Elsevier

Health Policy

Volume 122, Issue 4, April 2018, Pages 412-421
Health Policy

No-shows in appointment scheduling – a systematic literature review

https://doi.org/10.1016/j.healthpol.2018.02.002Get rights and content

Highlights

  • The findings of 105 studies on no-show in appointment scheduling are synthetized.

  • The main determinants of no-show are high lead time and prior no-show history.

  • Adults of younger age and lower socioeconomic status are most likely to no-show.

  • Lack of private insurance and longer distance to clinic increase no-show likelihood.

  • Across all specialties, the average no-show rate is of the order of 23%.

Abstract

No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.

Introduction

No-show appointments (also commonly referred to as broken or missed appointments) are a burden to essentially all healthcare systems, significantly impacting revenue, cost and use of resources [[1], [2]]. It is a well-known fact that no-show decreases the provider’s productivity and efficiency, increases healthcare costs, and limits the health clinic’s effective capacity [[3], [4]]. Negative effects are also felt by patients who keep their appointments, such as dissatisfaction with high waiting time and perception of overall decrease in service quality [[2], [5], [6]]. In addition to creating financial costs for providers, non-attendance generates social costs related with unused staff time, ineffective use of equipment and possible misuse of patients’ time [6].

There is a general consensus in literature regarding the fact that no-show does not occur arbitrarily and several studies have identified the need to statistically analyze the factors that influence its behavior in order to improve healthcare processes and dampen the effects of missed appointments. A number of the most recent of such studies attest to the existence of a relationship between no-show rates and patient behavior [[4], [7], [8], [9], [10]]. By evaluating this relationship through univariate and/or multivariate statistical methods, several works have proposed interventions to mitigate the negative effects of missed appointments [[2], [4]], such as: overbooking [[11], [12], [13], [14]], open access [15], appointment reminders [5], best management practices, among others.

There is a markedly growing interest from the healthcare community in uncovering and understanding the issues involved in no-show behavior. However, given the variability in context and specificities of health care delivery and systems, it is unlikely that a general agreement may be reached regarding the variables that statistically influence no-show behavior. Nevertheless, by aggregating studies that report on a range of different medical specialties and continents, and make use of distinct methodologies for data analysis, it is possible to identify the determinants that have been most frequently considered significant and their effect on no-show. Moreover, although a comprehensive synthesis of the state-of-the-art in this field would be of great value to researchers, practitioners, and hospital administrators alike, to the best of our knowledge, no updated systematic literature review (SLR) exists.

This paper addresses the aforementioned shortcomings by providing a SLR of no-show in appointment scheduling. The goals are threefold: for one, we provide an overview of the characteristics of existing studies in terms of their methodology, continent where the study was undertaken, medical specialties involved, dependent variables considered, and values of no-show rates. In addition to that, we report on the most common tendencies across surveyed studies and detect patterns that emerge. Finally, we discuss our findings in light of previous literature reviews [[16], [17], [18]].

Of note, we adopt the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [19] and organize the remainder of this paper as follows. In Section 2 we detail how data collection and study selection were performed, and report on the methods used for handling such data. Section 3 contains a complete account of the studies screened, assessed for eligibility, and included in this review, with reasons for exclusions at each stage, along with the results of our analysis. Finally, we summarize our main findings and present a general interpretation of the results with implications for future research in Section 4.

Section snippets

Methods

This work entails a SLR of existing studies on no-show in appointment scheduling. As such, we rely on qualitative, non-statistical tools for integrating, evaluating and interpreting results currently available in literature [20]. In what follows we describe our search strategy, recount eligibility criteria for study selection, and elaborate on our methodology for analyzing the surveyed studies.

Results and discussion

Our search using the Scopus database yielded a total of 727 papers, three of which were duplicates, so that 724 papers were screened for eligibility based on their title and/or abstract. The remaining 230 papers were screened based on their complete text using eligibility criteria as well as the additional constraints defined in Section 2.2. A total of 105 papers and three literature reviews on the subject of interest were retained. Although these review papers were not SLR, they offer a basis

Conclusion

This work integrates and summarizes the findings of 105 papers dealing with determinants of no-show in appointment scheduling. The average no-show rate across all studies was found to be 23.0%, and further analysis revealed that this rate was highest in the African continent (43.0%) and lowest in Oceania (13.2%). We also verified that psychiatry and primary care were the most investigated specialties, and that various statistical methods were used in the reviewed papers to identify significant

Conflicts of interest

The authors declare no competing conflicts of interest.

Acknowledgements

This work was supported by the National Council for Scientific and Technological Development (CNPq) [grant numbers 443595/2014-3 and 304843/2016-4 to FLCO, grant numbers 306802/2015-5 and 403863/2016-3 to SH]; Carlos Chagas Filho Foundation (FAPERJ) [grant number E-26/202.806/2015 to FLCO]; Coordination for the Improvement of Higher Education Personnel (CAPES); and the Pontifical Catholic University of Rio de Janeiro.

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