Tackling A&E waiting times with Power BI and Trustmarque

The customer challenge

The customer is a healthcare organisation providing hospital and community services in east England. Like many acute health providers in the UK, they have witnessed a steady rise in the demand for A&E services. As a result, they were facing physical space constraints, staff provision issues and were not meeting their target to treat or admit attendees within a maximum time of four hours.

While understanding the importance of data and metrics to improve future performance, the team was limited by older systems and the reliance on various individual reports, making a cohesive analysis difficult. A spokesperson told us: “We were not easily able to identify early warnings and trends because we were looking at things in isolation”.

They wanted to change their reporting process with an approach that could unify their findings and enable staff to self-serve to access the right data at the right time. Ultimately, their goals were to maximise the space and personnel in the dept and improve performance against the previous performance levels.

Why Trustmarque was selected as a partner

The customer decided the best strategy was to first “analyse the data” at their fingertips so they could better understand what was happening and make an informed decision on the appropriate course of action. However, they needed support to help them to identify, interpret and leverage their data.

They were familiar with Paul Brady, Senior BI Consultant at Trustmarque, who has supported many healthcare organisations with their data and analytics projects. Paul is well-known and respected in the industry for his depth of knowledge: having worked with different data in different situations across many A&E departments and other areas. Paul has an unrivalled understanding of what data is available, what it represents and how it can be used to find practical opportunities and improve outcomes.

The solution

Trustmarque’s unique approach was to “blend narrative with data” says Paul Brady, who wanted to put context and understanding behind the data in a way the staff could understand and act upon it. This was achieved through a two-part process. Firstly, a substantial on-site consultation in which Paul collated evidence from observations and interviews with staff and patients. And secondly, utilising data rich analytics through Microsoft’s Power BI to create a series of bespoke interactive dashboards. Paul created a statistically robust set of baselines from 3 years’ worth of attendance data. This showed typical daily, weekly and annual trends; the changes that were taking place; and the defined mathematically volumes and trends.

For the first time, this healthcare customer now has access to an interactive self-service platform for data, rather than the flat reports that limited their ability to progress. They can now triangulate and condense all reports in one place so it’s easier to identify trends and correlations. “It’s great to see the data presented in a different way and not in a flat report. Not only is it all in one place, it’s more dynamic and you can better identify questions or issues that might arise,” explains a customer spokesperson.

The organisation is better informed about areas that they want to focus their attention on. They are particularly interested in paediatrics and how they can filter the data to look at specific areas like time of arrival and present and complete. Other areas of interest include frequent attenders in a bid to reduce demand.

The outcome and benefit

Simplified self-service – all staff now able to access critical information via a ‘single shop window’ to make smarter decisions about staffing and patient care.

Trend information – ability to bust myths about trends, i.e. misconceptions about high volumes of older people were disproved when data showed every demographic was increasing in volume regardless of class, age, postcode, illness. This has allowed them to plan more knowledgeably what sort of care is needed in what volumes.

Identify frequent attenders – with the ability to better identify frequent attenders, the customer can provide multidisciplinary individualised care plans that factor collaboration across local services, i.e. mental health provision, policing etc.

Maximise space and the personnel in dept – matching staff requirements to better understood demand peaks has helped plan improved staff provision, for example it was found that current staff numbers needed to be increased at night.

Planning and capital build – the data has helped them make smarter decisions about their use of space and has been used to inform the planning of their A&E expansion