This seems like it might be a simple scheduling problem. All that’s needed is to ensure a chair is available for each patient at the scheduled time of their appointment. When chairs are fully booked, appointments can’t be offered until one is freed. A talented nursing staff should be able to manage the workload if the chairs are clustered into pods and each pod has the right number of nurses with the appropriate skillset. In fact, nothing could be further from the truth!
The demand side of the infusion equation is volatile and difficult to predict
Since a typical infusion regimen runs for 6-8 weeks, the patient population is constantly evolving. Each patient has a unique treatment protocol that requires a built-to-order formula of medications and a specific duration for their infusion. Patients often need to see their oncologist in the clinic prior to each infusion, and each oncologist in turn has a clinic schedule for a different set of days. The timing of patient arrivals is also hard to predict.
The supply side of the equation is constrained, difficult to predict and heavily interdependent
For an infusion treatment to proceed, all four elements of supply – the chair, the pump, the drugs and the nurse have to be available and ready when the patient arrives. But a finite number of infusion chairs have a fixed number of operational hours in the day, only so many infusion pumps are ready for deployment and a fixed number of pharmacy hoods can be used to formulate needed medications. The nursing roster has a specific number of nurses and, on any given day, one or more nurses may call in sick.
Matching a volatile demand signal with a constrained, interdependent supply signal is already hugely difficult, even more so given both signals are hard to predict with a high level of precision.
Infusion scheduling could be thought of as a challenging game of Tetris.
Each infusion appointment has a fixed duration which is represented by the length of the Tetris block. The trick is organizing blocks in a way that best fills the holes in the schedule and allows the center to operate efficiently.
Winning the game of Infusion Scheduling Tetris requires superb daily execution of the following:
- Forecast the volume of patients for each day of the week. Mining historical data from the past 6-12 months can enable the creation of accurate forecasting models that take into account the various factors such as seasonality and changes in the number of oncologists on staff.
- Forecast the duration mix for each day of the week. Historical data is also a goldmine of information to help accurately estimate the number of 1-hour, 2-hour, 3-hour treatments.
- Adjust for the reality of the accuracy — or lack thereof — of duration estimates. Comparing the bell curve of the actual treatment duration against the expected duration is hugely informative, and allows you to adjust the length of a given Tetris block to match the likely duration of the treatment.
- Build constraint-based optimization algorithms. Many constraints need to be factored into the optimization algorithm for each of the four supply elements – the chair, the pump, drugs and the nurse. These may vary from center to center but are always essential to safety and effectiveness and must be factored into a center’s unique infusion schedule.
- Build optimized templates. Following the prior steps produces an optimized template with prescriptive guidance on the ideal options for an upcoming appointment, based on both the duration of the treatment and the available slots for that duration on the specific day.
- Run each day based on the actual reality. Infusion scheduling is not a “set-and-forget” activity. While optimized templates can help steer patients into the best slot given on all the constraints described above, the reality on any specific day might be different. This could be due to volumes or the duration mix deviating from the forecast level, or simply the lab, pharmacy or clinics being busier than expected. Over time, the templates will naturally drift from the optimum and must be tuned through algorithmic learning that identifies the root causes for the drift and automatically adjusts the underlying models.
The impact of optimizing infusion scheduling is enormous
Optimizing the schedule can enable an infusion center to complete 10-15% more treatments within the existing hours of operation and staffing levels. This is vital to opening access for new patients who need to begin their infusion regimen quickly. Optimization also helps reduce wait time for patients, a critical element for patient satisfaction. Staff satisfaction will also improve, which in turn improves safety and reduces attrition. Providing infusion services to patients can be incredibly stressful, and nurses must be rested and non-harried to prevent their making mistakes.
Mastering the game of Tetris is a key to successful infusion scheduling, and in providing superior services to a patient population, and a clinical staff, that needs all the help and support to manage and overcome a uniquely challenging situation.
For more detail on optimizing the operational performance of infusion centers, see Chapter 8 of the book “Better HealthCare Through Math”. You can also reach us at email@example.com for more information on how hundreds of infusion centers that are part of the leading cancer institutions in the country have deployed this approach.