Development and validation of nonattendance predictive models for scheduled adult outpatient appointments in different medical specialties. (2nd November 2022)

• Record Type: Journal Article
• Authors: Giunta, Diego Hernán; Huespe, Ivan Alfredo; Alonso Serena, Marina; Luna, Daniel; Gonzalez Bernaldo de Quirós, Fernan
• Is Part Of: International journal of health planning and management. Volume 38:Number 2(2023)
• Page Start: 377
• Page End: 397
• Publication Date: 2022-11-02
• Subjects: appointments -- nonattendance -- predictive model; Health planning -- Periodicals; Health services administration -- Periodicals; Santé publique -- Planification -- Périodiques; Santé, Services de -- Administration -- Périodiques; 362.1068
• DOI: 10.1002/hpm.3590
• Languages: English
• ISSNs: 0749-6753

Abstract

Abstract: Introduction: Nonattendance is a critical problem that affects health care worldwide. Our aim was to build and validate predictive models of nonattendance in all outpatients appointments, general practitioners, and clinical and surgical specialties. Methods: A cohort study of adult patients, who had scheduled outpatient appointments for General Practitioners, Clinical and Surgical specialties, was conducted between January 2015 and December 2016, at the Italian Hospital of Buenos Aires. We evaluated potential predictors grouped in baseline patient characteristics, characteristics of the appointment scheduling process, patient history, characteristics of the appointment, and comorbidities. Patients were divided between those who attended their appointments, and those who did not. We generated predictive models for nonattendance for all appointments and the three subgroups. Results: Of 2, 526, 549 appointments included, 703, 449 were missed (27.8%). The predictive model for all appointments contains 30 variables, with an area under the ROC (AUROC) curve of 0.71, calibration‐in‐the‐large (CITL) of 0.046, and calibration slope of 1.03 in the validation cohort. For General Practitioners the model has 28 variables (AUROC of 0.72, CITL of 0.053, and calibration slope of 1.01). For clinical subspecialties, the model has 23 variables (AUROC of 0.71, CITL of 0.039, and calibration slope of 1), and for surgical specialties, the model has 22 variables (AUROC of 0.70, CITL of 0.023, and calibration slope of 1.01). Conclusion: We build robust predictive models of nonattendance with adequate precision and calibration for each of the subgroups. Highlights: Outpatient non‐attendance has a significant impact on patients, physicians, health, and the effectiveness of the healthcare system. We evaluate more than 2.5 million ambulatory adult appointments to develop and validate non‐attendance predictive models. We developed non‐attendance predictive models for general practitioners, clinical, and surgical specialties. Non‐attendance prediction might allow tailoring ambulatory care appointment systems and interventions to improve adherence and minimise missing appointments.