A secure environment for digital research

The DRIVE technical team manages the GOSH DRE, a digital research platform supplied by Aridhia.

​It provides secure access to data recorded for more than 20 years and works alongside the Electronic Patient Record system at GOSH. This allows for data management, visualisation and analysis in research and operational projects, in collaboration with academics, clinicians and partner organisations. The platform integrates with advanced analytics tools allowing data to be interrogated for patterns, modelling and proof-of-concept testing of digital technologies.

The team have set up more than 300 workspaces on the platform and developed standard templates for data extraction to accelerate the process.

The DRE has established project organisational structures and processes aligned with best practice in data science.

These underpin projects undertaken by the DRIVE technical team including, for example, implementation of the FAIR principles to describe data formats. These stand for findability, accessibility, interoperability, and reusability, and serve as a guide for delivery of data science research outputs. The team works in open source adding to GitHub repositories.

The team continually engages with operational and clinical teams that input and use data to define and implement best practice in data management, research and innovation across the NHS, as well as international hospitals and organisations with a shared vision to improve healthcare through data science.

Our work follows the FAIR principles for data management. These are standards to make data – Findable, Accessible, Interoperable, and Reusable, FAIR. Following these standards will improve research data management, for example, increasing the efficiency of multi-centre clinical trials, which will result in patient benefit by speeding up discoveries.

The team use REDCap to support researchers to collect data in a structured way. The system will also link with electronic patient record data. This data can then be transferred into DRE workspaces for analysis. Find out more about the community. 

It's common for clinicians to come together regularly to discuss adverse patient outcomes so that they can quickly identify any safety concerns and improve outcomes for the future.

To prepare for these meetings, data analysts have to collate and understand information on patients' outcomes. This takes lots of time.

The DRIVE technical team worked with cardiologists at GOSH to set up a data base and dashboard that links together this information. This means that the cardiac team can access data more rapidly in informative patient profiles to be discussed in these meetings. 

Electronic health records massively increase the breadth of data available to inform diagnosis, treatment and care.  However, the majority of this data cannot currently be used to support clinical decisions as it would take years to collate and understand the data for one patient.

The team are building a new tool which can quickly analyse a wide range of clinical information in real time to improve the accessibility and provision of information for clinicians, as well as patients, families and carers.

Using REDCap, the DRE team are supporting the largest cohort study of Juvenile Dermatomyositis and related inflammatory conditions in children and young people in the UK.

The team have developed a new database to capture structured data from 16 centres around the UK, and where possible, integration of data from GOSH’s electronic patient record.

This database supports multiple national and international studies on genetics, immunity and muscle pathology in the condition. It will also help to understand the value of data that is routinely collected through patient’s appointments for research which can improve efficiency of research in the future.

Understanding a typical trend in diagnoses of illnesses is important to predict demand and potential clinical challenges.

The DRIVE technical team utilised the DRE to analyse records of diagnosis at GOSH since 2000 to model and predict occurrence of events over the space of a year. This exploration showed that many of common diagnoses have a seasonal trend. It also showed that there was a significant difference in trends since March 2020, likely due to the pandemic. 

This type of analysis can help with service planning and supporting clinical decisions.

GOSH, along with leading children’s hospitals in the United States, Canada and Australia form the International Precision Child Health Partnership (IPCHiP). It is the first major collaboration around genomics in child health and aims to accelerate discovery and therapeutic treatment for rare diseases. The first study of the partnership looked at infantile epilepsy and whether earlier genetic diagnosis improves patient outcomes.

The DRIVE technical team team designed the architecture for, and implementation of, a completely new way to record genomic information so that it can be used to spot vital clues and patterns that can lead to potential new treatments.

Our team hosted and mentored two fellows from Faculty AI, a leading British artificial intelligence (AI) company. The collaboration developed two projects.

Project one found that AI could spot trends in lab test results and eventual diagnoses.

Project two built a computer model that could predict the probability that a patient may need a treatment, and when, based on their diagnoses.

Both of these technologies could be used in the future to support clinicians to make decisions that could get children and young people the right care, sooner.