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Patient Waiting Lists & Healthcare
In the previous page we saw how to apply
replenishment to log marshalling. Of
course people are not logs – we know that.
People are much more perishable, especially when they are unwell. However, is there something that we can
learn from log marshalling in general that can also be applied to
healthcare? Let’s run a test, let’s
compare the patient waiting/referral process against log marshalling and see
if it is different or similar. If it
is similar then maybe we already have a reference environment from which we
can extract the principles and apply to healthcare. Let’s see.
Firstly, however, if you have arrived at this page
directly rather than sequentially through the replenishment page and the
distribution page, then please consider reading the replenishment page
first. This will ensure your
understanding of the technical solution (the planning and control system)
that we are going to apply in this case.
Forearmed with such knowledge you will be in a much better position to
evaluate the description of the current problem and also the potential for
the detail of the solution.
The “producers” or the source node in this system –
the general practitioner, or local doctor – is in this case a service
operation having no set-up and appears to produce stock for the system –
patients – at a random rate and in units of one. Now, however, there is no longer a
geographic many-to-one relationship between producer and the next node, the
specialist. But rather there is a many
to many relationship. Any general
practitioner may wish to refer a patient to any one of a number of different
specialists depending upon the nature of the illness.
Let’s try to draw this network.
 All we have done here is to have
changed the relationship from one-to-one in log marshalling to many-to-one in
public healthcare and changed the labels.
As a generalization then, the marshalling network seems to describe
the flow of patients. There is however
one interesting and critical difference in this network, the actual
production part isn’t at the source nodes any more, here labeled the local
doctor. The actual production is at
the end node, the intervention/admission.
Health professionals “bristle” when manufacturing
analogies are used in health, “we don’t make cans of beans you know!” However, we need to use manufacturing
terminology for a moment to describe part of this system in consistent
terms. When the intervention is
carried out in a theatre, that part of the process is a production
process. It has a set-up, it has
particular equipment and staff for particular procedures, and it may even
have particular rooms. We need to know
this to distinguish it from the supply chain portion, the referral and
waiting list part, because the way in which the supply chain portion and
production parts are managed are intrinsically different.
How can we be sure that intervention is a production
process and not a project process?
Well, if we look at an operating list we can characterize it in terms
of patients per day, rather than days per patient. Therefore we can be quite certain that from
the system’s perspective the intervention node is a production process. This is important because it is likely that
the intervention node will always be the control point – the drum in
drum-buffer-rope terminology – regardless of whether the constraint is
internal (we don’t have enough capacity) or external (we don’t have enough
patients).
How can we be sure that this node will always be the
control point? Well, at a guess, it is
the most capital extensive step, either a new theater in surgery or a new
ward in medical cases. It certainly
will be the operationally most expensive step in terms of on-going staffing
and support. Therefore it is unlikely
that new theaters will be built in rapid response to demand. The control point is therefore unlikely to
shift somewhere else as a consequence of additional theatres being built.
So it looks as though there is some validity in
investigating marshalling or a convergent supply chain as a descriptor for
patient waiting lists. However, this
brings us to a significant issue – waiting.
On the very first page, the introduction to these webpages, we
mentioned that we can batch in time or we can batch in quantity. Supply chains are dominated by batching in
time, and the patient waiting list is a supply chain. Batching in time is so pervasive in
healthcare that we absolutely accept it as normal. We fail to even question the reasons for
its existence. Whenever we batch in
time we cause waiting to occur.
Reducing batching in time, along with removing the policy constraints
that limit output; will substantially reduced patient waiting lists. Would this be a worthwhile cause to
pursue? I think so. Are you interested?
Good, then we need a plan of attack.
Plan Of Attack
There is only one plan of attack, the 5 step focusing
process that we have used to date in the analysis of all of our logistical
endeavors. Let’s repeat it here for
good measure;
(1) Identify the system’s constraints.
(2) Decide how to Exploit the system’s constraints.
(3) Subordinate everything else
to the above decisions.
(4) Elevate the system’s constraints.
(5) If in the previous steps a constraint has been broken Go back to step 1, but do not allow inertia to cause a
system constraint. In other words; Don’t
Stop.
What is the constraint in this system? What are we trying to protect? The constraint isn’t a lack of “customers”
in this instance; there is no shortage of patients. It something else; it is expensive and
finite capacity – limited physical space somewhere, possibly “funded beds”
(as opposed to beds that exist but are deemed to be unfunded) for medical
conditions or theatre space for surgical conditions. That almost answers the second question –
how to exploit the constraint. We lose
output from the system whenever we have patients who need intervention, but
who are not in the right place at the right time to receive that
intervention. Healthcare is a service;
we can’t store the intervention for use at a later time.
In order to exploit the system we need to ensure
that we can never waste an opportunity to carry out an intervention. We will need to deduce how to best do this
– a strategy for exploitation. We will
also need to develop how to best subordinate the rest of the system once the
exploitation strategy is in place.
To properly and successfully exploit and subordinate
the valuable capacity in this system would mean that we begin to have shorter
waiting times and eventually spare capacity.
First however in order to be able to determine the exploitation and
subordination strategy we need to examine the properties of waiting list
networks in a little more detail.
Let’s do that.
The Waiting List Network
Let’s acknowledge right at the outset that there are
two ways that we could effectively exploit the constraint – one way is to
decrease the input and the other is to increase the output. We could express this as follows;
(1) Approaches that avoid illness
in the first place.
(2) Approaches that mitigate or cure illness
once contracted.
Although there is now an increased emphasis on
pro-active prevention rather than reactive intervention, the fact remains
that much illness still requires active intervention and moreover there is a
backlog of work. This backlog arises
from rising general expectations from taxpayers and improved levels of
care/technology from practitioners.
Technology is a double edge sword here, it both substantially reduces
the effort in simple interventions freeing up bed space that was unimaginable
20 or 40 years ago, and at the same time making possible interventions that
tie up bed space that was unimaginable 20 or 40 years ago.
We need to recognize that the patient waiting list
network in the main is concerned with non-acute admissions although how we
handle this network strongly impinges upon acute work also. Public health systems must deal with
non-acute, acute, and emergency patients; all at the same time. But this is not unusual. I have not yet seen a process that didn’t
operate a concurrent but differential priority system of some sort. Concurrent differential priorities are the
rule not the exception in serial processes and health professionals need to
recognize this commonality. In any
system; manufacturing, service, or supply chain, the only way to manage
differential priorities concurrently is to have adequate sprint capacity
and/or buffering.
Let’s examine our non-acute network then, from the
perspective of a patient/taxpayer (tired pun intended). It is a bit radical to take a patient’s
perspective but let’s press on. The
patient books an appointment with the local general practitioner who
determines that the problem more correctly needs specialist assessment. The doctor might ask “public or private”
implying some differential service but we won’t go there. Our patient paid taxes damn it and is going
to go public. Well, please wait 2
weeks and you will get a letter from the hospital telling you when to attend
an outpatient’s clinic. Two weeks pass
and the letter arrives – please attend a clinic in 6 weeks time! Six weeks pass and the specialist
appointment date arrives. And isn’t it
funny how all the other people in the clinic seemed to have similar
conditions. Anyway an assessment is
made and intervention is recommended within 6 months. Ah, that doesn’t mean 3 or 4 months, that
means something like 5½ months or 6 months. That’s 6 months waiting plus 1½ months waiting plus ½ a months waiting. That’s 8 months waiting all up. That is, if the specialist didn’t refer you
back to the general practitioner.
Why all the waiting?
Well, we are just trying to be efficient didn’t you know!
If you look at the marshalling system it looks like
an “A” – upside down I grant you.
However A-plants describe a situation where there is general
convergence in manufacturing.
Patients’ waiting lists don’t manufacture anything, but as a supply
chain they seem to exhibit the same behavior as A-plants. Let’s look at an A-plant description; under
traditional management practices in A-plants the tendency is to misallocate
resource time in an attempt to maximize efficiency and utilization
figures. Large batches are used to
keep the measurements high resulting in a poor component mix and constant
shortage of the right parts (1).
Furthermore these large batches move in waves throughout the plant
causing temporary bottlenecks to wander from resource to resource. Since material is constantly out of
balance, overtime is used to ‘catch up’ so that shipments can be made on time
(1). We can expect similar things to
happen in patient waiting lists.
What evidence do we have of local efficiency measures
to substantiate this assertion? Well
just reflect for a moment on the number of measures that we must compile for
reporting and how many of these directly relate to improving wellness in the
community. Aren’t most really local
efficiency figures? Aren’t most of
them sheer frustration to substantiate even on a good day?
What, then, if we were to increase the frequency of
clinics in such as system – and indeed interventions as well. Nothing major, moving things from once a
fortnight to once a week or from once a week to twice a week. What effect would that have if we could
simultaneously address the backlog?
Think about it.
But Demand Will Increase!
Well, there is a very peculiar notion in healthcare
that if we improve patient service levels then demand will also
increase. We need to examine
this. This notion belongs on another
planet.
Would you ever wish upon yourself a serious
illness? Pretty damn unlikely. So if patient waiting lists decrease, and
service improves, are people going to become ill more often just to avail
themselves to the new levels of service?
Pretty damn unlikely also. So
where does this curious notion arise from?
One situation where it might arise from is if we are
currently failing to meet a real need (as opposed to a desire, or a want, or
what the marketers would call a latent need – one when you go out and buy
something you didn’t even know that you wanted). If we are failing to meet a real need and
we increase availability then of course there will be an apparent rise in
demand. However, that demand was
already present, it simply wasn’t being met.
Failure to meet a present and real demand is not a reason to restrict
services if it is possible to improve access those services using existing
resources.
There is another aspect to this apparent
paradox. Healthcare has very strong
negative reinforcing loops operating in it.
Failure to respond to a need at an early stage means that the need when
eventually met consumes far more resources.
This might help explain why demand is perceived to increase. It is not necessarily the total incidence
that is increasing but rather the severity of the individual incidences once
they reach an agreed level for intervention.
We will return to this thought later.
You Don’t Understand We Have Too Many Acute Patients
Yes but, we have too many acute patients. This is an interesting problem. Some people become acutely ill
suddenly. Others become acutely ill
over time – time spent waiting for non-acute intervention. Governments find it hard not to fund acute
work and not so hard not to fund non-acute work, so we can guess why acute
work load is, in part, so significant.
Moreover, we already know how to break this vicious cycle – earlier
intervention. Too many acute patients
is really just an excuse. There are
others too.
The local population is too old; the local
population is too young. We have
diverse socio-economic challenges in our area, the area is too rural and
dispersed, the area is too urban and condensed. We can’t retain good staff, we can’t attract
talented doctors to our specialty, our specialty is under-recognized, our staff are older and more expensive than the mean and
so forth.
What about; our young doctors are attracted to the
large cities, or (if you are in a large city) our young doctors are attracted
overseas. And you can’t get good
locums anymore. Our buildings are over
40 years old, our buildings are an earthquake risk, the air conditioning is
antiquated, our total corridor length is much greater than anywhere else (its
true trust me). The flu season was
early/late this year – but never on time.
We have an unreasonable orthopedic load, we are a national center for
……… but this isn’t recognized in the funding.
In fact, you have probably worked out that there is
no end to this list. But don’t worry
these are not the problems either.
They may be symptoms of people’s frustrations, but they are not the
core problem, and therefore solving them is not the solution (but that has
never stopped anyone yet).
Let’s move on.
Cart Before The Horse
Improving the patient waiting list network is indeed
a fine ideal, especially if it ensures that we don’t miss an opportunity to
do an intervention because we didn’t have a patient ready – even though there
are 100’s of patients on the list (it happens!). However, the reason supply chain follows
production on these webpages is that we have to sort out our production first
if we are to improve our supply chain.
Now there are exceptions to this; for instance where we don’t own the
production stage or it is beyond our span of control or sphere of
influence. In these instances then,
yes, indeed we have to do our best in spite of the limitations. However, this
is not the case in public health systems, the production stage and the supply
chain stage are integral. So we should
address the production side first. And
if we can’t win there, then that shouldn’t be an excuse not to look at the supply
chain mechanics nonetheless.
In order to increase the production side we must
address a policy constraint. Let’s
have a look then at that.
There Is No Goal In Public Health
It has been said that if there is no goal then the
absence of a goal is the
constraint. We also noted in the
measurements section that the goal of a system is in fact open-ended. You can’t have enough of the goal. In contrast the necessary conditions that
support the goal can be viewed as having limits. Once we satisfy a necessary condition
additional satisfaction does not improve the rate at which the organization
moves towards its goal.
A problem arises
however in not-for-profit organizations of which a public health service is
just one example. Scheinkopf notes
that in not-for-profit organizations “there is a tendency to believe that the
measures are so intangible and that attainment of purpose is such a
subjective call, that such measures are simply not discussed. The focus ends up to be on measuring and
managing the things we call ‘tangible,’ such as money (2).”
New Zealand
health boards must currently meet an 11% “capital charge” on some types of
new investment (the rate-of-return incidentally is one that some public
companies in the free market can currently only dream of). This means that the Government must pay the
health boards pro rata 11% too much to cover this capital charge, which the
boards then pay back to the Government showing that they are indeed
efficient. This financial efficiency
can be met by restricting access by raising the level (points) for non-acute
admissions. At best, meeting the
capital charge is a necessary condition and a perverse one at that.
Because the
capital charge is imposed upon the system from outside and must be met, it is a necessary condition for success. However, necessary condition aside, it is
just a Government policy, this doesn’t mean that its validity shouldn’t be
challenged, nor the cost mentality behind it.
However the very real danger is that once the necessary condition has
been satisfied (boards run a “balanced” budget) there is no driver for further
improvement. The necessary condition,
due to its prominence, is mistaken as a goal – which emphatically it is not.
It is no
exaggeration to say that public health is missing a goal. Instead it has, as an objective, a
necessary condition – meet budget. We
can illustrate this further.
“The
Nelson-Marlborough District Health Board confirmed all elective surgery will
be postponed for about six weeks over summer.
The
moves come at a time when some patients are waiting up to five years for
non-urgent surgery, and the board is preparing to cut people from its waiting
lists if their conditions are not considered serious enough to warrant
treatment in the public health system.”
“The
Health Ministry contracted the board to do fewer operations than it had the capacity
to perform. As a result it was already
significantly over budget less than four months into the financial year.
The
purpose of the cuts was to reduce surgery to contracted levels and save money
(3).”
If you believe
in reductionist/local optima viewpoint you will also believe that each
operation has a cost and by avoiding operations we can avoid all the costs
associated with them and thereby save money.
If you understand the systemic/global optimum viewpoint then you know
that such efforts will hardly save a penny.
Sure it will save on some variable costs. However, using the quote above as an
example, we should ask what will the buildings do for 6 weeks, what will the
staff do for 6 weeks, and what will the air conditioners do for 6 weeks? They are not variable expenses. And of course what is the final cost to the
system when the work is finally undertaken – is it more or is it less?
Let’s have a
look then at evidence of, not of postponement, but of removal from a list.
“Many
gallstone patients in Auckland must now suffer at least four attacks of
severe pain and vomiting in a year to qualify for surgery.
Or
they must have two attacks of gall bladder inflammation, or experience worse
symptoms or complications.
Less
that four pain and vomiting episodes, called biliary colic, and you would
probably fall below the cut-off point – set in response to Government funding
levels – for elective surgery at North Shore Hospital (4).”
In the same
article but a different hospital.
“Waitemata
officials started to introduce their new scheme in November after struggling,
like all district health boards, with having more patients than can be
treated. They hope to extend it to
other types of surgery later.
Under
it, about 40 patients have already been taken off the surgery waiting list
because they are not considered sick enough.”
Note that the
qualifier is 4 attacks in one year – and then you go onto an “elective”
waiting list; but there was no mention of how long the list is until
intervention. Removing people from
waiting lists who are deemed not ill enough to warrant treatment conforms
exactly to one of Senge’s system thinking archetypes – eroding goals (5).
So I think it
is safe to say that the objective illustrated here is characterized by a
limiting necessary condition and that necessary
condition is to meet budget. We don’t
have a goal at present.
Who Should Set The Goal Then?
So if we don’t
have a goal at present in the health system, who should set the goal
then? Well, the answer is clear, the owner
of the system should set the goal. And
the owners are the taxpayers aren’t they?
Sure, but the Government of the day administers the health service on
behalf of the taxpayers; so it is the Government who is the proxy owner of
the system in this instance, and it is the Government that
should set the goal.
The Government
currently sets a number of necessary conditions that are financial in nature
because articulating a non-financial goal and the fundamental measures to support
it is deemed to be too difficult. But
is it really that difficult? Let’s
try.
How Do We Set The Goal?
Let’s try and
set a goal for public health so that we can move along back to our objective
of showing marshalling as a viable model for public waiting lists. How do we do that? How do we set the goal? I guess that we need to ask where we want
the public health service to be at the present. That would be a good place to start.
A politically
correct goal might then become; a timely and appropriate outcome. But what is the outcome? Is it community wellness? If it is community wellness, are we seeking
to maximize it? That certainly seems
open ended as a goal should be.
However, it might also imply incorrectly that funding should be
maximized and clearly there is a problem here because most people don’t want
taxes to increase which is exactly where the funding must come from.
Then, how
about; improve community wellness, as an appropriate outcome? Improving community wellness seems
sufficiently open-ended at this point in time (maybe even bottomless), we
could certainly do with a lot, lot, more of it. Why don’t we run with this for a while and
see if it will work for us. Thus the
trial goal for a public health system is to; improve community wellness now
and in the future. Let’s write that.
 Establishing a goal is fine; however, we now need to ask what are the
absolute necessary conditions or inputs that will give rise to this
goal. In order to obtain this goal it
seems that there are at least 2 necessary conditions that we must
satisfy. We alluded to these in
defining the goal. A timely and
appropriate outcome implies a timely and appropriate input. The appropriate input could be pro-active prevention or the reactive
intervention that we carry out.
The timeliness depends more upon availability at this moment than
anything else. So let’s add these two
necessary conditions to our goal.
 It seems that the appropriateness of the intervention isn’t so much in
contention as the timeliness. It seems
then that one necessary condition is currently satisfied – the
appropriateness. Medical professionals
do not appear averse to taking up new approaches or technologies in either
treatment or prevention. However, the
other necessary condition – timeliness, isn’t currently satisfied.
In fact,
satisfying this non-financial necessary condition looks a little
untenable. The proverbial rock and a
hard place. We need to improve the
outcome – community wellness – with a level of availability and therefore
timeliness that many would consider is currently “insufficient.” It therefore would be too easy to write
another necessary condition leading into the current one saying “secure
sufficient funding” in order to increase the level of availability and
therefore increase the timeliness – however, it would be quite another thing
to actually receive that funding.
We should also
remember from the measurements page that a not-for-profit organization such
as a public health service must watch its operating expenditure against its
existing fixed level of funding least it runs a deficit (6). So running in the red and hoping is out as well. How then do we ensure sufficient timeliness
and maintain our operating expenditure at the same time?
Let’s go back
to one of the most important statements in Theory of Constraints;
Productivity = Throughput / Operating
Expense
We have
muddied the water a little because our goal is now non-financial and
throughput, as defined, is a financial measure (sales – totally variable
costs excluding direct labor).
However, we can jury-rig another equation that will do just about as
well – we will substitute output for throughput;
Productivity = Output / Operating
Expense
We can measure
our output – patients. We can measure
our input – operating expense. If
output goes up and input goes down or stays the same then we have increased
our productivity and we have also moved towards our goal.
Let’s be clear
however, increased productivity doesn’t mean working harder. It does mean though, knowing sufficient
about the system, its dependencies, and the variability in and between
dependencies that we can protect the most valuable or most important part,
that part that we have the least capacity to spare. Let’s make “not working harder” an explicit
necessary condition to our goal so that this aspect is not misinterpreted or
misrepresented. Let’s draw it.
 This then is the goal and the necessary conditions for a public health
system. We have identified a
non-financial necessary condition – timeliness – that is currently not being
satisfied.
How Then Do We Measure Progress Towards The Goal?
Using
productivity as a measure of progress towards the goal is a bit of a blunt
weapon – in fact it is more an indication of the method than the measurement
that we should use. The fundamental measurement then is our non-financial
necessary condition, the one that we are failing to meet currently –
timeliness.
The Government – the owner of the system – must set maximum national patient wait-times that must be
met. We can measure this performance
and it is non-financial. Moreover we
can see that meeting an increase in demand at static maximum wait criteria
and funding must mean an increase in productivity. Also meeting a lowered maximum wait
criteria at static demand and funding means an increase in productivity. We can measure progress towards or away
from the fundamental measurement with two local measures; patient-days-wait,
and patient-days-late.
Yes But, The Government Already Uses Maximum Patient Wait Times
So how is our
proposal different then; the Government already uses maximum patient wait
times for many aspects of healthcare?
That is true, but how is the issue managed at present? Timeliness is currently managed not by
increasing productivity but by decreasing productivity. It is managed by raising the criteria for
consideration, so that the patient wait times may remain high and constant
but the level of unwellness in the waiting list becomes greater over time and
the number of people treated becomes fewer and fewer – we saw direct evidence
of this in the earlier quotes.
Moreover, the
maximum wait times are measured purely by the number of patients. Our local measures; patient-days-wait and
patient-days-late are much more revealing about the true nature of the
waiting list. But we ourselves must
wait a little before we can investigate this aspect further.
Broader Issues
First,
however, there is a broader aspect to productivity that applies to a public
health system. Public health systems
are not “stand-alone,” public health productivity impinges upon the productivity
of the whole nation/state. Consider
for instance a country with first class productivity in one of the primary
industries such as; agriculture, fisheries, forestry, or mining, or first
class productivity in any one of the secondary manufacturing industries. These activities generate national
income. Why do we constantly strive to
increase the effectiveness of these national income generating activities if
a major consumer of this income, healthcare, operates on assumptions once
thought valid in a previous century – and I mean the 19th not the 20th
century. Other sections of the economy
have moved on.
Currently most
hospitals are implementing some form of patient information management system
and some form of enterprise-wide scheduling system. Enterprise-wide scheduling systems were
described in the section on production, essentially they are finite
scheduling solutions based upon a reductionist/local optima approach. As we know from manufacturing, reliance on
these techniques depends on excellent data integrity but generally results in
increased work-in-process because they fail to protect the system from
variation even through they have ample protection embedded within the
schedule – in short they fail to protect the constraint – output goes down,
work-in-process increases. Increased
work-in-process in this environment means more patients-in-waiting and
waiting for longer.
The reality is
that in both manual and automated scheduling systems many theater
opportunities are lost due to poor protection of the constraint. These losses are buried in the general
theater utilization hours, we have to scratch the surface to find them, but
they are real, and they do present a real opportunity to improve output at
current operating expense. And that
brings us to our critical erroneous assumption.
A Critical Erroneous Assumption
There is an
assumption that we totally fail to challenge – the assumption that we are
sufficiently productive and that we can not improve further. The pervasiveness of this assumption can be
demonstrated every time someone says; ”yes we could
process more patients if only we had access to more funding.” The hospital in the earlier quotation is
very likely to have sufficient productivity – it could do more operations
than contracted for (don’t be fooled by contracted cost, you need to see the
flow of money in and out of the system).
The real issue is that if one hospital provides a better level of
service than others it is defying a charter that requires equitable access to
all people in all parts of the country.
This means other hospitals are currently not as productive. The most productive hospital and all other
hospitals in between must be hobbled to the level of the least productive
hospital in the system in order to ensure equitable access. Think about it.
Yes but, all
the other hospitals could improve to the same higher level couldn’t
they? Well you would think so; this
would be the ideal situation. However,
there are two reasons why this doesn’t happen. Firstly under a reductionist/local optima
costing process, if we improve our productivity our unit costs will go down
and next funding round we will receive less to do the same number of
procedures rather than the same amount to do more. This is a very real fear of hospital
management.
The other
reason is more important. Currently in
the health system there is little knowledge of the rules of engagement that
we first saw in the measurements section.
Let’s repeat them here;
(1) Define the system.
(2) Define the goal
of the system.
(3) Define the necessary
conditions.
(4) Define the fundamental
measurements.
(5) Define the role of the constraints.
As you can
see, in health at present we have just a few financial-based necessary
conditions; we are missing so much of the whole picture. Why won’t we do this if it is so
simple? Are we scared? No, I don’t think so. It might be that many people simply don’t
know how to evaluate the role of the constraints in this system yet – or that
they do know how to but common practice runs counter to this.
Well,
fortunately we are using our common sense rather than common practice, so
let’s continue with our examination of patient waiting lists and
marshalling. We really ought to stop
looking at the problem and start looking at the solution.
Semantics
How can we describe the actions of the nodes in the
patient waiting list network? We have
suggested that the supply chain here is a marshalling supply chain, or more
accurately marshalling and consolidation. Patients are marshaled in by referral from
numerous local doctors and consolidated into specialties and then lists. The consolidation is carried out in
accordance to a push-to-need basis.
General practitioners feel that a particular patient
needs specialist expertise (and it is the expertise of the general
practitioner to know when this is required) and “launches” the patient into
the process and hopes that the outcome will be favorable (and timely). As in all other supply chain solutions here
we need to replace this with some sort of pull-and-replace system. The constraint, the most valuable and
limited resource, must pull the patients via the waiting list network to a
position where they are ready to receive intervention as soon as possible. Maybe we should call this a pull-to-cure or
a pull-and-cure system.
If at some future point in time there are
insufficient patients to fully load the system then we are moving in the
right direction. And if currently we
can at least stop waiting list expansion (without fiddling with the criteria)
and affect a contraction then we know that we will eventually reach that
future point. The key is that the system must initially pull at a faster rate than the incidence
rate of the problem. How
are we going to achieve that?
Well, unlike the distribution problem or the log
marshalling problem, where the constraints in the system were non-production
constraints, here the constraint is a production constraint. The intervention produces something; it
produces favorable outcomes – but currently it produces an insufficient
number of them. Thus we need to break
our solution into two subsystems;
(1) Production subsystem – Intervention.
(2) Supply chain subsystem – Patient Waiting List Network.
And as you know, common sense tells us that the
answers are already in the system. So
let’s have a look.
General Solution – Part One; Intervention & Drum-Buffer-Rope
A solution with an unusual name and very powerful
consequences. Drum-buffer-rope is the
Theory of Constraints production solution, it is a logistical solution. It is fully described in the section on
production; it is really a way of thinking more than anything else – a way of
thinking that enables substantially increased output from constrained
situations without recourse to additional funding or manpower. There is a good example from neurosurgery
in the United Kingdom (7).
The Radcliffe Infirmary went from canceling 64
elective neuro-surgical procedures over a 3 month period to canceling none in
the same period the next year.
Out-of-hours operations were drastically cut and output went up by
16%. Would a reduction in non-acute
cancellations be useful to you? Would
reduced out-of-hours operating be useful to you? Would an increase in output be useful to
you? This is not a trivial solution.
We could get away here with just briefly mentioning
some aspects of the drum and the buffer.
The drum is the constraint, it beats out the rate at which the system
works at. In our case the constraint
is most probably a surgical theater or a medical bed. Let’s draw this using our systemic model
that we developed earlier. The
constraint – our drum – is the rate limiting step.
 A buffer is quite tightly defined
– in this situation it is a measure of time, the time for a patient from the
moment of admission to the beginning of intervention. To properly exploit our scarce
resource in surgical cases we must admit patients in good time so that they
are always ready for intervention.
However, to properly subordinate the scarce resource we must
also not admit too many patients at any one time.
Watch the distinction; it is very, very,
important. After all, one of our local
ward measures is average “bed nights” or some such similar measure. Having a lot of patients for a short time
is locally positive; having few patients at any one time for longer is
locally negative. The current local
measures do not support the global objective of the system. If we have too many patients waiting for
too short a period we will absolutely miss some interventions “because the
patient wasn’t ready”. Hell, the
patient was ready. It was the system
that wasn’t ready. Our output goes
down.
If we have fewer patients waiting longer between
admission and intervention we won’t miss an intervention, output goes
up. System operating expense remains
the same. It seems counterintuitive,
but if it was intuitive we would have done it – right?
We could summarize this as follows;
Introducing constraint buffers and decreasing
process batch size automatically
aligns the process with the goal
What do we mean by process batch? Well, I guess that an operating list is a
process batch. The other sort of batch
size that we might refer to is a transfer batch, and in a service operation
like this a transfer batch will be in units of 1 – the patient. To decrease the size of the process batch
means that instead of operating all day only on Tuesdays for instance – and
causing uneven ward work-load, how about Tuesday and Monday and Wednesday
mornings instead. Forget the detail,
it is simply that we are trying to decrease the number of patients at any one
point and increase the frequency.
Really we are trying to better balance the
flow. Again be careful, we never
balance capacity but we always try to balance the flow – just the opposite
from local optimization. Of course
there are practical limits to this, but we should make sure that the limits
are real and not policy. We need to
make sure that the policy is not some assumption rooted in the 1960’s or the
1950’s. Increasing the production
frequency is the primary driver that flows on back up into the supply chain –
the patient waiting list network. We
had better look at that next.
General Solution – Part Two; Patient Waiting Lists & Replenishment
The constraints in this system are in the
intervention stage, the stage located within a hospital, and this is the
stage that we must exploit. Therefore,
all other stages are non-constraints and we must subordinate these to the
constraint. The patient waiting list
network, like the log marshalling network, must subordinate to the constraint
until such time as there is a substantially reduced waiting list and
additional patients present at admission at a rate that is less than rate of
intervention.
To properly subordinate we must ensure that the
waiting list network never “starves” the production node. It starves the production node when it fails
to produce a patient for admission in good time. It happens.
Talk to a scheduling clerk and you will hear stories
like “I need a patient for the operating list on Tuesday fortnight – and I
have rung and rung around the patients on the waiting list but do you think
that I can find one!” It’s amazing,
but true, and very frustrating for those trying to do their very best. Thus our intuition as well as a good dose
of common sense suggests that we should move patients through the waiting
list network as quickly as possible to the place of greatest aggregate safety
for both the patient and the system – just prior to admission.
In fact, in medical cases, it is likely that the
supply chain prior to admission will also form part of the constraint
buffer. This type of situation is not
so uncommon in manufacturing especially where the first step in the process
is so capital-intensive that to “buy another one” is prohibitive. The expense in both cases here is the
bricks and mortar and the considerable number of skilled staff required to
run the facilities around the clock.
Let’s consider some questions then;
What would happen if we could increase the frequency
of clinics prior to acceptance for intervention? As an example, instead of holding a clinic
once a month for a day (because it is efficient for staff) what about holding
a clinic for half a day every fortnight, or until mid-morning every
week? It is kind of like waiting for one 747 or one of two 737’s. The total waiting time is less for the
smaller more frequent service.
What potential could that have?
What about if we could remove nodes completely or
combine nodes so that they occur at the same time and place, maybe carry out
some tests on the same day in the same place for instance? What potential could that have?
Hold on to these thoughts for a moment.
As in linear supply chain, distribution, and log
marshalling, we need to introduce into this system the Theory of Constraints
supply chain solution – replenishment.
If you are unfamiliar with fixed-frequency variable-quantity
replenishment then please check the explanation on the replenishment page –
it is important.
Each node in the waiting list network becomes a
buffer for the next node containing sufficient patients to ensure that it can
supply the next node down while it pulls patients from the next node up. The constraint, the drum, in the production
portion is the originator of this pull signal. Let’s draw the supply chain portion then.
 If we carry out replenishment
correctly we will move safety to the area that is most important, the area
closest to admission. Let’s draw that.
 And if we increase the frequency
of the clinics and other waiting list processes then these buffers can be
very small indeed and passage from one end of the list to the other will be
very rapid. That way once a referral is
made the patient can move through the system quickly and be available to be
“worked upon” – either an operation or a medical treatment if required as
soon as possible. We can summarize
this;
Introducing replenishment buffers and increasing
resupply frequency automatically
aligns the process with the goal
Now if we return to those thoughts that you are
holding on to, there is probably a big red flag saying’ “yes but there are
too many patients-in-waiting in the system now to make such a process
work.” Yes there are. But unless we get the appropriate mechanism
in place even before it is apparently needed things simply can’t
improve. If we were to size our
buffers today we would find that they are way over-full. But at least we know where we are heading.
In every situation where the system is drowning in
work-in-process, people are reluctant to give up the system that causes the
work-in-process that drowns them; “because there is so much work in the
system that doing this will have no effect.”
Exactly wrong.
We recognize how chaotic huge numbers of
patients-in-waiting are because periodically we “fiddle” with the criteria to
try and reduce the numbers.
Unfortunately that just feeds a negative reinforcing loop – we get more
acute patients. The only solution is
to maintain the criteria and increase productivity. You will be very, very surprised at the
effects. Patients are not logs, and
they are not cars, but that doesn’t mean that we can dismiss the
principles. Well in fact we can
dismiss them, but they won’t dismiss us.
We need to cut the strong negative reinforcing loops
and replace them with strong positive reinforcing loops. We need to look for systemic/global optimum
solutions not reductionist/local optima solutions. We need to look at trying to reframe the
environment and not to continually applying band-aids. The solutions are already in the system,
and those solutions although they represent change, represent a change in
meaning only.
Let’s Put It All Together
Let’s try and pull all of this together by showing
the system in its proper order; the supply chain patient waiting list network
feeding into the intervention stage.
Likely as not there is another supply chain at the other end –
district nursing, but let’s leave that for another day.
 Doing this it becomes clear that
there is a feedback between the two.
We need to make sure that we don’t ever waste our scarce intervention
stage, and at the same time we need to ensure that the supply chain doesn’t
ever fail to provide an appropriate patient at an appropriate time.
Local Performance Measures
Earlier we parachuted in a goal for public service
healthcare and looked at how to measure whether we are moving towards the goal
or away from it. The goal and
necessary conditions might provide a measure for a whole system, but how do
we know in a system as complicated as a large public hospital or a district
health board that the parts – the subsystems – are also aligned and moving in
the right direction? Really we are
asking; how do we know that the non-constraints are subordinated to the
constraints. For this we need local
performance measures.
Another way of looking at local performance measurements is that they should judge the quality of
the execution of the exploitation plan (8).
What is the plan in this case?
Surely it is to provide a timely and appropriate outcome. We can’t comment here on the
appropriateness but we certainly can on the timeliness.
Timeliness is
reflected in two particular measures;
(1) Unit-days-wait.
(2) Unit-days-late.
In fact of the two, late-days is more important, but
waiting-days always seems easier to explain first. These two measures are simply a
re-verbalization of the two measures that we have consistently applied to any
subsystems in production or supply chain processes. In fact, we used these exact measures to
introduce the concept of local performance in the measurements section. Let’s have a look at these again in detail.
Let’s say for
instance that a certain outpatients’ clinic for referrals has 50 people on
the waiting list at any one time and last year these people waited on average
for 12 weeks, this year we still have 50 people on the waiting list at any
one time but they now wait on average for 16 weeks. What is the total waiting time here?
Well, we know
that last year that there was on average 12 weeks by 5 days per week by 50
people = 3000 patient-days-wait. In
comparison, this year there are 4000 patient-days-wait on the list. Is the performance better or worse? It’s worse of course. If we can stop patient-waiting-days from
increasing, or better still reduce it, then we must have improved the system. Let’s add this measurement to a linear
representation of our health system (both patient waiting list network and
hospital intervention).
 So waiting-days is one measure that we can use to evaluate a subsystem
with, or indeed even departments within a subsystem.
Another aspect
of timeliness is that regardless of how long we must wait, do we still
receive attention “in time” at the end of the wait or are we late? Let’s continue with our analogy. Let’s assume that last year our patients
were expected to be seen by a specialist within a recommended guideline of 12
weeks of referral. Some, however,
weren’t seen within this time-frame.
Let’s say that 3 patients were seen after 13 weeks and 2 were seen
after 14 weeks. Again we might argue
that just 5 out of 50 or 1 in 10 patients were not seen within the
recommended guidelines. However, a
more realistic measure is that 3 were 1 week late and 2 were 2 weeks
late. This gives us 1 week by 5 days
per week by 3 patients plus 2 weeks by 5 days per week by 2 patients = 35
patient-days-late. Is this bad? Of course it is, it should be zero. We can add this measurement to our system.
 So unit-days-late is another measure that we can use to evaluate the
performance of a subsystem with – anywhere that there is a clear hand-off to
another subsystem.
If the subsystems are aligned to the goal of the system we should
expect patient-days-wait to decline and patient-days-late to be zero. Now, these measures are excellent at
monitoring subsystems – nodes in the waiting list network for instance – but
there is no reason why they can not be used for the whole system as
well. If we use them for the whole
system, maybe divided by specialization, then they also provide us with a
non-financial measure of system success.
They don’t measure wellness in the community directly but rather
indirectly as the absence or decrease in unwellness. We should strive to reduce the unwellness,
wouldn’t you agree?
Let’s hope
that one day we can see in district health board meetings a 12-24 month
running graph tabled for each major subsystem showing patient-days-wait
and patient-days-late. Then we will know at a
glance whether we are all moving in the right direction or not.
We can test
for obfuscation
with a simple graph.
In the graph below we have some initial criteria for
admission to an elective list – patients who have managed to reach the
“access threshold.” Over time the
total number of patient-days-wait increases as the effects of system
dependency, variability, and an absence of knowledge of how to protect the
constraint cause output to be lower than input into the list.
 At some time the length of wait
and the number of patients waiting becomes too great. There is a reassessment of the “access
threshold” and a limit is imposed.
 The limit is imposed via new
criteria for the access threshold.
Some of the previous patients are “parked” in new categories such as
the “residual waiting list.” Nevertheless,
the patient-days-wait continues to increase as before, and for the same
reasons, but now artificially depressed for a time by the adjustment.
 Patient-days-wait increases, that
is until, once again, the length of wait and the number of patients waiting
becomes too great. There is another
reassessment of the “access threshold” and a new limit is imposed.
 The new limit is imposed via new
criteria for the access threshold.
Some of the previous patients are once again “parked” in new
categories with new and different names such as “active review.”
And of course once again, patient-days-wait
continues to increase as before, and for the same reasons, because we have
still failed to address the fundamentals underlying the problem.
 The important point is that if we
extrapolate from the earlier data we should be able to get a good estimate of
the “real” patient-days-wait had the criteria remained unchanged. An apple with apple comparison. Let’ draw what we mean below.
 So even if people say the we can’t
compare different sets of criteria, because the criteria themselves have
change, that is the make-up of the rules in the “access threshold,” then we
don’t need to allow that to divert attention away from the very real
conclusion that the wait list based upon the initial criteria is much greater
and can be estimated with relative ease.
What should we see then, if we instead address the
fundamentals in the system? What
should we see if we identify, exploit, and subordinate to the system’s
constraints? Let’s have a look.
 Rather than impose a new access
threshold and “bump” people off the list, we have identified and protected
our weakest link and increased its output.
Patient-days-wait haven’t been reduced to zero, but they have been brought
under control, the backlog removed and a new, lower, and stable rate
achieved. At each steep downward step
indicates that a constraint in the system has been overcome and output has
risen (and days-wait decreased). Then
after some time the cycle “flicks” from being negative and vicious to being
positive and virtuous (removal of excessive waiting time induced acute load)
and the rate of intervention becomes greater than the rate of admission to
the list.
Don’t allow the “we can’t compare criteria” to obfuscate
the real issues.
Yes But, Do We Do Tonsillectomies Or Hip Operations?
So far we have
mainly considered patient-waiting-days as some undifferentiated mass. Of course different specializations will
“own” different lists. Different lists
have different degrees of difficulty.
We could knock-off the easy ones first and substantially reduce total
patient-waiting-days. Well such things
do happen. But what do you think will
happen next? We will run out of easy
jobs to pick. Then we get down to more
serious matters. The more responsible
and boring approach is to work away at the total mix.
Patient Waiting Lists Are Not Simple Replenishment
Patient waiting list networks are not simple linear
replenishment through a series of dependent nodes. They are first and foremost a convergent
marshalling and consolidation supply chain.
Moreover, the characteristics of the supply chain are strongly
affected by the characteristics of the integral production step – the intervention. Why can we be so sure that this isn’t
simple replenishment? Let’s have a
look;
(1) There is a many to many
relationship from the source nodes, the local doctor, to the specialist
assessment nodes.
(2) We must consolidate from numerous
source nodes to a limited number of intermediate nodes.
(3) We must currently subordinate the
whole system to the intervention stage.
(4) We must position the maximum
buffer protection in the place that best protects the whole system, just
before the intervention node.
(5) We must protect the more
disaggregated source nodes differentially by a higher frequency of resupply.
Of these, positioning the protection for the system
in the place that does the most good is probably the most important; this
helps avoid a major problem, having a lot of patients on the list but no one
immediately available.
Let’s Think It Through
So what do we
do first? Two things; a chicken and
egg scenario. Here goes.
We must raise
productivity at the intervention node.
We do this by overcoming our key policy constraint (or simply ignoring
it), implementing a proper global production approach – drum-buffer-rope (a
spreadsheet will do), and improving flow from our patient waiting list
network. However, to improve our flow
from our patient waiting list network we must raise productivity at the intervention
node and implement replenishment and buffer management in the supply
chain. Yes it’s a circular
argument. And do you know how to break
it? Just go out and do something,
somewhere, anywhere. It’s shockingly
simple, do something rather than talking it to death and you will begin to
move the system towards its goal.
Once you have
tackled this crux, increasing productivity will become self-reinforcing;
improving flow in the patient waiting list network will also become
self-reinforcing. Think smaller
batches, either in time or quantity and you are on your way, think global
results not local efficiency and you can deduce the solution for yourself.
What Are The Unavoidable Outcomes?
If we approach
the problem as suggested then the unavoidable outcomes are;
(1) Increased patient output.
(2) Absence of “no-shows” in
wards/theater (zero patient-late-days).
(3) Decreased waiting lists (decreased
patient-waiting-days).
(4) Decreased waiting times on the
list (decreased patient-waiting-days).
(5) Lower severity of illness at
intervention.
(6) Shorter overall bed stay.
(7) Lower overall operational
expenditure.
We will arrive at a position where we can
pull-to-need or pull-to-cure with increasing rapidity.
But Wait, Reality Isn’t This Simple!
If this explanation seems quite simple and
straightforward then that it excellent; then we know that we have developed a
broad understanding of patient waiting list networks with replenishment. If experience tells us that reality is much
more complicated than this generalized explanation, then that too is
excellent. Now we are in a position to
better investigate how to apply this methodology to our own particular
situation.
Give Us Some Examples
The Radcliffe Infirmary whose results we have
already discussed is one of the published examples of Theory of Constraints
in healthcare. The closest
approximation to this approach in New Zealand that I am aware of is the
operational and logistical expertise that the Health-Med Group has brought to
the privately run Venturo public urology service. Although Theory of Constraints was not used
there is so much plain common sense that this example should be very much
better known.
In 1993 Venturo was awarded a population-based
contract to undertake the treatment of all referred patients in need of
specialist urology services within its operating area. Traditionally services had been (and still
are) contracted for in-advance by the number of procedures or pre-determined
volume rather than a fixed fee based upon the total population served.
During the first year the service sought to minimize
its risk by dealing with the existing waiting list backlog. Outpatient visits increased by 32%,
inpatient admissions increased 9%, and day-patients increased 16%. In total 70% more people were treated from
the waiting list than the previous services had in the previous year. Total waiting time between GP referral and
operation was reduced by 44% from 37 weeks to 26 weeks (9).
How did they do this?
Good Management.
Clinics, wards, and operating theatres have dedicated urology staff
and the booking systems ensure maximum utilization of both staff and
facilities. Some investment was made
in ultrasound and laser equipment which has improved the ease and timeliness
of diagnoses and treatments. Protocols
were developed for GP referral.
However, one of the most significant gains was the standardization of
clinical practices amongst urologists (9).
Now read back through the earlier arguments. It appears that Venturo ensured that
patient and system risk was reduced and managed by moving patients to the
place of maximum safety – pre-admission or thereabouts, and by increasing the
output of the intervention stage. Its
just common sense. More people were
treated sooner, and access to urology specialist services increased (9). Moreover the contract fee for this service
has remained fixed for the last 7 years.
Summary
There is a very powerful policy constraint in place
at the moment which we must break. We
must be prepared to demand that each hospital tries to raise its productivity
to the level of the very best. Clearly
we can’t all be the very best. But the
spread, or tail, down from the very best must be quite tight. Currently, rather than raise productivity,
the system policy actively hobbles each hospital to the level of the very
worst.
We don’t’ need “free” enterprise to make this
work. We do need, however, some
enterprise of thought. If we look at
this at a national level we buffer our health risk through our taxes. We gain maximal benefit from minimal
payment. It would be a shame to allow
a profit margin to accrue to “free” enterprise just because the public
enterprise is constrained by its own well intentioned policy.
In order to raise productivity we must adequately
exploit and subordinate the very valuable and limited capacity that we
already do have. We can do this
without recourse to additional funding.
Part of the exploitation and subordination is to have patients ready
for intervention from the patient waiting list network as soon as
possible. We can do this by
implementing a change in mind-set away from local efficiency measures and
towards global effectiveness measures.
A crude initial indicator is whether our total
patient-waiting-days on a list is decreasing or not. There is no other measure that doesn’t seek
to obfuscate reality is some fashion.
Returning to the Radcliffe Infirmary example. One further outcome was produced; "The
difference was visible, the staff just looked much better … and happy staff
means happy patients (7)." One of
our necessary conditions for success in Theory of Constraints is to provide
employees with a secure and satisfying workplace now and in the future. It seems that at Radcliffe they achieved
all of the necessary conditions.
Now why can’t you?
References
(1) Stein, R. E., (1994) The next phase of
total quality management: TQM II and the focus on profitability. Marcel Dekker, pg 36.
(2) Scheinkopf, L., (1999) Thinking
for a change: putting the TOC thinking processes to use. St Lucie Press/APICS
series on constraint management, pg 25.
(3) Surgery slashed in bid to reduce hospitals’ costs. New Zealand Herald, 20th October 2003.
(4) Galling
delays for surgery. New Zealand Herald, 26 February 2004.
(5) Senge, P. M., (1990) The fifth
discipline: the art & practice of the learning organization. Random House, pp 383-384.
(6) Newbold,
R. C. (1998) Project management in the fast lane: applying the Theory of
Constraints. St. Lucie Press, pg 228.
(7) Phipps,
B., (1999) Hitting the bottleneck. Health
Management Magazine, February, pp 16-17.
(8) Goldratt,
E. M., (1990) The
haystack syndrome: sifting information out of the data ocean. North River Press, pp 144-155.
(9) Hoskins,
R., Blaxall, B., and Sceats, J., (1996) Venturo, evaluation of a pilot
specialist budget-holding contract: report of the first two years. Midland Health, Health and disability
Analysis Unit Evaluations Series Number 1, 29 pp.
This Webpage Copyright © 2003-2009 by Dr K. J.
Youngman
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