Every pharmacy owner I speak with now has more data flowing into their business than at any other point in the sector’s history. Pharmacy First consultations, GP Connect updates, EPS, NHS App activity, wholesaler feeds and PMR data are all generating information that could help owners build a much clearer picture of how their pharmacies are performing.
The difficulty is that having more data does not automatically mean having better pharmacy data insights. Ask a relatively straightforward question such as, “What actually happened across the business last week, clinically, financially and operationally?” and answering it can mean looking across several different systems.
That isn’t because anybody has failed to capture the information.
It is because each system has been designed to do a particular job well, rather than to provide a complete view of the business. For me, that is one of the most important considerations as we talk about AI in community pharmacy. AI has enormous potential to help us make better use of the information pharmacies already hold, but its value will ultimately depend on the quality and connectivity of the data sitting behind it.
You can see the challenge clearly by following the data associated with a single prescription.
The clinical information is captured through the PMR or Pharmacy First system at the point of care, but that record does not necessarily tell you what the transaction cost or what will ultimately be reimbursed. Cost information is held separately in wholesaler invoices, while Drug Tariff, concessions, clawback and final payment information arrive on another timetable. GP-facing information may then be fed back through GP Connect separately again.
Across thousands of items and multiple services each month, those separate records create a substantial visibility gap. True margin, for example, may not become apparent until two or three months after the buying decisions that influenced it have already been made.
That matters because pharmacy owners are making decisions in the present. Waiting for different systems and datasets to catch up with one another means decisions can end up being based on an incomplete picture of what is happening across the business.
This is why I think we need to be careful not to treat AI adoption as the objective in itself.
There are already genuinely useful applications emerging, including ambient scribing and predictive ordering. But putting greater levels of automation around fragmented information does not address the underlying problem. If the data feeding an AI tool is disconnected, incomplete or difficult to verify, technology risks accelerating the same limitations that already exist.
Where I believe AI becomes much more interesting is when it sits on top of data that has already been joined, cleaned and verified.
Instead of giving an owner another dashboard to interpret, AI could make that information easier to interrogate, allowing someone to ask in plain English what happened in a particular branch last week and receive an answer grounded in verified information. It could analyse thousands of lines of data to identify patterns that might otherwise go unnoticed, from over-ordering and missed concessions to a branch beginning to move away from expected performance.
There is potential on the clinical and compliance side too - rather than teams having to piece together evidence when a commissioner asks for proof that a service was delivered, joined-up data creates the basis for that record to be assembled from information that was captured properly in the first place.
The important distinction is that AI isn’t fixing fragmented data.
Joining up the data is what creates the foundation for AI to become genuinely useful.
For pharmacy owners, the goal therefore shouldn’t simply be to collect more information or introduce another piece of technology. It should be to create a more complete and dependable view of the business from the information that already exists.
That starts by bringing datasets together at the level where decisions are actually being made, whether that is by branch, drug, patient or service. From there, pharmacy data insights need to become part of the operating rhythm of the business, rather than something reviewed retrospectively, particularly when Tariff prices, concessions, consultation volumes and stock can change monthly or faster.
The emphasis also needs to move away from showing everything towards identifying what genuinely requires attention. A particular branch, drug, service or value is far more useful to a decision maker than another dashboard full of information to work through. Just as importantly, predicted figures need to be reconciled against what was actually paid, delivered and recorded so that decisions are based on verified information rather than assumptions.
We have already seen the commercial value of creating that visibility. In financial data work with a 25-branch pharmacy group spending £20 million a year on drugs, recovering just 0.5% through greater visibility of seepage, ordering compliance and correct payment claims added £100,000 a year to the bottom line, before a single clinical or operational dataset was included.
Every pharmacy group will, of course, have its own numbers, pressures and controls. The wider point is what becomes possible when information that was previously scattered across the business starts to form one connected picture.
As AI becomes a bigger part of community pharmacy, I believe that foundation will matter far more than simply having access to the latest technology. The businesses that benefit most will be those that can connect what is happening with what it costs and what it is worth, giving AI reliable information to work with and their people clearer insight on which to act.
If you’d like to explore what greater visibility could reveal in your own pharmacy business, get in touch to learn more about our pharmacy business intelligence solutions and book a demo with the RWA Pharmacy team.
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