Date Published: April 4, 2019
Publisher: Public Library of Science
Author(s): Henry Lenzi, Ângela Jornada Ben, Airton Tetelbom Stein, Antonio Palazón-Bru.
Patient no-show is a prevalent problem in health care services leading to inefficient resources allocation and limited access to care. This study aims to develop and validate a patient no-show predictive model based on empirical data. A retrospective study was performed using scheduled appointments between 2011 and 2014 from a Brazilian public primary care setting. Fifty percent of the dataset was randomly assigned to model development, and 50% was assigned to validation. Predictive models were developed using stepwise naïve and mixed-effect logistic regression along with the Akaike Information Criteria to select the best model. The area under the ROC curve (AUC) was used to assess the best model performance. Of the 57,586 scheduled appointments in the period, 70.7% (n = 40,740) were evaluated including 5,637 patients. The prevalence of no-show was 13.0% (n = 5,282). The best model presented an AUC of 80.9% (95% CI 80.1–81.7). The most important predictors were previous attendance and same-day appointments. The best model developed from data already available in the scheduling system, had a good performance to predict patient no-show. It is expected the model to be helpful to overbooking decision in the scheduling system. Further investigation is needed to explore the effectiveness of using this model in terms of improving service performance and its impact on quality of care compared to the usual practice.
Patient no-show is defined as a scheduled appointment that the patient neither attended or canceled on time to be reassigned to another patient [1,2]. It implies ineffective use of human and logistic resources in a scenario where the demand for health care is greater than the supply. Beyond that, the patient non-attendance could compromise the core principles of primary care: the accessibility and the continuity of care . Whenever a patient misses an appointment, two patients fail to access health care: the no-show patient and the patient who could not book an appointment. Also, patient non-attendance leads to a discontinuity of care, which is associated with worsening of health outcomes such as increasing of hospitalization rates due to exacerbations of chronic conditions [4–6]. There are also additional costs, e.g., time spent on mitigation strategies and health care staff idle time .
This study explored the factors associated with no-show at a primary care setting in Southern Brazil and developed and validated a patient no-show predictive model based on empirical data. It revealed that previous patient attendance and same-day appointments were the most important predictors of a no-show in the service investigated. More importantly, the results showed that the best model, developed from data already available in the scheduling system, had a good performance with a probability of 81% to correctly identify the true positives and negatives of a patient no-show.
This study developed and validated a patient no-show predictive model based on data from a public primary care setting in Southern Brazil. It mainly revealed that using the information already available in the scheduling system, the best fit model presented a good performance to predict no-show when empirically validated. Additionally, the methodology applied in this study may be useful to other health care services to develop predictive no-show models based on their specific population. It is expected this approach to be helpful to overbooking decision in scheduling systems. Further investigation is needed to explore the effectiveness of using this model in terms of improving service performance and its impact on quality of care compared to the usual practice.