Skip to main content

Some of the many negative consequences of Brexit for the NHS

I've started to list some of the many negative consequences of Brexit for the NHS

  1. The future employment of the 50,000 EU and EEA nationals who work in the NHS (including around 9,000 doctors).
  2. The development and implementation of reciprocal agreements to recognise the qualifications of doctors and other health professionals from the EU.
  3. More limited rights for UK health professionals to live and work in other EU countries.
  4. The access to NHS care of the three million EU nationals in the UK and the access to European healthcare of the two million UK nationals living in other EU countries.
  5. Disruption to Pan-European initiatives to protect public health.
  6. Loss of funding from the EU for medical research.
  7. Relocation out of London of the European Medicines Agency.
  8. Reduced funding for the NHS if predictions of lower economic growth for the UK are correct.
  9. Threats to the development of health IT standards.
  10. Imported NHS drugs and supplies may be more expensive.
  11. The UK may be a less favoured site for clinical trials and other industry supported research.


Comments

Post a Comment

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...
Post a Comment
Read more

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...
Post a Comment
Read more

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...
Post a Comment
Read more