Designing and evaluating complex interventions to improve health care

Complex interventions are “built up from a number of components, which may act both independently and interdependently.”1 2 Many health service activities should be considered as complex. Evaluating complex interventions can pose a considerable challenge and requires a substantial investment of time. Unless the trials illuminate processes and mechanisms they often fail to provide useful information. If the result is negative, we are left wondering whether the intervention is inherently ineffective (either because the intervention was inadequately developed or because all similar interventions are ineffective), whether it was inadequately applied or applied in an inappropriate context, or whether the trial used an inappropriate design, comparison groups or outcomes. If there is a positive effect, it can be hard to judge how the results of the trial might be applied to a different context (box 1)

Cascade diagrams for depicting complex interventions in randomised trials

Clarity about how trial interventions are delivered is important for researchers and those who might want to use their results. A new graphical representation aims to help make complex interventions clearer. Many medical interventions—particularly non-pharmacological ones—are complex, consisting of multiple interacting components targeted at different organisational levels. Published descriptions of complex interventions often do not contain enough detail to enable their replication. Reports of behaviour change interventions should include descriptions of setting, mode, intensity, and duration, and characteristics of the participants. Graphical methods, such as that showing the relative timing of assessments and intervention components, may improve clarity of reporting. However, these approaches do not reveal the connections between the different “actors” in a complex intervention.8 Different audiences may want different things from a description of an intervention, but visualising relationships between actors can clarify crucial features such as the fidelity with which the intervention is passed down a chain of actors and possible routes of contamination between treatment arms. Here we describe a new graphical approach—the cascade diagram—that highlights these potential problems

Developing and evaluating complex interventions: the new Medical Research Council guidance

<p><i>Evaluating complex interventions is complicated. The Medical Research Council's evaluation framework (2000) brought welcome clarity to the task. Now the council has updated its guidance</i></p> <p>Complex interventions are widely used in the health service, in public health practice, and in areas of social policy that have important health consequences, such as education, transport, and housing. They present various problems for evaluators, in addition to the practical and methodological difficulties that any successful evaluation must overcome. In 2000, the Medical Research Council (MRC) published a framework<sup>1</sup> to help researchers and research funders to recognise and adopt appropriate methods. The framework has been highly influential, and the accompanying BMJ paper is widely cited.<sup>2</sup> However, much valuable experience has since accumulated of both conventional and more innovative methods. This has now been incorporated in comprehensively revised and updated guidance recently released by the MRC (<a href="www.mrc.ac.uk/complexinterventionsguidance">www.mrc.ac.uk/complexinterventionsguidance</a>). In this article we summarise the issues that prompted the revision and the key messages of the new guidance. </p&gt

Randomised controlled trials of complex interventions and large-scale transformation of services

Complex interventions and large-scale transformations of services are necessary to meet the health-care challenges of the 21st century. However, the evaluation of these types of interventions is challenging and requires methodological development. Innovations such as cluster randomised controlled trials, stepped-wedge designs, and non-randomised evaluations provide options to meet the needs of decision-makers. Adoption of theory and logic models can help clarify causal assumptions, and process evaluation can assist in understanding delivery in context. Issues of implementation must also be considered throughout intervention design and evaluation to ensure that results can be scaled for population benefit. Relevance requires evaluations conducted under real-world conditions, which in turn requires a pragmatic attitude to design. The increasing complexity of interventions and evaluations threatens the ability of researchers to meet the needs of decision-makers for rapid results. Improvements in efficiency are thus crucial, with electronic health records offering significant potential

Are you serious? From fist bumping to hand hygiene: considering culture, context and complexity in infection prevention intervention research

Infection prevention is an under-resourced research and development topic, with limited evidence for practice in the most basic of measures. A survey of IPS R&D members indicated that what might appear to be simple interactions and interventions in healthcare, such as hand shaking and hand hygiene, should be considered complex interventions taking account of behaviour at the individual and social level as well as contextual factors. Future studies need to be designed utilising comprehensive approaches, for example, the Medical Research Council complex interventions framework, tailored to the country and more local cultural context, if we are to be serious about evidence for infection prevention and control practice

a systematic review of complex interventions

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Process evaluation for complex interventions in primary care: understanding trials using the normalization process model

Background: the Normalization Process Model is a conceptual tool intended to assist in understanding the factors that affect implementation processes in clinical trials and other evaluations of complex interventions. It focuses on the ways that the implementation of complex interventions is shaped by problems of workability and integration.Method: in this paper the model is applied to two different complex trials: (i) the delivery of problem solving therapies for psychosocial distress, and (ii) the delivery of nurse-led clinics for heart failure treatment in primary care.Results: application of the model shows how process evaluations need to focus on more than the immediate contexts in which trial outcomes are generated. Problems relating to intervention workability and integration also need to be understood. The model may be used effectively to explain the implementation process in trials of complex interventions.Conclusion: the model invites evaluators to attend equally to considering how a complex intervention interacts with existing patterns of service organization, professional practice, and professional-patient interaction. The justification for this may be found in the abundance of reports of clinical effectiveness for interventions that have little hope of being implemented in real healthcare setting

The Reporting Quality Assessment of Complex Interventions’ Articles in Traditional Chinese Medicine

Objective. To realize the current situation and problems of complex interventions’ clinical trials. Methods. Searching at Chinese Journal Integrated Traditional and Western Medicine and Journal of Traditional Chinese Medicine from 2007 to 2012 by hand, we identified complex interventions’ articles, and then we used the proposed criteria of complex interventions and CONSORT FOR TCM to evaluate. Results. All data is presented as counts with percentages and details in tables. Conclusion. Our evaluation presented that complex interventions have many defects: the selection of the intervention’s components lacks rationale, complex interventions were short of fundamental researches, components’ interactions were ambiguous, and the advantages of complex interventions were not mentioned. Furthermore, explanation of sample size, blind, quality control, ethical approval, and inform consent were neglected in different degrees