DDBA 8561 Week 3 Surgery Capacity Analysis Example

The Operating Room Waits on Recovery: A Capacity Analysis of an Ambulatory Surgery Center Arguing That First-Stage Recovery Bays, Not Operating Rooms, Set the Day's Pace Through Blocking

Student Name

Walden University

DDBA 8561: Seminar in Managing Healthcare Delivery Systems

Instructor Name

Month Day, Year

The Operating Room Waits on Recovery: A Capacity Analysis of an Ambulatory Surgery Center Arguing That First-Stage Recovery Bays, Not Operating Rooms, Set the Day's Pace Through Blocking

The Route and the Default Answer

A composite ambulatory surgery center in Phoenix, owned jointly by a hospital and a group of surgeons, performs orthopedic, ophthalmic and general surgery cases on weekdays. Surgeons complain of waiting between cases, and the center's leadership assumes the five operating rooms are the constraint: they are the most expensive resource, they are busy, and surgeons wait there. This analysis argues that the pace of the day is set one step later, in first-stage recovery, and that the operating rooms appear constrained because they are blocked.

Capacity by Step

[after]

Times for first- and second-stage recovery are observed times, which include minutes when patients are clinically ready to move but have nowhere to go. Clinical recovery time in first stage averages about 60 minutes; the other 25 are holding time. Figures are drawn from the center's time stamps over eight weeks, with service times for discharge teaching and transport labeled as staff estimates.

Table 1

Capacity and Utilization by Step

Recovery Occupancy Across the Day

[after]

From 11:00 to 13:00, all six first-stage bays are full. During those hours, a case finished in the operating room cannot leave, because no recovery bay is free.

Figure 1

First-Stage Recovery Bays Occupied by Hour

The Blocking Argument

When first-stage recovery is full, a patient whose surgery is finished stays in the operating room with the anesthesia team until a bay opens. The center's records show operating rooms held an average of 18 minutes per case for cases finishing after 10:30. Surgeons waiting to start their next case see a busy operating room and conclude rooms are scarce. In fact the room is not working; it is serving as an overflow recovery bay.

The pressure originates further downstream still. Second-stage recovery chairs fill by late morning because patients who are ready to go home wait for rides; observed second-stage time of 110 minutes includes about 35 minutes of waiting for a driver. When second-stage chairs are full, first-stage patients cannot move, and first-stage bays fill.

Two Readings of a Constraint

Goldratt and Cox (1984) held that one resource in any system caps its output, so gains elsewhere are wasted until that resource is freed. On average capacity alone, the operating rooms, at 5.0 cases an hour, would not be the constraint; first-stage recovery, at 4.2 observed, would. But observed capacity depends on blocking downstream. Hopp and Spearman (2008) emphasized that variability and blocking between stations with limited buffers can reduce effective capacity well below what average service times suggest, and that a station's measured capacity may reflect what happens after it. Here the cascade runs from rides to second-stage chairs to first-stage bays to operating rooms. Goldratt alone would point to one station; Hopp and Spearman explain why the constraint moves upstream from where its cause lies.

What Follows for Managers

Adding a sixth operating room would add little, because its cases would join the queue for recovery. Two cheaper changes target the cause: requiring confirmed ride times at scheduling and moving patients awaiting rides to a lounge staffed by one nurse, which would free second-stage chairs; and staggering operating room start times so cases do not finish in clusters. Hopp and Lovejoy (2013) described staggering arrivals to a shared downstream resource as a basic way to reduce peak congestion in hospital operations.

Testing the Argument

The blocking argument can be tested against the center's own days. If recovery is the pacing step, then on days when ride waits are short, because more patients have confirmed drivers, operating room holds should fall even with the same case volume. The center's records allow that comparison: on the eleven days in the sample with fewer than five patients waiting for rides at noon, average holds fell to about 7 minutes per late-morning case. That pattern supports the blocking argument; if holds had stayed the same, the operating rooms would deserve another look.

Limits

Eight weeks of time stamps may not represent seasonal case mix. The ride waiting estimate relies on nurses' notes, which may be incomplete.

References

Goldratt, E. M., & Cox, J. (1984). The goal: A process of ongoing improvement. North River Press.

Hopp, W. J., & Lovejoy, W. S. (2013). Hospital operations: Principles of high efficiency health care. FT Press.

Hopp, W. J., & Spearman, M. L. (2008). Factory physics (3rd ed.). McGraw-Hill/Irwin.