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Appointment reminder systems are effective but not optimal: results of a systematic review and evidence synthesis employing realist principles

Overview of attention for article published in Patient preference and adherence, April 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#35 of 1,244)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
15 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
137 Mendeley
Title
Appointment reminder systems are effective but not optimal: results of a systematic review and evidence synthesis employing realist principles
Published in
Patient preference and adherence, April 2016
DOI 10.2147/ppa.s93046
Pubmed ID
Authors

Sionnadh McLean, Andrew Booth, Melanie Gee, Sarah Salway, Mark Cobb, Sadiq Bhanbhro, Susan Nancarrow

Abstract

Missed appointments are an avoidable cost and resource inefficiency which impact upon the health of the patient and treatment outcomes. Health care services are increasingly utilizing reminder systems to manage these negative effects. This study explores the effectiveness of reminder systems for promoting attendance, cancellations, and rescheduling of appointments across all health care settings and for particular patient groups and the contextual factors which indicate that reminders are being employed sub-optimally. We used three inter-related reviews of quantitative and qualitative evidence. Firstly, using pre-existing models and theories, we developed a conceptual framework to inform our understanding of the contexts and mechanisms which influence reminder effectiveness. Secondly, we performed a review following Centre for Reviews and Dissemination guidelines to investigate the effectiveness of different methods of reminding patients to attend health service appointments. Finally, to supplement the effectiveness information, we completed a review informed by realist principles to identify factors likely to influence non-attendance behaviors and the effectiveness of reminders. We found consistent evidence that all types of reminder systems are effective at improving appointment attendance across a range of health care settings and patient populations. Reminder systems may also increase cancellation and rescheduling of unwanted appointments. "Reminder plus", which provides additional information beyond the reminder function may be more effective than simple reminders (ie, date, time, place) at reducing non-attendance at appointments in particular circumstances. We identified six areas of inefficiency which indicate that reminder systems are being used sub-optimally. Unless otherwise indicated, all patients should receive a reminder to facilitate attendance at their health care appointment. The choice of reminder system should be tailored to the individual service. To optimize appointment and reminder systems, health care services need supportive administrative processes to enhance attendance, cancellation, rescheduling, and re-allocation of appointments to other patients.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 136 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 37 27%
Researcher 21 15%
Student > Bachelor 15 11%
Student > Doctoral Student 14 10%
Student > Ph. D. Student 7 5%
Other 19 14%
Unknown 24 18%
Readers by discipline Count As %
Medicine and Dentistry 41 30%
Nursing and Health Professions 24 18%
Social Sciences 11 8%
Psychology 9 7%
Computer Science 7 5%
Other 17 12%
Unknown 28 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2019.
All research outputs
#995,314
of 15,937,237 outputs
Outputs from Patient preference and adherence
#35
of 1,244 outputs
Outputs of similar age
#23,776
of 266,476 outputs
Outputs of similar age from Patient preference and adherence
#3
of 64 outputs
Altmetric has tracked 15,937,237 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,244 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,476 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.