Embracing a brave new world

Adapting technologies 

Many robotics manufacturers are adapting their offer for the pharmacy sector. “AI technologies are increasingly being incorporated to enhance safety, accuracy and efficiency within pharmacy workflows, including those that use robotics,” says Anderson. “AI can support the validation of prescriptions, identify and prioritise urgent or dose-change prescriptions, and automate aspects of stock management. This allows pharmacy teams to focus more on clinical roles and patient care while improving the safety and efficiency of medicines supply.” 

Meditech systems pre-empt dispensing patterns to assist with ordering. “Rather than sporadic daily ordering, we can forecast what you’re going to use in the month and feed your stock control or cascade ordering system,” says Hodivala. “This gives consistency of supply, better pricing because you are bulk ordering, and is far more efficient.” 

The company’s MT.Speed system uses a high speed channel dispensing mechanism for fast moving stock lines. “Machine learning will identify both faster and slow-moving stock lines and use the most appropriate method of dispensing to make sure pharmacists get those fast-moving stock lines quickly,” Hodivala explains. This approach is revolutionising the way pharmacies are able to automate their dispensing and the way hubs work in community pharmacy, he says.

He gives the example of one pharmacy group that has centralised all of its repeat dispensing: “It has reduced the group’s stock in the spoke stores by about 70 per cent because stock is now being ordered through the hub. Hub ordering  is more efficient, the time from prescription to patient is reduced, and it frees up time for clinical services.”

One of Meditech’s big automation targets is the integration of fast robotic dispensing with a process called ‘scan to bag’. This involves digitally checking medicines packs and creating a single label, rather than printing streams of labels and possibly putting the wrong labels on the wrong boxes. By using the ‘scan to bag’ process, “you can eliminate the final accuracy check of the prescription because it’s been digitally checked all the way through”, Hodivala says.

At Centred Solutions, the FlowRx Hub already uses AI to support how patient labels are applied to medication packs, says Laban.

A machine learning model is used to identify the text orientation on medication packs so that FlowRx’s automated labelling unit can affix the patient label with its text running in the same direction as the pack, making it easier for patients to read. The automated results closely align with how pharmacy staff apply labels by hand. However, the AI solution is “significantly faster, taking only six milliseconds to identify the text orientation of the pack compared to 450 milliseconds with the previously used methodology”.

“It is being used by all of our FlowRx Auto Hub pharmacy customers”, says Laban.“We are seeing incredible progress from technology companies when it comes to applying AI in a range of areas, including medical imaging, drug discovery and tackling diseases. The dispensing of medication should be no different. That is why we are now exploring further uses for machine learning models in our FlowRx workflow.” 

These potential uses include learning where to place the label on the medication pack, so that in the future it could be placed specifically over the ‘affix dispensing label here’ area. Ideas also include determining the height of the medication pack from the image captured by one of the barcode scanners on the automated labelling unit, and learning to control a counting device to count different kinds of pills.

Pharmacy systems suppliers are “developing AI-enabled decision support tools to assist with clinical decision-making, automate repeat prescription management and analyse patient data to identify opportunities for intervention and improved outcomes”, says Professor Anderson. “Ensuring these systems work together seamlessly, are secure, and co-produced with pharmacists, patients and developers is essential to support high-quality care and meet the needs of pharmacy practice.”

Tracey Robertson, managing director of pharmacy software provider Cegedim Rx, says Cegedim Rx’s Intelligence Hub leverages AI and machine learning “to transform pharmacy data into clear, actionable management insights”. 

“This empowers pharmacy teams to make proactive decisions that optimise patient outcomes, maximise revenue opportunities and enhance overall operational efficiency,” she says.

Fin McCaul is managing director of Prestwich Pharmacy in North Manchester, where Meditech robots are used to put stock away and retrieve stock in the dispensing process. “This releases a minimum of one full-time equivalent person in our team so they can focus on patient services,” says McCaul. Using PMR systems effectively and getting the most out of them is “critical in a pharmacy business”, he adds.

“They can assist with stock management, patient management, pharmacy checks, clinical assessments and accuracy checks.” Other technology in use at the pharmacy includes buying portals, which assist in locating stock at or below tariff price. “It is been beneficial in sourcing appropriate stock so that we are not losing money from overpriced stock,” says McCaul.

Ade Williams, pharmacist at Bedminster Pharmacy in Bristol, has been using his current PMR system for about 18 months. “It picks up prescribing errors and prompts us to consult patients to seek additional information,” he says.

“It also helps us to improve our clinical knowledge, especially for less common treatments. When scanning treatments, the system may highlight that a particular medication is less commonly prescribed or that a dosage is less commonly given. This prompts you to do additional checks.”

Challenges

While AI presents exciting opportunities for pharmacy, it is essential to address several issues to ensure its safe and effective use, says Professor Anderson. These include safeguarding patient data and ensuring privacy standards are upheld; mitigating bias within AI systems through diverse and representative datasets; and maintaining transparency so that patients understand how AI is supporting their care.

“Strong governance and regulatory frameworks are needed to oversee the development and deployment of AI tools, and pharmacy professionals must remain accountable for the decisions they make, whether supported by AI or not,” she says.

“AI technologies deployed within healthcare, including pharmacy services, for the safe and effective use of medicines, must comply with regulatory and legislative requirements as these evolve with the evidence base over time. There is also a need to ensure the pharmacy workforce has the education, training and protected learning time required to develop the skills necessary to use AI safely and effectively.”

With AI rapidly developing, the ability to keep up and use it effectively is “really challenging” for pharmacy teams, says McCaul.

Williams believes there is “still a lot of reluctance” about using AI, and some pharmacists are unable to see how it can optimise some of the current work processes or free up more time for them.

“Like any new technology, AI needs to be understood before the full benefits can be realised,” says Laban. “There is a risk that if it is implemented too soon, without full understanding, it could detract from the first class service currently provided by pharmacists and their teams.” 

“AI’s potential is immense,” says Robertson, “but it is only as powerful as the data it is built upon.
In pharmacy, fragmented data silos limit AI’s ability to drive meaningful impact. Unless technology providers collaborate and embrace modern, cloud-based infrastructure, we risk AI being a ‘buzzword’ rather than a game-changer.

“For AI to truly support pharmacy, systems must allow seamless, real-time data sharing across businesses and the wider healthcare ecosystem. Legacy systems that store static data in isolated databases will hold us back. The future lies in open, connected platforms that empower AI to work intelligently across the pharmacy network.”

Bojana Vukelić is product manager, pharmacy automation retail, at dispensing robot provider BD Rowa. Speaking from a personal perspective, she says integrating AI into pharmacy systems won’t be easy. “System upgrades will be needed to ensure seamless compatibility, requiring investment and planning,” she says, adding that data privacy is another major concern. “AI relies on sensitive patient data, so strong security and regulatory frameworks will be essential,” she warns. 

Some pharmacists are concerned that the use of robotics and AI could potentially replace them, says Cyrus Hodivala. “However, the reality is, patients like to speak to a human being. Pharmacists will never be replaced.”

To have “true AI” in pharmacy, a unified patient medical record is needed so that AI can analyse all the details and come to medical and clinical conclusions, says Hodivala. However, as yet, such a record is unavailable in the UK, which he says is “a major stumbling block in the enablement of true AI in a clinical environment”.

Given the financial pressures facing pharmacy, the cost of AI and automation could be seen as a barrier to investment too. However, Hodivala makes the point that: “The payback models of the return on investment are remarkably fast if they are applied correctly.”

As Meditech offers its robots on a rental scheme, he adds: “There is no leveraged debt against the business or high deposits to pay. And it is great for first-time pharmacy buyers who are taking over a new contract.”

Future outlook

Looking ahead, there are opportunities for AI to support drug discovery, optimise clinical trial design, analyse prescribing data to improve cost-effective care, and enhance patient education by producing information tailored to individual needs, says Claire Anderson of the RPS.

AI datasets could help pharmacists to offer personalised, targeted care that aligns to identified risk profiles, agrees Ade Williams. AI applications could also help to identify patients at risk of certain conditions. For example, AI-powered PMR systems could help to find patients who are at higher risk of raised blood pressure because of the medications they are taking.

One of the biggest opportunities lies in smarter inventory management, says Bojana Vukelić. “AI could analyse sales patterns, seasonal trends and external factors – for example, flu season spikes, prescription trends, supply chain delays – to predict medication demand proactively. Instead of reacting when stock runs low, AI-driven systems could anticipate shortages before they happen and suggest optimised ordering,” she says. 

Cyrus Hodivala says autonomous clinical checking of inbound prescriptions without pharmacist interaction is on the horizon. And rather than machines learning what the pharmacist would do and replicating it, AI could, for example, flag the need to question a patient about their prescription. It could potentially also support diagnostic decisions made when delivering Pharmacy First services, he says.

In the end, AI adoption is not just about technology, “it is also about trust”, says Tracey Robertson. “Many pharmacists welcome AI’s efficiencies but are understandably cautious about liability and accountability. We must work alongside legal and regulatory bodies to ensure AI in pharmacy operates within a framework that protects both professionals and patients.” 

For Vukelić, the future of AI in pharmacy is promising. “As technology evolves,” she says, “pharmacies will see greater efficiency, improved patient safety and enhanced workflow automation. But this will require careful planning, investment and ongoing training to maximise AI’s benefits.”

To pharmacists thinking about it, Hodivala says: “Don’t let your assumptions about what you’ve seen 10 years ago drive your decisions today because automation and robotics have moved on in leaps and bounds since then.”