<click on the above graph for a larger image>
The above graph is an example of finished data analysis for three different orthopedic groups (Arizona Cutters, ORO Pods, Tucson Bones) which are deciding in which hospital, and with which anesthesia group (APA, PAA, ABC) they should do their cases.
Each colored dotted line represents data from an individual orthopedic case. The closer the lines are to each other, the faster their surgeon’s turnover. The dots on each line represent particular milestones that can be used for other analysis, but can be ignored at present.
ORO Pods works only with the anesthesia group PAA in two different hospitals (UMC, ORO Valley). By looking at the chart, they can see that there are 7 cases finished at UMC in the same amount of time that 3 cases are finished at ORO Valley. Since the anesthesia group PAA is the only group they work with, they can conclude that PAA knows how to turn over cases quickly, and that the slow turnover at ORO Valley Hospital is due to the hospital, not PAA.
Tucson Bones can see that their cases are being turned over quickly by the anesthesia group ABC at the hospital TMC, so they don’t need to make any changes.
Arizona Cutters has a dilemma. From the data it sees that it’s cases are being turned over slowly, but they don’t know if it’s because the anesthesia group APA is slow, or if the hospital TCH has a problem turning over cases. They need to try another anesthesia group at TCH or try another hospital with the same or different anesthesia group.
An anesthesia group could use similar charts to decide which orthopedic groups and hospitals they want to work in. Anesthesia groups are often reimbursed as much for starting a case as for an hour of sitting on a case. For example, in the case of ORO Pods and PAA, if PAA received 4 units for starting each case it would receive 28(7×4) units for starting the cases at UMC, but only 12 (3×4) for starting the cases at ORO Valley. Where this usually comes into play is when there is a particularly slow surgeon. Suppose that PAA was asked by a new orthopedic surgeon to cover him at UMC. They try it a couple of times, but he is particularly slow (the colored lines would be more horizontal) and only gets 4 cases done in the same time that the other surgeons get 7 cases done. The lost income (even though they are at the fast turnover hospital) would be [(7×4)-(4×4)=12] 12 units because of the slow surgeon even though they were working for just as long during the day.
A hospital could use the charts the same way. They could court the faster anesthesia groups and faster surgeons to their facilities.
An orthopedic group could also use this data to monitor their own members. The individual dots represent milestones during a case. If a particular surgeon continuously has a long horizontal section during his cases, he may be taking a less than optimal approach during the surgery or using a prosthesis that is difficult to work with. Post-op complications may also correlate with increased times during parts of the surgery which might make it easier to locate causes for the complications.