Managing waiting times to predict no-shows and cancelations at a children’s hospital
Abstract: Since long waits in
hospitals have been found to be related to high rates of no-shows and
cancelations, managing waiting times should be considered as an important tool
that hospitals can use to reduce missed appointments. The aim of this study is
to analyze patients’ behavior in order to predict no-show and cancelation rates
correlated to waiting times.
Design/methodology/approach: This study is based on the data from a US
children’s hospital, which includes all the appointments registered during one
year of observation. We used the call-appointment interval to establish the
wait time to get an appointment. Four different types of appointment-keeping
behavior and two types of patients were distinguished: arrival, no-show,
cancelation with no reschedule, and cancelation with reschedule; and new and
established patients.
Findings: Results confirmed a strong impact of long waiting times on
patients’ appointment-keeping behavior, and the logarithmic regression was
found as the best-fit function for the correlation between variables in all
cases. The correlation analysis showed that new patients tend to miss
appointments more often than established patients when the waiting time
increases. It was also found that, depending on the patients’ appointment
distribution, it might get more complicated for hospitals to reduce missed
appointments as the waiting time is reduced.
Originality/value: The methodology applied in our study, which combines
the use of regression analysis and patients’ appointment distribution analysis,
would help health care managers to understand the initial implications of long
waiting times and to address improvement related to patient satisfaction and
hospital performance.
Author: Miguel
Rodríguez-García, Aldo A McLean-Carranza, J. Carlos Prado-Prado, Pablo
Domínguez-Caamaño
Journal Code: jptindustrigg160050
