In today’s healthcare landscape, general practitioners (GPs) are navigating an increasingly complex and demanding environment. With a growing patient population and a shrinking workforce, the pressure on primary care has never been greater. According to a recent analysis by the British Medical Association, a single full-time GP is now responsible for an average of 2,255 patients, 14% more than in 2015.
The rising demand is compounded by the increasing complexity of patient needs, often involving multiple chronic conditions. This has placed an even greater burden on GPs, who are expected to manage more intricate cases with limited time and resources. This surge in demand, coupled with administrative burdens and the need for personalised care, has created a perfect storm in general practice.
As a GP, I’ve experienced firsthand how this pressure can impact the quality of care we provide. Time with patients is one of the most important aspects of our work, yet it’s often in short supply. When stretched thin, it becomes harder to build trust, listen attentively, and tailor treatment plans to individual needs. This can lead to overlooked symptoms, reduced patient satisfaction, and poorer health outcomes.
However, amidst these challenges, the latest wave of innovation is offering a potential solution. The UK government has recently published guidance encouraging the use of generative AI technologies in both primary and secondary care. Clinically validated AI tools that support decision-making are beginning to reshape how we practice medicine, making it easier to access trusted, evidence-based information at the point of care.
Empowering GPs through AI
AI presents a promising yet practical solution to many of the systemic issues we face in primary care. These technologies have been designed to integrate seamlessly into clinical workflows, reducing friction and enabling faster, more informed decision-making.
In my experience, these innovations have the potential to make a tangible difference in three key areas:
Diagnosis support: AI tools can assist in identifying differential diagnoses, especially in complex cases involving co-morbidities and multiple medication use. This may reduce the risk of clinical errors and enhances diagnostic accuracy.
Improving knowledge: With instant access to the latest medical research and clinical guidelines, AI can help clinicians stay informed to deliver best in class patient care.
Administrative relief: By streamlining documentation and reducing admin tasks, AI can enable clinicians to devote more quality time to patients.
These capabilities are no longer theoretical and are being used in real-world settings to improve efficiency and enhance the GP-patient relationship.
AI-powered moments that are shaping clinical practice
By taking the time to explore the potential of AI tools, they’ve become a key role in my clinical practice. Through this process of exploration and understanding how and where AI can support my work as a GP, these tools are now built into my day-to-day workflow. My use of AI isn’t just a technological upgrade, they are truly practice-changing moments that have reshaped how I diagnose, treat and connect with patients.
Managing complex co-morbidities efficiently
I had a 76-year-old patient present to me with a skin infection on their leg, with poor kidney function and on multiple medications, including blood thinning drugs. Usually, I would look up each medication individually and cross-reference potential drug interactions which is a time-consuming process. But with AI, I am able to navigate fragmented systems efficiently to identify potential drug interactions and prescribe treatment safely. This enables me to spend more valuable time interacting with patients.
Preventing escalation through insights intervention
A patient came into urgent care with acute constipation; they had previously been treated by a GP and nurse practitioner with laxatives, but their condition worsened. In a case like this, I could have assumed the initial treatment simply hadn’t worked and prescribed another laxative, delaying effective care. However, with AI, I can efficiently review patients’ medications to understand potential underlying causes. On this occasion, I discovered that the likely cause was a side-effect of his mental health medication, and with a simple dose adjustment, in consultation with his specialist, I was able to resolve the issue and avoid unnecessary escalation.
Reducing clinical bias and broadening diagnostic thinking
I had a 5-year-old patient present to me with unilateral hip pain that had persisted for 8 weeks, where all standard tests had come back normal. In this scenario, I might have continued along a similar diagnostic path as previous clinicians. However, by adding AI into my process, it offers an alternative diagnosis, helping to identify how a patient’s ethnicity could influence disease risk and progression. This insight led to timely and appropriate care.
These practice-changing moments have taught me that AI isn’t about replacing me as a GP – it’s about amplifying what we do best. It’s about giving us tools to be more present, more informed and more effective in our roles.
Considerations for the use of AI in clinical practice
As interest in AI continues to grow and healthcare systems begin to embrace its potential, clinicians must approach these tools with both optimism and caution. While AI can be a powerful ally in improving efficiency and enhancing care, its implementation must be guided by responsible and ethical use, clinical judgment, and a clear understanding of its limitations.
Key considerations for responsible AI use in clinical settings include:
Clinical validation: Tools must be developed with input from medical professionals and tested in real-world settings to help ensure they support safe, effective decision-making at the point of care.
Transparency and explainability: Clinicians must understand how AI tools generate their outputs but also the underlying processes and data that inform those decisions. The ability to “look under the hood” – to see and interrogate the mechanisms behind AI recommendations is vital. This transparency allows clinicians to apply AI tools appropriately, critically assess their reliability, and foster trust in their application to patient care. Without such explainability, the safe and effective integration of AI into clinical workflows is significantly compromised.
Data privacy and security: Patient data must be handled with the highest standards of confidentiality and compliance.
Bias and health equity: AI must be designed to reduce disparities in healthcare. Developers and medical professionals should work together to identify and mitigate bias in training data and algorithms.
By keeping these principles in mind, we can help ensure that AI enhances clinical practice, supports better outcomes and maintains the trust that is foundational to patient care.
Transforming practices for better patient care
As a GP working in the UK, I understand the pressures on resources, time and the needs of patients and carers. The challenges we face in ensuring we deliver the best possible care to all our patients are significant.
However, through my work with Elsevier and as a member of the Clinical Best Practice Council, I’ve seen firsthand the potential for technology to play a role in supporting the work of GPs. By combining medical expertise with advanced technology, we can develop tools that are evidence-based, clinically relevant and practical for real-world use.
The goal is simple: to help clinicians make informed decisions more efficiently, so we can spend more time where it matters most – caring for patients.
Clinical AI tools are here now, and their adoption in primary care is inevitable. By implementing purpose-built AI solutions specifically designed for clinical use, we can reduce administrative noise and surface trusted information at the point of care, allowing us to focus on what truly matters – listening to our patients and making informed decisions that support them.
The journey is just the beginning, but the potential is clear. With the right tools and thoughtful implementation, AI can help us meet the challenges of general practice and deliver the kind of care our patients deserve.
