Segmenting outpatient appointment behavior: a data-driven approach to understanding no-shows, delays, and walk-ins in specialty care
International Journal for Quality in Health Care, cilt.38, sa.1, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 38 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1093/intqhc/mzaf133
- Dergi Adı: International Journal for Quality in Health Care
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, CINAHL, EMBASE, MEDLINE, Psycinfo, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest)
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Background Outpatient appointment disruptions—particularly no-shows, late arrivals, and walk-ins—undermine clinic efficiency and equity in access to care. Despite decades of research, predictors of attendance behavior remain poorly understood, especially in respiratory care settings. Methods We conducted a cross-sectional study of 520 patients attending a pulmonology outpatient clinic in Turkey over 12 weekdays. Patients were classified as on-time attendees, late attendees, no-shows, or walk-in visitors. Data on sociodemographic, clinical, logistical, and behavioral factors were collected via structured surveys administered in person or, for no-shows, by phone; some variables (e.g. employment and insurance) had substantial missingness. Logistic regression identified predictors of non-attendance. An unsupervised clustering analysis (Partitioning Around Medoids using Gower distance) was used to identify patient subgroups with distinct attendance patterns. Results Among 520 patients, 386 (74.2%) arrived on time, 64 (12.3%) were late, 48 (9.2%) missed appointments, and 22 (4.2%) were walk-ins. On-time attendees were older and more likely to have received appointment reminders. No-shows were younger, more educated, and less likely to receive reminders. The leading causes of lateness were transportation issues (45.9%) and difficulty locating the clinic (31.1%). In multivariable analysis, younger age (odds ratio [OR] = 0.964; 95% confidence interval [CI] = 0.938–0.992; P=.010) and absence of reminders (OR = 4.275; 95% CI = 2.013–9.081; P<.001) were independently associated with non-attendance. Clustering analysis identified three phenotypes: “Older Dependent” (older, comorbid, low socioeconomic status), “Young Autonomous” (younger, high socioeconomic status, high no-show rate), and “Access-Challenged” (employed, low insurance coverage, high lateness). Conclusion Appointment reminders and age are strong, actionable predictors of attendance. Behavioral segmentation revealed latent patient profiles with distinct needs. Personalized scheduling strategies—especially targeted reminders and flexible systems—may improve outpatient efficiency and reduce inequities. These findings support the integration of behavioral clustering into appointment optimization frameworks.