What if you could tell which patients were likely to cancel or not show up for their appointment? Would you treat them differently? Maybe utilize more appointment reminders or enforce stricter policies? Now, a new tool might be able to tell you who will be your no-show appointments.
A new algorithym could reduce no-show rates
A team of researchers from the Johns Hopkins University’s Malone Center for Engineering in Healthcare has developed a new algorithm that can decrease no-show rates and increase appointment availability. As part of their study, Levin and his team closely examined operations at two JHCP clinics, where they discovered that, on average, 20% of patients failed to show up for their scheduled appointments.
What to do with high-risk no-who patients
If an algorithm can show you which patients are “high-risk” for canceling or not showing up for their appointment, what then? What can your office do to manage these patients? First, you will want to put strict policies in place. These policies should act as an incentive to encourage your patients to show up for their appointment or cancel in the proper window. Second, you should schedule low-risk patients to efficiently use your resources.
Use automated appointment reminders
Automated appointment reminders are a key tool for your practice. Use this tool to give your staff back their time. These additional reminder calls the day before the appointment for patients likely to no-show allow for patients to reschedule as needed and remind them one more time about their appointment. But you can remove the task of making phone calls, leaving voice mails, and playing phone tag. You can also use an online booking system to allow your patients to cancel and reschedule at their convenience.
The best way to know what will work for you and your business is to start with a free trial. Appointmentreminders.com offers a 30-day free trial with no credit card required and no obligation to continue. Decrease your no-show appointments by as much as 80% and increase your bottom line.