waiting lists and healthcare

8/8/2019 Waiting Lists and Healthcare

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A Guide to Implementing the Theory of Constraints (TOC)

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Bottom Line Production Supply Chain Tool Box Strategy Projects & More ... Healthcare

Lead Times Finished Goods Replenishment Replenishment& Distribution

Replenishment&Marshalling

Replenishment& Healthcare

Patient Waiting Lists & Healthcare

In the previous page we saw how to apply replenishment to log marshalling. Of course people are not logs weknow that. People are much more perishable, especially when they are unwell. However, is there something thatwe can learn from log marshalling in general that can also be applied to healthcare? Let s run a test, let s comparethe patient waiting/referral process against log marshalling and see if it is different or similar. If it is similar thenmaybe we already have a reference environment from which we can extract the principles and apply tohealthcare. Let s see.

Firstly, however, if you have arrived at this page directly rather than sequentially through the replenishment pageand the distribution page, then please consider reading the replenishment page first. This will ensure yourunderstanding 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 currentproblem 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 aservice operation having no set-up and appears to produce stock for the system patients at a random rate andin units of one. Now, however, there is no longer a geographic many-to-one relationship between producer andthe 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.


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All we have done here is to have changed the relationship from one-to-one in log marshalling to many-to-one inpublic healthcare and changed the labels. As a generalization then, the marshalling network seems to describetheflow of patients. There is however one interesting and critical difference in this network, the actual productionpart 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 youknow! However, we need to use manufacturing terminology for a moment to describe part of this system inconsistent terms. When the intervention is carried out in a theatre, that part of the process is a productionprocess. It has a set-up, it has particular equipment and staff for particular procedures, and it may even haveparticular rooms. We need to know this to distinguish it from the supply chain portion, the referral and waiting listpart, because the way in which the supply chain portion and production parts are managed are intrinsicallydifferent.

How can we be sure that intervention is a production process and not a project process? Well, if we look at anoperating list we can characterize it in terms of patients per day, rather than days per patient. Therefore we canbe quite certain that from the system s perspective the intervention node is a production process. This isimportant because it is likely that the intervention node will always be the control point the drum in drum-

LocalDoctor

LocalDoctor

LocalDoctor Local

Doctor

LocalDoctor

LocalDoctor

LocalDoctor

SpecialistAssessment


Pre-admission

Admission

LocalDoctor


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buffer-rope terminology regardless of whether the constraint is internal (we don t have enough capacity) orexternal (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 capitalextensive step, either a new theater in surgery or a new ward in medical cases. It certainly will be theoperationally most expensive step in terms of on-going staffing and support. Therefore it is unlikely that newtheaters will be built in rapid response to demand. The control point is therefore unlikely to shift somewhere elseas 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 descriptorfor patient waiting lists. However, this brings us to a significant issue waiting. On the very first page, theintroduction to these webpages, we mentioned that we can batch in time or we can batch in quantity. Supplychains are dominated by batching in time, and the patient waiting list is a supply chain. Batching in time is sopervasive in healthcare that we absolutely accept it as normal. We fail to even question the reasons for itsexistence. Whenever we batch in time we cause waiting to occur. Reducing batching in time, along with removingthe policy constraints that limit output; will substantially reduced patient waiting lists. Would this be a worthwhilecause 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 ourlogistical endeavors. Let s repeat it here for good measure;

(1) Id entify the system s constraints .

(2) Decide how to Exp loit the system s constraints .

(3) Sub or d inateeverything else to the above decision s.

(4) Elevate the system s constraints .

(5) If in the previous steps a constraint has been broken Go b ack to step 1, but do not allow inertia to cause asystem constraint. In other words; Don t Sto p .

What is the constraint in this system? What are we trying to protect? The constraint isn t a lack of customers inthis instance; there is no shortage of patients. It something else; it is expensive and finite capacity limitedphysical 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, butwho are not in the right place at the right time to receive that intervention. Healthcare is a service; we can t storethe 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 anintervention. We will need to deduce how to best do this a strategy for exploitation. We will also need todevelop how to best subordinate the rest of the system once the exploitation strategy is in place.


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To properly and successfully exploit and subordinate the valuable capacity in this system would mean that webegin to have shorter waiting times and eventually spare capacity. First however in order to be able to determinethe exploitation and subordination strategy we need to examine the properties of waiting list networks in a littlemore 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 oneway is to decrease the input and the other is to increase the output. We could express this as follows;

(1) Approaches that avoi d illness in the first place.

(2) Approaches that m itigate or c u re illness once contracted.

Although there is now an increased emphasis on pro-active prevention rather than reactive intervention, the factremains that much illness still requires active intervention and moreover there is a backlog of work. This backlogarises 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 upbed space that was unimaginable 20 or 40 years ago, and at the same time making possible interventions that tieup 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 admissionsalthough how we handle this network strongly impinges upon acute work also. Public health systems must dealwith non-acute, acute, and emergency patients; all at the same time. But this is not unusual. I have not yet seen aprocess that didn t operate a concurrent but differential priority system of some sort. Concurrent differentialpriorities are the rule not the exception in serial processes and health professionals need to recognize thiscommonality. In any system; manufacturing, service, or supply chain, the only way to manage differentialpriorities 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 abit radical to take a patient s perspective but let s press on. The patient books an appointment with the localgeneral practitioner who determines that the problem more correctly needs specialist assessment. The doctormight ask public or private implying some differential service but we won t go there. Our patient paid taxesdamn it and is going to go public. Well, please wait 2 weeks and you will get a letter from the hospital telling youwhen to attend an outpatient s clinic. Two weeks pass and the letter arrives please attend a clinic in 6 weekstime! Six weeks pass and the specialist appointment date arrives. And isn t it funny how all the other people inthe clinic seemed to have similar conditions. Anyway an assessment is made and intervention is recommendedwithin 6 months. Ah, that doesn t mean 3 or 4 months, that means something like 5 months or 6 months. That s6 months waiting plus 1 months waiting plus a months waiting. That s 8 months waiting all up. That is, if thespecialist 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 asituation where there is general convergence in manufacturing. Patients waiting lists don t manufacture anything,


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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 tomaximize efficiency and utilization figures. Large batches are used to keep the measurements high resulting in apoor component mix and constant shortage of the right parts (1). Furthermore these large batches move in wavesthroughout the plant causing temporary bottlenecks to wander from resource to resource. Since material isconstantly out of balance, overtime is used to catch up so that shipments can be made on time (1). We canexpect 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 amoment on the number of measures that we must compile for reporting and how many of these directly relate toimproving wellness in the community. Aren t most really local efficiency figures? Aren t most of them sheerfrustration 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. Whateffect would that have if we could simultaneously address the backlog? Think about it.

Bu t De m an d Will Increase!

Well, there is a very peculiar notion in healthcare that if we improve patient service levels then demand will alsoincrease. 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, andservice 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, ora want, or what the marketers would call a latent need one when you go out and buy something you didn t evenknow that you wanted). If we are failing to meet a real need and we increase availability then of course there willbe an apparent rise in demand. However, that demand was already present, it simply wasn t being met. Failure tomeet a present and real demand is not a reason to restrict services if it is possible to improve access those servicesusing existing resources.

There is another aspect to this apparent paradox. Healthcare has very strong negative reinforcing loops operatingin it. Failure to respond to a need at an early stage means that the need when eventually met consumes far moreresources. This might help explain why demand is perceived to increase. It is not necessarily the total incidencethat is increasing but rather the severity of the individual incidences once they reach an agreed level forintervention. We will return to this thought later.

You Don t Und erstan d We Have Too Many Ac u te Patients

Yes but, we have too many acute patients. This is an interesting problem. Some people become acutely illsuddenly. Others become acutely ill over time time spent waiting for non-acute intervention. Governments findit hard not to fund acute work and not so hard not to fund non-acute work, so we can guess why acute work loadis, in part, so significant. Moreover, we already know how to break this vicious cycle earlier intervention. Toomany acute patients is really just an excuse. There are others too.


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The local population is too old; the local population is too young. We have diverse socio-economic challenges inour area, the area is too rural and dispersed, the area is too urban and condensed. We can t retain good staff, wecan t attract talented doctors to our specialty, our specialty is under-recognized, our staff are older and moreexpensive 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 areattracted overseas. And you can t get good locums anymore. Our buildings are over 40 years old, our buildingsare an earthquake risk, the air conditioning is antiquated, our total corridor length is much greater than anywhereelse (its true trust me). The flu season was early/late this year but never on time. We have an unreasonableorthopedic 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 problemseither. They may be symptoms of people s frustrations, but they are not the core problem, and therefore solvingthem 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 anopportunity 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 thatwe 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 casein public health systems, the production stage and the supply chain stage are integral. So we should address theproduction 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 Pub lic Health

It has been said that if there is no goal then the absence of a goal is the constraint. We also noted in themeasurements section that the goal of a system is in fact open-ended. You can t have enough of the goal. Incontrast the necessary conditions that support the goal can be viewed as having limits. Once we satisfy anecessary condition additional satisfaction does not improve the rate at which the organization moves towards itsgoal.

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 sointangible 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 (therate-of-return incidentally is one that some public companies in the free market can currently only dream of). This


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means that the Government must pay the health boards pro rata 11% too much to cover this capital charge, whichthe boards then pay back to the Government showing that they are indeed efficient. This financial efficiency canbe met by restricting access by raising the level (points) for non-acute admissions. At best, meeting the capitalcharge is a necessary condition and a perverse one at that.

Because the capital charge is imposed upon the system from outside and m ust be met, it is a necessary conditionfor success. However, necessary condition aside, it is just a Government policy, this doesn t mean that its validityshouldn t be challenged, nor the cost mentality behind it. However the very real danger is that once the necessarycondition has been satisfied (boards run a balanced budget) there is no driver for further improvement. Thenecessary 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 necessarycondition meet budget. We can illustrate this further.

The Nelson-Marlborough District Health Board confirmed all elective surgery will be postponed for about sixweeks over summer.

The moves come at a time when some patients are waiting up to five years for non-urgent surgery, and the boardis preparing to cut people from its waiting lists if their conditions are not considered serious enough to warranttreatment in the public health system.

The Health Ministry contracted the board to do fewer operations than it had the capacity to perform. As a resultit 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 byavoiding operations we can avoid all the costs associated with them and thereby save money. If you understandthe 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 dofor 6 weeks, what will the staff do for 6 weeks, and what will the air conditioners do for 6 weeks? They are notvariable expenses. And of course what is the final cost to the system when the work is finally undertaken is itmore 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 toqualify 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 healthboards, 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


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sick enough.

Note that the qualifier is 4 attacks in one year and then you go onto an elective waiting list; but there was nomention of how long the list is until intervention. Removing people from waiting lists who are deemed not illenough 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 andthat necessary condition is to meet budget. We don t have a goal at present.

Who Sho u ld 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 theGovernment of the day administers the health service on behalf of the taxpayers; so it is the Government who isthe proxy owner of the system in this instance, and it is the Govern m ent that sho u ld set the goal.

The Government currently sets a number of necessary conditions that are financial in nature because articulating anon-financial goal and the fundamental measures to support it is deemed to be too difficult. But is it really thatdifficult? 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 asa viable model for public waiting lists. How do we do that? How do we set the goal? I guess that we need to askwhere 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 openended as a goal should be. However, it might also imply incorrectly that funding should be maximized and clearlythere is a problem here because most people don t want taxes to increase which is exactly where the funding mustcome from.

Then, how about; improve community wellness, as an appropriate outcome? Improving community wellnessseems 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 publichealth 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

Improve communitywellness now and in the

future


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will give rise to this goal. In order to obtain this goal it seems that there are at least 2 necessary conditions that wemust satisfy. We alluded to these in defining the goal. A timely and appropriate outcome implies a timely andappropriate input. The appropriate input could be p ro-active p revention or the reactive intervention that wecarry out. The timeliness depends more upon availability at this moment than anything else. So let s add thesetwo necessary conditions to our goal.

It seems that the appropriateness of the intervention isn t so much in contention as the timeliness. It seems thenthat one necessary condition is currently satisfied the appropriateness. Medical professionals do not appearaverse to taking up new approaches or technologies in either treatment or prevention. However, the othernecessary condition timeliness, isn t currently satisfied.

In fact, satisfying this non-financial necessary condition looks a little untenable. The proverbial rock and a hardplace. We need to improve the outcome community wellness with a level of availability and thereforetimeliness that many would consider is currently insufficient. It therefore would be too easy to write anothernecessary 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 receivethat funding.

We should also remember from the measurements page that a not-for-profit organization such as a public healthservice must watch its operating expenditure against its existing fixed level of funding least it runs a deficit (6). Sorunning in the red and hoping is out as well. How then do we ensure sufficient timeliness and maintain ouroperating expenditure at the same time?

Let s go back to one of the most important statements in Theory of Constraints;

Pro d u ctivity = Thro u ghp u t / O p erating Exp ense

We have muddied the water a little because our goal is now non-financial and throughput, as defined, is a financialmeasure (sales totally variable costs excluding direct labor). However, we can jury-rig another equation that willdo just about as well we will substitute output for throughput;

Pro d u ctivity = O u tp u t / O p erating Exp ense


future

T imelyprevention/intervention

now and in the future

Appropriateprevention/intervention



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We can measure our output patients. We can measure our input operating expense. If output goes up andinput goes down or stays the same then we have increased our productivity and we have also moved towards ourgoal.

Let s be clear however, increased productivity doesn t mean working harder. It does mean though, knowingsufficient about the system, its dependencies, and the variability in and between dependencies that we canprotect 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 ormisrepresented. Let s draw it.

This then is the goal and the necessary conditions for a public health system. We have identified a non-financialnecessary condition timeliness that is currently not being satisfied.

How Then Do We Meas u re Progress Towar d s The Goal?

Using productivity as a measure of progress towards the goal is a bit of a blunt weapon in fact it is more anindication of the method than the measurement that we should use. The f u nd am ental m eas u re m ent then is ournon-financial necessary condition, the one that we are failing to meet currently timeliness.

The Govern m ent the owner of the system m u st set m axim u m national p atient wait-ti m es that must be met.We can measure this performance and it is non-financial. Moreover we can see that meeting an increase indemand at static maximum wait criteria and funding must mean an increase in productivity. Also meeting alowered maximum wait criteria at static demand and funding means an increase in productivity. We can measureprogress towards or away from the fundamental measurement with two local measures; patient-days-wait, andpatient-days-late.


future

Provide employees witha secure and satisfyingworkplace now and in

the future

Appropriateprevention/intervention


T imelyprevention/intervention



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Yes Bu t, The Govern m ent Alrea d y Uses Ma xim u m Patient Wait Ti m es

So how is our proposal different then; the Government already uses maximum patient wait times for many aspectsof healthcare? That is true, but how is the issue managed at present? Timeliness is currently managed not byincreasing productivity but by decreasing productivity. It is managed by raising the criteria for consideration, sothat the patient wait times may remain high and constant but the level of unwellness in the waiting list becomesgreater over time and the number of people treated becomes fewer and fewer we saw direct evidence of this inthe 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 weourselves must wait a little before we can investigate this aspect further.

Broa d er Issu es

First, however, there is a broader aspect to productivity that applies to a public health system. Public healthsystems are not stand-alone, public health productivity impinges upon the productivity of the wholenation/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 manufacturingindustries. 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 onassumptions once thought valid in a previous century and I mean the 19th not the 20th century. Other sectionsof the economy have moved on.

Currently most hospitals are implementing some form of patient information management system and some formof 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. Aswe know from manufacturing, reliance on these techniques depends on excellent data integrity but generallyresults in increased work-in-process because they fail to protect the system from variation even through they haveample 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 andwaiting for longer.

The reality is that in both manual and automated scheduling systems many theater opportunities are lost due topoor protection of the constraint. These losses are buried in the general theater utilization hours, we have toscratch the surface to find them, but they are real, and they do present a real opportunity to improve output atcurrent operating expense. And that brings us to our critical erroneous assumption.

A Critical Erroneo u s Assu m p tion

There is an assumption that we totally fail to challenge the assumption that we are sufficiently productive andthat we can not improve further. The pervasiveness of this assumption can be demonstrated every time someonesays; yes we could process more patients if only we had access to more funding. The hospital in the earlierquotation is very likely to have sufficient productivity it could do more operations than contracted for (don t befooled by contracted cost, you need to see the flow of money in and out of the system). The real issue is that if


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one hospital provides a better level of service than others it is defying a charter that requires equitable access toall people in all parts of the country. This means other hospitals are currently not as productive. The mostproductive hospital and all other hospitals in between must be hobbled to the level of the least productive hospitalin 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; thiswould be the ideal situation. However, there are two reasons why this doesn t happen. Firstly under areductionist/local optima costing process, if we improve our productivity our unit costs will go down and nextfunding 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 syste m .

(2) Define the goal of the system.

(3) Define the necessary con d itions .

(4) Define the f u nd am ental m eas u re m ents .

(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 somuch 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 mightbe that many people simply don t know how to evaluate the role of the constraints in this system yet or thatthey 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 ourexamination of patient waiting lists and marshalling. We really ought to stop looking at the problem and startlooking at the solution.

Se m antics

How can we describe the actions of the nodes in the patient waiting list network? We have suggested that thesupply chain here is a marshalling supply chain, or more accurately marshalling and consolidation. Patients aremarshaled in by referral from numerous local doctors and consolidated into specialties and then lists. Theconsolidation 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 generalpractitioner to know when this is required) and launches the patient into the process and hopes that theoutcome will be favorable (and timely). As in all other supply chain solutions here we need to replace this withsome sort of pull-and-replace system. The constraint, the most valuable and limited resource, must pull thepatients 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


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right direction. And if currently we can at least stop waiting list expansion (without fiddling with the criteria) andaffect a contraction then we know that we will eventually reach that future point. The key is that the syste m m u stinitially p u ll at a faster rate than the inci d ence rate of the p ro b lem . How are we going to achieve that?

Well, unlike the distribution problem or the log marshalling problem, where the constraints in the system werenon-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 breakour 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 Solu tion Part One; Intervention & Dr u m -Bu ffer-Ro p e

A solution with an unusual name and very powerful consequences. Drum-buffer-rope is the Theory of Constraintsproduction 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 constrainedsituations without recourse to additional funding or manpower. There is a good example from neurosurgery in theUnited Kingdom (7).

The Radcliffe Infirmary went from canceling 64 elective neuro-surgical procedures over a 3 month period tocanceling none in the same period the next year. Out-of-hours operations were drastically cut and output went upby 16%. Would a reduction in non-acute cancellations be useful to you? Would reduced out-of-hours operatingbe 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 theconstraint, it beats out the rate at which the system works at. In our case the constraint is most probably asurgical theater or a medical bed. Let s draw this using our systemic model that we developed earlier. Theconstraint 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 momentof admission to the beginning of intervention. To properly exploit our scarce resource in surgical cases we mustadmit patients in good time so that they are always ready for intervention. However, to properly subordinate thescarce 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

Admission Intervention Nursing Discharge


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or some such similar measure. Having a lot of patients for a short time is locally positive; having few patients atany one time for longer is locally negative. The current local measures do not support the global objective of thesystem. 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 outputgoes 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 wewould have done it right?

We could summarize this as follows;

Intro d u cing constraint bu ffers an d d ecreasing p rocess b atch size a u to m aticallyaligns the p rocess 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 batchsize 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 onTuesdaysfor instance and causing uneven ward work-load, how about Tuesday and Monday and Wednesday morningsinstead. Forget the detail, it is simply that we are trying to decrease the number of patients at any one point andincrease the frequency.

Really we are trying to better balance the flow. Again be careful, we never balance capacity but we always try tobalance the flow just the opposite from local optimization. Of course there are practical limits to this, but weshould make sure that the limits are real and not policy. We need to make sure that the policy is not someassumption rooted in the 1960 s or the 1950 s. Increasing the production frequency is the primary driver thatflows on back up into the supply chain the patient waiting list network. We had better look at that next.

General Solu tion Part Two; Patient Waiting Lists & Re p lenish m ent

The constraints in this system are in the intervention stage, the stage located within a hospital, and this is thestage that we must exploit. Therefore, all other stages are non-constraints and we must subordinate these to theconstraint. The patient waiting list network, like the log marshalling network, must subordinate to the constraintuntil such time as there is a substantially reduced waiting list and additional patients present at admission at a ratethat is less than rate of intervention.

To properly subordinate we must ensure that the waiting list network never starves the production node. Itstarves 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 doyou think that I can find one! It samazing, but true, and very frustrating for those trying to do their very best. Thus our intuition as well as a gooddose of common sense suggests that we should move patients through the waiting list network as quickly aspossible 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 constraintbuffer. This type of situation is not so uncommon in manufacturing especially where the first step in the process is


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so capital-intensive that to buy another one is prohibitive. The expense in both cases here is the bricks andmortar 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 aclinic for half a day every fortnight, or until mid-morning every week? It is kind of like waiting for one 747 or oneof 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 andplace, maybe carry out some tests on the same day in the same place for instance? What potential could thathave?

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-quantityreplenishment 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 ensurethat it can supply the next node down while it pulls patients from the next node up. The constraint, the drum, inthe 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 toadmission. Let s draw that.

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And if we increase the frequency of the clinics and other waiting list processes then these buffers can be very smallindeed and passage from one end of the list to the other will be very rapid. That way once a referral is made thepatient can move through the system quickly and be available to be worked upon either an operation or amedical treatment if required as soon as possible. We can summarize this;

Intro d u cing re p lenish m ent bu ffers an d increasing res u pp ly freq u ency a u to m aticallyaligns the p rocess 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 butthere 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 wewere to size our buffers today we would find that they are way over-full. But at least we know where we areheading.

In every situation where the system is drowning in work-in-process, people are reluctant to give up the systemthat causes the work-in-process that drowns them; because there is so much work in the system that doing thiswill have no effect. Exactly wrong.

We recognize how chaotic huge numbers of patients-in-waiting are because periodically we fiddle with the

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criteria to try and reduce the numbers. Unfortunately that just feeds a negative reinforcing loop we get moreacute patients. The only solution is to maintain the criteria and increase productivity. You will be very, verysurprised at the effects. Patients are not logs, and they are not cars, but that doesn t mean that we can dismissthe 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. Weneed to look for systemic/global optimum solutions not reductionist/local optima solutions. We need to look attrying to reframe the environment and not to continually applying band-aids. The solutions are already in thesystem, and those solutions although they represent change, represent a change in meaning only.

Let s Pu t 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 listnetwork feeding into the intervention stage. Likely as not there is another supply chain at the other end districtnursing, 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 everwaste our scarce intervention stage, and at the same time we need to ensure that the supply chain doesn t everfail to provide an appropriate patient at an appropriate time.

Local Perfor m ance Meas u res

Earlier we parachuted in a goal for public service healthcare and looked at how to measure whether we are

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moving towards the goal or away from it. The goal and necessary conditions might provide a measure for a wholesystem, but how do we know in a system as complicated as a large public hospital or a district health board thatthe parts the subsystems are also aligned and moving in the right direction? Really we are asking; how do weknow 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 executionof 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- d ays-wait .

(2) Unit- d ays-late .

In fact of the two, late-days is more important, but waiting-days always seems easier to explain first. These twomeasures are simply a re-verbalization of the two measures that we have consistently applied to any subsystemsin production or supply chain processes. In fact, we used these exact measures to introduce the concept of localperformance 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 timeand last year these people waited on average for 12 weeks, this year we still have 50 people on the waiting list atany 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 orworse? It s worse of course. If we can stop patient-waiting-days from increasing, or better still reduce it, then wemust have improved the system. Let s add this measurement to a linear representation of our health system (bothpatient 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 withina subsystem.

Another aspect of timeliness is that regardless of how long we must wait, do we still receive attention in time atthe end of the wait or are we late? Let s continue with our analogy. Let s assume that last year our patients were

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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 14weeks. Again we might argue that just 5 out of 50 or 1 in 10 patients were not seen within the recommendedguidelines. However, a more realistic measure is that 3 were 1 week late and 2 were 2 weeks late. This gives us 1week by 5 days per week by 3 patients plus 2 weeks by 5 days per week by 2 patients = 35 patient-days-late. Is thisbad? 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 anywherethat 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 listnetwork for instance but there is no reason why they can not be used for the whole system as well. If we usethem for the whole system, maybe divided by specialization, then they also provide us with a non-financialmeasure of system success. They don t measure wellness in the community directly but rather indirectly as theabsence 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 eachmajor subsystem showing p atient- d ays-wait and p atient- d ays-late . Then we will know at a glance whether weare all m oving in the right d irection 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 systemdependency, variability, and an absence of knowledge of how to protect the constraint cause output to be lowerthan input into the list.

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At some time the length of wait and the number of patients waiting becomes too great. There is a reassessmentof 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 newcategories 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.

PatientDays Wait

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Patient-days-wait increases, that is until, once again, the length of wait and the number of patients waitingbecomes 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 againparked 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, becausewe have still failed to address the fundamentals underlying the problem.

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New Rationing Limit

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The important point is that if we extrapolate from the earlier data we should be able to get a good estimate of thereal patient-days-wait had the criteria remained unchanged. An apple with apple comparison. Let drawwhat

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 attentionaway from the very real conclusion that the wait list based upon the initial criteria is much greater and can beestimated with relative ease.

What should we see then, if we instead address the fundamentals in the system? What should we see if weidentify, exploit, and subordinate to the system s constraints? Let s have a look.

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Rather than impose a new access threshold and bump people off the list, we have identified and protected ourweakest link and increased its output. Patient-days-wait haven t been reduced to zero, but they have beenbrought under control, the backlog removed and a new, lower, and stable rate achieved. At each steep downwardstep 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 Bu t, Do We Do Tonsillecto m ies Or Hi p Op erations?

So far we have mainly considered patient-waiting-days as some undifferentiated mass. Of course differentspecializations will own different lists. Different lists have different degrees of difficulty. We could knock-off theeasy ones first and substantially reduce total patient-waiting-days. Well such things do happen. But what do youthink will happen next? We will run out of easy jobs to pick. Then we get down to more serious matters. Themore responsible and boring approach is to work away at the total mix.

Patient Waiting Lists Are Not Sim p le Re p lenish m ent

Patient waiting list networks are not simple linear replenishment through a series of dependent nodes. They arefirst and foremost a convergent marshalling and consolidation supply chain. Moreover, the characteristics of thesupply chain are strongly affected by the characteristics of the integral production step the intervention. Whycan 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 assessmentnodes.

(2) We must consolidate from numerous source nodes to a limited number of intermediate nodes.

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(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, justbefore 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 mostimportant; this helps avoid a major problem, having a lot of patients on the list but no one immediately available.

Let s Think It Thro u gh

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 (orsimply 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 buffermanagement in the supply chain. Yes it s a circular argument. And do you know how to break it? Just go out anddo something, somewhere, anywhere. It s shockingly simple, do something rather than talking it to death and youwill begin to move the system towards its goal.

Once you have tackled this crux, increasing productivity will become self-reinforcing; improving flow in the patientwaiting list network will also become self-reinforcing. Think smaller batches, either in time or quantity and you areon your way, think global results not local efficiency and you can deduce the solution for yourself.

What Are The Unavoi d ab le Ou tco m es?

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.

Bu t Wait, Reality Isn t This Sim p le!

If this explanation seems quite simple and straightforward then that it excellent; then we know that we have


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developed a broad understanding of patient waiting list networks with replenishment. If experience tells us thatreality is much more complicated than this generalized explanation, then that too is excellent. Now we are in aposition to better investigate how to apply this methodology to our own particular situation.

Give Us Som e Exam p les

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 theoperational and logistical expertise that the Health-Med Group has brought to the privately run Venturo publicurology service. Although Theory of Constraints was not used there is so much plain common sense that thisexample should be very much better known.

In 1993 Venturo was awarded a population-based contract to undertake the treatment of all referred patients inneed 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 basedupon 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 total70% more people were treated from the waiting list than the previous services had in the previous year. Totalwaiting time between GP referral and operation was reduced by 44% from 37 weeksto 26 weeks (9).

How did they do this?

Good Management. Clinics, wards, and operating theatres have dedicated urology staff and the booking systemsensure maximum utilization of both staff and facilities. Some investment was made in ultrasound and laserequipment which has improved the ease and timeliness of diagnoses and treatments. Protocols were developedfor GP referral. However, one of the most significant gains was the standardization of clinical practices amongsturologists (9).

Now read back through the earlier arguments. It appears that Venturo ensured that patient and system risk wasreduced and managed by moving patients to the place of maximum safety pre-admission or thereabouts, and byincreasing the output of the intervention stage. Its just common sense. More people were treated sooner, andaccess to urology specialist services increased (9). Moreover the contract fee for this service has remained fixedfor the last 7 years.

Su mm ary

There is a very powerful policy constraint in place at the moment which we must break. We must be prepared todemand that each hospital tries to raise its productivity to the level of the very best. Clearly we can t all be thevery best. But the spread, or tail, down from the very best must be quite tight. Currently, rather than raiseproductivity, 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 welook at this at a national level we buffer our health risk through our taxes. We gain maximal benefit from minimalpayment. It would be a shame to allow a profit margin to accrue to free enterprise just because the public


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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 capacitythat we already do have. We can do this without recourse to additional funding. Part of the exploitation andsubordination 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 globaleffectiveness measures.

A crude initial indicator is whether our total patient-waiting-days on a list is decreasing or not. There is no othermeasure 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, thestaff just looked much better and happy staff means happy patients (7)." One of our necessary conditions forsuccess in Theory of Constraints is to provide employees with a secure and satisfying workplace now and in thefuture. 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. MarcelDekker, pg 36.

(2) Scheinkopf, L., (1999) Thinking for a change: putting the TOC thinking processes to use. St Lucie Press/APICSseries 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, pp144-155.

(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, 29pp.

This Webpage Copyright 2003-2009 by Dr K. J. Youngman

waiting lists and healthcare

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