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      The clinically led worforcE and activity redesign (CLEAR) programme: a novel data-driven healthcare improvement methodology

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          Abstract

          Background

          The NHS is facing substantial pressures to recover from the COVID-19 pandemic. Optimising workforce modelling is a fundamental component of the recovery plan. The Clinically Lead workforcE and Activity Redesign (CLEAR) programme is a unique methodology that trains clinicians to redesign services, building intrinsic capacity and capability, optimising patient care and minimising the need for costly external consultancy. This paper describes the CLEAR methodology and the evaluation of previous CLEAR projects, including the return on investment.

          Methods

          CLEAR is a work-based learning programme that combines qualitative techniques with data analytics to build innovations and new models of care. It has four unique stages: (1) Clinical engagement- used to gather rich insights from stakeholders and clinicians. (2) Data interrogation- utilising clinical and workforce data for cohort analysis. (3) Innovation- using structured innovation methods to develop new models of care. (4) Recommendations- report writing, impact assessment and presentation of key findings to executive boards. A mixed-methods formative evaluation was carried out on completed projects, which included semi-structured interviews and surveys with CLEAR associates and stakeholders, and a health economic logic model that was developed to link the inputs, processes, outputs and the outcome of CLEAR as well as the potential impacts of the changes identified from the projects.

          Results

          CLEAR provides a more cost-effective delivery of complex change programmes than the alternatives – resulting in a cost saving of £1.90 for every £1 spent independent of implementation success. Results suggest that CLEAR recommendations are more likely to be implemented compared to other complex healthcare interventions because of the levels of clinical engagement and have a potential return on investment of up to £14 over 5 years for every £1 invested. CLEAR appears to have a positive impact on staff retention and wellbeing, the cost of a CLEAR project is covered if one medical consultant remains in post for a year.

          Conclusions

          The unique CLEAR methodology is a clinically effective and cost-effective complex healthcare innovation that optimises workforce and activity design, as well as improving staff retention. Embedding CLEAR methodology in the NHS could have substantial impact on patient care, staff well-being and service provision.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12913-022-07757-1.

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          Most cited references2

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          The job demands-resources model of burnout.

          The job demands-resources (JD-R) model proposes that working conditions can be categorized into 2 broad categories, job demands and job resources. that are differentially related to specific outcomes. A series of LISREL analyses using self-reports as well as observer ratings of the working conditions provided strong evidence for the JD-R model: Job demands are primarily related to the exhaustion component of burnout, whereas (lack of) job resources are primarily related to disengagement. Highly similar patterns were observed in each of 3 occupational groups: human services, industry, and transport (total N = 374). In addition, results confirmed the 2-factor structure (exhaustion and disengagement) of a new burnout instrument--the Oldenburg Burnout Inventory--and suggested that this structure is essentially invariant across occupational groups.
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            Methodological triangulation: an approach to understanding data.

            To describe the use of methodological triangulation in a study of how people who had moved to retirement communities were adjusting. Methodological triangulation involves using more than one kind of method to study a phenomenon. It has been found to be beneficial in providing confirmation of findings, more comprehensive data, increased validity and enhanced understanding of studied phenomena. While many researchers have used this well-established technique, there are few published examples of its use. The authors used methodological triangulation in their study of people who had moved to retirement communities in Ohio, US. A blended qualitative and quantitative approach was used. The collected qualitative data complemented and clarified the quantitative findings by helping to identify common themes. Qualitative data also helped in understanding interventions for promoting 'pulling' factors and for overcoming 'pushing' factors of participants. The authors used focused research questions to reflect the research's purpose and four evaluative criteria--'truth value', 'applicability', 'consistency' and 'neutrality'--to ensure rigour. This paper provides an example of how methodological triangulation can be used in nursing research. It identifies challenges associated with methodological triangulation, recommends strategies for overcoming them, provides a rationale for using triangulation and explains how to maintain rigour. Methodological triangulation can be used to enhance the analysis and the interpretation of findings. As data are drawn from multiple sources, it broadens the researcher's insight into the different issues underlying the phenomena being studied.
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              Author and article information

              Contributors
              eve.corner@33n.co.uk
              clear.team@hee.nhs.uk
              Journal
              BMC Health Serv Res
              BMC Health Serv Res
              BMC Health Services Research
              BioMed Central (London )
              1472-6963
              19 March 2022
              19 March 2022
              2022
              : 22
              : 366
              Affiliations
              [1 ]33N Ltd, London, UK
              [2 ]GRID grid.83440.3b, ISNI 0000000121901201, Research Dept. of Primary Care and Population Health, , University College London, ; London, UK
              [3 ]Economics By Design, London, UK
              [4 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Targeted Intervention, , University College London, ; London, UK
              Article
              7757
              10.1186/s12913-022-07757-1
              8933657
              35305625
              ce2c2798-4b45-4f7d-86dc-44177aa470b4
              © The Author(s) 2022

              Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

              History
              : 10 January 2022
              : 9 March 2022
              Categories
              Research
              Custom metadata
              © The Author(s) 2022

              Health & Social care
              transformation,workforce,innovation,healthcare,education,new models of care
              Health & Social care
              transformation, workforce, innovation, healthcare, education, new models of care

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