also assist with the clinical studies conducted as part of the system evaluation.
Clinicians from the University of Glasgow, led by Professor John Kinsella, will provide expertise in anaesthesia, pain and critical care and the design of the clinical trial as well as contributing to the evaluation of the clinical control application.
Aridhia enables translational healthcare by analysing big data and making this available to clinicians in a practical format in a real healthcare setting. The project also demonstrates an important extension to the platform’s ability in receiving and storing high frequency data, analysing this and returning results directly to the original bedside device.
Through the project, Aridhia aims to develop a framework that will enable clinically important physiological models and analysis to be implemented more quickly into clinical practice.
Modern traumatic brain injury (TBI) units have sophisticated bedside monitoring equipment, which means large volumes of high-frequency patient data is available. Currently, summaries of patient data such as blood pressure and intracranial pressure are displayed on monitoring devices to aid clinical decision-making, but, with the analysis of the raw high-frequency data yielding additional, previously unseen clinical information, this has the potential to revolutionise the treatment of TBI patients.
The project collaboration was successful in winning the R&D funding from Innovate UK earlier this year.
Unchained Labs Unveils LEA 9 Software Making Workflows EasyNews
Unchained Labs, the life sciences company that’s all about getting biologics researchers the right tool for the job, released its LEA 9 software, making Freeslate and Junior automation systems way more accessible.READ MORE
Human Microbiome Project: Millions of New Genes IdentifiedNews
A new study of the human microbiome, the trillions of microbial organisms that live on and within our bodies, has uncovered millions of previously unknown genes.READ MORE
Machine Learning to Increase the Pace of Brain Imaging AnalysisNews
New approach could allow doctors or researchers to quickly identify the data they need, and then rapidly fill in the fine details, making the process faster and more accurate.READ MORE