We have a system that joins hospitals, primary and secondary care providers together. Each hospital might have it's own prices, as will their consultants, etc. Insurers have standard price lists, but some consultants and practices have direct deals with them, which can affect the price (and the creditor for the invoices), we also support rule-sets for modifying prices based on location, clinician, day of the week, availability, benefit maxima, credits, etc. We support the notion of inherited prices, where a standard template can be setup and then used as a basis for more bespoke plans etc. All of which can be manually overridden. If additional services are added then that can affect appointment lengths and costs. If the patient self-pays they may get different prices to what they would on their insurance.

Some of our customers do occupational health where they go out and get contracts with the big city firms to provide schemes for their employees. Each deal they get is different and negotiated to the nth degree, which can include various plans/tarrifs/credits, etc. Some services the patients can book themselves online, some can only be booked by their HR manager, some as follow-ups by clinicians, etc. Also rules around what can go on invoices, how billing items are grouped, who can see what (i.e. should an HR person see what services you've had etc.)

The additional complexity comes with matching that all up with efficient scheduling. We have some customers where they have appointments that involve up to 8 separate clinicians or resources; so they need to be scheduled in and priced without leaving massive gaps in the schedules. We also support call-centres that could see 1000s of practices and can query all of them. The end result looks very much like an airline booking model, but behind it all is a mass of complexity.

The combinatorial issues are large, and have definitely caused us some headaches over the years. So I fully understand how these systems can get very resource hungry. We're not in the same league as Expedia or Jet in terms of scale (although we hold more patient records than Jet has customers, if that means anything) - and as I mentioned it's relatively easy for us to silo groups of users, so I suspect some complexities just go away for us because we can filter down the potential results quite quickly, but still 2200 sounded like a lot - it was just a gut feeling. I don't understand how a shop selling a product can have difficulty calculating a price, but just as my customers have no idea of the complexity involved in calculating prices, I almost certainly don't understand the complexities that the team at Jet have to deal with.

The way we've dealt with it is to build massive data-sets of various static combinations, so if patient X on chargeband Y wants service Z then they'll be changed N. That does mean some large data updates when someone changes their pricing and rules; but it means doing less work live - although it can still be significant. This all runs on two 32 core servers.