Using routinely gathered data to empower locally led health improvements.

Data are routinely used throughout the NHS to report on and monitor performance. For example, detailed information regarding hospital episodes is reported via the Secondary Use Services (SUS) programme. Local commissioners use this data to monitor hospital contracts. In primary care, data such as glycaemic control of diabetes patients is extracted from general practice clinical systems to calculate practice payments for the 'Quality and Outcomes Framework' (QOF). We suggest that this routinely gathered data should also be used to help clusters of practices to learn from locally led innovation and to motivate long-term partnerships for interorganisational health improvement. Following the recent NHS reforms, the number of data sources that could facilitate this is likely to increase in size, variety and complexity. In this paper, we describe some of the existing data sources that could be used to do this; we also describe some of the dangers of using data in this way, and our conclusions about the best way forward.

Using routinely gathered data to empower locally led health improvements.

Data are routinely used throughout the NHS to report on and monitor performance. For example, detailed information regarding hospital episodes is reported via the Secondary Use Services (SUS) programme. Local commissioners use this data to monitor hospital contracts. In primary care, data such as glycaemic control of diabetes patients is extracted from general practice clinical systems to calculate practice payments for the 'Quality and Outcomes Framework' (QOF). We suggest that this routinely gathered data should also be used to help clusters of practices to learn from locally led innovation and to motivate long-term partnerships for interorganisational health improvement. Following the recent NHS reforms, the number of data sources that could facilitate this is likely to increase in size, variety and complexity. In this paper, we describe some of the existing data sources that could be used to do this; we also describe some of the dangers of using data in this way, and our conclusions about the best way forward.