BACKGROUND: Patients who do not keep physician appointments (no-shows) represent a significant loss to healthcare providers. For patients, the cost includes their dissatisfaction and reduced quality of care. An automated telephone appointment reminder system may decrease the no-show rate. Understanding characteristics of patients who miss their appointments will aid in the formulation of interventions to reduce no-show rates. METHODS: In an academic outpatient practice, we studied patient acceptance and no-show rates among patients receiving a clinic staff reminder (STAFF), an automated appointment reminder (AUTO), and no reminder (NONE). Patients scheduled for appointments in the spring of 2007 were assigned randomly to 1 of 3 groups: STAFF (n=3266), AUTO (n=3219), or NONE (n=3350). Patients in the STAFF group were called 3 days in advance by front desk personnel. Patients in the AUTO group were reminded of their appointments 3 days in advance by an automated, standardized message. To evaluate patient satisfaction with the STAFF and AUTO, we surveyed patients who arrived at the clinic (n=10,546). RESULTS: The no-show rates for patients in the STAFF, AUTO, and NONE groups were 13.6%, 17.3%, and 23.1%, respectively (pairwise, P<.01 by analysis of variance for all comparisons). Cancellation rates in the AUTO and STAFF groups were significantly higher than in the NONE group (P<.004). Appointment reminder group, age, visit type, wait time, division specialty, and insurance type were significant predictors of no-show rates. Patients found appointment reminders helpful, but they could not accurately remember whether they received a clinic staff reminder or an automated appointment reminder. CONCLUSIONS: A clinic staff reminder was significantly more effective in lowering the no-show rate compared with an automated appointment reminder system. Copyright 2010 Elsevier Inc. All rights reserved.
RCT Entities:
BACKGROUND:Patients who do not keep physician appointments (no-shows) represent a significant loss to healthcare providers. For patients, the cost includes their dissatisfaction and reduced quality of care. An automated telephone appointment reminder system may decrease the no-show rate. Understanding characteristics of patients who miss their appointments will aid in the formulation of interventions to reduce no-show rates. METHODS: In an academic outpatient practice, we studied patient acceptance and no-show rates among patients receiving a clinic staff reminder (STAFF), an automated appointment reminder (AUTO), and no reminder (NONE). Patients scheduled for appointments in the spring of 2007 were assigned randomly to 1 of 3 groups: STAFF (n=3266), AUTO (n=3219), or NONE (n=3350). Patients in the STAFF group were called 3 days in advance by front desk personnel. Patients in the AUTO group were reminded of their appointments 3 days in advance by an automated, standardized message. To evaluate patient satisfaction with the STAFF and AUTO, we surveyed patients who arrived at the clinic (n=10,546). RESULTS: The no-show rates for patients in the STAFF, AUTO, and NONE groups were 13.6%, 17.3%, and 23.1%, respectively (pairwise, P<.01 by analysis of variance for all comparisons). Cancellation rates in the AUTO and STAFF groups were significantly higher than in the NONE group (P<.004). Appointment reminder group, age, visit type, wait time, division specialty, and insurance type were significant predictors of no-show rates. Patients found appointment reminders helpful, but they could not accurately remember whether they received a clinic staff reminder or an automated appointment reminder. CONCLUSIONS: A clinic staff reminder was significantly more effective in lowering the no-show rate compared with an automated appointment reminder system. Copyright 2010 Elsevier Inc. All rights reserved.
Authors: Khadijah Breathett; Rachel D'Amico; T M Ayodele Adesanya; Stefanie Hatfield; Shannon Willis; Rodney X Sturdivant; Randi E Foraker; Sakima Smith; Philip Binkley; William T Abraham; Pamela N Peterson Journal: Circ Heart Fail Date: 2017-06 Impact factor: 8.790
Authors: Kara E MacLeod; David R Ragland; Thomas R Prohaska; Matthew Lee Smith; Cheryl Irmiter; William A Satariano Journal: Gerontologist Date: 2014-02-20