# Understanding No-Show Patterns in Healthcare: A Retrospective Study from Northern Italy

**Authors:** Antonino Russotto, Paolo Ragusa, Dario Catozzi, Aldo De Angelis, Alessandro Durbano, Roberta Siliquini, Stefania Orecchia

PMC · DOI: 10.3390/healthcare13151869 · Healthcare · 2025-07-30

## TL;DR

This study analyzed why people miss healthcare appointments in Northern Italy, finding that younger patients and longer wait times are linked to higher no-show rates.

## Contribution

The study identifies specific demographic and service-related factors influencing no-show rates in healthcare appointments in Northern Italy.

## Key findings

- Younger patients (<18 years) and adults (18–65 years) had significantly higher odds of missing appointments than elderly patients (>65 years).
- Each additional day of waiting increased the likelihood of no-show by 1%.
- First-time visits had a higher no-show risk compared to follow-up visits and diagnostics.

## Abstract

Objectives: The aim of this study was to analyse no-show patterns in healthcare appointments, identify associated factors, and explore key determinants influencing non-attendance. Study Design: This was a retrospective observational study. Methods: We analysed 120,405 healthcare appointments from 2022–2023 in Turin, Northern Italy. Data included demographics, appointment characteristics, and attendance records. Logistic regression identified significant predictors of no-shows, adjusting for confounders. Results: A 5.1% (n = 6198) no-show percentage was observed. Younger patients (<18 years) and adults (18–65 years) had significantly higher odds of missing appointments than elderly patients (>65 years) (OR = 2.32, 95% CI: 2.17–2.47; OR = 2.46, 95% CI: 2.20–2.74; p < 0.001). First-time visits had a higher no-show risk compared to follow-up visits and diagnostics (OR = 1.11, 95% CI: 1.04–1.18; p < 0.001). Each additional day of waiting increased the likelihood of no-show by 1% (OR = 1.01, 95% CI: 1.01–1.01; p < 0.001). Conclusions: No-show percentages are influenced by demographic and service-related factors. Strategies targeting younger patients, longer waiting times, and non-urgent appointments could reduce no-show percentages.

## Full-text entities

- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

27 references — full list in the complete paper: https://tomesphere.com/paper/PMC12345966/full.md

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Source: https://tomesphere.com/paper/PMC12345966