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Exploring the Feedback Effects of Reconfiguring Health Services:
The Case of Cardiac Catheterization Procedures
KS. Taylor, B.C. Dangerfield
Department of Social Policy, London School of Economics and Political Science, Houghton
Street, London WC2A 2AE, U.K. Centre for OR & Applied Statistics, University of Salford,
Faculty of Business & Informatics, Salford M5 4WT, U.K.
k.taylor-alumni@|se.ac.uk, b.c.dangerfield@salford.ac.uk
The reconfiguration of health care, shifting services ‘closer to home’, is a well established
trend, which has been motivated by the desire to improve the provision of services. However,
these efforts may be undermined by the improvements in access stimulating demand. Existing
analyses of this trend have only considered isolated parts of the system and have led to
recommendations to control demand with stricter clinical guidelines or to meet demand with
capacity increases. By failing to appreciate the underlying feedback mechanisms, these
interventions may only have a limited effect. We demonstrate the contribution offered by
system dynamics modeling by presenting a study of two cases of the shift in cardiac
catheterization services in the U.K. We describe several mechanisms by which demand is
stimulated and clarify the roles for stricter clinical guidelines and capacity increases. We
also demonstrate the potential benefits of changing the goals that drive activity.
INTRODUCTION
The reconfiguration of health care is a well established trend whereby services are being
shifted towards the primary level and ‘closer to home’ [1,2]. This trend has been motivated
by the broad desire to improve the provision of services by expanding services and by
improving service quality. However, the efforts to improve services overall may be
undermined by the unintended consequences of service shifts, in particular, by the
improvements in access stimulating demand. This general feedback phenomenon has been
frequently reported in the health care literature [3-8].
The evidence of shifts in services influencing demand has tended to be anecdotal or
unsubstantiated assertions with existing analyses limited to isolated parts of the system, based
upon economic appraisals and surveys. Insufficient attention has been given to the underlying
feedback mechanisms. Furthermore, existing analyses have led to calls to control demand by
introducing stricter clinical guidelines or to meet demand with capacity increases [9-11].
However, these interventions may only have a limited impact due to the complexity of the
underlying feedback mechanisms. System dynamics [12] is specifically designed for the
analysis of feedback. Therefore, the application of this method could provide a better
appreciation of the consequences of service shifts and thus offer useful insight into ensuring
that service shifts actually improve the provision of services overall. In this paper, we
describe a system dynamics modeling study [13] of a specific service shift across the patient
referral chain in the U.K. health care system, the National Health Service (NHS).
The patient referral chain runs from the primary level (care by general practitioners or GPs)
to the secondary level (district general hospitals) and from there on to the tertiary level (more
specialized hospital-based care carried out at specialist centers). Our study considered the
case of the shift in cardiac catheterization (CC) services from the tertiary level to the
secondary level for low risk investigations. A CC investigation is an invasive procedure used
to diagnose heart disease. The majority of elective (non-emergency) investigations are low
risk. Whilst a few patients who present at hospital as emergencies will subsequently stabilize
and become elective referrals for a CC investigation, elective referrals typically arise from
patients first seen at a hospital outpatient appointment. We considered the development of CC
services at two English district general hospitals. For reasons of confidentiality, they are
referred to as ‘Veinbridge Hospital’ and ‘Ribsley Hospital’.
CARDIAC CATHETERIZATION SERVICES AT RIBSLEY AND VEINBRIDGE
Prior to the introduction of district services, cardiologists from Ribsley and Veinbridge
Hospitals developed and maintained their CC skills, by using tertiary facilities to conduct CC
investigations. They both worked at the same tertiary center. The level of both Ribsley and
Veinbridge’s CC activity was determined by the contracts between the individual tertiary
centers and the Ribsley and Veinbridge purchasing authorities. The elective demand for
Veinbridge’s patients was met. However, there was a persistent problem of under-capacity
for Ribsley’s patients. Consequently, temporary capacity increases had been provided at the
tertiary level.
Improving Access with District Services
District CC services were originally introduced, using a mobile catheter laboratory, to
compensate for the temporary closure of one of the tertiary-based catheter laboratories for
repairs. Geographical considerations dictated that separate mobile sessions were required at
Ribsley and Veinbridge Hospitals. At Ribsley, the district service sustained the capacity
increases and thus produced dramatic improvements in access to elective services (Figure
la). However, this trend was reversed when the tertiary laboratory reopened and the district
service was withdrawn. A further temporary service was offered at a later date to compensate
for a second period of construction work at the tertiary center and to provide additional
capacity. This achieved further temporary improvements in access.
Figure 1. Consequences of District Services: Improving Access and Stimulating Demand
(a) fs
120 _
= 0
=
icy
a2 wo
53
zie *
= 4 SO
(3)
ow»
z
a ot
ZB 1 4 7 © B 6 BD 2 6 ®B® M M
Month
400
7 350
(b) 6-point centered
moving average
/
250
3
(Patients/Mth)
Veinbridge New OP Referral Rate
=
e
Ss
S
22 25 28 3 34 37 40
Month
Black blocks indicate the use of district services
At Veinbridge Hospital, the district CC service formed part of a long-term strategy to expand
capacity and to develop cardiac services at Veinbridge. Consequently, after the tertiary
laboratory re-opened, the district CC service continued and was developed into a permanent
service, with an integrated catheter laboratory.
Improvements in Access Stimulating Demand
The collaborators described several different mechanisms by which the improvements in
access stimulated demand. These mechanisms involved the influences of a number of factors,
in particular, the average waiting time, knowledge of patients and GPs of CC and the new CC
service, and the skills of the CC operators. We refer to these effects as the waiting time,
knowledge and skills effects on demand. The stimulation of demand was also reflected in the
hospital data. No other changes occurred during the 40-month observation period that could
have accounted for the changes in demand.
In both cases, there was sufficient supply to meet the CC investigation waiting time goals
whilst the district CC services were in place. However, in the Veinbridge case, the increase in
demand for an outpatient appointment (Figure 1b) produced severe detrimental effects on
access to this service. This loss in access had not been anticipated.
THE SIMULATION MODEL
The model conceptualization process involved collecting archival data, observational work,
informal discussions with junior hospital staff, and formal interviews with senior health
professionals including consultant cardiologists, hospital managers and health service
purchasers.
The simulation model was constructed using the STELLA software [14]. The model contains
nearly 300 variables including 18 stocks (or levels) in the main structure and 52 overall. The
main patient flows are shown in Figure 2.
Figure 2. The Main Patient Flows
CL] Stock / Level
New Referrals ou Flow / Rate
from Inpatients to
an Elective CC Inv fe) Model Boundary
CCInv CC Investigation
Other OP
Waiting List
Removals
OP Outpatient
Referrals from OP
Referrals for an Waiting List to an
OP Appt Elective CC Inv
O O
oP
CC Inv Waiting List
Removals
0
CC Inv
Waiting List Waiting List
Figure 3. The Main Sectors
Waiting Times Sector
For OP appointments & for
elective CC investigations
Inputs to waiting time effect
on demand (____skillsSector
Turnover of junior district operators
Loss & gain in district CC operator skill
Inputs to skills effects on demand
I Delivery Sector )
Elective CC activity at tertiary level
{ Referrals Sector Elective CC investigations at district level
OP referrals & activity Preparation & availability of district facili
Provides learning experiences upon which
Referrals for elective CC investigations from —ea>) Provides learning exper nee
idles : junior district CC operators gain skills
inpatients & outpatients
cae rielia id confidence
Calculates waiting time, skills & knowledge = }*-———_,_ @"
es oh dawind Inputs to knowledge effects on demand
==> Physical Flow Costs Sector
—> Information Flow Patient activity costs
District preparation & running costs
OP Outpatient Affordability limits
The model is divided into five main sectors (Figure 3). The endogenous variables include
several referral multipliers, the waiting list lengths, average waiting times and patient activity
rates. Beyond the model’s explanatory boundary are a number of exogenous factors including
the service capacities. This arose from the model purpose, which was to examine the
consequences of the shifts in services and associated feedback effects and not the policy
decisions to introduce a district service. Other exogenous variables reflected simplifying
model assumptions. These included the base demand for an outpatient appointment that
aggregated demand for new and follow-up appointments, and certain referral multipliers.
The modeling perspective of system dynamics is on continuous, aggregate phenomena.
However, there have been calls to modify the system dynamics paradigm by incorporating
discrete and stochastic elements into its models and by disaggregating further. This has been
to extend the range of feedback phenomena that may be studied and to satisfy clients’ desire
for further detail [15-17]. In our study, it was necessary to make some modifications, for the
former reason, as the emphasis was on decisions and processes at the local (individual
hospital) level rather than the national level. This resulted in the introduction of discrete
elements into the model including appropriate built-in STELLA functions. For example, to
model the arrival and departure of trainee CC operators, a function was used which generated
a pulse input of a specified size at a specified time.
Calculation of the Elective CC Investigation Referral Rate
The elective CC investigation referral rate is the rate of referrals from the inpatient route
(from patients who were admitted into hospital and subsequently stabilized) added to the rate
of referrals arising from patients seen in outpatient clinics. The former was assumed to be
constant and the latter is a fraction of the outpatient activity rate. This referral fraction is a
reference referral fraction (described later) adjusted multiplicatively by several referral
multipliers.
The model contains five base case referral multipliers. The first three are endogenous
variables (Figure 4). The first multiplier involves the influence of the average waiting time
(Figure 4a). This reflects how low waiting times could stimulate demand and how high
waiting times could suppress demand. For the Veinbridge case, the waiting time was not
considered a factor in referral decisions, which was consistent with the more ‘aggressive’
(confident and enthusiastic) referral behaviour at Veinbridge. This multiplier is set to a
default value of 1 (zero effect).
The second multiplier concerns the influence of the knowledge of patients and GPs of the
benefits of CC and the new CC service (Figure 4b). By developing a local CC service, GPs
and patients became more knowledgeable about the benefits of CC and overcame anxiety
about the risks, and thus more demanding for this service. The extent of this effect on demand
increased as the district service grew generating more publicity and, through ‘word of
mouth’, more reports of patients who had benefited. This effect was delayed as GPs and
patients perceived changes in the availability of district services. Subsequently, this multiplier
is modelled as a function of the perceived district CC rate. The effect was greater at
Veinbridge because the CC service was permanent and heavily marketed.
Figure 4. Base Case Waiting Time, Knowledge and Skills Referral Multipliers
(a) Waiting Time Effect (b) Knowledge Effects
Veinbridge
11 “ 12
1 = Se LL
0.95 1
\ TF mn
Veinbridge Ribsley || Ribsley
i i if
1 5 15
Average Waiting Time Perceived District-Based
Reference Waiting Time* CC Investigation Rate
(c) Skills Effect
1.208
1
0.167
ley
30 50 60 80 100
Average Skills Per Junior
District Operator}
* - Neither stimulates nor suppresses demand; + - 100 represents a fully skilled CC operator;
Values above | - Stimulation of demand; Values below | - Suppression of demand
The third multiplier depicts how changes in the skill base of those who select patients for a
CC investigation could alter demand (Figure 4c). We expand on the reports of Hamblin et al
[18] who described how when staff gain new skills, as a result of taking on new duties, they
identify more patients in need of treatment. We considered the rate of the gain in skills and
the Joss in skills associated with staff turnover. The effect of skills on demand is a weighted
average of the skills multipliers of three categories of staff: expert CC operators; junior
trainee CC operators; and, non-CC operators. Experts and non-CC operators were assumed to
make referrals at constant rates with the former referring more than the latter due to their
higher enthusiasm for CC and their greater skills in identifying patients in need (non CC
operators will often be general physicians rather than cardiologists). The referral multiplier
represents the changes in referrals of junior trainee CC operators as they climbed up the
learning curve gaining specialist knowledge and confidence. The two functions reflect the
different referral patterns, one representing Ribsley, involving a period of under-confidence
and the other, representing Veinbridge, also involving a period of over-confidence. The
multiplier reflects the greater confidence ‘spilling over’ during the learning process. Periodic
skills effects were introduced by the existence of training programmes and the rotation of
junior staff between hospitals.
The model contains two other referral multipliers that are, for simplicity, modelled
exogenously. The first is the effect of significant capacity losses on referrals. It was assumed
that a significant loss of capacity in the Ribsley case (e.g. the withdrawal of the district
service) resulted in a reduction in referrals of the lowest priority, lowest risk cases as a ‘knee
jerk’ reaction. For the Veinbridge case, as there were no significant capacity losses, this
multiplier is set to a default value of 1 (a zero effect). The second multiplier is the effect of
other factors on referrals. For the Veinbridge case, it reflects how introducing a permanent
CC service and opening the integrated laboratory prompted shifts of higher risk cases to the
district level. For the case of Ribsley, this multiplier is set to the default value of 1, as the
district service was only temporary.
The reference referral fraction represents periods where: there was no district service; normal
capacity levels existed at the tertiary level; all district screeners were fully skilled; and, there
was neither stimulation nor suppression of demand due to the waiting time or knowledge
effects. This would correspond with a situation for which GPs’ and patients’ knowledge of
CC relied totally on that derived from a tertiary-based service. An expert estimate applies to
the case of Ribsley and for the case of Veinbridge, this figure is subjected to a multiplier to
account for the more ‘aggressive’ referral environment. The formulation chosen for the
referral fraction reflects the assumption that the ‘knee jerk’ reaction to the capacity losses
dominated over the effects of waiting times, knowledge and other factors on referrals for an
elective CC investigation. This avoids double counting patient referrals [19].
The Basic Mechanisms
The basic feedback mechanisms represented in the model are shown in Figure 5.
Figure 5. Basic Feedback Structure
Average
D Waiting Time
- B2
Referral * Elective CC * CC Investigation
Fraction Referrals Waiting List
2 +
+
= “em DELAY
Outpatient B3 Other Outpatient — a
Waiting List Waiting List Removals
+h. ag District CC
Rin Investigation Rate
Patient and GP
Skills of Pressure %X______ Patient and G DELAY
+
Juniors Knowledge +
D
Learning Experiences
of Juniors = +
BI - Activity adjustment; B2 - Waiting time effect on demand; B3 - Other outpatient waiting list removals;
RI - Skills effect on demand; R2a & R2b - Knowledge effect on demand for elective CC investigations and
outpatient appointments respectively; For the Veinbridge case, loops B2 and R1 remained inactive. The average
waiting time was not considered in making referral decisions and referrals were not influenced by changes in
juniors’ skills and confidence because the expert operator made all final decisions about referrals.
Model Parameterization and Testing
The justification underlying the model parameters varied as efforts were made to utilize all
the available data, as recommended by Homer [20]. The approximations and assumptions
made served for the purpose of the model. Various parameters arose from archival data
provided by the collaborative centers. Other parameters were based upon estimates given by
the study collaborators including some of the nonlinear referral multiplier functions. There
was insufficient numerical data to estimate these functions econometrically, as described by
Sterman [21]. In several cases where numerical data did not exist, parameter values were
derived from simple calculations using the actual activity rates and by assuming that parts of
the system were in equilibrium and the desired waiting time goals were maintained. Values of
parameters were also obtained from the preliminary simulation runs.
Confidence was gained in the model via established methods [19,22]. In testing the ability of
the model to replicate the patterns of historical behavior there was a good qualitative fit in
both cases. Figure 6 shows some selected graphs for the Ribsley case.
Figure 6. Selected Graphs of Historical Fit for the Ribsley Case
Simulated
(Months)
Avg Time Spent on CC I
(Patients,
Elective CC Inv.
Actual data is smoothed with a 2 point-centered moving average. Black blocks indicate the use of district
services during month 14 to month 23 and month 34 to month 38; Avg - Average; CC Inv - CC Investigation
For the Veinbridge case, hypothetical reference modes were constructed due to the absence of
suitable real data. These modes, which were based upon the descriptions of the interviewees,
were verified during the base case analysis.
MODEL-BASED EXPERIME
Insight into the basic causes of the base case scenario was gained via a series of partial model
test simulations and sensitivity analyses. A series of policy experiments followed which
showed how the senior health professionals could have effectively intervened to improve the
provision of services. Sensitivity analysis confirmed the insights generated by the various
experiments. We provide an overview of the experiments and present the results of the key
policy experiments.
10
The key model outputs were: the cumulative referrals, cumulative patient activity, cumulative
overall costs, and the basic trends displayed by the average waiting times and waiting lists.
Waiting times and waiting lists were both considered as each reflect different aspects of
pressure on the system, respectively, the delay for services and the number of patients
waiting. In our case studies, whilst the focus was on meeting waiting time goals, those
concerned about costs were also interested in the length of the waiting list. For example, if
the waiting time target was met, an increase in the waiting list would indicate that increases
in patient activity were necessary to prevent the average waiting time rising above its target.
In interpreting the results of the experiments, we considered the varying needs of the different
senior health professionals [8,23,24]. Firstly, the need to improve health i.e. identify patients
in need of treatment, provide health care promptly and appropriately, direct resources towards
the most urgent cases, and increase activity and thus meet higher activity targets. Secondly,
the need to control the overall costs incurred i.e. whilst agreeing to the district service set up
costs, ensuring that services were used appropriately. Thirdly, the need to improve efficiency
ie. deliver care at the lowest cost/case. We thus refer to health improvement, overall cost
control and efficiency improvement perspectives.
To summarize the graphical output of different simulation runs, for several variables, a
simple ‘pressure summary index’ was defined by the area between each graph and a tolerance
level as specified by the relevant goal (Figure 7). We assumed that pressure would only be
exerted if the goal were exceeded. Comparing areas thereby quantified the degree to which
improvements had been made over the duration of the simulation run, accounting for short-
term and longer-term effects and changes in expectations, which would be reflected by
adjustments in the tolerance level.
11
Figure 7. Using a Pressure Summary Index to Measure an Improvement
Under Test Conditions
@ (b)
Pressure Reduced Pressure Increased
Pressure by Test Changes Pressure] by Test Changes
Proxy Base Case proxy
A Test
Pressure Reduced
by Test Changes
Base Case
E. Test
ieee Nagging A ls Tolerance
ta Level
Time Time
For (a): PSI for Base Case = Area A+ Area B
PSI for Test Case = AreaB
Improvement with Test Conditions = Area A
For (b): PSI for Base Case = Area C + Area E+ Area F
PSI for Test Case Area C + Area D + Area F
Improvement with Test Conditions = Area E- Area D
PSI - Pressure summary index; Pressure Proxy - Waiting list length or average waiting time
Ribsley Case
The Need for Capacity Increases
The Ribsley experiments indicated that the extent of the under-capacity was such that
demand management strategies alone, even the use of the most stringent clinical guideline,
could not have altered the undesirable rise in the CC waiting list and average waiting time.
Frequent capacity increases were necessary. However, it would be sensible to coordinate
capacity increases with efforts to manage demand to ensure that the benefits of increasing
supply were not cancelled out by stimulated demand.
Different approaches to capacity increases would have produced different effects (Figure 8
and Table 1). An obvious approach to increasing capacity would have been to provide a
permanent district service, as this would have been expected to maintain the access targets
permanently. However, from an overall cost control perspective, the benefits of increasing
the supply would have been cancelled out by stimulated demand. The CC investigation
waiting list would have exhibited a gradual rise thus indicating the need for further increases
12
in the elective CC investigation rate in order to maintain the desired waiting time.
Furthermore, the stimulated demand would have created a new problem, as outpatient
capacity shortages would have arisen. This would have been unanticipated and it would have
then called for further resources in an attempt to control access to outpatient services. From a
health improvement perspective, the reduction in the CC waiting times and increases in
activity associated with a permanent district service would have been attractive, but these
benefits would have been undermined by the loss in access to outpatient services.
Figure 8. Increasing Elective CC Capacity for Ribsley Case
0: Base Case 1: Permanent District 2: Expanded Tertiary 3: Further Temporary 0: Base Case 1: Permanent District 2: Expanded Tertiary 3: Further Temporary
Service... Service, District Services... Service Service District Services
110. a
Outpatient Waiting List
(Patiients)
(Patients)
CC Investigation Waiting List
0.00, 1350 2700 4050 $400
The outpatient waiting list graphs for runs 0 and 3 are the same.
Tablel. Selected Summary Statistics of Ribsley Policy Runs
% Change from Base Case
Performance Base Permanent Expanded Further
Measure Case District Tertiary Temporary District
Service Service Services
PSI for outpatient waiting list 155 >+8000 44 0
PSI for CC waiting list 891 +1.6 -40.5 -10.6
Cumul. referrals for an outpatient appt 19,937 49.7 -0.2 0
Cumul. referrals for a CC investigation 947 +13.4 +4.7 -14
Cumul. outpatient activity 18,784 +35 -0.2 0
Cumul. CC investigations 848 +14.9 +9.4 +43
Cumul. Costs 2,439,461 +6.4 +17 “tl
Cumul. - Cumulative; PSI - Pressure summary index
By using new referral guidelines, it might have been possible to suppress demand and
generate considerably lower costs. However, whilst the use of new moderate guidelines
would have enabled the access to CC services to be maintained, strict new guidelines would
have been necessary to eliminate the outpatient capacity shortages. The feasibility of
introducing strict new guidelines would have been doubtful even in a modest referral
environment such as Ribsley. Therefore, even with realistic safeguards in place to control
demand, a permanent district service could not have led to significant improvements from
either an overall cost control or health improvement perspective.
13
Limiting the Use of District Services
The access problems generated by a permanent district service suggests that a more effective
approach to increasing supply would have been to limit the use of district services. In theory,
this could have involved either expanding the tertiary-based service, and just using the district
service to compensate for tertiary facility closures, or offering further temporary district
services. (i.e. a continuation of the base case scenario). Compared to a permanent district
service, carrying out the same capacity increases at the tertiary level would have generated
fewer costs because by lowering demand and reducing expectations on the service, it would
not have been pushed as far. Therefore, this option would have been more attractive from an
overall cost control perspective. However, in practice, expanding the supply for elective
services at the tertiary level would have been difficult whilst meeting the demands for more
urgent cases. Expanding the supply at the district level would have been easier to achieve, as
the service would have been solely devoted to elective care. Those pursuing an overall cost
control agenda would have favored further temporary district services over the same overall
capacity increases translated into a permanent expansion at the tertiary level. This would
apply because the former would have generated lower costs due to the ‘knee jerk’ reductions
in referrals in reaction to the capacity losses.
The preference from a health improvement perspective would have been less clear. The
greater use of a district service would have provided more opportunities to devote tertiary
resources to more complicated cases. It would also have led to the stimulation of more
demand for outpatient services. Bringing more patients forward for assessment could have
led to the identification of further high-risk patients and also supported higher activity targets.
However, fewer referrals would have been made for CC services, as by introducing more
capacity losses there would have been more ‘knee jerk’ reductions in referrals in reaction to
these losses. It could be assumed that these reductions would have referred to lower risk
patients. However, their assessment as lower risk patients would have been based on
incomplete information i.e. without the benefit of the CC investigation, which was the most
accurate diagnostic tool available. It would have been possible that some high-risk cases that
presented minor symptoms would have slipped though the net. Therefore, from a health
improvement perspective, further temporary district services would have involved trade-offs.
Nevertheless, it could be assumed that those pursuing this agenda would have conceded that,
14
on balance, further temporary district services would have been the only practical way to
achieve improvements in access.
The attractiveness of further temporary district services would have been subject to district
services being efficient. Based upon the limited data provided, the district service provided
investigations at a lower cost/case compared to tertiary-based investigations. However, some
patients needed to undergo a second CC procedure for treatment as synchronous investigation
and treatment was only permitted at the tertiary level. Taking this factor into account, a
district service would only have been attractive from an efficiency improvement perspective if
it had avoided a high proportion of patients undergoing their CC investigation as an inpatient.
If that were not the case, from an efficiency improvement perspective, the desire would have
been to restrict the district service and to achieve improvements in access to CC services via
an expanded tertiary-based service if possible.
Veinbridge Case
The Need to Control Demand
The Veinbridge base case analysis indicated that whilst the long-term strategy to expand CC
services at Veinbridge Hospital relied upon increases in demand, the strategy would be
undermined unless it was coordinated with controls to limit the stimulated demand. Demand
for outpatient services (where patients are screened for a CC investigation) would have to be
controlled. Significant increases in the Veinbridge outpatient activity were not essential to
support the expansion of CC services at Veinbridge. Demand for the CC service also arose
from elsewhere (cardiologists from the surrounding districts also used the Veinbridge
facilities). The Veinbridge purchasers were concerned that the increases in demand for
outpatient services indicated a rise in inappropriate referrals. Consequently, they were
unwilling to provide unlimited funding for outpatient services.
Combining Controls on Demand with New Forces Driving Activity
Whilst the desired waiting time for CC was met, the CC waiting list exhibited increases
(Figure 9). The base case analysis suggested that with sufficient slack in the system, the
waiting time and waiting list goals could have been met simultaneously by changing the
15
forces that drive activity rates - seeking a desired waiting list length rather than a desired
waiting time. There was spare capacity for elective CC services and introducing controls on
demand would have produced some spare capacity for outpatient services.
Figure 9. Selected Variables for the Veinbridge Base Case Scenario
Avg. Time
Month 28-31 34-37 40-48 46 49 52 55 58 61 64 67 TD
Month
—| Total CC Inv. Rate
>
Month Month
Desired Waiting Time for OP Appt
Month
Black blocks indicate the use of district services (introduced at month 13 and with integrated catheter laboratory
opened at month 34); Avg - Average; CC Inv - CC Investigation; OP - Outpatient
The policy analysis thus considered the use of stricter clinical guidelines in combination with
changes to the goals that drive activity. The use of stricter guidelines was modeled by
assuming that the maximum degree to which demand could be stimulated was cut by 50%.
This was considered to form a reasonable adjustment. The factors limiting a further reduction
were the acceptability of such guidelines in such an ‘aggressive’ referral environment, and for
the case of CC services, the need to ensure sufficient demand to justify the development of a
permanent district service at Veinbridge. For comparative purposes, a further experiment
16
considered the use of stricter guidelines alone, and another experiment investigated the
effects of a 10% increase in outpatient capacity.
The experiments demonstrated that, the outpatient and CC waiting list and waiting time goals
could have been simultaneously (and feasibly) met only by using stricter clinical guidelines
in combination with seeking a desired waiting list length (Figure 10 and Table 2). In fact, this
policy would have overcompensated by producing an average waiting time that was lower
than required. The use of stricter new referral guidelines alone would have led to some
improvements but fewer than those derived from the combined policy. Increasing outpatient
capacity would have generated increases in the overall costs and CC costs in particular as
further patients were pushed along the referral chain.
Figure 10. Meeting the outpatient Waiting List and Waiting Time Targets
for Veinbridge Case
O:Base Case I: Increased 2: With Stricter 3: With Stricter Referral 0: Base Case I: Increased 2: With Swicter__3: With Stricter Referral
Outpatient Referral Guidelines Guidelines & Seeking Outpatient Referral Guidelines Guidelines & Seeking
Capacity 1 Desired List Length # Capacity a Desired List Length
00 : 3 is =
woe a
z ota 2
2 i
FE a. © _ =
52 be a =
& ssoofeyum aay zi Zao OE or
BE gg
E E
6 °
E
=
i %
00% * E oot
‘too oo 700 Tina mao £m Too 3.00 shoo 700
Table 2. Selected Summary Statistics of Veinbridge Policy Runs
% Change from Base Case
Performance Base Increased = With Stricter With Stricter Referral
Measure Case Outpatient Referral Guidelines & Seeking
Capacity Guidelines _a Desired List Length
PSI for outpatient waiting list 11,263 -68.4 -84.2 -98
PSI for time spent on outpatient waiting list 17 -100 -100 -100
PSI for CC inv. Waiting list 5,043 42.4 -52.2 -96.6
Cumul. referrals for an outpatient appt 30,295 0 -6.5 -6.4
Cumul. referrals for a CC inv. 4,140 +0.9 -19.6 -19.5
Cumul. outpatient activity 29,092 #15 -5 47
Cumul. CC investigations 3,922 +0.9 -18.6 -16.8
Cumul. Costs 6,801,121 +1 -10.2 -9.3
CC inv. - CC investigation; Cumul.
17
. - Cumulative; PSI - Pressure summary index; * - Division by zero;
Achieving Significant and Sustainable Improvements
Therefore, the implementation of the long-term strategy to expand CC services could have
been improved by coordinating the shift in CC services with the use of new referral
guidelines and changes to the forces that drive activity rates. This would have produced
significant and sustainable improvements in behavior that would have been attractive from
both an overall cost control and a health improvement perspective. The effects of this policy
would have been particularly attractive from an overall cost control perspective, as it would
have led to improvements in access and reductions in costs. For those pursuing a health
improvement perspective, there would have been trade-offs. They would have valued the
improvements in access to outpatient services and reduction in the CC investigation waiting
list but the reductions in referrals and activity would have been in conflict with the desires to
meet higher activity targets and identify more high risk patients.
As with the case of Ribsley, taking into account the need for some patients to undergo further
CC, a mobile-based district service at Veinbridge was only more efficient than a tertiary-
based service if it avoided a large proportion of patients being catheterized as inpatients.
When the integrated catheter laboratory at Veinbridge opened, it provided the opportunity to
improve the efficiency of the district service as the cost/case declined with the volume of
patient activity. This eliminated any conflict there might have been in considering both a
health improvement and an efficiency improvement perspective.
DISCUSSION AND CONCLUSIONS
Little attention has been given to the actual mechanisms of feedback effects associated with
service shifts. This is in spite of the increasing emphasis in health care on the need for
‘joined-up’ or ‘whole systems’ thinking where different parts of the system and their
interactions are considered [9,25,26]. This paper has demonstrated the contribution system
dynamics modeling can provide. Our study offers a plausible causal framework to support the
hypothesis that shifting services can stimulate demand. Also, in offering empirical evidence
of this phenomenon, we argue that such policies should be coordinated with other policy
changes to control demand and to ensure that suitable forces drive patient activity.
18
As with all models, this model is based upon certain simplifying assumptions. For example,
in reality, there would be additional feedback mechanisms. Nevertheless, the model still
successfully replicated the problematic behavior for both cases. Furthermore, the findings of
this research are not specific to the subject or timing of the case studies as the interactions
between supply and demand are widely generalizable. Therefore, a number of broad policy
lessons and recommendations may be derived.
Policy Lessons and Recommendations
It has been previously argued by Wolstenholme [27] in a community care setting, that
increasing capacity is not necessarily the most effective way of improving access. Our study
provides further evidence of this paradox. Wolstenholme highlights the greater leverage of
flow (rate) variables compared to that of stock variables. The Veinbridge case showed that
capacity increases (a stock variable) and stricter referral guidelines (flow variables) would
have provided similar leverage in improving behavior in terms of reducing the waiting list
and average waiting time. Furthermore, significantly better leverage could have been
obtained by combining stricter guidelines with changes to the goals that drive activity (both
flow variables). This result arose from the existence of spare capacity and was not specific to
the case of the shift in CC services. It was a general consequence of the interplay between
supply and demand variables that determine the waiting list length and average waiting time.
Although discussions of spare capacity may seem incongruous with reports of long NHS
waiting lists and waiting times, some spare capacity is often released by service shifts, by
their ability to provide additional capacity and/or by prioritizing patients. By contrast, the
Ribsley case demonstrated that in cases of extreme imbalance between supply and demand,
only capacity increases could have provided the necessary leverage. However, demand
management strategies could still have played an important role by improving the leverage of
capacity increases.
Our study challenges the persistent tendency in health care towards a narrow focus on
isolated events, short-term results and single performance measures with calls made to shift
the emphasis from the waiting list length on to the waiting time [18]. For example, the
Veinbridge analysis showed that maintaining a waiting time goal did not necessarily mean
that the system was free from pressure. The rise in the waiting list suggested that it was under
19
pressure and that the waiting time goal was only being maintained because more money was
being poured in to raise activity levels.
The case studies also illustrated the influence of pressure by patients on clinical decisions and
the problems that can arise from the inability to cope with this pressure and the poor
management of demand. It has been argued that clinical and policy decisions should be
driven by the preferences of patients and the public [28,29]. Questions thus arise about how
the shift in the balance of care can continue whilst providing high quality care to patients
whose expectations for health care are traditionally high.
Generalizations
Whilst the NHS formed the context to our case studies, our research findings have
implications for health service reconfigurations in other countries in spite of the differences
between their health systems. The NHS is a publicly funded system. Other types of health
systems are privately funded or insurance-based. The stimulation of demand in response to
improved access is a common response in the NHS and it is a consequence to services being
free at the point of delivery. However, it could be argued that services in other health systems
are all free in one sense or another. The individual feedback mechanisms that we have
discussed can be generalized to other health systems. Even the impact of waiting times on
referrals can be generalized in spite of the fact that waiting lists are a typical characteristic of
the NHS; the waiting time can translate to other health systems into the price customers pay
for services.
Potential Changes in Health
In both cases, the stimulated demand prompted increases in activity. The question of whether
or not the increase in the CC investigation rate actually improved health was not fully
addressed as serial data of clinical events and long-term patient outcomes were not
monitored. However, several comments could be made. In the Ribsley case, the reduced
waiting time for catheterization contributed to shorter delays for those requiring invasive
treatment (coronary angioplasty or coronary bypass surgery). This could offer the potential
for health benefits as treatment delays can lead to some patients deteriorating.
20
The fraction referred on for a CC investigation increased in both cases. As the case mix of the
patient populations did not change, this meant that the threshold for a CC investigation had
changed towards less severe cases (defined without the benefit of a CC investigation).
Catheterizing more of these patients could lead to improvements in health if it identified
further patients in need of invasive treatment; some patients with severe heart disease only
display minor symptoms and these patients may thus fail to be identified by other diagnostic
methods. The fraction of patients referred on for invasive treatment remained constant so,
associated with the increased catheterization rate was an increased invasive treatment referral
rate. This would suggest that, from the less severe cases, further patients in need of such
treatment had, indeed, been identified. However, for even less severe cases, it might be better
to delay bypass surgery until the disease is more advanced as repeat bypass surgery, which
can occur given the progressive nature of heart disease, carries higher risks. Therefore,
delaying catheterization (and therefore delaying bypass surgery) could, paradoxically, be
beneficial in the long-term management of the disease.
Further Work
There are several possibilities for further work. The model could be employed to explore
other service shifts, of which there are numerous examples. The model boundary could be
extended to endogenize the follow-up process of patients after their discharge from an
outpatient appointment and the process of changes in the referral threshold for CC and district
CC as the district service evolves. The model could be disaggregated to elucidate the
dynamics of changes in clinical priority between routine and urgent elective cases. Finally,
the pressure summary indices could also be investigated further.
21
ACKNOWLEDGEM.
We would like to thank all those who collaborated in this study, for their time and input, and
The Wellcome Trust for their sponsorship of this research (Reference Number 041243).
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If you are interested in obtaining further information about the model, please contact the
author by email (k.taylor-alumni@lse.ac.uk)
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