2. Secondary Uses of DCM Data
2.5. Secondary use of DCM data and need for an effective IT solution
as benchmarking and that there exist some examples that demonstrate the potentiality of DCM data for further analysis within research context. This section discusses some of the additional motivations for the secondary use of DCM data and the need for an effective data management system for this purpose.
Every year more and more individuals and organisations are trained to use DCM, which may increase the use of DCM and thus the amount of data generated. These larger amounts of data increase DCM’s potential uses for secondary purposes. Further, mappers are spread around the globe and there is evidence that DCM data is being collected from various types of care settings such as residential facilities (Lai et al. 2004; Chenoweth et al. 2009; Barnes 2013) and hospital wards (Woolley et al. 2008). The use of DCM is also promoted in non-dementia care settings, such as neuro-rehabilitation wards (Westbrook et al. 2013), assisted-living facilities (Zimmerman et al. 2005) and intellectual-disability residential services (Jaycock et al. 2006; Finnamore and Lord 2007). While there is yet a lack of knowledge on how regularly DCM is used at national and international level, its use in various types of settings and patient groups shows that it can provide rich and multi- purpose data. Such a rich dataset can allow one to see the possibilities for its
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secondary use if it is accumulated over a period of time and collected from various mappers and organisations.
As mentioned previously in Chapter 1, a large amount of rich DCM data can also be part of existing and future initiatives/efforts to improve the quality of dementia care by utilising the existing datasets. However, to facilitate the secondary uses, either for benchmarking, research or any user-required purposes, DCM data need to be collected in an electronic, standardised and integrated format. This is also to deal with the issue which Sandra and Gramon (2007: 95) state is one of the major barriers in using healthcare data for secondary purposes, that of “locating existing data”. The integration of DCM data within a specific resource can deal with this issue. In order to support the secondary use of healthcare data, a number of studies suggests collecting data in a standardised format, developing appropriate ethical and legal frameworks to support data-sharing (Safran et al. 2007), collecting additional information alongside patients’ healthcare data for in-depth analysis, re-defining technical architectures and communicating and promoting the opportunities and benefits of secondary data (Health Industries 2009).
A DCM data resource for secondary uses can potentially be a solution to providing access to integrated and historic DCM data without expending effort on conducting DCM method. This suggestion is based on the observation that DCM is criticised for being a time- and resource-consuming tool compared with other dementia-care improvement tools (Beavis et al. 2002; Edvardsson and Innes 2010). Studies have highlighted that DCM training is expensive (Edvardsson and Innes 2010). Furthermore, conducting
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observations is an intensive process that requires time and dedication from the mappers
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(Beavis et al. 2002). Moreover, implementing DCM within care practice is also resource consuming (Beavis et al. 2002; Cooke and Chaudhury 2012), requiring leadership and managerial skills (Bradford Dementia Group 2014). A DCM data resource can provide an opportunity to explore data to identify new insights to suggest aspects of care that might be improved (Khalid 2010). However, DCM data require management at the point of collection and storage in order to render them shareable and then available in a data resource.
Considering the importance and usefulness of DCM data, Brooker (2005) also emphasised the management of DCM data in order to fully exploit their richness and highlighted the need for innovative, efficient and reliable IT solutions.
2.6. Summary of the chapter
This chapter began by analysing the empirical studies that provide examples of the secondary use of DCM data for research purposes. The lack of reporting of any related issues and concerns regarding the secondary use of DCM data within these studies was noticed. The chapter then went on to explore a potential secondary use of DCM data for benchmarking the quality of dementia care. This exploration revealed that data effectiveness is the key to a successful benchmarking process. The chapter then outlined the characteristics of effective data, such as suitability, availability, quality (completeness and accuracy) and comparability and referred to these as key data requirements for benchmarking. It then examined the practicality of DCM data in the light of data requirements for benchmarking. This commenced with a discussion of the suitability of DCM data for
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benchmarking, including exploring potential DCM indicators, their usability and likely effectiveness for assessing and improving the quality of care within formal dementia-care settings. The chapter argued that, while sufficient evidence exists to indicate that DCM could provide some key quality indicators (e.g., the WIB score) and that there exists good evidence to indicate that it can be used effectively to assess changes in care overtime, further research is required to examine the potential applicability of DCM data for benchmarking.
The chapter then moved on to arguing that there is evidence demonstrating the use of DCM data for comparison purposes across time and across organisations, thus suggesting its use for comparability for benchmarking. Further, this chapter also highlighted the significance of data availability and its quality for benchmarking as well as for other secondary uses such as research purposes. The chapter then turned to arguing the case for establishing user perceptions of benchmarking, as these influence the type of benchmarking by giving an indication of the types and characteristics of data needed to be stored within the warehouse.
The chapter ended by highlighting the significance of integrated DCM data for secondary uses and argued the need for an effective data-management system for such a purpose. Data warehousing, as a technical solution for managing DCM data for secondary purposes, is examined in Chapter 3.
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