[Prediction of No-Show in an Orthopedic Outpatient Clinic].

Authors

Yigal Chechik, Shlomi Abuhasira, Orna Tal, Ran Ankory

Published in

Harefuah. Volume 165. Issue 7. Pages 441-446.

Abstract

Missed appointments in orthopedic outpatient clinics impose a considerable burden on healthcare systems by reducing operational efficiency, wasting clinical resources, and potentially compromising patient outcomes. Although personal and demographic characteristics are known to influence non-attendance, the specific factors that predict missed appointments remain insufficiently understood.
This study aimed to identify key variables associated with no-show rates in orthopedic outpatient clinics and to evaluate the performance of a predictive model designed to improve appointment adherence.
A retrospective cohort analysis was conducted using data extracted from a hospital scheduling system, encompassing appointments from January 2015 to December 2019 and from 2022. Demographic information and attendance status were analyzed to identify trends and predictive factors linked to missed visits.
During the years 2015-2019, 1,234,425 scheduled appointments were reviewed, with an overall no-show rate of 30.25%, while orthopedic outpatient clinics exhibited a slightly lower rate of 24.97%. Based on demographic attendance data, a predictive scoring model was developed and subsequently tested using 2022 data. The model classified 64% of appointments as "Attenders" (no-show rate 13.9%) and 36% as "Non-Attenders" (no-show rate 29.4%), showing a statistically significant difference.
: These results highlight the potential of predictive analytics to enhance appointment scheduling and optimize healthcare resource allocation in orthopedic settings. By identifying high-risk patients in advance, healthcare providers can implement targeted interventions to reduce non-attendance. Incorporating real-time data into predictive algorithms may further strengthen clinical decision-making and improve continuity and quality of patient care.

PMID:
42557629
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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