Patients’ Nonattendance in Outpatient Specialist Consultations: A National Cohort Analysis of a Health System - Silva JMB; De Souza Bermejo PH; Moreira MF; Prata DN; Trevisan DMDQ; dos Santos OA (Dove Medical Press, 2024)
João Marcelo Barreto Silva,1,2 Paulo Henrique De Souza Bermejo,3 Marina Figueiredo Moreira,3 David Nadler Prata,1 Daniela Mascarenhas de Queiroz Trevisan,1 Otávio Augusto dos Santos2 1Program in Governance and Digital Transformation, Federal University of Tocantins, Palmas, TO, Brazil; 2Department of Assistance Regulation and Control, Ministry of Health, Brasília, DF, Brazil; 3Administration Department, Faculty of Economics, Administration, Accounting and Public Policy Management of University of Brasília, Brasília, DF, BrazilCorrespondence: João Marcelo Barreto Silva, Department of Assistance Regulation and Control, Ministry of Health, St. de Adm. Federal Sul Q1-G, Sala 147B, Brasília, DF, 70058-900, Brasil, Email joao.marcelo@saude.gov.brBackground: Analyzing patients' nonattendance at medical appointments helps address an issue impacting the management and sustainability of health systems globally, providing valuable insights for healthcare managers. This study aims to identify factors at both patient and health system levels that contribute to understanding missed appointments.Methods: The analysis was conducted using data from secondary care consultations within the Brazilian Unified Health System between April 2018 and March 2020. Primary care includes general medical consultations, while secondary care involves specialized services provided by doctors with advanced expertise. We examined demographic factors (age, sex, race/color, socioeconomic level) and health system practices (waiting time, hospitalization, distance to service, medical specialty, and severity of clinical condition) to assess their impact on patient attendance. A weighted analysis and receiver operating characteristic (ROC) analysis were applied to determine the relative risk of nonattendance.Findings: Of 5,003,159 consultations, 435,523 (8.7%) were missed. Nonattendance was highest among patients facing long distances to the service (13.3%, [RRR] 1.227), younger age (16- 30 years: 11.8%, [RRR] 1.041), and waiting times (> 30: 10.9%, [RRR] 1.738). Socially vulnerable patients were more likely to miss appointments (9.6%, [RRR] 1.055) compared to less vulnerable groups (8.6%). Practice-level factors had a slightly greater impact on nonattendance (ROC: 0.621) than patient-level factors (ROC: 0.5674). The overall predictive model achieved a C statistic of 0.6228, resulting in a fair predictive ability. However, the model showed only modest prediction of no-shows, indicating the need for more detailed data to improve accuracy. Gauging which group suffers the highest risk of nonattendance was a secondary goal of this analysis.Interpretation: Young, socially vulnerable patients with long commutes and extended waiting times are at higher risk of nonattendance. Effective management of these risk factors and targeted preventive actions are essential to reduce absenteeism and improve health system efficiency.Keywords: absenteeism, health policy, medical appointment, health management