Why is it that every time I go to a doctor, I am given an appointment for a precise time and then every time, every single time, the doctor shows up at least 20 minutes late? Does the healthcare system hate me? Do they not want to fix the problem? Or are they just simply incompetent?
To dig deeper into the question, we at LeanTaaS dove into the operations of more than 50 healthcare providers this past year. We looked at resource utilization profiles at three different types of clinicscancer infusion treatment, oncology, and hematologyto understand the problem and how best to solve it.
The truth is that most healthcare providers have the patients interest at heart and are trying their level best. However, optimal patient slotting is a lot more complex than might appear on the surfacein fact, Googol sized in complexity. The good news is its a problem solvable with advanced data science; the sobering news is it MUST be solved if we are to handle the incoming onslaught of an increasing, aging patient population all carrying affordable insurance over the next 20 years.
There are few things I take for granted in life, and waiting to see a doctor is one of them. The average wait time for a routine visit to a physician is 24 minutes. I am sure I am not the only one who has sat in a doctors waiting room thinking, You said you would see me at 3:00 p.m.why am I being called at 3:24? This happens every time; I bet you could have predicted it. So, why didnt you just ask me to come at 3:24 instead?
A Press Ganey study of 2.3 million patients at 10,000 sites nationwide found that a 5 minute wait can drop patient satisfaction by 5 percent, a 10 minute wait by 10 percent, and more than 10 minutes by 20 percent.
That 24-minute stat is, in fact, not so bad compared to anyone who has had to get an infusion (chemo) treatment, visit a diabetes clinic, prepare for surgery, or see just about any specialist. That wait time can be hours.
Just visit any hospital or infusion center waiting room, and you will see the line of patients who have brought books, games and loved ones along to pass that agonizing wait time before the doctor sees them.
I spent the past year researching this problem and saw for myself just how overworked and harried nurses and doctors operating across the healthcare system are. I spoke to several nurses who have had days they were not able to take a single bathroom break. Clinics routinely keep a missed meal metrichow often nurses miss lunch breaksand most of the ones I spoke to ring that bell loudly every day.
I even heard of stories of nurses suing hospitals for having to go a whole day without breaks or meals.
The fact is that long patient wait times are terrible for hospitals too. Patients waiting for a long time is symptomatic of chronically inefficient patient flow through the system and that has serious negative impact on the hospitals economic bottom line and social responsibility:
Average US Operating Room Utilization. Source:mgma.com
Hospital leaders know this well. Every administrator I spoke to in my researchCEO / CAO / CNOhas some kind of transformation effort going on internally to improve patient flowlean teams, 6-sigma teams, rules for how to schedule patients when they call into various clinics, and so on. They know that if patients could be scheduled perfectly in a way that doctors could see them on time, the resulting smoothing of patient flow throughout the system would making their facilities, staff, and the bottom line much better off.
Its not for a lack of motivation that the system is broken. Its just a hard, complex math problem.
The system is broken because hospitals are using a calculator, standard EHR templates, and a whiteboard to solve a math problem that needs a cluster of servers and data scientists tocrunch.
To illustrate why this is such a complex problem, lets take the case of a mid-sized infusion (chemo) treatment center I studied during my research.
This infusion center has 33 chairs and sees approximately 70 patients a day. Infusion treatments come in different lengths (e.g., 12 hours, 34 hours and 5+ hours long) and the typical daily mix of patients for these three types are 35 patients, 25 patients and 10 patients, respectively.
The center schedules patients every 15 minutes starting at 8:00 a.m. with the last appointment offered at 5:30 p.m. So there are 39 possible starting time bands: 8:00 a.m., 8:15 a.m., 8:30 a.m., etc, ending at 5:30 p.m.
The center can accommodate three simultaneous starts due to the nursing workload of getting a patient situated, the IV connected, and other prep. That makes a total of 39 *3 = 117 potential appointment start slots.
In order to come up with a quick estimate on the number ways the center can slot these 70 patients into the 117 slots available, we can do the following:
Quick refresher: nCk = n! / (k! * (n-k)).
To get a lower bound on the possibilities, we can ignore the specific permutations of the 35 patients for now.
For the purposes of a quick lower bound estimate, multiplying all three parts provides the rough number of ways in which the schedule for a typical 70 patient day could be accommodated at 15 minute intervals with three simultaneous starts at this center.
The size of the solution space is of the order of 2.6 * 10^61.
Now, add to this the reality of a hospitalsome days nurse schedules are different from others, the pattern of demand for infusion services varies widely by day of week, doctors schedules are uneven across the week, special occurrences like clinical trials or changes in staff need to be considered, and very quickly, you are looking at a problem thats very hard to solve with simple heuristics and rules of thumb.
Very few hospitals I spoke to understand or consider this math. Rather, in trying to make the patient happy, most providers have been trained to use a first come, first serve approach to booking appointments. Sometimes they use rules of thumb based on their knowledge of busy times of day or week e.g., start long appointments in the morning and shorter ones later.
As far as possible most hospitals will do what they can to accommodate a patient who wants a specific appointment slot, based on their schedule or because for example they want to minimize the gap between their clinic and infusion appointment. Typical EHRs provide the equivalent of a Google Calendar for appointment scheduling with no real intelligence to guide the decision making.
The problem is every time a scheduler uses this kind of first-come-first-serve method to scheduling, they are in effect creating one of the 10^61 possible patterns above. If they were scheduling patients for one chair, one nurse, and the same treatment type some of their rules of thumb could work.
But reality is a lot more complicatedthe right schedule would need to consider varying treatment times across patients, include multiple treatment rooms / chairs, varying staff schedules, lab result availability and so on.
So without sophisticated tools, there is an almost zero chance a scheduler can arrange appointments so treatment durations fall like Tetris blocks that align perfectly over the course of the day, and seamlessly absorb patients as they arrive, orchestrating doctor, nurse and room availability, while accounting for all the other constraints of the operation.
In effect, they are scheduling blind, not taking into account the effect if appointments already scheduled before, during or soon after that slot. Its like adding traffic to rush hour and almost always results in a triangle shaped utilization curvemassive peaks in the middle of the day and low utilization on either side.
Typical utilization of infusion chairs in an infusion treatment center with 63chairs
Each of the 50 hospitals I spoke to identified precisely with this utilization curve. In fact, they identify with the midday rush and slower mornings and evenings so well that they have given them affectionate namesone called it their Mount Everest, another Mount Rainier.
From a cancer centers standpoint, this chair utilization curve has several issues even beyond long patient wait times:
In effect, when hospitals think they are scheduling in patient-centric ways, they are doing exactly the opposite.
They are promising patients what they cannot deliverinstead of giving the patient that 10:00 a.m. Wednesday appointment, an 11:40 a.m. appointment may have been much better for the patient and the whole system.
As we will see in my further analysis, the patient could have had a 70 percent shorter wait time, the hospital could have seen 20 percent more patients that week, every nurse could have taken a lunch break every day, and a lot less (if any) overtime would have been required.
The solution to the patient slotting problem lies in data science and mathematics, using inspiration from lean manufacturing practices pioneered by Toyota decades ago, such as push-pull models, production leveling, reducing waste, and just-in-time production.
In mathematical terms, it means taking those 10^61 possibilities detailed earlier and imposing the right set of constraintsdemand patterns, staffing schedules, desired breaks, and whatever is unique to the hospitals specific situationto come up with a much tighter set of possible patient arrangements that solve for maximizing the utilization of hospital resources and therefore the number of patients seen.
In the case of the infusion center, the algorithm optimizes utilization of infusion chairs making sure they occupied uniformly for as much of the day as possible as opposed to the peaks and valleys in Figure 3. In essence, rearranging the way the Tetris blocks (patients) come in so they appear in the exact order they can be met by a nurse, prepped and readied for a doctor whose schedule has been incorporated into the algorithm.
The first step in doing this is mining the pattern of prior appointments in order to develop a realistic estimate of the volume and mix of appointment types for each day of the week going forward.
The second step is imposing the real operational constraints in the clinic (e.g., the hours of operation, doctor and nurse schedules, the number of chairs, various rules that depend on clinic schedules, as well as patient-centric policies such as treatments longer than four hours should be assigned to a bed and not a chair).
Finally, constraint-based optimization techniques can be applied to create an optimal pattern of slots, which reflect the number of appointment starts of each duration.
In the case of the infusion center, that means how many one-hour duration, three-hour duration, and five-hour duration slots can be made available at each appointment time (i.e. 7:00 a,m., 7:15 a.m., 7:30 a.m., and so on).
Optimized shape of utilization curve for the same center as in Figure 1. 20% lower peak, much smoother utilization of resources, significant capacity freedup
Doing this optimally results in moving the chair utilization graph from the triangle that peaks somewhere between 11:00 a.m. and 2:00 p.m. in Figure 3 to a trapezoid that ramps up smoothly between 7:00 a.m. and 9:00 a.m., stays flat from 9:00 a.m. until 4:00 p.m., and then ramps down smoothly from 4:00 p.m. onwards in Figure 4.
Coming up with realistic slots that keep patients moving smoothly throughout the day cuts patient waiting times drastically, reduces nurse overtime without eliminating breaks, and keeps chair utilization as high as possible for as long as possible. Small perturbations in this system are more like a fender bender at midnight, a small annoyance that causes a few minutes of delay for a small number of people instead of holding up rush hour traffic for hours.
The above graphs are sanitized versions of real data from a cancer infusion treatment center at a real hospital that used these techniques to solve their flow problems. The results they achieved are staggering and point to the massive economic and social opportunity optimal patient flow presents.
Post implementation of a product called LeanTaaS iQueue, they now experience:
Imagine applying this kind of performance improvement to every clinic, hospital, surgery suite in the country and the corresponding positive impact it will have on population health with the increased patient access to the system.
This problem is going to get a lot worse for a simple reasonthe demand for medical services has never been stronger, and its only going to increase. Just looking at the US market:
Access to Healthcare is a looming crisismultiple drivers of significant demandgrowth
Most healthcare providers are waking up to the fact that their operations need a data-driven, scientific overhaul much the same way as auto manufacturing, semiconductor manufacturing and all other asset-intensive, flow-based systems have experienced.
The good news is that there are tools, software and resources that can be used to bring about this transformation. Companies like LeanTaaS are at the forefront of this thinking and are applying complex data science algorithms to help hospitals solve these problems.
Hospitals that are serious about solving patient flow issues and the related problems now have access to the best computational minds and tools at their disposal.
I see a world in which our healthcare system can see every patient on time without imposing hardship on care providers, disruption on current processes or increasing cost of services.
Heres to that world!
I would love to hear your thoughts on this important subjectemail me at firstname.lastname@example.org