How Can the Healthcare Industry Optimize Scheduling with Data?

After a recent bout with chickenpox and much time spent in doctors office waiting rooms, I started asking myself the question: how are medical appointments made? And how is it that waiting rooms are filled up? I came to the conclusion that while it's often done manually, data can indeed help. 

My doctor, or actually his secretary, once explained to me how he managed the doctor's schedule. He uses a system of common sense rules. He described it to me as follows

• I know that on Monday morning we will have a certain type of patients, who want to take their time. So I plan for appointments to go a little longer.

• I know that so-and-so will not be at the appointment one in three times

• People arrive late more often when you schedule appointments on the half-hour than on the hour. I think of that from time to time

• At the end of the week, I know that the doctor needs more calm on Friday in the late morning before starting the weekend

In short, my doctor's schedule is optimized by twenty years of human experience!

On the contrary, I recently (briefly) was able to experience making an appointment at a New York healthcare center, which was quite different:

• All calls are made to a centralized call platform.

• The doctor who will take the appointment, or the reason for the consultation, are not known when the appointments are made.

• Appointments are organized in a very standardized way: three appointments per hour,with a small break between each one (a bit like the commercial breaks in a TV show).

After a quick interview with the receptionist at the center, the scheduling process does not employ advanced technologies: just email and positioning in a shared Outlook calendar.

In short, it's the darker side of digitalization: a platform that connects efficiently and anonymously, but foolishly as a result.

Can Data Help?

These findings lead to more important questions: How can the healthcare industry  optimize scheduling efficiency, and can data help? From my point of view, this involves two approaches:

• First, healthcare practitioners and small healthcare centers can adopt appointment management platforms, such as iatrico.com, queuedr.com, or doctolib.fr. These make it possible to better manage patient reminders, cancellations, etc.

• Larger healthcare centers and networks can implement platforms giving them better knowledge of their patients. Modeling patient behavior would make it possible, among other things, to anticipate "no-shows" or better control the time scheduled for the appointments.

Without compromising anonymity and the need to respect privacy that presides over any project in the healthcare field, a healthcare network could use the data to:

• Make patient routing systems, that prioritize the most urgent cases.

• Plan for seasonal epidemics in advance in order to anticipate the needs of patients

• Better anticipate patient no-shows, to construct, perhaps, approaches similar to those used in civil aviation (over-booking).