Exciting new paper just out
Published On Tue 14 Sep 2021
Desiderata for the development of next-generation electronic health record phenotype libraries:
HIC support this Joint Statement from the Medical and Social Research Community
Published On Thu 10 Jun 2021
GP health data are crucial for the planning and provision of health services and to enable research discoveries that save and improve people’s lives
Combatting the COVID-19 pandemic has depended upon the ability to collect, link, access and use health data for research. It has allowed the NHS to identify and protect millions of people at high risk from COVID-19, to deliver and monitor the safety and effectiveness of the COVID-19 vaccination programme, and to identify life-saving treatments for COVID-19, including dexamethasone. These benefits must not stop with COVID-19. They must also extend to people living with other conditions such as mental illness, cancer, heart disease and diabetes.
As representatives of the UK’s medical and social research community, we support the goals of NHS Digital’s GP Data for Planning and Research (GPDPR) – an improved collection of primary care data from general practice systems. NHS Digital’s plans for making health data available for research are not new. NHS Digital has been collecting health data and enabling access by approved organisations for healthcare planning and research for public benefit for many years. What is new is the addition of GP data, which – as has been seen with COVID-19 – can support an even wider range of research to benefit patients.
We are therefore concerned to see the recent portrayal of this as a ‘data grab’. We believe that the trustworthy use of patient data for research that is in the public interest will enable better care, better treatments and better outcomes for the citizens of the UK.
Ensuring the confidence of GPs and patients is crucial and so we are pleased to see there will be more time taken to build transparency, clear communication and ongoing engagement, particularly around how the data will be accessed and commercial use. Whilst undertaking research involving health and social care data it is essential to demonstrate trustworthiness. The UK has expertise in applying safeguards in a scalable and verifiable way. This is exemplified through the UK Health Data Research Alliance’s leadership on Trusted Research Environments (TREs) centred on the “five safes framework”, data use registers, and meaningful public and patient engagement and involvement.
For those who do not wish their data to be included, the National Data Opt-out, which was developed with much consultation across the NHS over several years, provides an important way for people to opt-out of the wider use of their data across the whole NHS system, at any time. However, we hope that with better information on both the benefits and the safeguards of this improved approach, people will choose not to opt-out. It is vital that healthcare planning and research includes and represents all people so that we find treatments, improve care, and deliver positive outcomes for everyone.
HIC are to lead and collaborate on 3 major new projects researching chronic pain
Published On Thu 27 May 2021
HIC are delighted to announce their leadership and collaboration in 3 major new projects researching chronic pain.
University of Dundee scientists and clinicians have secured £5 million in funding to aid research that aims to establish the causes of vulnerability to chronic pain and advance treatment.
Two Dundee-led projects within the University’s School of Medicine have been awarded the funds by the Advanced Pain Discovery Platform (APDP), a funding mechanism created by UKRI, Versus Arthritis and Eli Lily.
Chronic pain affects millions of people in the UK and is often linked to conditions that include headaches, arthritis, cancer, nerve pain, back pain, fibromyalgia and more. To help address treatment challenges and improve the lives of people affected by pain conditions, a better understanding of the mechanisms of pain is needed.
The Alleviate APDP Pain Research Data Hub is led by HIC and aims to deliver a consortium-based platform of national scale, generating discovery and translational science that will break through the complexity of pain and reveal new treatment approaches to address a wide spectrum of chronic and debilitating clinical conditions.
Dundee’s Consortium Against Pain Inequalities (CAPE) project was awarded almost £3M, and Alleviate - APDP Pain Research Data Hub, was awarded £2M. Additionally, more than £1 million of funding will come to the School of Medicine from PAINSTORM, another APDP consortium led by the University of Oxford. HIC is supporting both projects.
The CAPE team will establish whether exposure to adverse childhood experiences contributes to higher levels of chronic pain in the most deprived communities, which fuels more frequent prescriptions of opioid analgesics and may contribute to drug misuse and increasing drug associated deaths.
Professor Tim Hales, Principal Investigator of CAPE, said, “We are delighted with the success of our proposals. This will enable us to build on our preclinical work, which links early life adversity to increased vulnerability to long term pain and adverse effects of powerful opioid pain killers.
“CAPE will now explore the impact of more complex adverse childhood events on chronic pain and responses to treatment in adult patients. If these relationships are evident from our research, the evidence will inform public health approaches and the development of better treatments for chronic pain in vulnerable populations.”
Research to assist Oxford’s PAINSTORM project will also be undertaken in Dundee, directed by Professors Lesley Colvin and Blair Smith, joint leaders of the School of Medicine’s Chronic Pain Research Group. The consortium aims to discover the causes of neuropathic pain, one of the most common and most distressing types of pain, with a view to preventing it and improving its treatment.
The project will make good use of the unique resources already available at Dundee, including the Clinical Research Imaging Facility led by Professor Douglas Steele, enabling large population studies of pain in diabetes, and innovative assessment of people who develop pain because of chemotherapy treatment for cancer.
Data from CAPE, PAINSTORM and two other ADPD consortia will be captured, hosted, and curated by Alleviate, led by Professor Emily Jefferson, Director of the School of Medicine’s Health Informatics Centre.
Professor Jefferson said, “Our UK-wide pain data hub will deliver world class health data infrastructure and services for pain research, guided by leading experts in pain research and in partnership with the NHS, the APDP consortia, industry partners and people with lived experience of chronic pain.”
Success securing these awards positions the School of Medicine as a major centre for translational chronic pain research.
"Data Saves Lives: The Fight Against COVID-19"
Published On Tue 2 Feb 2021
Emily Jefferson, from the University of Dundee, covers the importance of timely, good quality data for both research and public health. Emily describes her group's research and the journey of the Health Informatics Centre (HIC) over the past decade. She describes how the innovative technologies developed by the group are being used to protect patient confidentiality and maintain public trust in the use of health data for research. Such technologies and expertise are now being utilised at scale to support the UK-wide fight against Covid-19 using population-wide health data.
Cohort Discovery on the HDR Innovation Gateway
Published On Mon 26 Apr 2021
Delighted to announce exciting new functionality within the Health Data Research gateway, delivered by the Dundee jointly led CO-CONNECT project. "Cohort Discovery" is a new tool that allows researchers to search by specific population criteria across multiple datasets. Read the full story
Using machine learning approaches for multi-omics data analysis
Published On Fri 30 Apr 2021
Excited to announce a new publication lead by HIC: Using machine learning approaches for multi-omics data analysis: A review