National Theatre Data Analysis For D&C Modelling

Engineering and insights to support elective recovery and hub-based working with GIRFT and NHSE

One of the ways in which the NHS is trying to reduce the list of patients waiting for surgery is by enabling increased theatre throughput. A method of achieving this is bundling high-volume, low-complexity surgeries together and thus operating on these more efficiently. To understand the impact of this nationally as well as monitor implementation over time, GIRFT and the NHS more generally needed to collect theatre data on a national scale and refresh it regularly.

Several teams in NHSE and GIRFT were stood up to work on this. Edge Health was asked to assist this work by supporting part of the data collection, the engineering once the data was available, help assess the data quality and analyse the data to provide insights into the effects of theatre efficiency, both generally and at hub-based level.

Our Process

Data Collection: We ensured consistent data collection of all theatre systems

We supported the data collection by working with key stakeholders to identify what data was available and required to answer the question at hand. We then designed a data request for a one-off collection and collaborated with the national data collection teams to obtain data from their regular collection.

Engineering: We designed a system that could be robust, interpretable and updated regularly

Theatre data across all trusts, updated every two weeks requires significant streamlined plumbing under the hood to ensure accuracy, replicability and ability to use the data across teams. We therefore engineered a solution that fit into a Microsoft Azure based data platform, utilising Storage Accounts for reference and input files, Azure Data Factory to orchestrate and carry out the processing and Azure Synapse as a data warehouse from which the data could be consumed from.

Data quality: we put a process in place that flags and automatically improves data quality

As part of the process, we created a virtuous loop of data quality improvement. For every cycle update (i.e., trusts submitting data, engineering pipeline refreshing it and insight being generated), we produce automatic flags and updates that enable feedback to trusts about data quality issues.

Insights: We generated insights that are used around the country

Using the cleaned, processed data, we worked with clinical leads, theatre experts and Trusts to develop analysis to demonstrate opportunity across all theatres in the country. Particularly, we were interested to model what would happen if Trusts were able to schedule and operate their high-volume, low-complexity cases in a routine way, thus hitting the activity levels suggested by early hubs and clinicians trailing the concept.

The analysis suggested that Trusts could significantly increase throughput and reduce the waiting list quickly. That work has since fed into new funding models for hubs and advanced roll-out of the measures across the country. Our robust system ensures the NHS is able to update and track improvements over time across 110 providers and 39 systems as it moves forward towards elective recovery.