Skip to main content

Why we need workload-based funding for general practices in England

The NHS is currently aiming to develop a new capitation-based formula for funding general practices in England. My view is that a revised formula won’t address the fundamental problem with the current method of funding primary care: the disconnect between workload and funding. All the new formula will do – no matter how well-designed - is shuffle money between general practices. Some practices will gain substantial sums, some will lose substantial sums; but most practices will see no major changes in their funding. Capitation-based formulas for general practices are a 20th century solution that the government is trying to continue to use in the 21st century. We need to move away from a capitation-based funding model to one based on actual workload. Under such a model, any work done by general practices – whether generated through government policy, patient demand or transfer of work from specialist settings into the community – would be paid for at its full cost. There would then be no need for any ‘funding formula’. The more work a practice did, the more it would get paid. This is how primary care funded in many other developed countries and results in improved access to primary care services. Critics of workload-based funding for general practices might argue it would dramatically increase costs as well as being administratively complex to administer. However, the alternative is the continuation of current trends, with worsening access for patients to primary care services; and an exacerbation of GP recruitment and retention problems.

This blog was originally posed as a rapid response on the BMJ website.

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

UK Covid-19 Inquiry - The Importance of a Strong Primary Healthcare System

In my witness statement for Module 10 of the UK Covid-19 Inquiry, I discuss the pandemic's impact through from the perspectives of  primary care and public health, drawing on my extensive experience as a senior academic at Imperial College London and as a practising GP and NHS Public Health Specialist.  I emphasise that the pandemic disproportionately affected people who were clinically vulnerable, the disabled, ethnic minority communities and those living in deprived areas. The pandemic highlighted how structural inequalities, multigenerational housing, and employment in high-risk frontline roles exacerbated health disparities.  My statement also critiques the weakening of public health infrastructure - particularly for the control of infectious diseases - and the lack of integrated health data systems to identify at-risk groups such as the clinically vulnerable. I also advocated for a more robust preventive healthcare model that prioritises community-based primary care ...
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