Using no-show modeling to improve clinic performance

Abstract

'No-shows' or missed appointments result in under-utilized clinic capacity. We develop a logistic regression model using electronic medical records to estimate patients' no-show probabilities and illustrate the use of the estimates in creating clinic schedules that maximize clinic capacity utilization while maintaining small patient waiting times and clinic overtime costs. This study used information on scheduled outpatient appointments collected over a three-year period at a Veterans Affairs medical center. The call-in process for 400 clinic days was simulated and for each day two schedules were created: the traditional method that assigned one patient per appointment slot, and the proposed method that scheduled patients according to their no-show probability to balance patient waiting, overtime and revenue. Combining patient no-show models with advanced scheduling methods would allow more patients to be seen a day while improving clinic efficiency. Clinics should consider the benefits of implementing scheduling software that includes these methods relative to the cost of no-shows.

Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence

“…We are not aware of any studies investigating a role for NLP in measuring appointment adherence. [19][20][21][32][33][34][35][36][37][38][39][40] Thus, our work highlights a novel method using NLP for this purpose.…”

Targeted Reminder Phone Calls to Patients at High Risk of No-Show for Primary Care Appointment: A Randomized Trial

“…No-shows result in fragmented continuity of care, reduce access for other patients in the practice, and decrease provider productivity. [8][9][10][11][12][13][14][15][16] Studies in primary care and specialty clinics have demonstrated that staff phone calls are an effective intervention for reducing no-shows. [17][18][19][20][21][22][23][24][25][26][27][28][29][30] However, this convincing work is difficult to apply, because staff phone calls are timeconsuming and costly.…”

“…18,19,30,31 Nevertheless, even in practices with low baseline no-show rates, the risk of no-show is heterogeneous-that is, some patients are more likely of noshows than others. 9,10,28,29,[32][33][34] Prior work has effectively demonstrated that no-shows are predictable; validated models can accurately predict the likelihood that a patient will fail to keep a scheduled appointment. 9,[35][36][37][38] Subspecialty clinics have made use of the predictability of no-shows by integrating predictive modeling to target interventions to those at high risk of no-show.…”

“…9,10,28,29,[32][33][34] Prior work has effectively demonstrated that no-shows are predictable; validated models can accurately predict the likelihood that a patient will fail to keep a scheduled appointment. 9,[35][36][37][38] Subspecialty clinics have made use of the predictability of no-shows by integrating predictive modeling to target interventions to those at high risk of no-show. In a study of five oncology practices, a patient navigator successfully reduced no-shows by 42 % in a subset of patients at high risk for nonadherence, and in a dermatology practice, predictive modeling has been used for algorithmic double-booking of appointments.…”

Organizing integrated processes in cancer care

“…To mitigate the effects of no-shows and cancellations, clinics can try to influence patient behavior or modify their scheduling strategy [79]. Patient behavior can be influenced by education, reminders, and financial rewards or penalties.…”