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Abstract Background Collaborative approaches in quality improvement have been promoted since the introduction of the Breakthrough method. The effectiveness of this method is inconclusive and further independent evaluation of the method has been called for. For any evaluation to succeed, data collection on interventions performed within the collaborative and outcomes of those interventions is crucial. Getting enough data from Quality Improvement Collaboratives (QICs) for evaluation purposes, however, has proved to be difficult. This paper provides a retrospective analysis on the process of data management in a Dutch Quality Improvement Collaborative. From this analysis general failure and success factors are identified. Discussion This paper discusses complications and dilemma's observed in the set-up of data management for QICs. An overview is presented of signals that were picked up by the data management team. These signals were used to improve the strategies for data management during the program and have, as far as possible, been translated into practical solutions that have been successfully implemented. The recommendations coming from this study are: From our experience it is clear that quality improvement programs deviate from experimental research in many ways. It is not only impossible, but also undesirable to control processes and standardize data streams. QIC's need to be clear of data protocols that do not allow for change. It is therefore minimally important that when quantitative results are gathered, these results are accompanied by qualitative results that can be used to correctly interpret them. Monitoring and data acquisition interfere with routine. This makes a database collecting data in a QIC an intervention in itself. It is very important to be aware of this in reporting the results. Using existing databases when possible can overcome some of these problems but is often not possible given the change objective of QICs. Introducing a standardized spreadsheet to the teams is a very practical and helpful tool in collecting standardized data within a QIC. It is vital that the spreadsheets are handed out before baseline measurements start.

Abstract Background The aim of this paper is to show how researchers balance between scientific rigour and localisation in conducting pragmatic trial research. Our case is the Quattro Study, a pragmatic trial on the effectiveness of multidisciplinary patient care teams used in primary health care centres in deprived neighbourhoods of two major cities in the Netherlands for intensified secondary prevention of cardiovascular diseases. Methods For this study an ethnographic design was used. We observed and interviewed the researchers and the practice nurses. All gathered research documents, transcribed observations and interviews were analysed thematically. Results Conducting a pragmatic trial is a continuous balancing act between meeting methodological demands and implementing a complex intervention in routine primary health care. As an effect, the research design had to be adjusted pragmatically several times and the intervention that was meant to be tailor-made became a rather stringent procedure. Conclusion A pragmatic trial research is a dynamic process that, in order to be able to assess the validity and reliability of any effects of interventions must also have a continuous process of methodological and practical reflection. Ethnographic analysis, as we show, is therefore of complementary value.

Background: We examined the effectiveness of a structured collaboration in general practice between a practice nurse, a peer health educator, the general practitioner (GP) and a GP assistant in providing intensified preventive care for patients at high risk of developing cardiovascular diseases., Design: A randomized controlled trial in three healthcare centres (18 GPs) in deprived neighbourhoods of two major Dutch cities., Methods: Two hundred seventy-five high-risk patients (30-70 years) from various ethnic groups were randomized to intervention (n=137) or usual care group (n=138). We determined group differences in outcomes [10-year absolute risk (Framingham risk equation), blood pressure, lipids and body mass index] at 12-month follow-up., Results: The 10-year absolute risk was reduced by 1.76% (standard error: 0.81) in intervention and by 2.27% (standard error: 0.69) in usual care group; the difference in mean change was 0.88% [95% confidence interval: -1.16 to 2.93]. In both groups significant reductions were observed in the following individual risk factors: total cholesterol, total cholesterol/high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol, with no relevance between group differences., Conclusion: The cardiovascular risk profile of intervention and control patients improved after 1-year follow-up. However, no extra effect of the structured preventive care on the risk for cardiovascular diseases was achieved.