Wait Time Analytics

“How long do I have to wait for an appointment?” Our office uses two measures of health care wait time. The wait to get an appointment and the wait once the patient arrives. I have already described the technique we use to measure waiting in the office and the application of six sigma techniques (e.g. a goal that 95% of patients are in the office less than 50min). Today I will discuss measuring wait times for an appointment.

Comparing the wait to get an appointment between offices is difficult because no one seems to agree on how to define it. Time to next available appointment? The mean or median time waited? Some other metric?

Previous posts have described Korner Wait Time, 3rd to Next Available and Mean Time to Wait (MTW). Mean Time to Wait is the difference in days between when the appointment was created and when it occurred. The advantages of MTW are that it’s easily programmed into an Excel spreadsheet to download the data from an EMR (ApptDate – CreateDate) and it can measure an endless supply of appointment types for those who use block booking. The down side is that MTW is a retrospective analysis so changes can lag behind reality by the length of the wait. Because of that of that lag our office also directly measures 3rd to next. The advantage of 3rd to next is that you can see wait time problems in real time. The down side is that without detailed schedule templates and appointment types it has to be measured manually. It is of greater utility in open access booking where there are only a few types of appointments.

Another disadvantage of MTW is that it requires a normal distribution. Mean time to wait can be skewed with a bimodal patient population. Consider a patient class that has both an urgent and non-urgent patient pool (e.g. asthma). Together, the mean is in the trough of a bimodal population which would be an inaccurate reflection of health care wait time:





But separated, the two populations each have their own mean which is more reflective of the average time waited by patients for an appointment.


Interpreting wait time measures is tougher than it looks. Consider office or ER wait times with two different types of appointments each ‘competing’ for the same appointment blocks.

In the first graph the wait is balanced with the two types of appointments increasing and decreasing in proportion to one another.

In the second graph type A is decreasing while type B increases.

This is a common problem in block booking practices where over-booking type A appointments blocks out type B appointments. Typically, appointment type A is easier for a patient to book (less morbidity, less recovery, less time off work, less cost, etc…) and shorter duration. Since type A is easier to book it fills up the appointment slots faster than type B appointments. The more that short, type A appointments are booked the less time will remain for longer type B appointments. The effect is a widening in the wait time between the two appointment types and a lack of access for type B patients.

Having watched this scenario play out several times over the years it tends to occur with a) poor management of a block booking schedule, b) inexperience in the administrative centre c) moving from a slow season to a busy one. Our office also uses the 3rd to next technique to catch these problems as soon as they happen.

Another pattern frequently seen is when two different blocks of appointments of different duration become equal or invert. This usually means that there are open slots in the schedule which can be filled with other blocks of appointments. In the graph below, the soonest that patients choose to book an appointment is 6-8 days.

Our office is procedural based and a specialist office so complete open access would not be effective. Because we combine open access with block booking I've found that monitoring MTW and a real time monitor allows us to control the blocks of time. Monitoring wait times within a practice is a simple metric that maintains wait time equity between patient pools it also lets you better control standards of care for wait times.

Does No Access Count as Waiting

I've posted this argument on several other blogs so I've decided to add it to my own. I recently read a blog from the National Center for Policy Analysis (US based). According to the NCPA's web site "the NCPA's goal is to develop and promote private alternatives to government regulation and control, solving problems by relying on the strength of the competitive, entrepreneurial private sector. "



Not surprisingly they've posted about the sorry state of affairs in Canadian Health Care. Specifically the lack of access to physicians and services throughout the country and the high cost of care. They use research from a Toronto (Ontario, Canada) based think-tank called the Fraser Institute (for the Fraser Institute's web site and other wait time references see this link).

Specifically they address the long health care wait times in comparison to the United States and limited access to physicians. Given the recent problems in Massachusetts where mandatory insurance coverage has caused a (roughly) 10% surge in the number of patients trying to access primary care. The result is a doctor shortage, waiting lists and similar problems to Canada in primary care.

I would argue that waiting is waiting for a patient. It does not matter to them whether they have to wait because of lack of funding, doctors or services in a publicly funded system or because they have to save enough money to get into the system in the first place. If the NCPA is going to argue the point that Canada is lacking in services and access I think they need to include the counter-point from the United States that wait time estimates need to include those that don't join the queue until money is available. If you look at the Commonwealth Fund estimates roughly 10% more Canadians have access to primary care than Americans even if it is delayed. I'm not sure how the math works but I'd suggest that whatever number you choose to estimate the wait times in the US needs to have a fudge factor of at least 10% added to it to estimate to account for those that can't even get in the door.

Bottle Necks

You can look for bottle necks in entire processes (e.g. from 1st referral to a procedure) or drill-down to in-office detail (why someone will wait an hour for a quick look at their throat). But the first step to managing waits is to look for the bottle neck that is causing the problem.

First, look at a process a patient is going through and map it out. Below is an example of a basic flow map .



Use different symbols to represent different types of process. In this case the ovals are for administrative steps and the rectangles are for clinical. The arrows in between are for waiting. Since the map can be drilled down to an insane amount of detail it's better to first get a global overview and look for the bottle neck. The bottle neck will have either a high average time in process or a high degree of variation with a long wait before it. Once you find the bottle neck work to map it out as well.



It is not necessary to follow individual people through the clinic 20 or 30 times to make the measurements. Instead, get however is doing the process (better yet an objective third party) to write down the times. Or sit at one process and measure people going through it repeatedly. Eventually you will arrive at an average time in the process and you'll have a range. For the more mathematically inclined put the figures into a spreadsheet and calculate the standard deviation.

By creating process flow maps, finding the bottlenecks and working to resolve them you'll find that the number of people that can go through the system improves as does their satisfaction. The process is not about spending less time with people it is about making that time flow better.

How My New Big Office Screwed Things Up

Check out the picture of our new big office reception area (moved in June of 2007). I've already given away the ending in other posts but this is what happened. We were in a smaller office with two consult rooms and x-ray very near the front desk. We would never have enough room to put people in when post-op visit and consults came together so people would sit out front and wait.







With the new office we put in 7 consult and 2 post-op rooms assuming everyone would already be seated and things would flow alot better. Big error in judgement. Look at the floor map below.

Everyone has to walk a lot further. People walk in and register, sit down, get an xray, sit down, go into the consult room and have a medical history, wait for the doctor, have their consult then leave. The front desk can't just direct them to the proper room -- they have to be escorted there (or they'll get lost/ wander into someone elses room).

I decided to look at our sigma values for wait. Anything greater than 50 minutes is too long. In 2005 we had a sigma value of 53915 defects per million (almost 4-sigma, not great but respectable). In 2006 it increased a bit; I thought it was becaue of patient volume and the new office would cure that. In 2007 it turned into 120294 per million! Almost down to 2-sigma.

We're working at it but it goes to show you that the numbers don't lie and assumptions are just that. Everytime a patient has to sit in another area and wait or everytime they have to move it adds another step to the process. That introduces a greater chance of error and the person that is responsible for that step being unavailable. Complexity can greater rather than cure problems (as we found out). The solution to the problem (I think) is to improve the flow of patients. We've eliminated the wait when we can at the front (have them seated in the consult room right away) and thought hard about when to have people arrive so there is no back ups at the front desk. I'll post our 2008 number some time soon and hopefully they'll be better.

Waiting too Long – Intro to Six Sigma

In my blog of February 27th I wrote about keeping patients waiting too long. I described the CSAT scales and why the tangible parts of health care are what patient usually use to form their opinions of us and our clinics. Wait times (and in particular waiting while in the office) are of particular concern. They are easily controlled with good time management.

First, not every clinic is going to have a lot of wait in the office. A clinic needs to be greater than 80% utilized before it will see significant wait times (this is just an approximatation but I’ll get to that in a minute). Remember, that wait increases exponentially as utilization increases.

But as clinic utilization increases so do the chances of excessive wait. How do you judge an excessive wait? Assume that patients arrive at a health care clinic expecting a certain amount of wait. It will be different for every specialty but for a basic clinic we’ve found that from the time of the booked appointment to the time they leave a wait of greater than 50 minutes has an effect on the patients’ follow-through.

The way we determined this, was not by using the CSAT but by treatment follow-through using a group of homogenous patients that presented to the clinic for consultation prior to minor surgery (in this case removal of wisdom teeth). The group was then divided into 10 minute segments and their follow-up rate compared. For those patient that left within 20 minutes of their appointment start time just over 80% followed through, 30min 75% and so on. When the group that was less than 50 minutes was compared to the group greater than 50 minutes there was a significant difference in follow-through. In a quick-and-dirty study such as this I make no attempt to establish the cause of their unhappiness, only that it is related to the time spent in the clinic. I can say this data was found when comparing multiple variables including location of clinic, doctor, age and gender. Time waiting seems to be an independent variable. The biggest assumption in this is that the length of consultation, x-rays and administration is approximately the same.

Our clinic, therefore defines a failure in service to be keeping a patient waiting for consultation greater than 50 minutes. In industries, they measure failures as defects per million units. A high failure rate is 3 sigma which is 66,000 defects per million (where most industry operates) and a low failure rate is 3.4 defects per million. You do not, however, have to wait for 1 million patients to come through the door, an adequate sample can be taken (in our case we data-mined approximately a thousand but several hundred is usually more than enough) and created this curve of waiting.

In the curve you can see that our sigma value has actually gone up! Or in other words, as the years have progressed we’ve kept more people waiting longer than we like. Why? Our office increased utilization and moved to a bigger space. One would assume that a bigger office and more capacity would help with in-office wait but it seems to disrupt the patient flow (we’re working hard on decreasing the in-office wait with process analysis but that’s another blog).

I would suggest that each of you examine your different patient pools and make an estimation of how long a wait is too long for each group. Then track how many patients fall outside of that range and work to six-sigma levels of not keeping patients waiting.

Google Missed the Boat on Health

At the risk of having my blog removed by the very people that host it (OK, I admit it, I have secret fantasies that the upper echelon at Google reads and cares about my blog) I have to say I was disappointed with the Google Health announcement.

Google has teamed with the Cleveland Clinic to create portable health records. The new Google platform uses GData protocol to allow people to store and share their medical records from a central repository. The screen shots of the new platform resemble a Google page to allow the patient to import records, search for information and find resources.

While I cannot argue the utility of being able to have a portable record, nor can I argue the nobility of Google for taking on this cause I can’t help but think they’ve absolutely missed the boat. The truth, as I see it, is that most of the population rarely visits their physician during the year and when they do it’s for a specific problem. While their past medical history is important it is rarely critical or difficult to review verbally. From the provider point of view, we usually make our clinical decisions by listening and examining the patient. The medical record is absolutely necessary but doesn't usually “make” the diagnosis.

I wish that Google had put their efforts into improving access and efficiency in the system. For a lot of the world, it’s not the portability of medical records but access to care that’s at stake. Improving efficiency at the primary care level would go a long way to helping with that cause. Take a look at www.google.com/analytics. When I read my web-statistics there is an amazing amount of data about visitor type, usage, time of day, time on site, and many other demographics. I have long dreamed of having a similar Google dashboard to organize and control patient flow, wait times and treatment delay. Giving clinics the ability to visualize patient flow will help reduce wait times.

Health institutions can afford to have IT, Six Sigma and Lean consultants do this type of work but it’s out of reach for smaller clinics who provide the majority of primary care. It’s not technically easy to do either, because of the disparity in clinic IT systems. If Google applied the same use of programming cookies and central processing to small clinics, dissimilar IT systems could have key performance indicators summarized the same way disparate web sites do. Google’s forte has always been putting information into the hands of the “little guy” so I hope they’ll one day take a look beyond the major health institutions and put some of their computing power into organizing primary care.

Block Booking Example to Improve Patient Flow

Back on February 20th I wrote about the principals of block booking and why they control waiting. In a nutshell, blocking time for different patient groups ensures that everyone who needs an appointment wait’s the correct amount of time. As long as a clinic is using 80% of their time, you can only see one group of people quickly at the expense of others. So setting waiting goals is not only good for patient flow but it will improve patient satisfaction.

Today I’ll walk through an example. A clinic set guidelines for two sets of patients – those that require a skin lesion to be checked (Mole Group) and those that call in with sore joints (Joint Group). The clinic decides that the goal waits will be 10 days for the mole group and 20 days for the joint group.

Here are the steps to create the blocks:

Count how many people from each group are currently booked going forward
(appointment blocks are in bold)
Example: Joint 40 people, Mole 30 people
Count how many from each group have been seen in the last 30 days
Example: Joint 120 people, Mole 15 people
Calculate you completion rate (average number completed per day)
Example: Joint = 120/30=4 per day, Mole = 15/30=0.5 per day
Calculate Wait Time at Current Completion Rate = Booked/Completion Rate
Example: Joint = 40/4 = 10 days, Mole = 30/0.5 = 60 days
Calculated the number of appointment slots per day required to meet Wait Time standards
Example: Joint = 40/2 = 20 days, Mole = 30/3 = 10 days

In the schedule you need to block 2 appointments per day for people with joint pain and 3 per day for mole evaluation. Some of you may ask why I calculated the completion rate in Step 3. The reason is that there is going to be a change to the flow of patients in the clinic. The back staff may on be equipped to see one mole patient every 2 days. If they are going to be seeing 3 per day it may have an impact on what they need and/or how the set-up for this type of patient. Also, for these two groups of patients the number of reserved slots is increasing from an average of 4.5 per day to 5 per day. As long as 80% of the schedule is being booked these extra slots will have to come from another patient group.

If you’re clinic moves to block booking you also need to have a group of patients that could use more urgent appointments (short notice list). If a block has not been filled by ½ of the desired wait time (e.g. there is an open block for the mole group at 5 days and joint group at 10 days), fill it in with someone from the short notice list. The graph below is an example of how block booking dampened the amount of variation for a group of same day surgery patients (blocking started in early 2006).


For those clinics with more sophisticated technology (data mining techniques) other systems can be used to count the patients waiting or already treated and their mean wait times. In our clinic, the mean wait time is measured directly from the appointments (date of appointment – booked date) and the number of patients in the queue is also measured directly. Block booking will improve patient flow and satisfaction. It also let’s the clinic set and manage priorities in a very logical and planned fashion.

Wait, Expectations, Satisfaction and Intention to Treat

Is it possible to keep someone waiting in health care so long that they will change their treatment because of bad service? This question goes to the heart of customer satisfaction in health care and it is not an easy answer. The caveat is that every specialty will be slightly different so the validations I provide may not extend to different professions.

While watching 60 Minutes (a TV show out of the US) they did a study on the happiest people on earth. Who, you ask, are the lucky citizens? The answer is of course the citizens of Denmark. When researchers delved further into the source of their happiness what they found was a bit of surprise. It was not that the Danish are blessed with extraordinary love, money or social safety nets – apparently they have low expectations.

Which brings me to the point of this blog; client satisfaction is not derived from extraordinary service (although it helps) it comes from exceeding the expectations of you’re clients. Mathematically, client satisfaction (CSAT) = Outcomes – Expectations.
Now, this all sounds good on paper but how does one judge outcomes and expectations in health care? Researchers have created a measure called the SERVQUAL (Service Quality) scale that has been validated (tested) in healthcare and dentistry. It has five dimensions; Reliability, Responsiveness, Assurance, Empathy and Tangibles. An excellent summary (e.g. readable) can be found in the British Dental Journal, 1999 by PRH Newsome and GH Wright.

In short it discusses whether it is possible for a patient to be dissatisfied. The obvious answer is yes. People want to be engaged in their own care and will pass judgment. It also discusses how clients will judge their health care provider which is not such an easy answer. Because clients generally don’t have the technical knowledge to develop expectations about skills and outcomes they don’t have a significant impact on the CSAT. Rather the tangible aspects of a clinic such as waiting, empathy, cleanliness, accessibility, physical facilities, etc… are what will satisfy you’re clients.

The SERVPERF itself is divided into the five dimensions, each with many questions then repeated for expectations and outcomes. I’ve found this too long to be practical in a busy practice. Because expectations do not add a lot to the reliability of the CSAT in health care they can be omitted with minimal impact. Instead a shortened version can be used (see RATER scale as an example).

Now that I’ve established that customer service can be accurately measured, why should you do it and how does it impact on wait times?

First, imagine you are a surgeon seeing consultations half the day then doing surgery the other half. If you keep the consultations waiting long enough some of them will not book with you because they perceive you as being incompetent. You, as the surgeon, seek to serve the community as well as you can and that means fully booking you’re days. You will have to see a greater number of consultations for each surgery. In other words, for each surgery you do, you will have to see 10% or 20% more consultation because of bad time management. This is a vicious cycle because the more consultations you have to see the more likely you are to run slower. However, there is also a threshold for each type of healthcare. For instance, someone waiting on a consultation for an aortic aneurysm repair is likely to be more forgiving than someone getting their wisdom teeth out. In my next blog I will discuss how to determine that magic threshold and how to monitor it a meaningful way.

RATER SCALE
Reliability
CDSG.ca is dependable (1-5)
When you have problems CDSG.ca is sympathetic and reassuring (1-5)
CDSG.ca Provides it services at the time it promises to do so(1-5)
Assurance
You can trust employees at CDSG.ca (1-5)
You feel safe in your transaction with CDSG.ca employees (1-5)
Tangibles
The CDSG.ca facilities are visually appealing (1-5)
The employees at CDSG.ca are well dressed and appear neat (1-5)
Empathy
Employees of CDSG.ca do not know what your needs are (1-5)
Employees of CDSG.ca do not give you personal attention (1-5)
Responsiveness
You do not receive prompt service from CDSG.ca employees (1-5)
CDSG.ca does not tell you exactly when services will be performed. (1-5)

Technology, Change and Wait – Project Failures

I’ve recently seen two major IT projects fail due to “lack of user interest”. In both cases, the IT people had put in a lot of hard work and had advanced technology but were left disappointed and bitter. In one case, the project was for digital entry of drug orders in a hospital. It decreased ‘defect’ rates but made the process run less lean. In the other, it was a web-based system to re-order third party supplies after surgery. There were more errors due to data entry and it ran less lean. The failures were not due to a lack of initiative or ingenuity but a failure to improve the process flow.

Whenever our clinic has a suggestion from IT we first put the change into a flow map of the overall process. The process is constructed from the patients’ perspective. Changes that we make to the process have to improve the quality of care or time it takes. If the change doesn’t accomplish either goal – there is no point to doing it. If the change doesn’t achieve both goals it will have an uphill battle.

That being said, sometimes you have to look hard to see the improvement.

Let’s look at two changes and how they affected process flow:



A suggestion to go to completely digital charts. A process flow map (see diagram for an example) showed that because we often see patients in rapid succession (with the nurse seeing them too) the steps of log-in, log-out and typing rather than writing slowed the process. The decision was made to continue with paper but certain aspects would be digitized. When we can have tablets with stable wireless networks at a reasonable cost the change will come.



We are a referral based specialty clinic so the acceptance of patients was usually done by phone or fax. There is a digital system to manage referrals. When we added email referrals as an option the system broke down due to entry errors, misfiling, etc… What we thought would improve flow actually hindered it. The process was reexamined and the software provider for the main patient database was involved. For a small cost, code was written such that emails are automatically brought into the system and when they’re reviewed by our booking staff (to contact the patient) an email is automatically sent back with details.

Our clinic invests heavily in IT infrastructure but the point is to improve access and care, not to “be digital”. I think it would help to have the IT people bedside for several days before starting any project. Have them come in and work a busy night in emergency or a lonely night on the wards. The point is not to generate empathy, but to help them construct a process flow map of how an IT project is going to be used.

In any process, improvement can be achieved in two ways. Either decreasing the error rate (or defect rate in six sigma terms) so that work doesn’t have to be repeated or increase efficiency (or in Lean terms to lean it down and decrease the variation). My experience is that the defect rate is the most often cited reason for IT changes whereas Lean changes are easier to ‘sell’ to the healthcare workers and to improve and control. Perspective is everything. Every project, big or small, needs to have a process flow map constructed to determine whether it’s really going to help the people for which it’s being designed. But in a healthcare, the patient is the metric that really counts so when a project is suggested construct the map from the perspective of the patient. Remembering that we are here to benefit patients is not just a "corporate goal" but a valuable tool in implementing new projects.

Meeting Wait Time Demands

The holy grail of wait time management is the merging of the need of the client and the availability of the provider. Imagine a system, where health care wait times of various patient groups are monitored then adjustments to the schedule are made (with block booking) to meet as many of the needs as possible.

The dilemma is that if a clinic is already running near 100% capacity seeing someone too quickly has to have an effect on someone else. In other words, seeing a patient 10 days quicker than they really need (or expect) will leave someone else, more needy, waiting 10 days too long. Ideally, a clinic sets the goals for health care wait times for each group of patients and strictly enforces them – to the betterment of the population of patients.

The problem is in the practical application of this method. Setting the goals is easy enough. But to measure the actual wait time is difficult. Let me preface the next section by saying I am neither a mathematician nor politician just someone trying to meet the needs of patients. The easiest solution is for the clinic to have someone look ahead in the schedule and estimate the wait for each block of patients. It is time consuming and can be inaccurate. Alternatively the average wait for a group of patients in the preceding month or two can be measured directly but it will lag months behind and doesn’t help when trying to ramp up scheduled time during variation. For the more mathematically inclined, you can use standard queuing theory models such as M/M/1, M/D/n, etc… but all assume a queue that is not growing (where as we are trying to change the number of providers or servers to match the need) or Little’s Law (which breaks down when the queue empties).

All that is left is the brute force method of measuring the number of available appointments per day and the number of booked patients then dividing one by the other to come up with an average wait time. This is the most dynamic but requires data mining expertise. It is, however, the most accurate from what we have found.

The benefit for clinics in both private and public sectors comes from improvements in efficiency and better patient flow. By meeting the needs of the clients rather than exceeding some and leaving other short. In industrial terms, they call this leveling process and it refers to decreasing the variation that’s in the system. The less variation that is in the system, the more efficiently it will run.

Block Booking vs Priority Booking

What is block booking and priority booking and what are their advantages? I was recently approached by a colleague when I was talking about the advantages of block booking. He said that he wasn’t using block booking and no intention of using it.

In block booking, certain slots during the day are reserved for certain types of patients/clients. For instance, a family doctor might reserve 3 x 15min slots for asthma patients, 2 x 30min slots for new patients, etc…. The slots are held until the latest possible moment then filled with more urgent patients (more on the timing of this later). The alternative is a first-come-first-serve model (whoever calls in gets the next available appointment).

My colleague said that he saw no advantage to it. His waiting list was longer than two months to see him, in his mind no amount of juggling the schedule would change that waiting time for the patients. If the mean wait would continue to be 60 days, why play with the schedule. Rather than arguing about lean theory, six sigma and the benefits of a pull system (which are the theoretical basis for block booking) I told him about Disney World. At Disney I could take my family and get a fast-pass for each ride. I could get only one pass at a time, which let me to the front of the line at the time specified. While waiting I could get on other rides that were less popular. If I timed things well, I would spend much of the day using the fast-pass. If everyone at Disney was doing the same thing all of us would have the same effect. Although the mean wait for rides didn’t change our satisfaction was greatly improved.

Block booking works in a similar way. A patient with an acute asthma exacerbation needs an appointment sooner than someone with a sore joint from arthritis than someone with chronic migraines. If the clinic knows how many patients arrive with a certain type of ailment per month they can reserve the time based on the desired level of service. Let’s say 60 patients arrive each month with arthritis pain for evaluation. If the desired wait time is 15 working days then 4 slots per day need to be reserved. Priority booking is the same beast by another name. In priority booking, certain patient types are brought to the front of the line at the expense of others. In block booking, the natural variation in patient arrivals will result in a longer wait. In priority booking, one group of patients wait time is kept static and the others are allowed to vary. Priority booking is necessary for patient types that cannot wait more than a certain amount (e.g. chest pain, cancer, etc…) although it is used for priority programs as well by some institutions.

Block booking and priority booking add benefit to the system in 2 ways. First, they improve client satisfaction. Second, they decrease the amount of variation in the system which improves overall efficiency.

Wait Time -- A Universal Yardstick?

How much health care is too much and how much is not enough. In the United States the cost is the problem whereas in Canada it's access to care. Are the two related?

The time from seeking care to treatment is a good indicator of the level of service. When the helath care wait time is low there can be an excess of resources and when it is high there are not enough (in a system that is full). In theory, health care wait times can be calculated (and even better predict them going foward) for each sector of the system and resources allocated appropriately. Currently many governments are calculating wait times in the emergency rooms, monitoring patient flow and time to surgery. But wait times can be manipulated. Surgeons can limit consultation time so the actual time on the wait list for surgery is low, emergency rooms can leave the patients with the ambulance crew prior to registration.

Instead, wait times need to be considered from the patient's perspective. Not only will it better reflect the actual wait involved but it might allow for more solutions. Imagine a patient that phones the doctors office. If the wait time for the front desk is only measured as the time on-hold with the office administrator we see only half the story and consequently half the solutions. In this instance, the only option is to either hang up or keep holding. If the wait time is measured from the time the decision to call is made other options such as email, fax, etc... also become apparent. Both email and fax would potentially decrease the wait time and relieve the burden on the administrative staff.

While patients may choose to accelerate or delay treatment and inadvertantly manipulte wait times, our data suggests that the mean wait is a good predictor of availability in the clinic. Most of the patient populations have well defined normal curves with predictable variations. Since representative survey populations are already common in politics; can the same solution not be extended to health care? We don't need to record every surgery and every wait time, just a representative sample. This would significantly simplify the burden on the information technology sector in health care (who is currently trying to connect many different clinics) and potentially provide accurate data over wider sectors. To determine how many patients need be surveyed and across which sectors, it would be simple enough to look at existing data for power and size calculations. From there a uniform, automated system could be implemented for all sorts of health problems not just those choosen as priority programs.

More and more institutions are rightfully focusing on wait times and so should smaller clinics. I think we'll find that as systems evolve a universal yardstick for health care delivery will not only let us see the problems, it will let us see the solutions.

The Biggest Bang for the Buck

What gives the best “bump” to efficiency in a clinic? If I could pick only one tool to decrease health care wait times what would it be?

Offices world wide have a broad spectrum of automation (computerization) and business practices. My old family doctor had his wife run the front desk, the waiting room had five chairs and there was one exam room. I rarely waited once I arrived at the office. On the other hand, I’ve seen emergency rooms with open beds, lots of staff, enough computerization to launch the space shuttle and still experienced 3-5 hour emergency waits.

I can think of many tools that should, in theory, improve the efficiency of a clinic and patient flow, thereby decreasing helath care wait times. There are certain tools that make processes work faster, such as computerized billing, email confirmation, digital lab results and the like. I consider tools like these “velocity tools”. They improve efficiency by leaning down the time of a process.

There are tools that make mistakes or errors on the part of the clinic less likely such as automatic recall schedules and fool-proof treatment tracking. In turn, less mistakes results in less repeated work or more time for clients. There are tools that allow better control of scheduling and patient flow such as computerized booking. These tools decrease the amount of variation in the system by allowing for control of booking and the creation of “pull” which improves efficiency.

Having been analyzing our own data for 7 years I believe that the tools that make mistakes less likely have the biggest benefit. In an already busy practice, velocity tools and variation tools can change individual processes greatly, but the overall effect is only 5-10% each. On the other hand, decreasing errors seems to change the burden 10-20% across the board.

In our case, it means digital treatment plans and recall schedules with strict algorithms to prevent errors. The system makes it impossible to forget about follow-up work to be conducted. Imagine a patient that comes in for a problem then neglects to follow-up. There is time spent in the office tracking down why the person hasn’t followed up and then repeating the work if too much time has elapsed. Not only do appointments and clinic work need to be repeated but so does the administrative burden. In healthcare, this lack of follow-up includes a significant number of people.

Looking back at the data, that single step of fool-proof treatment tracking changed our efficiency by 10-20%. I don’t think I can find another single tool with as large of an effect over the entire practice. So, the one tool I’d pick before all others is a computer system that tracks treatment and follow-up.

Talk about Wait Time and Delayed Care in Clinics

Welcome to the wait time and delayed care blog. This blog is about the techniques and tools of managing wait times in a professional setting. Doctors’ offices, lawyers, dentists, chiropractors, other health care professionals (as well as hairdressers) all run into the same problems because of quick appointments with a large number of people. My premise is that if the staff that manage scheduling and patient flow can make modest improvements, major changes can happen. Most of us can find ways of shortening a 20 minute appointment by 4 minutes. This is a 20% improvement. But if that same 20% improvements are achieved with the family doctors across Canada; that's an extra 3,400 (20% of 17,000) physicians worth of time.

If you are the person that is organizing or managing an office, business or clinic then this blog is intended for you. Our office works hard to run on time and get people in quickly, despite being in an under serviced area. Shortening wait times is not just a matter of increasing the number of people/providers that care for the patients it's a matter of better management.

There are four main concepts.
1. Time saved with one patient can be spent with another
2. Work that runs smoothly will run faster
2. Having to repeat work wastes time
3. To improve something; you have to be able to measure it.

Industry (e.g. Toyota, GE, etc...) long ago embraced these concepts through Lean, Six Sigma, DMAIC and other programs. But many businesses still don't use them to improve the bottom line. In the service sector we need to be even more diligent because wasted time affects our patients.

I hope you'll find the blogs helpful and I look forward to bouncing ideas off of anyone interested in the topic. Health care wait times, delayed care, waiting lists, block booking, priority booking, queuing theory, six sigma, lean, repeated work, critical time to treatment and many other topics are all up for discussion. My hope is many people will make small changes that can have a major impact.