Written evidence from PharmBot AI (PMA0005)
1. Introduction
This submission provides evidence on the role of artificial intelligence (AI) in supporting personalised medicine and improving access to clinical services within the National Health Service (NHS), with particular reference to community pharmacy.
The author is a Consultant Pharmacist and founder of PharmBot AI Ltd, a UK health technology company developing AI-enabled clinical decision support systems designed to assist pharmacists and other healthcare professionals in delivering structured clinical services.
Community pharmacy represents one of the most accessible parts of the NHS, with approximately 10,000 pharmacies across England delivering medicines optimisation, public health services and increasingly expanded clinical services through programmes such as Pharmacy First.
Despite this significant clinical footprint, digital innovation in community pharmacy remains relatively limited compared with other areas of healthcare. Artificial intelligence offers an opportunity to enhance the quality, consistency and efficiency of pharmacy-led clinical services through the use of structured consultation tools and clinical decision support systems.
This submission draws upon direct practical experience of developing, piloting and deploying an AI-enabled clinical consultation system within community pharmacy, providing a frontline perspective on both the opportunities and systemic barriers associated with AI adoption in the NHS.
2. Author Background and Relevant Experience
The author has developed and piloted an AI-enabled clinical consultation system within community pharmacy. The system has to date supported 76 structured clinical consultations within the Pharmacy First programme, with preliminary analysis suggesting a time saving of approximately 15 to 20 minutes per consultation compared with standard manual consultation workflows. This represents a meaningful reduction in pharmacist workload at a time when primary care capacity is under significant pressure.
The author has written on the emerging role of artificial intelligence in pharmacy practice, including articles published in the PM Healthcare Journal discussing how AI-enabled clinical workflows may support medicines optimisation and pharmacy service delivery.
Work associated with the development of AI-enabled pharmacy consultation systems has also attracted recognition within the UK healthcare innovation ecosystem, including nominations for the HSJ Partnership Awards (Best Pharmaceutical Partnership with the NHS) and the HealthInvestor Awards (Health Tech Provider of the Year).
Interest in the technology has also extended internationally, including engagement from researchers at the German Research Centre for Artificial Intelligence (DFKI), who requested a demonstration of the platform in connection with a presentation on AI in pharmacy.
These developments reflect growing interest in how AI technologies may support clinical decision-making within pharmacy practice.
3. The Role of Artificial Intelligence in Personalised Medicine
Personalised medicine involves tailoring healthcare interventions based on individual patient characteristics such as medical history, risk factors and treatment response.
In community pharmacy, AI-enabled clinical decision support has the potential to support personalised medicine by analysing individual patient data including symptoms, medical history, current medications and laboratory results and applying clinical guidelines to the specific circumstances of each patient. Rather than delivering standardised advice, AI-assisted consultation systems can support clinicians in identifying patient-specific risk factors, contraindications and treatment options.
Emerging applications within pharmacy include AI-assisted hormone health services, where individual patient laboratory results including testosterone levels, metabolic markers and cardiovascular risk indicators can be interpreted within a structured clinical framework to support personalised treatment decisions. Such applications represent a natural extension of the pharmacist’s expanding clinical role and illustrate how AI may support personalised medicine delivery within community-based settings.
In clinical practice, AI systems can support healthcare professionals by:
• analysing structured patient information
• supporting personalised clinical decision-making based on individual patient data
• identifying patient-specific risk factors or contraindications
• generating structured consultation documentation
• assisting clinicians in applying relevant clinical guidelines to individual circumstances
It is important that AI systems are viewed as clinical decision support tools rather than replacements for clinicians. Their role is to enhance the quality and consistency of clinical decision-making, not to supplant professional judgement. Within primary care settings, such systems may help clinicians manage increasing patient demand while maintaining high standards of clinical governance.
4. Community Pharmacy as a Platform for AI-Enabled Healthcare
Community pharmacy represents a significant yet underutilised platform for the deployment of digital clinical technologies.
Pharmacists are among the most accessible healthcare professionals, and pharmacies operate across both urban and rural communities. In recent years, the scope of pharmacy services has expanded to include clinical services traditionally delivered within general practice.
Examples include:
• Pharmacy First services for common conditions
• hypertension case-finding services
• contraception services
• vaccination programmes
• independent prescribing by pharmacists
The expansion of pharmacist independent prescribing further increases the clinical complexity of services delivered within community pharmacy settings, and creates additional demand for structured clinical decision support tools that can assist prescribers in making safe, evidence-based and personalised treatment decisions.
These services require pharmacists to conduct structured clinical consultations, assess patient symptoms, apply clinical guidelines and document consultations appropriately. AI-enabled consultation systems could support pharmacists in these activities by guiding structured assessments, assisting with clinical reasoning and generating documentation aligned with NHS requirements.
By supporting pharmacists in this way, AI systems could enhance the consistency and scalability of pharmacy-led clinical services, contributing to a more accessible and personalised primary care offer.
5. Barriers to Innovation in the NHS
Despite the UK’s strong academic research base and vibrant life sciences sector, many innovations struggle to achieve widespread adoption within the NHS. Several structural barriers contribute to this challenge.
Procurement Complexity
The NHS procurement landscape is fragmented, with multiple organisations responsible for commissioning services and adopting technologies. This can make it difficult for innovative technologies to scale across the health system.
The author has direct experience navigating the NHS procurement landscape as a small health technology company. The process of onboarding onto NHS contracting vehicles requires compliance with multiple overlapping standards including clinical safety standards, data security frameworks and interoperability requirements. While these standards are appropriate and necessary, the pathway lacks a dedicated support structure for small and emerging companies, creating a significant resource burden that larger organisations are better placed to absorb.
Regulatory Complexity
AI-enabled clinical systems must comply with multiple regulatory frameworks, including medical device regulation, data protection legislation and NHS digital standards. While these frameworks are essential to ensure patient safety, the regulatory landscape for AI technologies remains complex and evolving.
Digital Infrastructure and Interoperability
Many digital health innovations rely on integration with NHS information systems such as electronic patient records and prescribing platforms. Achieving interoperability between new technologies and existing systems remains a major technical and organisational challenge.
Commissioning and Funding Models
Some innovative digital tools improve efficiency or clinical workflow rather than directly generating new billable services. As a result, existing commissioning models may not provide clear incentives for adopting such technologies.
AI-enabled consultation tools that improve workflow efficiency and clinical consistency do not currently fit neatly within existing NHS commissioning frameworks, which tend to reward activity rather than quality or efficiency. This represents a structural disincentive to adopting technologies that could meaningfully reduce pressure on primary care.
6. AI-Enabled Clinical Decision Support in Pharmacy Services
Recent advances in artificial intelligence have enabled the development of systems capable of guiding structured clinical consultations.
AI-enabled pharmacy consultation platforms can support clinicians by:
• guiding symptom-based patient assessments
• analysing patient responses against clinical guidelines
• identifying appropriate personalised treatment options
• generating structured consultation records
• assisting with clinical governance requirements
Such systems may be particularly useful within services such as Pharmacy First, where pharmacists assess patients presenting with common conditions and determine appropriate treatment or referral pathways. By supporting consultation workflows and documentation, AI systems could help reduce administrative burden while supporting safe clinical decision-making.
7. Case Study: Emerging AI Infrastructure for Pharmacy Services
One example of emerging digital infrastructure within pharmacy is the development of AI-enabled clinical support platforms designed to assist pharmacists in delivering structured consultations and clinical services.
PharmBot AI is developing a platform intended to function as AI-assisted clinical workflow infrastructure for pharmacy services, supporting pharmacists in conducting consultations, applying clinical guidance and generating structured clinical documentation.
The system is designed to support services such as Pharmacy First by providing:
• structured clinical assessment workflows
• AI-assisted analysis of patient responses
• support for guideline-based and personalised treatment decisions
• automated generation of clinical consultation records (such as SOAP notes)
• tools to assist pharmacists in identifying when escalation or referral may be required
To date, the platform has supported 76 structured clinical consultations within the Pharmacy First programme, with preliminary analysis indicating a time saving of approximately 15 to 20 minutes per consultation. This early evidence suggests meaningful potential to reduce pharmacist administrative burden while maintaining high standards of clinical documentation.
Rather than replacing clinical judgement, the system is designed to operate as clinical infrastructure that supports healthcare professionals in delivering safe, consistent and auditable clinical services.
The development of such systems illustrates the potential for artificial intelligence to support the scaling of clinical services within community pharmacy, particularly as pharmacists take on expanded roles within primary care. At the same time, the process of developing such technologies highlights systemic barriers to innovation within the NHS, including regulatory complexity, procurement challenges and limited opportunities for piloting new digital clinical tools within frontline services.
8. Policy Recommendations
To support innovation in personalised medicine and AI within the NHS, the following specific policy measures should be considered.
9. Conclusion
Artificial intelligence has significant potential to support personalised medicine and improve healthcare delivery across the NHS.
While much attention has focused on AI applications within hospital settings and genomics research, there is also substantial opportunity to deploy AI within community-based clinical services such as pharmacy. AI-enabled clinical decision support systems could support structured and personalised consultations, improve documentation and help clinicians deliver evidence-based care more efficiently.
Early real-world evidence from the pilot of AI-enabled pharmacy consultation tools – including measurable time savings and structured clinical documentation – suggests that AI has a practical and immediate role to play in community pharmacy, not only as a future aspiration but as a deployable technology today.
However, realising this potential will require policy action to address barriers related to procurement, regulation and digital infrastructure. With appropriate support, AI technologies could play an important role in enabling a more accessible, efficient and genuinely personalised healthcare system.