24 INSIGHT manage their medication better so that they were meeting their targets. Part of the programme focused on identifying pre-diabetes patients – people who are not yet diabetic but have a high risk of developing the condition. That is something we can discover through a simple blood test. As we started focusing on this, more and more patients were screened and a number of them already had diabetes but hadn’t been recognised. Dr Anju Gupta of NHS Barking and Dagenham CCG has worked with the 37 practices in the borough to improve diabetes diagnosis rates fter introducing a new initiative in 2016, Barking and Dagenham CCG managed to reduce the number of undiagnosed diabetes cases in its area by 62%, down from 1,642 in 2012/13 to 624 in 2017/18. A The problem Our borough is one of the most deprived in London. When we looked at our diabetes testing rates, we could see we were doing poorly in comparison to other boroughs. Within the CCG there was variation between practices, with some doing much better than others. We had to come up with a plan that would not only help the CCG but also reduce variability between practices while improving patient outcomes. Healthcare Leader 2019 Issue 10 The solution We set up a central data sharing system that fed data back to our practices. This proved to be beneficial. Through an innovation fund, we recruited a third-party company that gathered data from all practices in the area. It fed that data back to the CCG for us to identify the problems in each practice and provide supervised support. We helped practices come together in network meetings, in which we showed them data and updated them on how far they were from meeting their targets, and helped motivate them to do better. Each practice nominated a diabetes clinical lead and most had an administrative lead as well. Practices would regularly be informed of how far they were from meeting their targets. The data would also help them recognise the number of patients they had to recall to do tests, or recall and The benefits Practices made improvements on the nine NICE care processes for diabetes: weight; blood pressure; smoking status; HbA1c; urinary albumin; serum creatinine; cholesterol; eye examinations; and foot examinations. Since we launched the scheme, an increasing number of patients have received the NICE care processes, with the figure rising from 15.7% in 2016/17 to 49.4% in 2017/18. More patients were trained to manage their condition in the same period – rising from 40.7% to 63.1%. There was also an increase in the number diagnosed with pre-diabetes – rising from 0.62% to 4.7%. We especially improved our checking of patients’ urine, and identifying early stages of diabetes related to kidney changes. Checking urine and identifying early kidney disease are important to recognise patients who are likely to have early heart disease. People with diabetes are likely to have other long-term conditions as well. Starting from the next financial year, our intention is to have a scheme that covers all long-term conditions, not just diabetes. Dr Anju Gupta is a GP and clinical lead for diabetes in NHS Barking and Dagenham CCG Additional reporting by Valeria Fiore How our CCG reduced undiagnosed diabetes cases by more than 60% The challenges Initially, practices were apprehensive and felt the scheme would increase their workload. However, as soon as we started seeing results, which we presented to the practices, everyone was very happy with their own progress. At first, practices were also unsure about allowing a third party to access their data and discuss it with them. Many practices raised information governance issues. We specified that the data was collected on an anonymous basis and that only the practices themselves could decode it to identify patients. We found that regularly feeding up-to- date data back to the practices in a way that is easy to interpret is a good way to reduce inter-practice variability. Comparing data with practices prompts them to change their approach and we have now extended the project to include atrial fibrillation.