A descriptive study of missed appointments: Families' Perceptions of barriers to care

Pesata, Virginia; Pallija, Geri; Webb, Adele

doi:10.1016/s0891-5245(99)90037-8

Cited by 112 publications

(84 citation statements)

References 6 publications

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“…Patients with increased levels of depression have been shown to report more dissatisfaction with their providers (27). Considering that missed appointments are also often associated with increased provider frustration (28), decreased empathy and patient-provider communication (29), and less continuity of care (29), this relationship may have important ramifications for depressed patients with diabetes.…”

Section: Moderation Analysesmentioning

confidence: 99%

Depression and Diabetes Treatment Nonadherence: A Meta-Analysis

et al. 2008

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OBJECTIVE -Depression is common in patients with diabetes and is associated with worse treatment outcomes. Its relationship to treatment adherence, however, has not been systematically reviewed. We used meta-analysis to examine the relationship between depression and treatment nonadherence in patients with type 1 and type 2 diabetes.RESEARCH DESIGN AND METHODS -We searched MEDLINE and PsycINFO databases for all studies published by June 2007 and reviewed references of published articles. Meta-analytic procedures were used to estimate the effect size r in a random effects model. Significance values, weighted effect sizes, 95% CIs, and tests of homogeneity of variance were calculated.RESULTS -Results from 47 independent samples showed that depression was significantly associated with nonadherence to the diabetes treatment regimen (z ϭ 9.97, P Ͻ 0.0001). The weighted effect size was near the medium range (r ϭ 0.21, 95% CI 0.17-0.25). Moderator analyses showed that the effect was significantly larger in studies that measured self-care as a continuous versus categorical variable (P ϭ 0.001). Effect sizes were largest for missed medical appointments and composite measures of self-care (r values ϭ 0.31, 0.29). Moderation analyses suggest that effects for most other types of self-care are also near the medium range, especially in studies with stronger methodologies.CONCLUSIONS -These findings demonstrate a significant association between depression and treatment nonadherence in patients with diabetes. Studies that used stronger methodologies had larger effects. Treatment nonadherence may represent an important pathway between depression and worse diabetes clinical outcomes.

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Section: Moderation Analysesmentioning

confidence: 99%

Depression and Diabetes Treatment Nonadherence: A Meta-Analysis

et al. 2008

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“…Thus, it is expected that cancelations and no-shows have a significant impact on hospitals' finance and revenue. In 1999, a children's health clinic found a total of 14,000 missed appointments, which represented a loss of over than one million dollars (Pesata, Pallija & Webb, 1999). In 2015, appointments at an academic paediatric neurology clinic were analyzed for one year (Guzek, Gentry & Golomb, 2015).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Managing waiting times to predict no-shows and cancelations at a children’s hospital

Rodríguez-García

McLean-Carranza

Prado

et al. 2016

JIEM

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Abstract:Purpose: Since long waits in hospitals have been found to be related to high rates of no-shows and cancelations, managing waiting times should be considered as an important tool that hospitals can use to reduce missed appointments. The aim of this study is to analyze patients' behavior in order to predict no-show and cancelation rates correlated to waiting times.Design/methodology/approach: This study is based on the data from a US children's hospital, which includes all the appointments registered during one year of observation. We used the callappointment interval to establish the wait time to get an appointment. Four different types of appointment-keeping behavior and two types of patients were distinguished: arrival, no-show, cancelation with no reschedule, and cancelation with reschedule; and new and established patients. Findings:Results confirmed a strong impact of long waiting times on patients' appointmentkeeping behavior, and the logarithmic regression was found as the best-fit function for the correlation between variables in all cases. The correlation analysis showed that new patients tend to miss appointments more often than established patients when the waiting time increases. It was also found that, depending on the patients' appointment distribution, it might get more complicated for hospitals to reduce missed appointments as the waiting time is reduced.-1107-Journal of Industrial Engineering and Management -http://dx.doi.org/10.3926/jiem.2075 Originality/value: The methodology applied in our study, which combines the use of regression analysis and patients' appointment distribution analysis, would help health care managers to understand the initial implications of long waiting times and to address improvement related to patient satisfaction and hospital performance.

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“…For those who may not qualify for paratransit (such as the retired population in our study who are transit-dependent), a lack of adequate transportation results in late and missed appointments. This affects not only the individual health and well-being of the patient, but the hospital or medical provider's bottom line (Pesata et al, 1999;Wallace et al, 2015). According to one study, "About 3.6 million Americans do not obtain medical care because of a lack of transportation in a given year.…”

Section: Serving Elderly and Disabled Populationsmentioning

confidence: 99%

What Do We Know About Location Affordability in U.S. Shrinking Cities?

Ganning¹,

Tighe²

2017

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In late 2013, the Department of Housing and Urban Development (HUD) launched the Location Affordability Index (LAI) portal. Their dataset uses models to estimate typical amount households spend on housing and transportation at the block group level, and calculates "H + T Affordability," the percent of household income spent on these items. In our previous research, we analyzed 81 shrinking cities to determine how location affordability differs across various neighborhoods. Our results suggest that households in declining neighborhoods, as compared to stable or redeveloping neighborhoods, face the greatest H + T affordability challenges in shrinking cities. Furthermore, in declining neighborhoods, virtually all of the additional affordability challenges encountered can be accounted for by differences in transportation affordability rather than housing. Since there is virtually no research to either validate or suggest bias in the LAI data, and a declining neighborhood in a shrinking city presents both a relatively common yet entirely dissimilar context to the norm, we feel that this data should be carefully calibrated to, and tested for, this setting to ensure that appropriate and efficient policy follow.In this report, we present the results of two research phases: a strictly quantitative first stage in which the LAI is disassembled and reassembled, taking stock of assumptions, methods, and accuracy. This work finds that the LAI generally over-estimates housing costs, but more for renters and more in metropolitan areas. Estimations of transportation cost burdens are built largely from unreliable data and using models which cannot be replicated, leading us to conclude that these cost burden estimates may not be reliable. In the second research phase, which is survey-based, we gather household-level data from 12 Census tracts in Cleveland, Ohio, to estimate household housing and transportation costs and cost burdens and gain a clearer sense of budget trade-offs where costs are unaffordable. The survey results support the first research stage. The LAI over-estimates housing costs for these neighborhoods by approximately 17% and transportation costs by approximately 126%, meaning the LAI estimates for transportation are not reliable. By and large, households trade essentials, investing, and paying bills (at all or in full) to cover their costs in housing and transportation.17.

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A descriptive study of missed appointments: Families' Perceptions of barriers to care

Cited by 112 publications

References 6 publications

Depression and Diabetes Treatment Nonadherence: A Meta-Analysis

Depression and Diabetes Treatment Nonadherence: A Meta-Analysis

Managing waiting times to predict no-shows and cancelations at a children’s hospital

What Do We Know About Location Affordability in U.S. Shrinking Cities?

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