The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
Robust workforce planning for the English medical workforce
Authors: Dr Graham Willis’, Dr Andrew Woodward , Dr Sién Cave”
* ok
Centre for Workforce Intelligence (CfW1) Decision Analysis Services Ltd (DAS)
Graham. Willis@ cfwi.org.uk SionCave@ das-ltd.co.uk
209 — 215 Blackfriars Road Grove House
London Lutyens Close
SE1 8NL Chineham Court
Basingstoke
Hampshire
RG24 8AG
Abstract
This paper describes the use of system dynamics in a major project for the UK
Department of Health to inform a review of the intake to medical and dental school. It
takes many years to train these professionals (typically 15 years or more for a hospital
consultant), so an under or over-supply cannot be corrected quickly or easily. The cost
of training and employing an individual is significant so the decisions to be made are
highly important.
The system dynamics approach meant that robust, evidence-based supply and demand
models could be created to test potential policies and their impact. It also meant that
the model was “transparent” and enabled the expertise of several hundred stakeholders
from the healthcare system to be captured and synthesised.
Significant decisions were made as a result of this work, including:
e A 2% reduction in medical school intakes to be introduced with the 2013 intake,
with a further review in 2014
e No immediate change to dental school intakes because of issues over data
quality highlighted by the modelling, with another review in 2013
e A rolling cycle of reviews of medical and dental student intakes should be
established; to be undertaken every three years.
Key Words: System dynamics, workforce planning, workforce training, doctor, dentist,
healthcare, health policy, Centre for Workforce Intelligence
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
1. Introduction
This paper describes a major project that has directly influenced decisions taken by the
Department of Health (DH) and the Higher Education Funding Council for England
(HEFCE) to adjust the numbers of doctors and dentists being trained in order to prevent
a future under or over-supply. The work was carried out by the Centre for Workforce
Intelligence (CfW1) with support from Decision Analysis Services Ltd (DAS).
The CfW1 is an independent agency working on specific projects for the Department of
Health and is an operating unit within Mouchel Management Consulting Limited. The
CfWI is the UK’s national authority on workforce planning and development, providing
advice and information to the health and social care system. The aim of the CfW1 is to
produce quality intelligence to inform better workforce planning, in order to improve
people's lives. The CfWI are supported by DAS who provide specialist system
dynamics consultancy. DAS is a team with a shared vision of solving strategic
challenges facing government and industry decision makers using systems modelling
and simulation methods.
This paper describes the work undertaken to model the future supply and demand of
doctors and dentists for the Health Education National Strategic Exchange (HENSE)
review group. The NHS in England employs 1.35 million staff, including over 145,000
doctors (NHS Information Centre, 2013). Supply of a single specialist doctor costs the
UK Government approximately £250K to £550K in training (University of Kent, 2011)
and over £2Million in lifetime salary', so over-supply is expensive. Equally, under-
supply has a considerable, if harder to quantify, impact on the health and well-being of
the population.
The purpose of the work was to provide intelligence to inform recommendations of the
HENSE review group on future student intakes to medical and dental schools. The work
was carried out from winter 2011 to the autumn of 2012. The work was acknowledged
as making a major contribution to this review (Department of Health. 2012).
Implementing the recommendations of the HENSE review group for a 2% reduction in
trainee doctors will lead to a reduction in training costs to the NHS of approximately
£50 million per year. These recommendations were informed by the modelling, and
have been agreed by the DH and HEFCE, who share responsibility for determining
medical and dental school intakes.
A completely new approach to workforce planning was developed by the CfWI for this
project, which we call Robust Workforce Planning. This is a method for identifying
potential future issues that need to be addressed by workforce planners. It allows them
to assess the impact of workforce policy options and reduce risk. This approach is new
for health and social care workforce planning in England. We first think about what
health and social care may look like in the future, including the workforce needed to
provide it. We then focus on policies to deliver the required workforce, and test them
* http://www.icnhs.uk/workforce, Staff earnings. 25 years of GP or Consultant salary of approximately
£108k pa. (NHS Information Centre, 2013a).
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
across a range of futures defined by a set of scenarios. This allows robust decisions to
be made that recognise the uncertainty of the future.
Central to the approach are system dynamics (SD) models that calculate workforce
supply and demand — in this project the medical and dental workforce. These models are
referred to in this paper as the MDSI models. The models were grounded in empirical
data and evidence. The MDSI models enabled analysis against a range of potential
futures and policy interventions. The models allow the rapid assessment of the
implications of scenarios and policies across the whole of the medical and dental
training systems and workforce.
1.1. Contents
Section 2 describes the challenge of health and social care workforce planning.
Section 3 provides an overview of the Robust Workforce Planning framework, which is
used by the CfWI to inform workforce planning decisions. The role of system dynamics
in the framework is described.
Section 4 describes the systems dynamics models that were developed during the
application of the Robust Workforce Planning Framework to provide projections of
future supply and demand of doctors and dentists.
Section 5 discusses the impact of the system dynamics modelling and the next steps for
the application of system dynamics in the CfWI.
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
2. The challenge — Health and social care workforce planning
2.1. Introduction to workforce planning with a health care focus
Effective workforce planning has been described as ensuring “The right people, with the
right skills, in the right places, at the right time” (Taylor, 2005). This is a challenge in
health care due to a complex mix of staff and staff functions, the large geographic area
that is covered and the changing policies that influence the supply and demand of care.
The risks of poor workforce planning are to put patient lives at risk, increase morbidity,
and spend huge sums of money to correct sub-optimal systems. Employees can suffer
from the stresses of understaffing, or in the case of oversupply, livelihoods can be put in
jeopardy if jobs are not available. To mitigate risk it is important to have foresight of the
key issues, and flexibility within the workforce and the training pipeline to adapt when
necessary.
2.2. The medical and dental workforce
The medical and dental workforce in England is large and very highly qualified.
Characteristics of training are long and varied training paths that take well over ten
years from starting university to becoming a trained specialist. There are over 60
medical specialties and 11 dental specialties that trainees compete to specialise in.
Progression through the training pipeline is subject to delays as trainees often take time
out for research, maternity or other experiences. When qualified there are many
different contracts available to doctors, such as consultant, GP, speciality doctor, and
many more. Attrition occurs throughout education, training and employment.
2.3 Why health workforce planning is difficult
Health workforce planning is very difficult because of the size of the workforce (over
one million), the number of professions and different skills involved, and the
complexities of estimating the requirements to meet the future health needs of the
population.
There are many routes through training, flows between these routes, and also migration
in and out of England and the UK. Behaviours of the workforce vary by age, gender and
the type of doctor or dentist. Transfer between contracts and training areas complicates
the system. The long timescales for training make it hard to measure the impact of
policy changes and to make corrections. The inertia, delays and complex influences in
the system mean that modelling is complicated.
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
3. Cf£WI workforce planning framework
Rather than attempt to predict the future, the CfWI has developed a scenario-based
approach that recognises the complexity of factors influencing demand and supply and
the intrinsic uncertainty of the future. This framework is referred to as the Robust
Workforce Planning framework. The key benefits of this approach are to support
longer-term planning, here looking out to 2040; to support more robust decision
making, taking account of the uncertainties of the future; and to help decision makers be
more alert to emerging risks as the future unfolds.
The study into the medical and dental workforces described in this paper is the first time
an approach of this kind has been used in healthcare workforce planning. The high-level
framework is illustrated in Figure 1. The framework consists of four linked stages, the
outputs from each stage feeding into the next. A major feature of the framework is the
high degree of stakeholder involvement, which is critical to arrive at a shared view of
future challenges, and in making policy decisions.
Robust workforce
planning
framework
Figure 1 - Robust Workforce Planning framework
3.1 Stage 1 - Horizon scanning
Horizon scanning explores the potential challenges, opportunities and likely future
developments that could influence workforce planning. These include technological,
economic, environmental, political, social and ethical (TEEPSE) influences on an
unfolding future. Some of these influences may be viewed as predetermined, such as an
ageing population, and some may be more uncertain, such as technology advances.
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
The horizon scanning can be broad or focused on specific areas. A web site is used to
collate a wide range of expert opinions and present key factors’. Reports are avaialble
on the horizon scanning conducted for the MDSI project (CfWI, 2012 and 2012a).
3.2 Stage 2 — Scenario generation
Scenario thinking focuses on how the future might evolve (Van der Heijden et al, 2002).
Scenarios are essential for workforce planning since it is not possible to predict the
long-term future accurately. Scenarios are particularly useful since a range of plausible
futures can be generated and demand and supply projections made. Workforce plans can
then be assessed against the scenarios for robustness. A baseline or ‘business as usual’
scenario is included to illustrate what might happen if trends continue as at present.
Scenarios are based around high impact and high uncertainly driving forces which shape
the future. The method used (Wright & Caims, 2011) creates plausible stories about the
future that capture what might happen in a memorable way. Facilitated workshops are
used to get wide involvement and agreement.
Following the scenario generation, the narrative stories need to be quantified. A unique
feature of the framework is the use of a Delphi process (Dalkey & Helmer, 1963) to
quantify key workforce variables. Experts make quantitative judgments and share the
reasoning behind them over several rounds to decrease uncertainty and refine the
values.
3.3 Stage 3- Workforce modelling
The purpose of workforce modelling is to project demand and supply for a range of
plausible futures, as described by the scenarios. Further modelling is then conducted to
determine the robustness of policy options for achieving a sustainable balance of
demand and supply. System dynamics modelling is used, since it is most appropriate to
complex systems with feedback, like health and social care workforce planning, and can
easily be extended or revised to address additional issues as they arise.
The model takes several kinds of input:
e Facts we know ~ baseline data to populate the model, including current training
and workforce numbers
e Assumptions we make — predictable trends and assumptions needed where key
data is not available or of poor quality
e Parameters that we can control — that define the policy choices needed to
secure adequate supply to meet forecast demand
e Uncertainties we can quantify — intrinsically uncertain parameters that may
vary by scenario.
2 www.horizonscanning.org.uk
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
The model is formally tested and documented. Sensitivity analysis is conducted to help
understand the impact of changes in data on model outputs, and thus which are the most
important for accuracy.
3.4 Stage 4- Policy analysis
Policy analysis focuses on analysing future uncertainties and the impact of policy
options, and presenting the findings. By considering multiple future scenarios, different
options can be tested to see which one is the most robust. There will be some which
lead to favourable outcomes across all futures and others where the outcome is less
clear. In these situations the relative probability of scenarios may need to be assessed,
and scans made for signals that might indicate a particular scenario unfolding.
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
4 MDSI system dynamics model
The Robust Workforce Planning framework described in Section 3 was applied in 2012
to the future supply and demand of doctors and dentists for the HENSE review group. A
fundamental part of the framework during the workforce modelling stage was the
development of models to calculate the change in supply and demand for the medical
(doctors) and dental workforces through to 2040. This Section provides a description of
the system dynamics models.
4.1 High-level requirements
The workforce models were required to:
e Calculate the supply and demand for the medical and dental workforces from
now through to 2040
e Segment the workforce by age and gender
e Represent the training pipeline from entering university through to delivering
service as fully qualified doctors and dentists
e Represent the complex career paths for doctors and dentists following
qualification
e Integrate with large datasets from a variety of NHS and other official data
sources
e Use the data from the Delphi workshops that define the attributes of the four
scenarios
e Enable policy analysis to be carried out to determine the impact of different
policies on the different scenarios
e Execute rapidly and produce outputs that can be readily analysed
e Be fully tested and documented, with an audit trail for all assumptions
e Allow the sensitivity of the input assumptions to be determined.
Due to the complexity of the model scope and scale it was decided that the system
dynamics approach was best suited to meeting the modelling requirements. Not only
does the method allow the complex processes to be represented and to integrate with the
complex datasets, but as it is based on a graphical representation of the system the
stakeholders can be more readily involved in the model validation.
Two models were developed, one for the medical workforce and the other for the dental
workforce. The models were built using Vensim DSS and Microsoft Excel. A user
interface was developed using Excel to enable non-SD analysts to more easily use the
model and carry out policy analysis.
4.2 Model description
This section provides a high level description of the MDSI models, and the approach
adopted to develop them. The standard development process, as illustrated in Figure 2,
was followed for the models:
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
Model Testing and
=> Pe nani
Figure 2 — Simulation development process
Each of the stages is described below, along with further detail regarding the model
structure and functionality.
4.2.1 Specification
The model specification clearly defined the purpose of the model, and what was in
scope, and equally importantly what was out of scope. The specification was based on
the initial model requirement outline in Section 4.1 and ensured that the developed
models only represented what was needed for the purposes of the HENSE review, thus
preventing scope creep and mitigating against the risk of late delivery.
4.2.2. Development
The initial stages of model development were to map out the relevant processes of the
training and career pathways with appropriate stakeholders from the medical and dental
systems.
The maps were created in Vensim and printed out to be shared with the stakeholders. In
addition, the process maps were presented at a series of national road shows hosted by
the CfWI, which enabled over 80 people to comment and amend the process maps.
Numerous stakeholders helped to sense-check the accuracy of the models themselves or
helped to provide or sense-check the data and modelling assumptions used. Key sources
of help were the DH’s Workforce Data and Analysis Team, the Health and Social Care
Information Centre, the British Medical Association (BMA), the General Medical
Council (GMC) and specific deaneries, University and Colleges Admissions Service
(UCAS), NHS Pensions, and members of the medical and dental project reference
groups.
The large degree of stakeholder engagement throughout the process mapping stage
ensured high levels of stakeholder buy-in to the modelling process.
Sample career path stock and flow diagrams are given in Figures 3 and 4 below:
The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
trang
specialist
Figure 3 — Dental model career pathway stock and flow diagram
2011= 7.765 ~ zotte35203
201187346
pot = 34086" 2o11= 8081 rosa 20116524 zor1= 12282 2011
+ Based onthe sum of eows ~ course drop
cuts eccourtadtor tthe and ofthe couse
2or= 19687
Figure 4 — Medical model career pathway stock and flow diagram
Following process mapping, the process maps were converted into a quantitative model.
The model was developed using a combined Vensim and Excel architecture. Excel was
used to create a user interface so that multiple scenarios and policies could be specified
and then simulated with Vensim.
The model was developed iteratively. As each functional area was completed the model
results were shared with experts to determine whether the behaviour for that functional
area was sensible and explainable.
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
The medical and dental models contain similar calculation structures. In both models
the future demand is calculated based on the current demand for service, future
population projections, changes in levels of need and changes in productivity (for
example through technological advances) and changes in service delivery. The demand
calculation is based on a framework from the Canadian research programme on health
human resources (Birch et al, 2011).
In both models the future supply is calculated based on the simplified career pathways
shown above in Figures 3 and 4. The actual career pathways represented within the
models are in fact much more complicated, and include attrition from the stocks, exits
out of the system, inflows from overseas, workforce re-joiners and re-sits. The
workforce levels are also broken out into more detailed career progression pathways.
Figure 5 provides a more detailed view of the complexity of the medical training and
career pathway as implemented in the Vensim SD model.
In addition, the supply is segmented by age (from 16 to 80 years) and gender. This
enables age and gender dependent impacts to be taken into account, for example
attrition and participation rates’, The models have been developed so that additional
segmentation can be added if required.
Finally, the models contain training allocation algorithms and capacity constraints at
each stage of the training pipeline. These enable the preference between types of
training to be included (for example there is a female gender preference for GP
Training). This allows the changes in future demography to be considered within the
model.
Figure 5 — Detailed stock and flow diagram for medical model
3 The extent to which the workforce work full or part time
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
The medical model contains 15 separate influence diagrams, 997 distinct variables and
is initialised with 903,525 items of data. This model takes approximately 10 seconds to
simulate. The dental model is of similar complexity
Each Vensim system dynamics model is linked to an Excel spreadsheet. The Excel
spreadsheet contains all the input data used by the Vensim model, including all data
references and a complete data audit trail. The table below provides a snapshot of some
of the data integrated into the MD SI models:
PSource
Accepted applicants to preclinical aot UCAS
dentistry
Medical school intakes 2007-11 Higher Education Funding Council for
England
. 2011 UK Foundation Programme Office
Foundation programme data ancl Resort
Medical and general practice (GP) 2008-11 Aas and Social Care Information
workforce census for England ea
National population projections 2010 Office for National Statistics
Hospital episode statistics for England 2010-11 pe and Social Care Information
The Excel spreadsheet also acted as a user friendly model interface and allowed the user
to:
Create, store and edit future scenarios
Create, store and edit potential policies
Select scenarios and policies to simulate
Simulate the SD model
Store the results of multiple simulations
View and analyse the results of multiple scenarios.
cooceoeoee
4.2.3 Documentation and testing
The models were fully documented and tested prior to use for formal policy analysis.
This was carried out to ensure that all model assumptions were formally documented
and signed off, and that the model had been implemented correctly.
Each model was documented in the following ways:
e A Technical Description was developed that described the model architecture,
model assumptions and how the model is used for analysis
e The Excel spreadsheet made extensive use of comments to describe the purpose
of the cells and contained audit trail cells so that references could be included for
each data item
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
e Each variable in the Vensim model was documented using the equation editor,
and the units were fully defined.
A robust, formalised approach to testing was adopted. The purpose of model testing was
twofold:
e To ensure that the model design has been transformed into a simulation model
with sufficient accuracy
e To ensure that the simulation model is sufficiently accurate for the required
purpose.
A test specification was developed based on the model documentation. The test
specification detailed all the tests to be carried out on the model, and included tests of
the model structure, formulation and behaviour. The test specification ensured that the
testing was carried out methodically, and that all areas of the model were tested.
The testing was carried out by a CfWI modeller who was independent of the simulation
development process. The results of the testing were captured in a spreadsheet. The
spreadsheet identified when and by whom the test was carried out. The outcome of each
test was also logged in the spreadsheet. If the test resulted in a fail then the fault was
corrected by the model developer. The test was then re-run by the model tester to ensure
that it had been corrected, and also that the correction had had no wider implications on
the model. The model tester also had the freedom to carry out additional tests on the
simulation model, and these were also captured in the testing results spreadsheet.
In addition to the tests identified in the specification the following analysis was carried
out:
e The results of the model were compared with previous simulation models that
represented the medical workforce
e The projections produced by the model for each stage of the training and
workforce pipeline, along with the associated assumptions, were reviewed with
relevant stakeholders (for example the chief dental officer)
e The sensitivity of the model outputs to the input data was tested.
The sensitivity analysis was of particular importance. There were varying levels of
confidence associated with the input data, and the sensitivity analysis was used to
determine whether the model outputs were particularly sensitive to any low quality data.
Figure 6 shows a sample sensitivity analysis output chart.
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
Sensitivity analysis results
[sees mare ron —
Lengiend
=
STitperceaonee id
Annual GP rejinersirom ”
outside English systern
oe — GP participation rate
aT se t | Go atrtion rate
[ame | eae
Impact ata quality
Figure 6 ~ Illustrative sensitivity analysis output
4.2.4 Policy analysis
Once each model had heen tested it was considered to be implemented correctly and fit
for purpose, and therefore suitable for policy analysis. Policy analysis required the
consideration of the impact of different policies against the four different scenarios
defined during the scenario generation phase of the workforce planning framework.
Sample policies that were tested as part of the HENSE review included changes to:
Productivity
Skill Mix*
Retirement A ge
Training preferences
Training duration.
eoeceee
A more detailed review of the policy analysis is provided in the online report available
at the DH website (Department of Health, 2012).
The Excel spreadsheet enables the simulation results to be presented in a number of
different ways which allowed for very efficient exploration of the scenarios and
policies. Sample simulation outputs are shown in Figure 7:
“ Skill mix refers to the ratios of different staff types used to deliver a service.
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
Supply vs. demand plots for multiple scenarios Changes in the age profiles over time
Supp
scenario &2
Suppiyafter ple
FTE
a
scenario a2
Year
Change in cost and supply versus demand discrepancy | Changes in the gender profile over time
for multiple policies and a single scenario
mr ercentageot
‘worforeetemsie
maPercentageot
‘worforeenae
z
i
3
z
i
:
é
Year
CChangein cumulative cost
Figure 7 — Illustrative simulation outputs
Furthermore, it is best practice in modelling to quantify the uncertainty that is inherent
in any forecast of the future, in this case: workforce demand and supply. Decision
makers need to understand this to inform their analysis of findings and to make
effective decisions. We considered the level of uncertainty through the use of Monte
Carlo simulation. Figure 8 provides an example of this uncertainty as a fan chart, giving
a probability distribution for supply under one specific scenario.
50000 GP Supply - Scenario 1 Uncertainty
45000
40000
wi
35000 = 90% of data contained
30000 | 1 75% of data contained
950% of data contained
25000
20000 SELLS
2010 2015 2020 2025 «2030-2035 ©=— 2040
Year
Figure 8 — Illustrative uncertainty analysis output
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The 31st International Conference of the System Dynamics Society, July 21-July 25, 2013
Robust workforce planning for the English medical workforce
Finally, as each of the values for each of the input variables over time can be accessed
from the user interface there is an infinite variety of different policies that can be tested
using the model. Therefore the model has great utility outside of the HENSE review.
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Robust workforce planning for the English medical workforce
5 Conclusions
This paper describes the use of system dynamics in a major project for the Department
of Health to inform a Health and Education National Strategic Exchange review of
whether current levels of medical and dental school intakes were in line with predicted
workforce requirements (Department of Health. 2012). It takes many years to train these
professionals so an under or over-supply cannot be corrected quickly or easily. The cost
of training and employing an individual are significant, estimated at over £2 million for
a doctor, so the decisions to be made are highly important.
System dynamics workforce models were developed specifically for this project. The
use of a system dynamics approach meant that robust, evidence-based supply and
demand models could be created to test future potential policies and their impact. It also
meant that the model was “transparent” and made it possible to synthesise the expertise
of several hundred stakeholders from within the health care system.
The modelling also provided insight into what levers can be used to control and guide
workforce numbers, identifying those that are most effective. A visualisation was
produced to show the impact of potential policy decisions, and this was used to inform
recommendations. Levers that are typically used to control workforce supply, such as
intake to medical school, were shown to be slow acting because of the time delays,
whereas changing productivity and reducing attrition by one percent year-on-year had a
much faster impact. The modelling also identified what data the model outputs were
most sensitive to, and where improvements would have a significant impact on accuracy
—and thus on future workforce decisions.
A number of significant decisions were made as a result of this work, in particular:
e A 2% reduction in medical school intakes (120 people), to be introduced with
the 2013 intake, with a further review in 2014
e No immediate change to dental school intakes because of issues over data
quality highlighted by the modelling, with another review in 2013
e A rolling cycle of reviews of medical and dental student intakes should be
established; to be undertaken every three years (not necessarily concurrently).
The model developed for this project has proven to be flexible and expandable, and will
be used to monitor future changes in the workforce and to inform future reviews.
Furthermore, the system dynamics approach is being used to develop additional supply
and demand models for other workforces across the UK health and social care systems,
including nursing, midwifery, pharmacy and a range of medical specialties.
5.1 Next steps
A number of areas are under consideration for further work including:
1. Improving the linkage between the horizon scanning and scenario generation
stages of the Robust Workforce Planning framework. In particular identifying
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Robust workforce planning for the English medical workforce
the most influential factors and driving forces, and investigating the stakeholder
perspectives which have the most impact on the workforce problem under
consideration, and greatest uncertainty of outcome.
2. Increasing the number of scenarios to explore a wider number of critical
uncertainties. The scenario method generates four scenarios by selecting two
clusters of driving forces with two outcomes of each. This might not give a
sufficient spread of challenging futures under certain circumstances. Bringing in
additional clusters of driving forces, possibly from several scenario exercises,
may enable a portfolio of potentially reusable scenarios to be created. Work is
also needed to understand which situations may require additional scenarios.
3. Measuring the effectiveness of a policy decision. In order to rank policies each
scenario is given a ‘score’ which measures the deviation from the optimum.
Policies that lead to the minimum deviation across the range of scenarios are
considered most robust. Metrics for the optimum workforce might involve a
number of factors, for example cost, the gap between supply and demand, and
the workforce age profile. Further work is needed on the selection and
proportion of these measures.
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Robust workforce planning for the English medical workforce
6 References
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