Data packs - Visualising data from Rotamap services
Robert Potts, June 2026
Introduction
Visualising and analysing the data held by NHS organisations is a complex challenge. There is an increasing need to use data to better understand and improve service delivery at all levels within organisations. Rotamap has developed a new way for users of our services to access and visualise their clinician rostering data in the form of bespoke customisable data packs, which can be sent on request and explored collaboratively. The purpose of these data packs is to focus on specific questions the organisation may have, and clarify information to facilitate data driven decision making.
At our Spring 2026 forum we showcased organisation-specific data packs using data extracted from the Report Builder within each organisation's Central Reporting site. A bespoke data pack was provided for each Trust in attendance, focussing on extra sessions and sick leave, to facilitate discussion of the data and its applications together as a group. The discussion in the room was driven by the attendees and covered a wide range of topics including how work and absence are recorded, different approaches to leave management, and the rollout of our services across organisations.
The data packs presented can be modified to measure the success of initiatives or monitor trends within an organisation. While the data packs presented at the event focussed on extras and sick leave, any of the data captured in Medirota and CLWRota that is accessible via the Report Builder can be included in these visualisations, such as theatre cancellations, attendance, and delivery. These can also be adapted to include or exclude specific subsets of the data, such as specific role or place categories, or run on a repeating basis to track changes over time. For example, how has the number of sessions delivered as extra by consultants in theatres increased since the last financial year?
Some examples of the analyses discussed are shown below.
Total and proportional extra sessions
Figure 1a. Bar chart showing the sum of the session values delivery as extra for each service over the course of a single financial year. A typical morning (half day) session has a session value of one. Services are listed alphabetically.
Figure 1b. Bar chart showing the percentage of work delivered as extra for each service over the course of a single financial year. Percentages taken from the total amount of work delivered per service. Services are listed alphabetically.
Figure 1a shows the total number of sessions delivered as extra across services within the Trust over the course of the year. Figure 1b shows the proportion of the work which is delivered as extra, work that requires additional payment, in the same services. The services listed on the y axis are in the same order throughout the data pack, so users can see how different metrics interact enabling more indepth analysis. For example, by looking at the first chart it suggests the Obstetrics and Gynaecology service is relying most on extras. However the proportional data, which helps to mitigate for the size of the service skewing total extra usage, shows that the Urology service is delivering the greatest proportion of their work as extra. These types of data transformation can be useful to expose hidden trends and can be used together to provide analysis that is greater than the sum of its parts.
Distribution of extra work
Figure 2. Heatmap showing the sum of the session value of extras delivered on each day of the week per service. Shading is calculated for each service independently. Services are listed alphabetically.
Figure 2 shows the total number of extra sessions on each day of the week over the course of the year. Within each row, the darker red colours show the days where more sessions are delivered as extra. For the majority of the services shown this is on Saturday. There are many reasons why the service may be running a high number of extra sessions on a Saturday such as waiting list initiatives, so the high number of extras here may be a positive metric for the service. High numbers of extras delivered from Monday to Friday may be of more concern, such as those recorded in ENT and General Surgery. The bar charts above show that General Surgery is recording a high number of extra sessions, and a relatively high proportion of their work is delivered as extra. By combining these analyses and focusing on a consistent topic users can begin to build a more complete understanding of what is happening within their services. Using the General Surgery example above, the data shown here may support a case for expanding their substantive workforce.
Total sick leave
Figure 3. Line chart showing the total day cost of leave taken in each week over a single financial year. Each role category has a different line to enable users to examine differences between roles.
Figure 3 shows the days lost to sick leave over the course of the year, filtered by role categories from Central Reporting. Using role categories facilitates comparing data between services within an organisation that might have inconsistent job roles. There is a large spike in the days lost to sick leave for Consultants between August and September. Between November and April the days lost to leave are very similar for all roles.
Based on these charts, the organisation might decide that they want to specifically look at consultant sickness due to the large increase in summer. They may also want to look at specific services in more detail, for example looking at the roles, places or times where General Surgery are delivering extra sessions. By adjusting and refining the scope of the data pack, users can get a more accurate insight into how their services and the overall organisation is running. Furthermore, data packs can highlight areas where data is not being captured accurately.
Rotamap can facilitate this by building the data packs, or by assisting users to set up Applications within Central Reporting to use third party visualisation and analysis software such as Power BI or RStudio. The example charts above were generated using the ggplot2 package within RStudio, which is a free and open source. These are extremely powerful tools for data visualisation and also analysis, allowing users to unlock the potential of their data to make informed decisions.
Contact us
If you would like us to generate or adjust an existing data pack for your organisation or if you have any questions about your pack or the above data, get in touch with the Rotamap support team at support@rotamap.net or +44 (0) 20 7631 1555.