Data clinics in Nottinghamshire Healthcare

We have been working with teams to help them with their data problems. This post describes some of the clinics and what has come about as a result of this work.

Our team recently piloted data clinics within the Trust in order to:

• Improve data quality and completeness
• Improve the means by which staff collect and record the essential information, making it more efficient and freeing them up to spend more time with their patients.

This was achieved by going “back to basics” with the people who collect and input data, the key principles are ensuring they know why they are collecting the data, making sure the data collection system works well for the teams and that the data can be used by both the data collectors and analysts. A variety of avenues were considered, such as reducing excess data collection and reducing duplication which make data gathering more laborious and tedious for clinicians.

When data collection is difficult for clinicians it often results in the data not being filled properly, correcting this increases the accuracy and completeness of the dataset. Structuring clinical records and decreasing their reliaance on free text input is also beneficial for data analysis but is also often faster and easier for clinical staff. The clinics are a collaborative venture with the clinical team and others such as analysts and system admin, the type of staff varies depending on the needs of each teams. My role was to facilitate the conversations using skills learnt from working closely with the clinical teams to learn to “translate” between clinical language and data/IT language. Subtle differences in expression between the two groups often lead to misunderstandings which could stifle progress (more examples). The key element was that the problem was generated by the team themselves. This ensures that the clinic is focused on solving their difficulties which should help them to improve their own systems rather than forcing a change on them.

As a test run we had two teams go through the process:

• A forensic mental health team which wanted to move away from using Excel to collect their data
• A community mental health team which wanted to collect some extra information to better understand the impact of their team without adding too much to their workload

The forensic team was a new service which had a lot of data requested of them and they wished to improve their data collection and assess its quality. The Team Leader had used team-specific forms in RiO (the clinical database which they use) previously and was interested in seeing if it was possible here. However, they were having a tough time explaining to the managers who were not familiar with such a system how to approve it and get it built into the system. The spreadsheet was found to have a lot of duplication and data being requested that was not necessarily attainable by the team. We looked together at what the purpose was and changed some of the data from free text to a more structured pick list from the valid values for that piece of data. We also had to explain to the managers that this change was not going to affect their reporting adversely. The patient record system was able to provide the team with what they needed and to automate some aspects to reduce workload for clinicians. Some outcomes measures already existed but others were not yet available on the system, an Excel sheet was made to collect them (an improvement over a folder in the corner of the room) with a reduction in demand for clinicians with simple automation of score summations. The team are thrilled that they can collect the data necessary for reporting and understanding their service in a more intuitive way, project managers are content they are getting the same information and more data is readily available for service improvement. Reports are being built which give the clinicians easy access to data which allows them to engage better and feel ownership of the data.

The community mental health team wanted to collect some more information to improve their ability to understand their outcomes. They needed to be able to distinguish between the cohorts of patients that were being referred. This ended up having a simple solution that had not been known to the clinical team – adding in more specific referral reasons. The patient cohort was clear and defined and could be determined at referral. They wanted some more information on one of the cohorts to understand the group further and to see how specific patients within the group progressed. To gather this data, a short form on the electronic patient record was created which takes one minute to fill in but adds a wealth of information. The team also got to play with the form before it went live to gain familiarity and to help them feel ownership of it. They also wanted to be able to predict when referrals may come in. As we got to know the pathways that brought patients into the service, we learned that we had information about patientes in the previous stage of the pathway. So, we managed to collect some information to understand the time between the previous stage and the referral. This means we can see when there is an uptick in people passing through the previous stage and predict a spike in referrals for the team to prepare for.