New Tool Helps Determine Which Patients Are Likely to Skip Appointments

Published in Insight - September/October 2018 Insight

Patients who don’t show up for their scheduled medical appointments drain health care providers’ time and resources, reducing appointment availability, increasing wait times and reducing patient satisfaction.

In an effort to solve this problem, a team of researchers from The Johns Hopkins University’s Malone Center for Engineering in Healthcare developed a new algorithm that can reduce no-show rates and increase appointment availability.

“The new approach developed with our partners at Johns Hopkins Community Physicians has allowed the clinic to add over 70 pediatric appointments to their schedule per week, improving outpatient access for more children while also reducing the no-show rate 16 percent for patients who are highly likely to miss scheduled appointments,” said Scott Levin, associate professor of emergency medicine at the Johns Hopkins University School of Medicine.

The team developed a machine-learning algorithm that takes various predictors into account — such as demographics, economic status and medical history — and calculates a probability “no-show score” for each patient. Based on these scores, providers can identify which patients are at high risk for not showing up for their appointments. Staff can then make additional confirmation calls to these patients, or may give the appointment slots to patients who urgently need to be seen.

So far, the model has been piloted at several Johns Hopkins clinics, including JHCP, the gastroenterology clinic and the hematology clinic, with the diabetes clinic and the Harriet Lane Clinic joining soon.

The researchers say that the next step is to integrate the model into Johns Hopkins Medicine’s electronic patient care records system before expanding it across Johns Hopkins Health System and, eventually, to hospitals across the nation.

Read the full story on The Johns Hopkins University’s Hub: New tool helps doctors determine which patients are most likely to forget or skip their appointments.