The CLEAR approach
CLEAR is a 22–26-week (theme-dependent) apprenticeship style methodology currently with five national themes: urgent and emergency care, mental health, anticipatory care, critical care and ophthalmology. The methodology respects the nuances of each theme through its design, ensuring that the approach is transferable to achieve successful outcomes in that area. There are 4-8 participating organisations per theme at any one time, and 2-4 clinicians per organisation, who are seconded to the CLEAR Faculty as associates. The inclusion of multiple organisations and associates serves to create cross-system learning and broadens the range of insights developed.
CLEAR themes are typically sponsored by national bodies, such as Health Education England (HEE) and NHS England and NHS Improvement (NHSE&I), to address strategic priorities for the NHS as outlined within the NHS Long Term Plan5. For nationally sponsored themes, a competitive expression of interest (EOI) process gives organisations equal opportunity to apply for participation. CLEAR projects may also be sponsored at a regional or system level to address local priorities.
The CLEAR apprenticeship model includes an in-depth, blended learning element which runs in parallel to the live project. The education is delivered using a combination of online learning platforms, workbooks, remotely delivered live workshops and tutorials, as well as face-to-face sessions. Associates are provided with learning outcomes and are guided through the education and live delivery by team supervisors and CLEAR Faculty. The educational package is designed to equip associates with the knowledge, skills and experience to deliver a successful CLEAR project as well as outcomes and recommendations for participating services and organisations.
The four stages of CLEAR are described and set out in the diagram below (figure 1).
Stage 1: Clinical engagement
During the clinical engagement stage, associates identify key stakeholders involved in leading, delivering and interacting with the service. A stakeholder register, comprising a purposive sample of staff working in the area/service, is identified for the CLEAR associates to engage with- this includes, but is not limited to, service leads, clinical staff e.g., nurse, doctors and allied health professionals, and non-clinical staff, e.g., porters and administrators.
Engagement involves using a series of qualitative techniques, such as interviews, focus groups, field observations and informal discussions. The goal of these engagements is to collect rich data about the service, such as patient pathways, workforce issues, problematic cohorts and staff well-being. This gives associates a rich description of how the system functions, and the staff perceptions of what works well and why, and what needs improving. The purposive sampling approaches allows CLEAR Associates to collect dissonant views which highlights contrasting opinions and provides a holistic picture. Ideally, associates aim for data saturation, which means that no new themes are emerging from the data. Typically, a CLEAR team conduct around 30-40 engagements.
Engagements are recorded with an automatic transcription function. These qualitative data are then recorded in a paraphrased fashion on a clinical engagement tool, which is an Excel spreadsheet that allows central recording of data collected by all associates involved in the project. A rapid thematic analysis approach is used to analyse the data. Initially, a sample of these data are coded by the associates independently. The individual codes that emerge are then condensed and agreed by all associates before applying this coding system to the remaining data. Once the open coding is complete i.e., all data is coded, family codes are formed, which are a collection of interconnected ideas. These are developed further into key themes that are presented back to the clinical sponsors, i.e., senior service leaders who act as the link between the organisation and the CLEAR team.
The themes are prioritised by the team and the clinical sponsor, and the most pressing undergo root causes analysis using fishbone diagrams6. This process allows associates to identify the perceived issues that contribute to the key themes and present them in a visual manner to be easily interpreted by the team. This is followed by the development of causative statements, which are statements that link the key issues, with the cause of the issue and the effect that issues have on the service. These statements create surrogate hypotheses which can be taken forward to the next stage of the CLEAR methodology, data interrogation.
Stage 2: Data interrogation
Data interrogation involves in-depth analysis of clinical and workforce data of organisations. The data used, and the way it is visualised for the purpose of interrogation are unique stages in the CLEAR methodology, and therefore the process of accessing data and visualisation of that data warrants further discussion prior to consideration of the interrogation approach.
The scope of the work is first determined with the site with the clinical and data teams working together to create a specification that will capture the required available data for successful completion of the project. The information governance documentation, which is completed as part of the contracting stage (refer below) reflects this data specification. Where possible, the data specification is aligned with nationally submitted datasets such as the Emergency Care Data Set (ECDS) and the Mental Health Services Data Set (MHSDS), which allows standardisation of the data requested between different sites. The process of local clinical validation also supports the organisation with improving the quality of this data submission with NHS Digital.
Prior to any data processing, appropriate and compliant data sharing documentation – including a data processing impact assessment and a sharing agreement – are collaboratively drafted by 33n and the participating organisation. Draft documents are reviewed for approval by senior stakeholders, from both organisations, with responsibility for information governance and data protection.
33n completes the NHS Digital Data Security and Protection Toolkit7 assessment every year and is Cyber Essential Plus certified. Data shared from a trust’s systems is stored on private, encrypted, access restricted servers in the London region. All data are deidentified i.e., a data subject cannot be directly identified by any member of the 33n team. This means that no NHS numbers, names, residential address, etc., are included.
The data required for the projects supports the understanding of the patient activity and the workforce availability at the organisation. Patient activity data is gathered from the patient administration system (PAS) and supplementary sources, such as national audit data. This data describes details of patient referrals, contacts and attendances, including their demographics, reasons for attendance, movements through the organisation, diagnoses, procedures and outcomes. Brought together, this builds a detailed, granular picture of the requirement for care within the organisation and how that care is being delivered in a way that has not been done before. The workforce data is brought together from a combination of electronic staff record (ESR) and finance data. This describes the workforce that delivers care by whole time equivalent (WTE) per role and outlines the monthly spend for substantive, bank and agency requirement. The workforce data provides an important baseline understanding of the workforce available within the organisation to deliver care for patients.
The types of data that feed into the dashboards will vary dependent on the scope of the project. This broadly falls into the following categories, shown in Table 1.
Types of clinical and workforce data
Data field examples
Patient activity data
Gender, racial/ethnic origin, age, Lower Layer Super Output Codes (LSOA), GP practice code, etc.
Patient investigations, diagnosis, and treatment data: vital signs, patient observations, investigation types and results, medical specialty, etc.
Patient flow data
Patient attendance, referrals, admissions, outcomes, time stamps, locations, type of contact etc, including patient and episode numbers (or equivalent)
Clinical coding data
Relevant clinical classifications and coding (diagnosis, procedure, consultant codes, frailty, clustering codes, etc).
Workforce composition data
Volume and types of staff position and roles, qualifications, rota details, contracted hours, shift time, etc.
Staff. bank and agency spend, locum spend, administrative costs, etc.
The data highlighted in Table 1 are accessed via established databases, such as: electronic staff records, electronic patient activity records/systems, ward movement databases, test request systems, national audit data, roster data, financial systems, and theme specific data sets, such as The Mental Health Services Data Set (MHSDS), and The Emergency Care Data Set (ECDS).
In most instances, the data required is extracted from the organisation by local Business Intelligence (BI) teams who have an in-depth knowledge of the organisation’s systems and software. Three consecutive years of data are requested to obtain a historical view of the activity and how this has changed over time. Once the data has been extracted by the organisation’s BI team, it is then transferred via a secure upload to 33n’s London-based servers for processing.
Data processing and visualisation process
Data processing is required to transform the data from its raw form into tables from which usable dashboards may be built. This includes a series of validation and transformation steps outlined in figure 2.
Data interrogation process
Quantitative data interrogation is completed using TableauTM Software (version 2021.3) (LLC). TableauTM allows data to be visualised in easy-to-read dashboards and visualisations that allow quantitative clinical data to be combined in multiple layers. The ability to apply multiple filters to the data provides insight into healthcare metrics of performance and the clinical pathways and processes that impact clinical departments or systems. Quantitative data visualised in TableauTM is site specific: no other healthcare provider’s data is included in TableauTM. A UEC (Urgent and Emergency Care) TableauTM would not, for example, include quantitative data shared by a primary care healthcare provider.
The associates are given access to TableauTM and advised to complete an exploratory interrogation of the data as whole. This gives insight into the CLEAR site's metrics of performance; for example, in the case of urgent and emergency care, the number of referrals or attendances, patient flow, large cohort groups that use the service, and discharge destination or attendance conclusion. The data can be compared against national Key Performance Indicators (KPI’s) or quality standards to give a benchmark of how the site compares against expected measures of performance.
The associates then apply agreed filters to the data to interrogate cohorts of interest or processes that have been identified via one of three methods (1) cohorts of interest identified by the CLEAR site in the initial scope document, (2) cohorts of interest identified during the clinical engagement phase (via coding of data and fishbone diagrams), and finally (3) cohorts that have arisen during the initial quantitative interrogation of the data described above. The filters used to create cohorts for interrogation are agreed as a CLEAR site team, and recorded on the data interrogation tool, to ensure that all associates belonging to the team are using identical filters ensuring data hygiene.
Cohorts can then be interrogated in further detail to review how they affect the performance of the CLEAR site e.g., size of the cohort, does the cohort have multiple pathways, does the KPI data vary for specific cohorts and what is the impact on the department.
Associates record their data findings on a data interrogation tool, which captures descriptive statistics, screenshots of data visualisations and short statements describing what the data shows and whether this links to the qualitative data.
At the end of the quantitative data phase, the associates triangulate (8) the qualitative and quantitative data. Associates create theoretical frameworks or hypotheses to explain the potential causes of key challenges that arose during the clinical engagement phase and then use the qualitative and quantitative data to support or refute these hypotheses.
The triangulated data should provide detailed information of the root cause of site challenges providing a clear starting point for the innovation phase that comes next.
Step 3: Innovation
During the innovation phase, associates are encouraged to use divergent thought processes to look for new and innovative solutions. The aim is to enable second order change through the design of a new model of caring for patients, and new ways of staffing through workforce redesign. The associates are provided with several tools for the creation, refinement and impact assessment of their ideas and solutions to the challenges highlighted through the triangulation of the qualitative and quantitative data8.
Tools for generating multiple and varied ideas are demonstrated and given to the teams to use, such as ‘fresh eyes’ and ‘steppingstones’9. After the use of these tools, there will be many options of varying plausibility and viability for conceptual solutions to the challenges.
The ideas generated then need to be looked at with a more convergent thought process to develop the concepts into workable solutions. The teams are provided with further tools on idea refinement including ‘dot voting’9 and linking or grouping solutions. This allows teams to select the concepts most likely to be successfully implemented but also to build on concepts by linking ideas into larger more coherent plans for change. At the end of this process, teams should have selected one or two solutions for each challenge and developed the concepts into more workable solutions.
The teams then perform an impact assessment of their solutions. This has a two-fold intention helping to further refine the ideas and solutions, but also select the most favourable options to put forward as recommendations. Tools provided for this include Levitt’s Diamond9, Yesterday Tomorrow9 and use of an ease of implementation versus desirability matrix.
By the end of these exercises, the teams should have new models of care developed as solutions to the challenges with a stratification of their ease of implementation against them.
A key part of ensuring success of an innovation is securing agreement of stakeholders to the issues and findings and socialising the innovations early so that they can be made as robust as possible for the recommendations stage. This is carried out through stakeholder meetings during the programme.
The next stage is the designing a workforce to the new processes.
Workforce redesign is an integral part of the innovation phase as it helps to consolidate the understanding of the challenge with the innovative new solutions. By working collaboratively with the local stakeholders, new models of care and workforce are designed to improve patient care and empower staff whilst being pragmatic and sustainable.
To achieve this outcome, a workforce methodology based on well-established concepts of healthcare demand and capacity modelling13,14 is utilised with the following considerations:
Describe the target cohort of patients as reviewed through the qualitative and quantitative analysis.
Understand in detail the target cohort characteristics from demographic factors, attendance behaviour, patient journey, activity and outcome generated during their interaction with the service.
Describe the new model of care for these patients generated from the innovation phase. This requires a thorough understanding of the new patient pathway and the intended aims of the new service.
Describe the patient demand, as characterised by attendance and activity demand. This is described as the care required per patient by each type of workforce, per location for every hour of every day. This allows for a flexible workforce model that expands and constricts to meet the varying demand of peak and off-peak hours in an operating service, whilst considering official guidelines of 80th centile of attendances15. By reviewing patient care requirements through activities generated, a skills-based approach can be engaged to address these needs.
This promotes innovative new roles to meet demand through upskilling or cross-skilling across professions.
Describe the workforce capacity in this new model. Various considerations can be employed here including estates capacity, minimum staffing requirements, safer staffing targets, workforce efficiency and local variations in roster patterns.
Match patient activity and attendance demand to workforce capacity to identify the appropriate, sustainable and safe effective workforce in the new model of care.
The teams will build a series of potential workforce models to deliver the new model of care that has been designed. These form different options that may be presented for consideration. For example, one option may reflect the utilisation of a new role which can then be compared to more traditional models.
The workforce data extracted from the site is used as a baseline of current staffing and spend. The new models of workforce are compared against the baseline to understand the change in workforce profile that would be required to staff the new model of care. The financial implications of these models are calculated using the NHS contract payscales (AfC, DDRB and GMS) estimated on-cost of 20%10,16.
The approach, as described above, emphasises a ground-up, patient-demand based workforce model which takes into consideration multiple demand and capacity variables along the way. The final workforce model is bespoke to the local team creating it, emphasising that solutions need to be clinically-led, supported by data and tailored to the local context. These workforce models are an important part of the recommendations for the project.
Step 4: Recommendations
The projects close with the generation of recommendations for change, in which the team brings together the work that has been completed in the previous stages and synthesise a clearly articulated case for change. The recommendations are written as a series of options which vary in their ease of implementation and investment. The impact of each option is described in terms of the anticipated change in workforce, financial cost, KPIs, staff and patient experience. The project team present the recommendations to the executive board for consideration and produce a written report that may be circulated to stakeholders. If the recommendations are accepted by the executive board, the written report may be used by the site to develop a business case to support implementation.
The implementation of recommendations is at the discretion of the participating organisation and is locally owned. However, as part of the final report, project associates are required to set out a site-specific high -level implementation roadmap including key time-based sequence of activities, key stakeholders and any process or estate considerations. A suggested implementation strategy is included within the written report, including recommended outcome metrics and re-evaluation time scales.
Evaluation of CLEAR
CLEAR programmes have undergone external evaluation to determine a potential return on investment.
The purpose of the evaluation was to:
 Assess the extent to which CLEAR projects deliver on the value promise and achieves the core aims of the programme
 Assess the return on investment (RoI) a CLEAR project may bring to a participating NHS organisation and sponsors
 Inform the future direction and development of CLEAR (not presented here).
A formative evaluation methodology was used, which included a qualitative study followed by an economic evaluation that the qualitative data helped to inform. A health economic logic model 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.
The data on which the analysis was performed included:
Interviews with people involved in the design of CLEAR (n=4) and previous CLEAR programme associates (n=6)
Interviews with people who have been or are currently CLEAR delivery or education leads (n=5)
Survey with previous CLEAR programme associates (n=14) Reports and recommendations from previous CLEAR projects 7
The health economic logic model
Reports from previous CLEAR projects
Health Economic Logic Model
The cost of each CLEAR project was calculated based on information provided by 33n Ltd about each of the components of a CLEAR project. Labour costs were calculated using the hourly cost of those involved based on their AfC band and the number of hours they were needed. Other costs included the cost of education delivery, information governance, data ETL, data visualisations and regional and system engagement.
To calculate any potential cost efficiency an appropriate alternative to CLEAR needed to be identified. We used a consultancy alternative with discounted rates exclusive to the public sector through the management consultancy framework. CLEAR roles were aligned with their consultancy roles and their hours needed were converted into days in order to use day rates.
Case studies of previous CLEAR projects were analysed to estimate the potential long-term return on investment (ROI) of projects.
Each case study looked at the projected benefits of the recommendations from the projects over the next 5 years, using a discount rate of 3.5% per year in line with guidance from the Treasury. Complex change interventions face rates of implementation failure of 30-90%, to account for this a 40% rate of implementation was assumed for the consultancy alternative. 93% of CLEAR associates believe recommendations from CLEAR are more likely to be implemented than those identified by other methods and 86% agreed CLEAR was a more effective way of delivering solutions. We therefore applied an implementation rate of 60% for CLEAR in our base case scenario. As a sensitivity analysis, a range of different implementation probabilities were applied. Savings from solutions implemented were calculated using costs from the Personal Social Services Research Unit (PSSRU) costs of health and social care 2020.
The evaluation was formative as insufficient time had elapsed for all the recommendations to have been implemented. The programme is re-engaging project sites to develop a summative evaluation of the impact of CLEAR.