Patient No-Show Prediction: A Systematic Literature Review

Abstract

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.

Predictive and Causal Analysis of No-Shows for Medical Exams During COVID-19: A Case Study of Breast Imaging in a Nationwide Israeli Health Organization

“…Understanding this issue can help us to better manage resources, target interventions to prevent the no-show, and reduce some of the associated revenue loss and cost. Predicting patient no-shows and identifying the major individual attributes associated with no-show have been the key approach to understanding this phenomenon 5,6 . Patients cohorts with retrospective data derived from electronic health records (EHRs) have been leveraged to train prediction models, with logistic regression being the most widely-used model 5,6 .…”

A Probabilistic Patient Scheduling Model with Time Variable Slots

“…The recent availability of Electronic Health Records (EHR) and advances in data science has made it possible to improve this wide variety of scheduling systems. This is because modern predictive techniques applied to EHRs are capable of estimating the probability of patient no-show, which can be used to improve the scheduling system [ 4 ]. Regarding deterministic systems, Savelsbergh and Smilowitz [ 19 ] are the first to define the probabilities of no-shows for six different categories of patients depending on their preferences (strong or weak) for three different time windows (AM, noon, or PM).…”

“…These patients are common to all the scheduling approaches. In the case of model with variable time slots, we assume that times follow a discrete uniform distribution in [ 4 , 6 ]. As described above, the buffer of patients to be passed to the scheduler is obtained from the waiting list The probabilistic model with variable time (1) assigns the day and time of the appointment to different patients in the buffer.…”

“…These patients are common to all the scheduling approaches. In the case of model with variable time slots, we assume that times follow a discrete uniform distribution in [ 4 , 6 ].…”

“…In order to reduce these numbers, health centers use reminders and sanctions. However, several studies have shown that these strategies only achieve a slight or moderate reduction in the no-show rates [ 3 , 4 ]. Moreover, it has been pointed out that sanctions may limit access to patients with limited income to medical centers [ 5 ], and automatic reminder systems might have an important economic impact on the health centers [ 6 ].…”