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How Data Science Is Transforming Cancer Treatment Scheduling

How data science is transforming cancer treatment scheduling

  • Mohan Giridharadas,

    Founder & CEO, LeanTaaS
    Former Sr. Partner at McKinsey and Co.

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Anyone who has ever had the misfortune of dealing with a cancer diagnosis — either personally or as a caregiver to a friend or loved one — must have at some point wondered why they invariably had to wait well past their treatment appointment time, every time.

The root cause is that the healthcare scheduling system is broken. Healthcare providers are using a calculator, spreadsheets and standard electronic health record (EHR) templates to solve a math problem that demands a cluster of servers and data scientists to solve effectively.

Most EHRs use scheduling templates that have no real scheduling intelligence built in. In their efforts to be patient-centric, they typically subscribe to a first-come, first-served “hair salon” methodology. Even the best among them use gut-based rules of thumb to accommodate patient and staff needs for a particular appointment slot. The result is, in effect, the opposite of a patient-centric situation — a domino effect of longer wait times and unhappy patients and doctors.

Traditional approaches just don’t hold water …

Let’s take a 35-chair infusion center that operates eight hours per day treating five types of appointments — 1 hour, 2 hours, 3–5 hours, 6–8 hours, or 9 or more hours. Four sets of patients can start their treatment at 10-minute intervals. That’s 256 possible start times or “slots” per day. The number of ways those patients can be accommodated is a number with over 100 zeros behind it. By comparison, if you were to use 1-gallon milk jugs to hold all of the oceans’ water, the number of jugs needed would have 40 zeros behind it. The chance of stumbling upon the most effective scheduling arrangement using the same approach as your neighborhood hair salon or oil change shop is, for practical purposes, nonexistent. Here’s what the math looks like:

 

With those odds, it is clear that simple spreadsheets or traditional EHR approaches can never create an optimal solution for scheduling appointments.

Now, compound the scope of possible appointments with the reality of hospital variables, such as practitioner schedules, staff changes, patterns of demand, equipment maintenance, room availability, lab results and clinical trial events, and you can see why this becomes a very difficult formula to solve with simple math — and why you continue to wait, wait and wait some more. Add to that patient preference — most patients want appointments in the 10 a.m. to 2 p.m. range — and you end up with a schedule full of more holes than a block of Swiss cheese.

But new approaches using data science can help lose the “wait.”

Inspired by the likes of Toyota and just-in-time lean manufacturing practices, data science and mathematics are changing the face of healthcare scheduling and, in effect, making healthcare more accessible to more patients.

By taking a holistic review, data scientists are mining all scheduling patterns and possibilities specific to a center, as well as considering operational constraints across patient demand, practitioner and staffing schedules, and capital asset availability. From there, an algorithm is created that optimizes a schedule for the center to serve patients more uniformly throughout the day, versus the peaks and valleys of traditional scheduling systems. The end result promises more patients served, reduced cost of service, optimized equipment and facility utilization, and a whole lot less sitting in the waiting room.

This mathematical approach to infusion center scheduling is already delivering impressive results; providers like Stanford Health Care, UCHealth, NewYork-Presbyterian, Fox Chase Cancer Center, UCSF, the Huntsman Cancer Institute and many others are seeing wait times decreased by as much as 55 percent during peak hours. Put that in real-world terms: A one-hour wait becomes 27 minutes. Who wouldn’t want a half-hour of waiting room time back?

Originally published in MedCity News.

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