Automation of costing mapping using a local costing manual and a Python app developed by the team

Somerset ICB, South West, Integrated

The problem

The costing team required quicker mapping of our general ledger to support various analysis, allocation methods, and activity codes in the costing guidance for a functional table in the costing software. To ensure timely costing outputs and submissions with changing general ledger, the mapping must be frequently reviewed and updated. Failing to do so may result in outdated or insufficient data, rendering it unusable for patient-level information costing (PLICs) analysis requiring detailed granularity.

The challenge

How might we accelerate the production of our cost ledger mapping table so that individuals in the wider finance team can understand the ledger mapping and we can address a key blocker in our ability to produce more regular costing outputs?

The outputs

We have an automated solution to generating our costing ledger and allocation mapping table for our costing system. We also have the costing manual which is used to keep an audit trail of changes to our mapping, and a tool that allows us to make transparent changes to our costing model without the need for downloading a massive table of coding.

The outcomes

The benefits to the team as a result of the innovation were numerous. We were able to make quick changes to our ledger mappings that reduced human error, and therefore the need for reimporting a large table. We were able to quickly use the costing manual when reviewing costing outputs to help us understand why allocations had been made or analytical coding assigned to certain cost items, rather than interrogating the costing system.

The Innovation

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What the peer-reviewers said

“I believe this is an excellent innovation as it will improve the data the NHS works with.”

“I think a wide issue was identified and a unique solution was developed to reduce the consequence of the issue. the time saved within the team should allow the value of costing to be better utilised, moving away from the production of costing information.”

“This would lower chance of human error which in turn will provide more accurate data, as well as make the data more readily available at a quicker time as it is automated.”

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