Skip to main content

End of MRC GPRD Access Scheme

For the past few years, the MRC has funded access to GPRD data for UK researchers. This has been very helpful in increasing expertise in the use of GPRD amongst UK researchers and boosting the number of academic papers that use the database. This scheme has now ended and the effects this might have on research were discussed in a recent BMJ news article. The UK has one of the highest uses of electronic patient records in primary care, and these records have been a great resource for biomedical researchers.

Before the licence scheme was implemented the main users of the database were based in the United States, Spain, and Switzerland but that after it was set up this was no longer the case. Although the MRC will continue to fund access to the data via its research grants schemes, the process for applying for access to the database will be much lengthier and because applying for the council’s grants is highly competitive it is likely that most grant applications will be unsuccessful. Hence, it is very likely that we will see a reduction in UK based research using the GPRD once current projects using data obtained under the old scheme end. In other areas of the NHS and public health, there are datasets that can be obtained at relatively low cost (or sometimes no cost). These include hospital episode statistics, mortality statistics, and cancer registrations.

We need a similar easy and cheap access to anonymised primary care records for research. The UK has a strong primary healthcare delivery system and a very high use of electronic patient records in this setting. We should therefore be leading the world in the secondary uses of data obtained from primary care.

Comments

Popular posts from this blog

What is the difference between primordial prevention and primary prevention?

Primordial prevention and primary prevention are both crucial strategies for promoting health, but they operate at different levels. Primordial prevention aims to address the root causes of health problems and improve the wider determinants of health. It focuses on preventing the emergence of risk factors in the first place by tackling the underlying social, economic, and environmental determinants of health. This involves broad, population-wide interventions such as: Policies that promote healthy food choices: Think about initiatives like taxing sugary drinks to discourage unhealthy consumption, or providing subsidies for fruits and vegetables to make them more accessible. Urban planning that prioritises well-being: This could include creating walkable neighborhoods with safe cycling routes, ensuring access to green spaces for recreation and relaxation, and designing communities that foster social connections. Social programs that address inequality: Initiatives aimed at reducing pov...

What makes a good doctor – and who gets to decide?

What Makes a Good Doctor? This is the question that Waseem Jerjes and I explore in the Journal of the Royal Society of Medicine . It is a key question that underpins the architecture of medical education, clinical practice, regulation, and professional identity. It cannot be answered by regulators, educators, or employers in isolation. It must be answered together – by doctors and patients – revisited throughout a career, and adapted as society and the profession change. Without that shared reflection, the danger is not simply disillusionment, but the erosion of the moral foundations of clinical work. As we enter an era when diagnosis will increasingly involve artificial intelligence and when performance metrics reward volume over value, reclaiming this question as a professional one is imperative. The integrity of our institutions – and of the practitioners within them – depends on reimagining excellence in inclusive, relational terms. A good doctor is not a flawless technician or a f...

Relevance Over Recall: Rethinking How AI Uses Clinical Data

Our article in the Journal of the Royal Society of Medicine argues that safe and effective AI in healthcare must incorporate mechanisms that emulate human judgement - down-weighting old, inaccurate or superseded information and prioritising what is recent, clinically relevant and reaffirmed - so that AI supports, rather than disrupts, high-quality patient care.  Clinicians constantly revise, reinterpret and filter past information so that only what is relevant, accurate and timely shapes present-day management decisions; medical records function as dynamic “working tools” rather than fixed archives. By contrast, many AI systems lack this capacity for selective forgetting and often treat all historical data as equally meaningful.  This can lead to outdated or low-confidence diagnoses being repeatedly resurfaced, persistent labels influencing clinical expectations, and irrelevant, long-resolved events cluttering summaries and decision-support outputs. Such indiscriminate recall...