Open Access: Part 2

The Relationship between Wait and Capacity

In Part 1 of my series on Open Access scheduling I posted about an implementation of open access at Baylor, Houston. Today I would like to take a closer look at the issue of practice capacity. Open Access scheduling is characterized by near zero wait times. One of the central arguments for it is that there can be zero wait times with the same capacity as a standard scheduling model. This is embodied in the motto; “Do today’s work today”. Since I believe that open access could be a major improvement to our health care system the relationship between capacity and delay deserves its own post.

Once an office is in steady state (the same number of patients are entering and leaving the practice) the wait time is proportional to the amount the system is utilized. If an office has 5 people per day seeking appointments, there will be zero delay. If it has 300 requests there will be considerable delay. After 3-5 years most offices enter a steady state where the demand for appointments is met by the supply of provider time. It is at this point that the wait time can be reduced to near zero.

In most queuing models delay begins at approximately 80% capacity. The point at which capacity is saturated without incurring delay can be pushed further to 100% by better efficiency and increasing the overal size of the system. Imagine two offices, each running at 90% capacity. Office A has more providers than Office B. Office A will have a lower wait times than Office B if all other factors are equal. Now imagine that the offices are the same size. If Office A is more efficient than Office B it will also have lower wait times.

The issue of efficiency is also central to the concept of open access. In order to have zero wait times, and maintain the same capacity, the office must be highly efficient. Efficiency, in this sense, is defined as the amount of variation in supply and demand for patient appointment slots. The variation in demand for appointment slots will be created by the patient. In flu season, for instance, the demand for appointments will spike. The variation in the supply of appointments will depend on how appointments are booked (short, long, variable length) and the availability of providers. The greater the variability in supplier demand, the lower the efficiency of the office.

As I write about capacity, therefore, the assumption is that all offices run at under 100% capacity because variation in patient demand and provider availability exists. I can also assume that all practices are capable of running with zero wait times below 80% capacity. So the real argument to open access versus conventional booking is at what point between 80-99% capacity wait occurs with good practice management. If open access offices had lower capacity compared to standard models, practices using it would have a lower total patient count and openings in the schedule. Instead, the data is strong that, in primary care, an open access schedule can handle the same capacity as a standard schedule but with zero wait times (which dramatically increases patient satisfaction). Tomorrow, I’ll be taking a closer look at experiences and data to support this contention and discuss the potential pitfalls of open access.

Part 1: Open Access Scheduling: Interview with Dr. Jeffrey Steinbauer
Part 3: Analyzing Open Access Scheduling