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Success of automated algorithmic scheduling in an outpatient setting
Patrick R Cronin1, Alexa Boer Kimball
150 Staniford St, 7th Fl, Boston, MA 02114.
Objective:
To determine if algorithmically generated double-booking recommendations could increase patient volume per clinical session without increasing the burden on physicians.
Study Design:
A randomized controlled trial was conducted with 519 clinical sessions for 13 dermatologists from December 1, 2011, through March 31, 2012.
Methods:
Sessions were randomly assigned to "Smart-Booking," an algorithm that generates double-booking recommendations using a missed appointment (no-shows + same-day cancella- tions) predictive model (c-statistic 0.71), or to a control arm where usual booking rules were applied. The primary outcomes were the average number and variance of arrived patients per session, after controlling by physician. In addition, physicians received a survey after each session to quantify how busy they felt during that session.
Results:
257 sessions were randomized to Smart-Booking and 262 sessions were randomized to control booking. Using a generalized multivariate linear model, the average number of arrived patients per session was higher in the Smart-Booking intervention arm than the control (15.7 vs 15.2, difference between groups 4.2; 95% CI, 0.08-0.75; P = .014).The variance was also higher in the intervention than control (3.72 vs 3.33, P = .38).The survey response rate was 92% and the physicians reported being similarly busy in each study arm.
Conclusions:
Algorithmically generated double-booking recommendations of dermatology clinical sessions using individual physician assumptions and predictive modeling can increase the number of arrived patients without overburdening physicians, and is likely scalable to other settings.
