Patient- and Visit-Level Variables Associated With Late Arrival to Pediatric Clinic Appointments

Abstract

We conducted a cross-sectional study to evaluate timeliness of patient arrival at a pediatric multispecialty clinic. Bivariate and ordered logistic regression analyses were conducted to determine the odds of late arrival by specified patient- and visit-level characteristics. A total of 64 856 visits were available for analysis, of which 6513 (10.0%) were late arrivals. The odds of late arrival were higher for patients who spoke English (odds ratio [OR] = 1.34, P < .001) compared with those who spoke Spanish, had Medicaid (OR = 1.54, P < .001) or no insurance (OR = 1.49, P < .001) compared with those with insurance other than Medicaid, and were late to their previous visit (OR = 2.46, P < .001). Visit-level variables associated with late arrival included appointment time earlier in the day (i.e. 8-10 am, OR = 2.77, P < .001 compared with 4-6 pm), earlier in the week (i.e. on Mondays, OR = 1.21, P < .001 compared with Wednesdays), and for certain subspecialty clinics ( P < .001). Numerous variables are significantly associated with late arrival for pediatric clinic appointments.

Association between age and outpatient clinic arrival time: myth or reality?

“…In our study, the late arrival rate of 5.1% was somewhat lower compared to other studies, and late arrival was associated with younger age. In contrast to our findings, Hang et al [ 4 ] showed that patients were most likely to be late to early morning appointments.…”

“…Unfortunately, the impact of age on patient earliness was not evaluated [ 3 ]. In a study on late arrival from a paediatric outpatient clinic [ 4 ], 10% of about 65,000 visits were late arrivals. The odds of late arrival were increased for patients who spoke English (compared to Spanish), Medicaid or no insurance and late arrival on their previous appointment.…”

“…On the other hand, patients arrive early more often than late, and patient earliness may also be undesirable, since it creates overcrowding in the waiting area and potentially reduces the patient logistics, which in turn can be a challenge to both the patient and the front-desk staff [ 1 ]. Previous research on outpatient clinic scheduling and patient flow has mainly focused on late arrivals [ 2 – 4 ] and no-shows/ non-attendance [ 5 – 10 ]. Age and gender have previously been suggested as potential factors for non-attendance [ 7 – 11 ].…”

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers

“…Late patient arrival is widespread and a prominent concern in several ambulatory clinics across the globe [1][2][3]. The reported rates of prevalence varied substantially among specialties, such as 10% in pediatrics [4], 22% in urology [2], and 38% in cardiology [5]. Clinics serving tardy arrivals may experience detrimental ramifications such as schedule disruption, ineffective doctor utilization, and increased waiting time for forthcoming patients who may have arrived on-time [6].…”

“…This is only possible if the clinic can identify the patients who are likely to be tardy for their appointment in advance. Given the significance of early detection, numerous efforts have been taken to predict the patient-specific risk of late arrivals [4,[10][11][12]. Evidence suggests both patient-level (insurance type, patient's primary language, and age) and visit-level (first-time patients, appointment time, day of the week) attributes to be associated with late arrivals [4,[10][11][12].…”