التفاصيل البيبلوغرافية
| العنوان: |
Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment |
| المؤلفون: |
Valero Bover, Damià, González, Pedro, Carot Sans, Gerard, Cano, Isaac, Saura, Pilar, Otermin, Pilar, Garcia, Celia, Gálvez, Maria, Lupiáñez Villanueva, Francisco, Piera Jiménez, Jordi |
| المصدر: |
Articles publicats en revistes (Medicina) |
| بيانات النشر: |
Springer Science and Business Media |
| سنة النشر: |
2022 |
| المجموعة: |
Dipòsit Digital de la Universitat de Barcelona |
| مصطلحات موضوعية: |
Serveis sanitaris, Administració sanitària, Health services, Health services administration |
| الوصف: |
Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and ... |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
9 p.; application/pdf |
| اللغة: |
English |
| تدمد: |
1472-6963 |
| Relation: |
Reproducció del document publicat a: https://doi.org/10.1186/s12913-022-07865-y; BMC Health Services Research, 2022, vol. 22; https://doi.org/10.1186/s12913-022-07865-y; http://hdl.handle.net/2445/185248; 724660 |
| الاتاحة: |
http://hdl.handle.net/2445/185248 |
| Rights: |
cc by (c) Valero Bover, Damià et al, 2022 ; http://creativecommons.org/licenses/by/3.0/es/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.90CEF66C |
| قاعدة البيانات: |
BASE |
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