Wait Time Differential a Good Measure of Administrative Skill

Measuring the efficiency of the front desk is not always simple. Hang up rates, patient satisfaction and similar metrics are indicators of administrative performance but are not specific nor do they correlate well to clinic efficiency. Global measures of clinic efficiency, on the other hand, are not sensitive to administrator competence. Our clinic has found a metric that appears to measure both clinic efficiency and administrative team performance. It is the difference in wait times between two different appointment types; the Wait Time Differential.

Imagine a clinic where there is a single block of time (say 2 hours) reserved for 2 appointment types (Red & Blue). Blue appointments are 30minutes in length and Red appointments are 15min. It is 'easier' to find a slot for a Red appointment than a Blue so the natural tendency is for the Blue wait time to be longer than Red. A well administered clinic fights the urge to simply fill the day and offers appointments such that the wait times are controlled for both appointment types. Statistically, the clinic will maintain both a low wait time for Red and Blue as well as a fixed* ratio between the two (e.g. Blue Wait Time = 1.5xRed Wait Time). When preforming poorly the gap between the two widens as excessive number of Red (short) appointments fill the day at the expense of Blue (long) appointments.

In the graph (real data) the goal is to maintain wait times between 10 and 20 days. Less than 10 and the schedule has open slots. Greater than 20 days and patient satisfaction drops.

Office1 maintains low wait times (red arrow; usually the gap is wide in August) and equalizes the wait times between the two appointment types. It is rare to see a reversal of the ratio except when wait times are low but is a sign of excellent administrative organization. Office 3 on the other hand, has a widening ratio from June to August (green arrow; due to new administrative staff).

We have found that a widened Wait Time Differential is a sensitive and specific indicator of administrative control. On the down side it is retrospective and will not indicate a problem for a period of 2-3x the average wait time. More immediate (but less specific) indicators are described here and here. Leveraging Excel and database mining for wait times allows management a powerful and objective means to monitor the capabilities of the administrative staff as well as clinic performance.

*the ratio does not have to be fixed. In fact, our clinic will manipulate the ratio depending on the demand of patients during certain times of the year. During winter, Blue appointment slot are in less demand and wait times of 20 days are easily tolerated. Near the end of summer 10 days is more desirable. Since Red slots are fixed throughout the year the ratio of Blue:Red changes throughout the year.

When the junior Administrator has more power than you

I had an interesting conversation while car-pooling with another surgeon and our junior front desk person. The surgeon speculated that we should be adding more blocks of time for one type of surgery. The junior front desk person said “we’ve been adding two ½ hour blocks after lunch for over a month now”. He asked, “Who told you to do that?” and she responded “my supervisor, Joanne”. How does it come to pass that our most junior front desk person is a month ahead of the surgeon with the schedule planning?

Power is that ability to make people do what you want, so she who controls the schedule, controls the power. Handing over the reigns of schedule control is a tough pill to swallow for many providers. While they may say the front desk controls it, critiques, both overt and implied, are frequent and one-sided.

While our clinic is not immune to provider critiques we try to use surgical wait times as the yardstick for blocks of time rather than a static template. By setting wait time expectations with our administrative supervisor, she can frequently monitor (weekly) and fine tune blocks of time. It is only the most drastic changes that the providers hear about and I suspect that is to ward off any surprises rather than to seek approval. Our administrators have a far better feel for how long people are waiting than we do.

I’d suggest the following method.
Where there is more than one person controlling the schedule, assign one to be the gate keeper of block booking (administrative coordinator)
Set expectations – our office uses wait times, some use revenue others use appointment types*
Have an initial meeting to create a template
Discuss how much the template can be changed without provider input
Review the results (based on expectations) every 3 months

Other than balancing the number of doctor days between clinics most changes to our block booking templates are made by the administrative coordinator. Handing the reigns over to the people that are on the front lines makes the schedule more nimble, improves office performance and the experience for everyone involved.

* Booking by appointment type is used by clinics that have static funding and either very high or low wait times. The schedule is built to please the provider when schedule will have no effect on patient satisfaction.

Using a Process Simulator for Block Booking

One of the problems with block booking is accurately estimating the time blocks required for all of the “what-ifs”. An example is a patient that is referred to our practice for surgery. Using our EMR we know that 5 +/-3 patients are referred each day and that 80% of them follow-through with surgery. Of the patients who have surgery, 80% have an uneventful recovery. The remaining 20% need either 1, 2 or 3 post-operative appointments for complications. An insignificant number need greater than 3 post-op appointments.

While I enjoy statistics, I have no practical means to determine the number of appointments required accounting for the natural variation in the number of referred patients and all of the possible outcomes. The solution is to use a process model simulator. In this case I set-up the scenario as outlined above and ran the scenario to simulate a week (5 days).

I then instruct the process model simulator to repeat the scenario 25 times to estimate the average patients in each outcome and the variation associated with each. The result is a practical method to block book the number of appointment slots per week based on the outcomes calculated in the process simulator. The chart below calculates the average number of appointment slots per week required (consultation [21], surgeries [17] and post-op [8])

What makes this scenario especially difficult to estimate manually is that the arrival rate of patients (5 +/- 3) is highly variable. Therefore, it is also useful to look at the standard deviation of the number of appointments required.

For instance, the total number of surgeries per week is 17.2 +/- 8.7. In a scenario where wait times cannot be tolerated (e.g. cancer care or obstetrics) a clinic may choose to plan on the average + 2SD (95% of patients will receive care in the specified time) blocks. Where wait times are better tolerated the average may suffice. Using a simulator allows administrators to create multiple scenarios with repeated measures then block the day according to the health care wait time tolerances desired.

The Ethics of Block Booking by Insurance Status

Recent cuts to Medicare bring up an interesting practice management problem. If a practice chooses to accept patients on social assistance medical insurance (such as Medicare) is it ethical to leave those patients waiting longer?

Reality

It would be easy enough to offer 1 or 2 appointments a day to publicly funded insurance plan patients and leave the rest for private pay (block booking based on insurance). The result would be longer health care wait times for public pay patients. The dilemma to providers, of course, is that publicly funded plans usually pay less. The alternative is either a cut in revenue for the clinic or complete denial of access to these patients.

Ethics

The ethical dilemma stems from the fact that those with less access generally have poorer health. When a practitioner denies access completely the ethical dilemma is transferred to another because the patient seeks care elsewhere. Where a practitioner chooses to govern access through block booking they retain the moral quandary of different levels of care for patients under the same care provider. Once one chooses to accept a patient, is it ethical to allow two patients with the same disease to have different outcomes based on access to the clinic?

A Moral Choice

My opinion is that a clinic could morally limit appointments as long as the process was transparent to patients, made accommodations’ for emergency/urgent cases and did not violate any legal statutes that preclude differentiation based on insurance status. Ideally, wait times for publicly funded patients would still fall within regionally acceptable limits for wait times based on disease. I do not believe the latter is moral requirement so long as the prerequisite of transparency is met.

Practical Concerns

Another problem is that staff controlling schedules have not undergone the same ethical indoctrination as providers. That is not to say that they are any less moral, only that they may not share the same values with respect to patients’ needs which could lead to behavior and booking patterns contrary to the providers’ value system. To align provider choices with booking practices administrative staff require healthy clinic socialization in addition to formal professional education.

Public Policy Concerns

As countries the world over convert to a mix of private and public funding the issue of primary care access based on coverage are going to become more pronounced. If it is to be public policy that income should not correlate with access public policy will need to be set to bring public funds in the range of private expectations and legislation created to equal the playing field. Assuming that coverage remains an acceptable reason to deny access, practitioners who continue to run dual coverage practices will need to consider the ethical implications of offering the service and professional colleges the standards by which dual coverage access will be provided.

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.

Open Access Scheduling

Next week I will be posting a two part interview with Dr. Jeffrey Steinbauer, Medical Director, Baylor Clinic, Houston, Texas and former Director of the Family Practice Unit. Dr. Steinbauer instituted open-access scheduling in 2003 and has published the results of his clinics implementation.

Countries around the world are identifying primary care access as one of the major hurdles to health care reform. In the US, reform will increase the demand on primary care and in Canada primary care access has been a problem for the past 10 years.

Open-access schedules have been touted as one possible solution to the influx of people seeking primary care. I wanted to get Dr. Steinbauer's impression of the benefits and pit-falls to open-access and compare it to block booking.

Block Booking for Procedural Patients

For ENT, opthomology, oral surgery and other specialists whose practice are dependent on scheduling procedures here is a suggestion for block booking. I've previously posted on why block booking controls health care and surgical wait times and how it improves satisfaction. For more detailed infomration click here. Our practice is largely determined by the load of 3 or 4 different types of procedures. The patients are seen once prior to the procedure for 15-30minutes. The procedure is then scheduled for 15-60 minutes and 10-15% of them require a 15minute post-operative visit.

The first part of the equation is the easiest to solve – how many procedures should be booked in a single day. This may be dependent on wait time, resources or populational need. Or you may simply want to maximize one type of procedure

Determining the Number of Procedures

First specifically define the procedure type. To calculate the wait time for that procedure, count the number of people in the schedule going forward and dived by the number completed per day. Fifty people in the schedule divided by 5 per day equals a 10 day wait. You can adjust the number of procedures per day based on this.

Population need is difficult to calculate but we’ve found it to be a surprisingly static figure. Calculate the number of procedures completed per 10,000 people per year. For instance the need for dental implants in the US is about 40/10,000 per year. From this you can calculate the number of procedures per year assuming you’re the only specialist in a community. On a smaller scale, calculate the number of procedures completed per year for a community, the size of the community and the clinics “market share” of patients seen.

Finally, when there is greater need than resources and you need to maximize the number of procedures in a day its’ a bit of a balancing act. Start with the procedures using all of the available time then back-track the number of consultations and other visits that would be required (see below for details on this). Finally start backing down on the number of procedures until the total time used with consults, procedures and other visits fills the total time available.

Determining the Number of Consultations:

I have friends that book 1 day/ afternoon for consultations and the rest for procedures but there’s not a lot of logic to it. Either they see too few consults and the days open up or too many and the wait list becomes excessively long. I’d suggest an agreeable wait time be determined between the consult and the procedure. Depending on how involved the procedure is 2-3 weeks will fill the day and give people enough time to arrange for time off work. Next, look back at you’re schedule to determine the number of people that actually follow-through with recommended treatment. This could vary greatly depending on the procedure needed but I’ve seen it range from 55-95%. Let’s assume that 85% of people follow though from the consultation. If you can complete 5 procedures per day then you need to see 5 divided by 0.85 = 5.8 per day (29 per 5 days).

Follow-Up

Finally calculate how many people return for follow-up and include that in the calculations as well in the same way consultation time was determined. For this example let’s assume that 12% of patients need a follow-up or 25 x 0.12 = 3 follow-up appointments

Putting it All Together

Last but not least add up all the time required and see if it still fits in the schedule.

Procedures: 60 min
Consultation: 15 min
Follow Up: 15 min

Based on 25 procedures per week
Procedures: 25 x 60 = 1500 min
Consultations 29 x 15min = 435 min
Follow-Up 3 x 15 min = 45 min

So the total time requirement is 1500 + 435 + 45 minutes =1980 min = 33 hours per week

When you do these calculations for multiple procedures you can add or steal time from one procedure or another to balance the amount of wait from the time of referral to the time of procedure.

Adjusting Blocks by Day of the Week

When patients are making requests to have a procedure on a certain day of the week how should you adjust the schedule in block booking? In our office we keep a uniform number of block available for each day of the week so a look at the wait time by each day of the week shows that there is a longer wait for Thursday and Friday appointments. In the case below, the procedure is for one office to have wisdom teeth out (the data was pulled for appx 2,600 patients over a two year period). The average time waited was 26 days but it varied from a low of 23 days for Monday and Tuesday to a high of 32 days for Friday. This is consistent with the anecdotal observation that patients prefer Fridays (two recover over the weekend) and are willing to wait for it.


Because this day of week variation doesn't happen for consultation appointments our office will increase surgical blocks late in the week and hold more consultative blocks early on. Since the wait is 40% longer for a Friday appointment compared to a Monday appointment 40% more blocks will be reserved for Fridays.

Block Booking for Chronic Care Patients

Mr. Jones needs to follow-up in a month for hypertension. How many blocks of time should you reserve, a month from now, for patients like Mr. Jones? Calculating blocks of time for chronic care is pretty straight forward.

First, identify a group of patients. The group doesn’t have to have the same disease but they do need to have the same time requirement in the schedule (15min, 20min, etc…), the same return frequency (1 month, 2 months, etc…) and the same resource requirements (does not need blood work, will need to see the nurse first, etc…). Once you’ve identified the group of patients use you’re electronic scheduler to create an appointment type with it’s own unique colour. This makes it easy for the front desk to identify how many patients are already booked in a day. As an example, all stable hypertension and asthma patients that are on 6 month recall schedules could be coloured purple in the scheduler.

First count back how many patients (of the group you’re blocking) were seen in the past. Count back 3x the length of the recall period. For instance if the follow-up is 1 months, count back 3 months. Next count back how many days were available to those patients during that time. If the practitioner was available 3 days per week for those patients x 12 weeks than there were 36 days available.

Finally divide the number of patients in the past 3 months by the number of days and this is the number of blocks to reserve per day.

Example:

Hypertension and type II IDDM follow-up 1 month:
In last 3 months: 472 patients of this type booked (includes no shows)
In last 3 months: 20 days available for this type of patient
Block booking: 472/20 days = 7.9 appts per day round up to 8 appts per day

As you go forward the front desk will get a “feel” or better have a technology tools in the scheduler to measure the wait time for this type of patient. As the wait time extends beyond a month slots are added and as it drops below slots are removed. Consistency and working to decrease no-shows will improve efficiencies and allow fewer slots per day. If the provider changes the number of days worked the slots will have to be adjusted as well.

Group Practice Levels Scheduling

Do you ever get pissed-off at a colleague because you’re positive you’re working harder or seeing more patients? Nah – me neither. I then read an article at Curious Cat Management about computer programmers and how they can have an order of magnitude (10x) difference in productivity while writing code and that it’s unrelated to skill or experience and I started to wonder if we had the same amount of variation between doctors.

I understand there is a difference in creative work versus menial work (and pulling teeth is menial) but I started to wonder what the difference would be in our own practice. We have an agreement between the doctors that we set global guidelines (block booking) for the schedule in conjunction with our staff & managers then let the front desk run with it. There are, of course, ups and downs and constant critiques but the blocks of time are set 3-4 times a year.

In OFFICE1 there are always multiple surgeons present. In OFFICES 2, 3 and 4 there is only one per day. I had a theory that there would be more pressure in OFFICE1 to conform to the scheduling guidelines. This would result in less variation in the number of appointments per day in OFFICE1 with more variation in OFFICES 2, 3 and 4. In other words, some of the doctors would have more variation in efficiency without peer pressure.

I pulled data for the year 2007 (251 days, 4 offices, 5 surgeons) and compared all offices and doctors. I then preformed multiple statistical tests to prove my theory. To my surprise, there is no difference in the amount of variation per office between doctors. In fact, when you compare each doctor in each office there is absolutely no clinically significant difference over OFFICES 1, 2 and 3. OFFICE 4 is smaller, so we expect to see fewer appointments but it’s uniformly less between the doctors. Putting numbers to it, if we have an average of 22 +/- 5 appointments per day, there is a variation of 0.8 appointments that can be accounted for by a different doctor. That is a whopping 4% of the variation. Compare this to the 10x seen in computer programmers. In fact, the total amount of variation in the average number of appointments seen per day is 7% for all four offices or 4% if OFFICE4 is excluded.

From this data I conclude that:

• We all have the same number of appointments and there are only modest differences between the offices


• That there is statistically no difference between the doctors in the number of appointments


• Setting scheduling policies globally has a profound effect on minimizing variation.


• Group practice applies another major pressure to work efficiently to the standards & expectations of others in the group.

I cannot stress enough the effect of minimizing variation (or leveling a process). It is the single biggest factor in poor patient satisfaction (because some patients will be left waiting much longer than expected) and resources planning. It is much easier to manage a practice when you know that the error on the number of appointments will be +/-0.8 instead of +/-10. In this case we’ve used good lean principals to level a process (scheduling) and block booking which increases efficiency. With the marriage of technology and good practice management this minimizes the health care wait times in our office.

Five Ideas: The Doctors to Get Patients an Appointment Faster

Here are five ideas for the doctors/providers to decrease how long people wait to get an appointment. I’ll be doing a series of entries in the coming weeks with ideas directed to doctors/providers, front desk (administrative staff) and clinic staff. Each entry will either help get appointments sooner, keep people waiting in the office less or improve efficiencies. The first entry is for doctors/providers to get appointments faster.

Here are the ideas:
1. Work: A major determinant of patient wait is doctor availability. Try matching days worked to seasonal variation in wait. In our practice the end of August is busy so we put a “moratorium” among the doctors on vacations. We also monitor wait by yearly quarters and try to match days between our offices. “Doctor Days” is the easiest measure but it can be hours, weeks or some other time frame. All wait times and other measures of efficiency are taken in the context of days worked. For instance our wait time went up 10% but our doctor days went down 15%. There is a net improvement in efficiency of 5%. I have also seen doctor days go up 20%, wait times remain unchanged and efficiency drop 20% meaning the extra days where literally useless (this can happen when the clinic is still underutilized). Days worked must be monitored to make any sense of efficiency and wait times. Have someone start recording doctor days on a monthly basis.

2. Set acceptable wait times and monitor: In my entry on block booking I described setting goals for different patient types. The provider and staff have to decide what an acceptable wait is. In a busy clinic it is important not to see clients too soon. It sounds counter intuitive but time saved with one patient can be spent with another so if you delay seeing one patient for a reasonable amount of time you can get another one an appointment in an acceptable amount of time. For instance an asthma exacerbation and sore knee. Neither wants to wait but from a clinical perspective if you let the knee wait a bit longer you can get the asthma patient in sooner. Take two actions: a) start monitoring wait times b) set ideal waits for major patient groups.

3. Don’t see more consults than you can do procedures: The concept that time saved with one patient can be spent with another applies to this suggestion. I sometimes meet clinicians that are procedure oriented who have no balance between the number of consultations they do and procedures they complete. If for instance, you can only do 5 procedures per day and you have 80% follow-through from consultation to procedure, you should see an average of 5 divided by .80 = 6.25 consults per day (6 for 3 days and 7 the 4th day). Seeing more than that does not help the patient get the procedure and fills time that could be spent elsewhere. Count the number of procedures & consultations in a week and see if they match.

4. Single day consult/surgery: I’ve done an entire entry on this under phone screening. If the clinic is geared towards completing a procedure on the same day it can save time for the clinic and improve patient satisfaction by completing it the same day. Look through minor procedures that are done in the clinic and determine if any can be condensed into a single day appointment with either better phone screen or improved organization.

5. Whole Day Blocks: Some times you simply can’t catch up. This is common with seasonal work loads or in group practices when one/many providers take vacation. If you are monitoring wait times by different patient groups and an isolated block of patients are waiting much too long dedicate an afternoon/day to just that patient group. It goes against the one-piece work flow idea but sometimes resources need to be temporarily reassigned. This is where practicality sometimes runs into a wall with Lean advocates. The argument is that reassigning resources in this manner disrupts work flow; increases variation and decreases efficiency which ultimately increases wait times. My argument is that if the reallocation is measured, proportionate and limited it quickly corrects and in-efficiency. It will not work unless you are using block booking (if you are not using block booking all patient wait longer not just the one group). Look for one patient group at one time of year and plan an entire day to deal with the work.

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.

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.