Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil-Reference-Cited by-同舟云学术

Development and validation of a patient no-show predictive model at a primary care setting in Southern Brazil

Published:2019-04-04 Issue:4 Volume:14 Page:e0214869
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Lenzi Henry,Ben Ângela Jornada^ORCID,Stein Airton Tetelbom

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference39 articles.

1. No-shows in appointment scheduling–a systematic literature review;LF Dantas;Health Policy,2018

2. The Easy Access Program: A Way to Reduce Patient No-Show Rate, Decrease Add-Ons to Primary Care Schedules, and Improve Patient Satisfaction;PJ Tuso;The Permanente Journal,1999

3. Non-attendance in general practice: a systematic review and its implications for access to primary health care;A George;Fam Pract,2003

4. Missed Appointments in Resident Continuity Clinic: Patient Characteristics and Health Care Outcomes;DL Nguyen;J Grad Med Educ,2011

5. No-shows to primary care appointments: subsequent acute care utilization among diabetic patients;LA Nuti;BMC Health Services Research,2012

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