“Realist syntheses without contextual comparison is unrealistic” – the utility of a realist synthesis when developing a care home minimum data set (MDS)
Mr Massirfufulay Kpehe Musa, Research Fellow, The Centre for Research in Public Health and Community Care (CRIPACC), University of Hertfordshire. Mr Musa works part-time as a research fellow; he is a PhD candidate at King’s College London.
In this blog, I am going to discuss how a realist approach to evidence review is valuable to understand how differences between care home systems of care and health care where resident Minimum Data Set (MDS) are already in place. I will argue that doing this helps to formulate an evidence-based, programme theory to inform how to introduce MDS into care homes.
What do we mean by a care home minimum data set (MDS)?
A care home minimum data set (MDS) is a comprehensive, standardised account of the characteristics and needs and ongoing care of residents living in long-term care (care home) (Musa et al.2020). It includes care home resource use – including prescription drug use – and care home residents’ outcomes in key areas (e.g. activities of daily living, cognitive performance, pain, cost of care, and infection) (Carpenter 2006; Doupe et al. 2018). Minimum Data Sets were first used in the USA, where there was a widespread quality of care concerns related to nursing homes’ inability to identify residents’ problems and needs (Morris et al. 1990). In order to address the nursing homes problems effectively, the federal government passed the Nursing Home Reform Act, a part of the Omnibus Budget Reconciliation Act of 1987. By October 1990, as part of a federal law mandated in all Medicare and Medicaid funded nursing homes, an early version of the MDS was implemented in the USA as the national assessment instrument (Morris et al. 1990). There are multiple versions of MDS, which are often country specific. However, all versions of MDS (e.g. interRAI, BelRAI, PLAISIR, etc) share a common language and are designed to support an integrated system of care that can support cross-sector clinical and managerial decision-making (Musa et al. 2020). In countries where an MDS has been adopted, research has demonstrated the use of an MDS to commissioners, researchers and service providers in enabling identification of care needs and residents at risk of ill health (de Stampa et al. 2018; Doupe et al. 2018; Fries et al. 2003; Hirdes et al. 1999; Vanneste and Declercq 2014).
What is a realist synthesis?
Realism is a methodological paradigm, which positions itself between positivism (the world is real and can be observed directly) and interpretivism (given that all we know has been processed through the human mind, we can never be sure exactly what reality is) (Graham and McAleer 2018). Realists are interested in causality and offer a theory-driven approach to programme development and aim to explore and understand the influence of context and underlying mechanisms on programme outcomes (Jagosh et al. 2014; Pawson and Tiley 1997). They seek to address elements of “how and why does this work and/or not work, for whom, to what extent, in what respects, in what circumstances and over what duration?” (Wong et al. 2016).
Context is central to understanding how and why a programme works (or not) for different individuals and in different circumstances (Gilmore 2019; Westhorpe 2014). Realists believe that outcomes are not the result of the implementation of a programme, but rather are explained through the context-mechanism relationships associated with an intervention or programme (Bergeron et al. 2020; Manzano 2016). This approach to evidence review moves beyond collating and summarising the available evidence in the literature, and asks to deliver a theory driven understanding of “what works, for whom, in what circumstances, and how?” (Jagosh et al. 2014; Pawson and Tiley 1997; Westhorpe 2014).
How does a realist synthesis help to develop an MDS?
There has been an attempt to introduce an MDS in the UK (Carpenter et al. 2003), but the approach to how information about care home residents’ medical history, care needs, and preferences is collected and used remain variable, and the information is often recorded in different formats and for different purposes (Burton et al. 2020).
The impact of the recent Covid-19 pandemic has in no doubt shone light on the lack of standardised data collection among UK care homes. Paramount to the success of any UK Care Home MDS is its feasibility and acceptability within the UK context. This includes addressing questions around access to digital technologies; data security; safeguarding privacy in a setting where individuals may lack capacity to consent to data sharing; and the capacity for integration of information across the primary, secondary and community health and care sectors and utility for improvement (Burton et al. 2020).
Our realist synthesis on the uptake and use of MDS in other countries focuses on what needs to be in place for it to be used effectively for residents’ care, recognising that data that are useful for commissioners and policy makers also need to be useful for staff providing care (Musa et al. 2020). There are so many examples of systems being introduced into health and social care systems that have not engaged with those who have to use them every day. Our realist synthesis is finding the many benefits to having MDS, but it is also highlighting unintended consequences particularly for care home staff. By consulting with UK care home stakeholders ( e.g. care home staff, researchers, regulators, care home managers, software designers, NHS staff) who are in position to give UK-specific views on what may foster or imperil the feasibility and sustainability of such system we hope to use the findings to ensure that a future MDS will be implementable and sustainable. This approach confirms Ray Pawson’s assertion that “realist syntheses without contextual comparison is unrealistic”.
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Funding Acknowledgement and Disclaimer
This study/project is funded by the National Institute for Health Research (NIHR) Health Service Research and Delivery programme (HS&DR NIHR127234) and supported by the NIHR Applied Research Collaboration (ARC) East of England.
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