Even though all I do is look for factors that effect health care wait times, it’s better to think about all of them as a black box. A friend of mine has a company that helps factories decrease their energy costs. I asked him how they calculate what impacts on energy costs when each factory is so different. I thought I could use some of the tools to ferret out the factors that effect wait times. He surprised me by saying they don’t even try to figure it out.
Energy usage, like wait times, will vary from business to business depending on all kinds of factors. In our clinics, I’ve found days worked, doctors, nurses, assistants, front desk staff, available rooms, size of the clinic, surgery type, and many more factors all impact (see the two graphs below) our surgical wait times. Even more factors effect the in-office wait times. The way my friend deals with the uncertainty is to create a “black box”. The black box is all the factors that impact on energy usage. Rather than trying to figure out what’s in the black box, they just measure what comes out. In his case it’s total energy consumption. In our clinic, the output is health care wait times. From a practice management point of view, this keeps it simple because you just create a really good measure of wait times then try different experiments. For our experiments we measure the difference in days between when an appointment is created and the day of the appointment itself. It’s great for “looking back” even if it’s not predictive of what’s to come.
Most importantly, I keep a record of major changes in the clinic. It’s nothing big, just an MS-Word file. But once a month, I record what we’ve done in the clinic that might impact patient flow or practice management. I’m not always certain it will have an effect, but I can always look back to see if it changed anything.
Energy usage, like wait times, will vary from business to business depending on all kinds of factors. In our clinics, I’ve found days worked, doctors, nurses, assistants, front desk staff, available rooms, size of the clinic, surgery type, and many more factors all impact (see the two graphs below) our surgical wait times. Even more factors effect the in-office wait times. The way my friend deals with the uncertainty is to create a “black box”. The black box is all the factors that impact on energy usage. Rather than trying to figure out what’s in the black box, they just measure what comes out. In his case it’s total energy consumption. In our clinic, the output is health care wait times. From a practice management point of view, this keeps it simple because you just create a really good measure of wait times then try different experiments. For our experiments we measure the difference in days between when an appointment is created and the day of the appointment itself. It’s great for “looking back” even if it’s not predictive of what’s to come.
Most importantly, I keep a record of major changes in the clinic. It’s nothing big, just an MS-Word file. But once a month, I record what we’ve done in the clinic that might impact patient flow or practice management. I’m not always certain it will have an effect, but I can always look back to see if it changed anything.
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