Quality collaboratives: lessons from research

Harvard University, Cambridge, Massachusetts, United States
Quality and Safety in Health Care (Impact Factor: 2.16). 01/2003; 11(4):345-51. DOI: 10.1136/qhc.11.4.345
Source: PubMed

ABSTRACT Quality improvement collaboratives are increasingly being used in many countries to achieve rapid improvements in health care. However, there is little independent evidence that they are more cost effective than other methods, and little knowledge about how they could be made more effective. A number of systematic evaluations are being performed by researchers in North America, the UK, and Sweden. This paper presents the shared ideas from two meetings of these researchers. The evidence to date is that some collaboratives have stimulated improvements in patient care and organisational performance, but there are significant differences between collaboratives and teams. The researchers agreed on the possible reasons why some were less successful than others, and identified 10 challenges which organisers and teams need to address to achieve improvement. In the absence of more conclusive evidence, these guidelines are likely to be useful for collaborative organisers, teams and their managers and may also contribute to further research into collaboratives and the spread of innovations in health care.

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    ABSTRACT: BackgroundA quality improvement collaborative is an intensive project involving a combination of implementation strategies applied in a limited inverted question markbreakthrough inverted question mark time window. After an implementation project, it is generally difficult to sustain its success. In the current study, sustainability was described as maintaining an implemented innovation and its benefits over a longer period of time after the implementation project has ended. The aim of the study was to explore potentially promising strategies for sustaining the Enhanced Recovery After Surgery (ERAS) programme in colonic surgery as perceived by professionals, three to six years after the hospital had successfully finished a quality improvement collaborative.MethodsA qualitative case study was performed to identify promising strategies to sustain key outcome variables related to the ERAS programme in terms of adherence, time needed for functional recovery and hospital length of stay (LOS), as achieved immediately after implementation. Ten hospitals were selected which had successfully implemented the ERAS programme in colonic surgery (2006 inverted question mark2009), with success defined as a median LOS of 6 days or less and protocol adherence rates above 70%. Fourteen semi-structured interviews were held with eighteen key participants of the care process three to six years after implementation, starting with the project leader in every hospital. The interviews started by confronting them with the level of sustained implementation results. A direct content analysis with an inductive coding approach was used to identify promising strategies. The mean duration of the interviews was 37 minutes (min 26 minutes inverted question mark max 51 minutes).ResultsThe current study revealed strategies targeting professionals and the organisation. They comprised internal audit and feedback on outcomes, small-scale educational booster meetings, reminders, changing the physical structure of the organisation, changing the care process, making work agreements and delegating responsibility, and involving a coordinator. A multifaceted self-driven promising strategy was applied in most hospitals, and in most hospitals promising strategies were suggested to sustain the ERAS programme.ConclusionsJoining a quality improvement collaborative may not be enough to achieve long-term normalisation of transformed care, and additional investments may be needed. The findings suggest that certain post-implementation strategies are valuable in sustaining implementation successes achieved after joining a quality improvement collaborative.
    BMC Health Services Research 12/2014; 14(1):641. DOI:10.1186/s12913-014-0641-y · 1.66 Impact Factor
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    ABSTRACT: Quality improvement and safety in intensive care are rapidly evolving topics. However, there is no gold standard for assessing quality improvement in intensive care medicine yet. In 2007 a pilot project in German intensive care units (ICUs) started using voluntary peer reviews as an innovative tool for quality assessment and improvement. We describe the method of voluntary peer review and assessed its feasibility by evaluating anonymized peer review reports and analysed the thematic clusters highlighted in these reports. Retrospective data analysis from 22 anonymous reports of peer reviews. All ICUs - representing over 300 patient beds - had undergone voluntary peer review. Data were retrieved from reports of peers of the review teams and representatives of visited ICUs. Data were analysed with regard to number of topics addressed and results of assessment questionnaires. Reports of strengths, weaknesses, opportunities and threats (SWOT reports) of these ICUs are presented. External assessment of structure, process and outcome indicators revealed high percentages of adherence to predefined quality goals. In the SWOT reports 11 main thematic clusters were identified representative for common ICUs. 58.1% of mentioned topics covered personnel issues, team and communication issues as well as organisation and treatment standards. The most mentioned weaknesses were observed in the issues documentation/reporting, hygiene and ethics. We identified several unique patterns regarding quality in the ICU of which long-term personnel problems und lack of good reporting methods were most interesting Conclusion: Voluntary peer review could be established as a feasible and valuable tool for quality improvement. Peer reports addressed common areas of interest in intensive care medicine in more detail compared to other methods like measurement of quality indicators.
    German medical science : GMS e-journal 01/2014; 12:Doc17. DOI:10.3205/000202
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    ABSTRACT: We introduce a new method for exploratory analysis of large data sets with time-varying features, where the aim is to automatically discover novel relationships between features (over some time period) that are predictive of any of a number of time-varying outcomes (over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of predictive features, (ii) which attribute will be predicted (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted attribute. After validating the method on 15 synthetic test problems, we used the approach for exploratory analysis of a large healthcare network data set. We discovered a strong association, with 100% sensitivity, between hospital participation in multi-institutional quality improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of mortality and morbidity observed after a 1-2 year lag. The proposed approach is a potentially powerful and general tool for exploratory analysis of a wide range of time-series data sets.

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