Who is involved in the Sandbox?
There are seven projects involved in the Health Data Research UK Sandbox, which are outlined briefly below. For more information on what is involved in the Sandbox click here.
Use Case 1: Early detection of neurodegenerative diseases
Lead: Rafael C Jimenez – Head of Research Informatics at “Alzheimer’s Research UK”.
Worldwide, an estimate of 50 million people are living with dementia. Yet, there are no treatments that can cure or halt the progression of dementia-causing diseases. Research indicates this is because we are looking for solutions at a very late stage when significant, irreparable brain damage has already occurred. Neurodegenerative diseases like Alzheimer’s begin in the brain around two decades before symptoms like memory loss start to show. By capitalising on cost-effective and non-invasive technological innovations, research data and machine learning methods, this project will develop a digital toolkit to identify people in the earliest, pre-symptomatic stages of diseases like Alzheimer’s.
The risk-related information derived from the digital data will be used to encourage lifestyle changes and to triage individuals into clinical trials or further medical testing. As such, the outputs of this project will benefit patients and clinicians and will produce a rich dataset for further research into the earliest stages of the disease to drive drug discovery.
Use Case 2: HealthSpan™ and Healthy Lifespan® Index metric validation using anonymised linked, longitudinal data
Lead: Nasrin Hafezparast – Co-founder and CTO – Outcomes Based Healthcare – an NHS Innovation Accelerator organisation
To deliver on recent healthcare shifts to improve Healthy Lifespans, as well as overall life expectancy (how long we spend healthy, not just how long we live), means understanding how, when, and why people first enter periods of ill health in their lives. This knowledge helps us develop approaches so people can stay healthier, for longer.
HealthSpan™ are objective, data-driven outcomes, providing novel and unique ways of measuring health of populations, through routinely collected, linked local health and care data. This suite of metrics provides reliable, cost-effective ways of measuring success of healthcare systems in supporting people to stay healthy.
HealthSpan data can be used to improve how long people stay in good health, and focusing on what matters most to people. These are vital and emerging outcomes to ensure financially sustainable future healthcare services.
Use Case 3: Detecting antidepressant treatment response from prescribing data
Lead: Mark Adams, Division of Psychiatry, University of Edinburgh
Depression is a common mental health disorder that is often treated with medication. Some patients respond well to this medication, while others do not. We aim to discover why people continue with or switch between antidepressant medications. We will do this by examining de-identified electronic health records of a large number of people. This will help us find out how genetic, environment, and other health factors influence which medications work best for which people.
This research will build the tools and knowledge needed to identify people who are not responding to antidepressant medication to better target treatments for depression. This will help people find the drug or therapy that they are most likely to respond to.
Use Case 4: RAPID-digital – Long term health outcomes in the UK NCRI RAPID trial in early stage Hodgkin lymphoma
Lead: John Radford, Division of Caner Sciences, University of Manchester
In the UK National Cancer Research Institute RAPID trial in early stage Hodgkin lymphoma (Radford et al, New England Journal of Medicine 2015) we found that using PET scanning it is possible to identify patients for whom chemotherapy alone rather than chemotherapy plus radiotherapy produces very good results in the short term. What we don’t know however is whether these outcomes are durable in the longer term and if reducing the use of radiotherapy has any impact on general health and healthcare usage.
In the RAPID-digital project we will address these important questions. By integrating data from GP, hospital and other national databases we will determine the long term health outcomes of the 602 patients who took part in RAPID. The results will influence worldwide practice in Hodgkin lymphoma, inform patients and serve as an exemplar of the power of digital technology in long term follow-up of clinical trials.
Use Case 5: The Emergency Care Data Research Resource
Lead: Tim Coats, Department of Cardiovascular Sciences – University of Leicester
The new Emergency Care Data Set (ECDS), using disease specific subgroups identified from DIH data, and linked to HES data will create a tool that researchers can use to identify high/low risk patient groups and to set priorities for interventions which might decrease hospital admission or length of stay.
Use Case 6: MendelScan: Rare disease EHR scanning platform for healthcare providers
Lead: Giovanni Charles, Chief Technology Officer, Mendelian
MendelScan flags electronic health records that suggest early, indicative signs of rare and hard-to-diagnose disease. A well designed, efficient detection system can improve patient outcomes, open avenues for preventative care and significantly shorten the costly journey of rare disease diagnosis. Mendelian researches clinical workflow improvements and machine learning methods in the medical domain to accelerate the diagnosis of rare disease.
Use Case 7: MyDiabetesMyWay and MyDiabetesClinical
Lead: Debbie Wake, Chief Operating Officer, MyWay Digital Health
MyDiabetesMyWay is a multi-award-winning data-driven self-management platform for people with diabetes, running across NHS Scotland (>50,000 registrants), and being rolled out in NHS England, supported by NHS Innovation Accelerator/ NHS Test Bed. MyDiabetesMyWay benefits patients health and reduces NHS costs.