• Exploring the potential for secondary uses of Dementia Care Mapping (DCM) data for improving the quality of dementia care

      Khalid, Shehla; Surr, Claire A.; Neagu, Daniel; Small, Neil A. (2019-04)
      The reuse of existing datasets to identify mechanisms for improving healthcare quality has been widely encouraged. There has been limited application within dementia care. Dementia Care Mapping (DCM) is an observational tool in widespread use, predominantly to assess and improve quality of care in single organisations. DCM data has the potential to be used for secondary purposes to improve quality of care. However, its suitability for such use requires careful evaluation. This study conducted in-depth interviews with 29 DCM users to identify issues, concerns and challenges regarding the secondary use of DCM data. Data was analysed using modified Grounded Theory. Major themes identified included the need to collect complimentary contextual data in addition to DCM data, to reassure users regarding ethical issues associated with storage and reuse of care related data and the need to assess and specify data quality for any data that might be available for secondary analysis.
    • A scoping review: Strategic workforce planning in health and social care

      Prowse, Julie M.; Sutton, Claire; Eyers, Emma; Montague, Jane; Faisal, Muhammad; Neagu, Daniel; Elshehaly, Mai; Randell, Rebecca (2022-04)
      Aim This aim of this scoping review was to undertake a detailed review of the pertinent literature examining strategic workforce planning in the health and social care sectors. The scoping review was tasked to address the following three questions: 1. How is strategic health and social care workforce planning currently undertaken? 2. What models, methods, and tools are available for supporting strategic health and social care workforce planning? 3. What are the most effective methods for strategic health and social care workforce planning? Methods The scoping review utilised the five-stage scoping review framework proposed by Arksey and O’Malley (2005). This includes identifying the research question; identifying relevant studies; study selection; charting the data and collating, summarizing, and reporting the results. The search included a range of databases and key search terms included “workforce” OR “human resource*” OR “personnel” OR “staff*”. Relevant documents were selected through initially screening titles and abstracts, followed by full text screening of potentially relevant documents. Results The search returned 6105 unique references. Based on title and abstract screening, 654 were identified as potentially relevant. Screening of full texts resulted in 115 items of literature being included in the synthesis. Both national and international literature covers strategic workforce planning, with all continents represented, but with a preponderance from high income nations. The emphasis in the literature is mainly on the healthcare workforce, with few items on social care. Medical and dental workforces are the predominate groups covered in the literature, although nursing and midwifery are also discussed. Other health and social care workers are less represented. A variety of categories of workforce planning methods are noted in the literature that range from determining the workforce using supply and demand, practitioner to population ratios, needs based approach, the utilisation of methods such as horizon scanning, modelling, and scenario planning, together with mathematical and statistical modelling. Several of the articles and websites include specific workforce planning models that are nationally and internationally recognised, e.g., the workload indicators of staffing needs (WISN), Star model and the Six Step Methodology. These models provide a series of steps to help with workforce planning and tend to take a more strategic view of the process. Some of the literature considers patient safety and quality in relation to safe staffing numbers and patient acuity. The health and social care policies reviewed include broad actions to address workforce planning, staff shortages or future service developments and advocate a mixture of developing new roles, different ways of working, flexibility, greater integrated working and enhanced used of digital technology. However, the policies generally do not include workforce models or guidance about how to achieve these measures. Overall, there is an absence in the literature of studies that evaluate what are the most effective methods for strategic health and social care planning. Recommendations The literature suggests the need for the implementation of a strategic approach to workforce planning, utilising a needs-based approach, including horizon scanning and scenarios. This could involve adoption of a recognised workforce planning model that incorporates the strategic elements required for workforce planning and a ‘one workforce’ approach across health and social care.
    • A study proposing a data model for a dementia care mapping (DCM) data warehouse for potential secondary uses of dementia care data

      Khalid, Shehla; Small, Neil A.; Neagu, Daniel; Surr, C. (2019-01-01)
      Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. There is growing emphasis on sharing and reusing dementia care-related datasets to improve the quality of dementia care. Consequently, there is a need to develop data management solutions for collecting, integrating and storing these data in formats that enhance opportunities for reuse. Dementia Care Mapping (DCM) is an observational tool that is in widespread use internationally. It produces rich, evidence-based data on dementia care quality. Currently, that data is primarily used locally, within dementia care services, to assess and improve quality of care. Information-rich DCM data provides opportunities for secondary use including research into improving the quality of dementia care. But an effective data management solution is required to facilitate this. A rationale for the warehousing of DCM data as a technical data management solution is suggested. The authors also propose a data model for a DCM data warehouse and present user-identified challenges for reusing DCM data within a warehouse.