MedCity Influencers

How data science is transforming cancer treatment scheduling

A mathematical approach to infusion center scheduling promises more patients served, reduced cost of service, optimized equipment, and facility utilization, and a whole lot less sitting in waiting rooms.

Time management.

 

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.

Sponsored Post

Physician Targeting Using Real-time Data: How PurpleLab’s Alerts Can Help

By leveraging real-time data that offers unprecedented insights into physician behavior and patient outcomes, companies can gain a competitive advantage with prescribers. PurpleLab®, a healthcare analytics platform with one of the largest medical and pharmaceutical claims databases in the United States, recently announced the launch of Alerts which translates complex information into actionable insights, empowering companies to identify the right physicians to target, determine the most effective marketing strategies and ultimately improve patient care.

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:

iQueue_Lose the Wait_1

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.”

iQueue_Lose the Wait_2

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?

Photo: solidcolours, Getty Images

This post appears through the MedCity Influencers program. Anyone can publish their perspective on business and innovation in healthcare on MedCity News through MedCity Influencers. Click here to find out how.

Topics