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.
RCT Entities:
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.
Authors: Andrew S Hwang; Steven J Atlas; Patrick Cronin; Jeffrey M Ashburner; Sachin J Shah; Wei He; Clemens S Hong Journal: J Gen Intern Med Date: 2015-03-17 Impact factor: 5.128
Authors: Sachin J Shah; Patrick Cronin; Clemens S Hong; Andrew S Hwang; Jeffrey M Ashburner; Benjamin I Bearnot; Calvin A Richardson; Blair W Fosburgh; Alexandra B Kimball Journal: J Gen Intern Med Date: 2016-08-08 Impact factor: 5.128