ABSTRACT
Background:
The no-show of patients to their scheduled appointments has caused a large increase in healthcare costs, worsening service quality and clinical efficiency.
Objectives:
This case study aims to identify the factors associated with patient no-shows in cardiology and neurology clinics, and develop a prediction model to estimate the no-show probability.
Methods:
We developed a retrospective analysis of 32,573 appointments from January 2019 to June 2022 in a Rio de Janeiro clinic. Logistic regressions were performed to analyze and model the influence of patient and appointment variables on no-show rates.
Results:
The factors most related to no-shows were the patient’s sex, age, medical specialty, month of the year, and type of insurance. Female patients have an increase of approximately 17% chance of no-shows compared to males. The age group with the highest no-show rates is between 21 and 30. Clinic consultations have higher no-shows when compared to medical procedures. Appointments in December tend to have higher non-attendance than in January, and patients with insurance from the five major companies presents greater no-show than those with smaller insurance. The prediction model presented the following performance indicators: AUC = 0.65, Sensitivity = 0.64, Specificity = 0.58, PPV = 0.11, and NPV = 0.95.
Conclusions:
This work contributes to understanding the factors related to non-attendance, assisting optimized management of appointment schedules.
Keywords:
no-show; appointments; clinical services; logistic regression; prediction model