Login to Access Video or Poster Abstract: MP96-09
Sources of Funding: none

Introduction

With the predicted shortage of urologists nationwide, efficiency in outpatient urology clinics is crucial. Our previous study demonstrated predictable patient demographics and diagnoses associated with missed clinic appointments. The significant characteristics of our study included: age, new versus established patient, and patient diagnoses. This study aims to utilize our previous data to develop a model to predict patient missed clinic appointments.

Methods

Utilizing our previous data, logarithmic regression analysis was performed to formulate an equation to predict the probability of a patient missing their appointment. Variables included age, new versus established patient, and groupings of 27 patient diagnoses. Using this equation, a retrospective analysis of clinic patient data was performed for four full-time academic urologists over a six-month period comparing predicted versus actual missed visits.

Results

A total of 2486 clinic appointments were compiled for four providers in the adult urology clinic over six months. Of the total, 408 were actual missed clinic visits at an overall no-show rate of 16.4%. The calculated number of patients missing their appointments was 488. Of the predicted 488 missed visits, the calculated number of patients was over by 130 with an average of 1.19 patients over per day, and under by 50 with an average of 0.46 patients under per day. The number of perfect days where the predicted number matched the actual number was 26/109 (23.9%), within +/- 1 patients 61/109 (56.0%), and within +/- 2 patients 87/109 (79.8%). Conversely, the model over predicted 4 or greater patient no-shows on 6/109 (5.5%) of days. Over-predicted patients per day ranged from 0.01-6.5 with a mean of 1.58.

Conclusions

This review further characterizes the predictable patient characteristics associated with missed clinic visits for an under-served academic urology patient population. This model works well over a large number of patients with a 79.8% efficacy within 2 patients. Applying this to a clinical setting would be limited by overestimating the number of patients that would be scheduled. The model still will require validation when put to test on data from different practice settings and larger patient data sets. Additionally, we predict there may be confounding factors (type of insurance, distance to appointment, previous missed appointments) that we plan to study in order to add to the accuracy of the model.

Funding

none