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Exploring the Effectiveness of Appointment Reminders

Abstract
Missed appointment, referred to as “no-shows,” are appointments that were not attended or previously cancelled at least 24-hours prior to the scheduled time. Missed appointments negatively impact patients as well as health care systems. According to Ullah et al. (2018), the financial impact of missed appointments on the healthcare system is more than $150 billion a year. Also, patients with chronic health problems (who are noncompliant with their scheduled appointments) may cause their conditions to worsen. Researchers have implemented several strategies to reduce the negative effects of no-shows. The purpose of this literature review was to explore the effectiveness of appointment reminders. The question driving this literature review was whether the implementation of appointment reminders via other means were more effective in reducing no-show rates, compared to the standard appointment reminder via telephone call. An electronic search was conducted using CINAHL and PubMed. Inclusion criteria consisted of English language, peer-reviewed, academic journal articles published from 2017 to the present. A variety of articles were found, and five of those were critiqued for this review. The literature was synthesized using the John Hopkins Nursing Evidence-Based Practice Model. The key finding of this review is that telephone calls are the most efficient and feasible form of appointment reminders (Lance et al., 2021 & Lavin et al., 2017). Since phone bills are a normal expense for most businesses, health systems should be able to implement the use of this strategy.
Keywords: appointment adherence, no-show, missed appointments, appointment attendance

Identiferoai:union.ndltd.org:ETSU/oai:dc.etsu.edu:es-conf-1107
Date23 April 2023
CreatorsLevasseur, Lisamarie
PublisherDigital Commons @ East Tennessee State University
Source SetsEast Tennessee State University
Detected LanguageEnglish
Typetext
Formatapplication/vnd.openxmlformats-officedocument.presentationml.presentation
SourceEpsilon Sigma at-Large Research Conference

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