Watching the tennis matches at Wimbledon over the last couple of weeks has inspired me to try and play a bit more often in order to improve my erratic, “weekend-warrior” level of play.
The challenge is in reserving a tennis court at a convenient time — early mornings or evenings during the weekend when it isn’t too hot or early mornings on weekdays before heading off to the office. Unfortunately, most other tennis players have the same constraints. The parallels between making tennis court reservations and scheduling medical appointments suddenly became very clear to me.
Scheduling systems in most health systems (that are forced to operate under the scheduling framework provided by their EHR or scheduling application) have a resource-based view of their world. For example, if the diagnostic imaging department has four CT scanners, there will be a template that lays out all of the operating hours of each day down the page and the four machines across the top, and each “slot” gets assigned to a specific patient as their appointment is made. This template could either be electronic or on paper, and the assignment to the specific machine could either be real or virtual — but the logic still applies. This approach is also applied to virtually every other “countable asset” in the health system — radiation oncology machines, infusion chairs, MRI machines and so on.
This is essentially the same process used by the racquet club: It has a dozen tennis courts, and as each reservation is made, the specific time slot is marked as “taken” and the process continues.
The reason that it works for the racquet club is exactly the same reason that it does not work for the health system.
Tennis court reservations are precisely one-hour long, i.e., they are predictable or deterministic. The expected duration of a medical appointment is, at best, an estimate (albeit accurate in many cases), i.e., it is random or stochastic.
Imagine if the racquet club were to suddenly decide to become “player-centric” and to adopt the following changes in its reservation policy:
1. We understand that traffic sometimes causes players to arrive late; we will therefore guarantee that they can still use their assigned court if they are late (up to a maximum of 30 minutes beyond the designated start time).
2. We want our players to fully enjoy their experience and will therefore not restrict our players to 60 minutes of court time but will instead allow them to “play until they are tired” (up to a maximum of 120 minutes of playing time).
These “simple” policies will throw the entire system into chaos. When you arrive at your designated court at your designated time, you have no idea if the current occupants on the court started on time or were 15-20 minutes late. You quickly realize that you may have to wait 60-90 minutes past your “scheduled time.” The only recourse left for the racquet club (unless they are willing to reverse their new policy) is to create one of two possible buffers: (a) a time-based buffer (blocking out 2.5 hours for each reservation to allow for the 30-minute delay in the start time and the extended play for 120 minutes instead of 60 minutes) or (b) an asset-based buffer (keeping one court free out of every cluster of four courts to allow for the variability in start and end times).
This is exactly what happens in health systems. Time-based buffers are created by “sandbagging” appointment lengths beyond the accurate prediction of the expected duration, while asset-based buffers are created by approving capital budgets that demand more machines even while the actual utilization of the existing machines is only ~50 percent.
Using a reservation approach that was designed for a deterministic (predictable) system in order to manage a stochastic (random) system is guaranteed to result in an underutilization of assets as well as long and unpredictable wait times.
Health systems need to use advanced algorithms to accurately forecast the volume of patients by hour for each day. They also need to be able to accurately estimate the duration of each type of appointment and then optimize the allocation of appointments across the relevant set of assets in a manner that accommodates the expected (and unexpected) variability in order to create as flat a utilization profile as possible. A flatter utilization profile enables a more intelligent allocation of staff in addition to minimizing the wait time experienced by patients since peak volumes create “rush hour conditions” which lead to extended wait times. Finally, they need to adopt a “control system” mindset of feedback loops to monitor, learn and adjust the forecasts in an ongoing manner.
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Mohan Giridharadas is an accomplished expert in lean methodologies. During his 18-year career at McKinsey & Company (where he was a senior partner/director for six years), he co-created the lean service operations practice and ran the North American lean manufacturing and service operations practices and the Asia-Pacific operations practice. He has helped numerous Fortune 500 companies drive operational efficiency with lean practices. As founder and CEO of LeanTaaS, a Silicon Valley-based innovator of cloud-based solutions to healthcare’s biggest challenges, Mohan works closely with dozens of leading healthcare institutions including Stanford Health Care, UCHealth, NewYork-Presbyterian, Cleveland Clinic, MD Anderson and more. Mohan holds a B.Tech from IIT Bombay, MS in Computer Science from Georgia Institute of Technology and an MBA from Stanford GSB. He is on the faculty of Continuing Education at Stanford University and UC Berkeley Haas School of Business and has been named by Becker’s Hospital Review as one of the top entrepreneurs innovating in healthcare. For more information on LeanTaaS, please visit http://www.leantaas.com and follow the company on Twitter @LeanTaaS, Facebook at https://www.facebook.com/LeanTaaS and LinkedIn at https://www.linkedin.com/company/leantaas.
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