## putting it all together…intra-case and inter-case….TOT and more

There are a thousand variations of the following examples, but I hope these four graphs will get across some ideas.  For potential economic effects, see:  Wasting \$2.7 million dollars a year.

Each of the following graphs has 5 bars. Each bar represents a case and is derived from, and can be easily altered by, the many factors that I’ve shown in my previous blogs. Each bar represents an identical case, all of which 1 surgeon is trying to finish in one day.

This first graph shows 1 surgeon, 1 anesthetist, and 1 OR room. The surgeon has block booked one OR room for the entire day.  He takes almost 16 hours to finish. The room is the constraint:

This second graph shows 1 surgeon, 1 anesthetist, and 2 OR rooms. The anesthetist runs from one room to the next, alternating rooms like the surgeon.  The total time for the cases is about 14 hours (2 hours less than when using only one room). Anesthesia is the constraint:

This third graph shows 1 surgeon, 3 anesthetists, and 3 OR rooms. The total time for the cases is about 9 hours (5 hours less than the second graph with 1 anesthetist and 2 rooms, and 7 hours less than the first graph with 1 anesthetist and 1 room). You can see that the first case ends at 11:09, and the third case begins at 10:58, therefore 3 rooms are needed for flipping in this graph if the surgeon demands that he never has to wait.  If the surgeon were willing to wait an additional 11 minutes (not bad for turnover) you could get by with 2 anesthetists and 2 rooms which would be much more cost effective.  The extra room, nursing crew, and anesthetist could be assigned to a different surgeon, while both nurses and anesthetists are kept constantly working. The surgeon is the constraint:

This fourth and last graph is similar to the third graph in that the surgeon is the constraint.  However, it’s the next day and  some cleaning was done intra-op before the patient left the room so that only 10 minutes instead of 15 were needed to  finish at the end of the case; better coordination decreased the anesthetic induction from 10 minutes to 5 minutes; and a different anesthetic technique decreased wake up time from 10 minutes to 5 minutes.  Notice that the first case ended at 10:54 and the third case started at 10:58.  That means that only 2 anesthetists and 2 rooms were needed to keep the surgeon continuously working with no TOT at all for him.  In this scenario, the total time for all cases is only 17 minutes less than the third graph, but the economic savings were significantly more because one less room, one less nursing crew, and one less anesthetist were needed to keep the cases going and the surgeon happy.  The big payoff would be in having an extra room, nursing crew, and anesthetist available for an additional day’s worth of cases and revenue from another surgeon. Also, the fact that any rational surgeon would rather spend 9 hours in your OR than 16 hours in your competitor’s would make it easier to recruit other surgeons to do all their cases at your hospital.

To summarize:  A more efficient scheduling paradigm (flipping rooms) can have dramatic effects on patient throughput and TOT. And, as is indicated in the last graph, targeting minor changes in efficiency (lean management) of specific tasks (I searched for non-surgeon tasks that I could realistically speed up) ended up saving the use of a room, nursing crew and an anesthetist.

For clarification of  TOT, see: sTOT, aTOT, rTOT

## About Brian D Gregory MD, MBA

Board Certified Anesthesiologist for 30 years. TOC design and implement for 30 years. MBA from U of Georgia '90: Finance, Data Management, Risk Management. Practiced in multiple US states and Saudi Arabia at KFSH&RC and KFMC Taught residents in two locations. Worked with CRNAs for 20 years.
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