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      Rate and predictors for non-attendance of patients undergoing hospital outpatient treatment for chronic diseases: a register-based cohort study

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          Abstract

          Background

          Failure to keep medical appointments results in inefficiencies and, potentially, in poor outcomes for patients. The aim of this study is to describe non-attendance rate and to investigate predictors of non-attendance among patients receiving hospital outpatient treatment for chronic diseases.

          Methods

          We conducted a historic, register-based cohort study using data from a regional hospital and included patients aged 18 years or over who were registered in ongoing outpatient treatment courses for seven selected chronic diseases on July 1, 2013. A total of 5895 patients were included and information about their appointments was extracted from the period between July 1, 2013 and June 30, 2015. The outcome measure was occurrence of non-attendance. The associations between non-attendance and covariates (age, gender, marital status, education level, occupational status, specific chronic disease and number of outpatient treatment courses) were investigated using multivariate logistic regression models, including mixed effect.

          Results

          During the two-year period, 35% of all patients (2057 of 5895 patients) had one or more occurrences of non-attendance and 5% of all appointments (4393 of 82,989 appointments) resulted in non-attendance. Significant predictors for non-attendance were younger age (OR 4.17 for 18 ≤ 29 years as opposed to 80+ years), male gender (OR 1.35), unmarried status (OR 1.39), low educational level (OR 1.18) and receipt of long-term welfare payments (OR 1.48). Neither specific diseases nor number of treatment courses were associated with a higher non-attendance rate.

          Conclusions

          Patients undergoing hospital outpatient treatments for chronic diseases had a non-attendance rate of 5%. We found several predictors for non-attendance but undergoing treatment for several chronic diseases simultaneously was not a predictor. To reduce non-attendance, initiatives could target the groups at risk.

          Trial registration

          This study was approved by the Danish Data Protection Agency (Project ID 18/35695).

          Electronic supplementary material

          The online version of this article (10.1186/s12913-019-4208-9) contains supplementary material, which is available to authorized users.

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          Most cited references14

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          Time Trends in Prevalence of Chronic Diseases and Multimorbidity Not Only due to Aging: Data from General Practices and Health Surveys

          Introduction Chronic diseases and multimorbidity are common and expected to rise over the coming years. The objective of this study is to examine the time trend in the prevalence of chronic diseases and multimorbidity over the period 2001 till 2011 in the Netherlands, and the extent to which this can be ascribed to the aging of the population. Methods Monitoring study, using two data sources: 1) medical records of patients listed in a nationally representative network of general practices over the period 2002–2011, and 2) national health interview surveys over the period 2001–2011. Regression models were used to study trends in the prevalence-rates over time, with and without standardization for age. Results An increase from 34.9% to 41.8% (p<0.01) in the prevalence of chronic diseases was observed in the general practice registration over the period 2004–2011 and from 41.0% to 46.6% (p<0.01) based on self-reported diseases over the period 2001–2011. Multimorbidity increased from 12.7% to 16.2% (p<0.01) and from 14.3% to 17.5% (p<0.01), respectively. Aging of the population explained part of these trends: about one-fifth based on general practice data, and one-third for chronic diseases and half of the trend for multimorbidity based on health surveys. Conclusions The prevalence of chronic diseases and multimorbidity increased over the period 2001–2011. Aging of the population only explained part of the increase, implying that other factors such as health care and society-related developments are responsible for a substantial part of this rise.
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            Patient adherence improves glycemic control.

            The purpose of this study was to assess the influence of appointment keeping and medication adherence on HbA1c. A retrospective evaluation was performed in 1560 patients with type 2 diabetes who presented for a new visit to the Grady Diabetes Clinic between 1991 and 2001 and returned for a follow-up visit and HbA1c after 1 year of care. Appointment keeping was assessed by the number of scheduled intervening visits that were kept, and medication adherence was assessed by the percentage of visits in which self-reported diabetes medication use was as recommended at the preceding visit. The patients had an average age of 55 years, body mass index (BMI) of 32 kg/m2, diabetes duration of 4.6 years, and baseline HbA1c of 9.1%. Ninety percent were African American, and 63% were female. Those who kept more intervening appointments had lower HbA1c levels after 12 months of care (7.6% with 6-7 intervening visits vs 9.7% with 0 intervening visits). Better medication adherence was also associated with lower HbA1c levels after 12 months of care (7.8% with 76%-100% adherence). After adjusting for age, gender, race, BMI, diabetes duration, and diabetes therapy in multivariate linear regression analysis, the benefits of appointment keeping and medication adherence remained significant and contributed independently; the HbA1c was 0.12% lower for every additional intervening appointment that was kept (P = .0001) and 0.34% lower for each quartile of better medication adherence (P = .0009). Keeping more appointments and taking diabetes medications as directed were associated with substantial improvements in HbA1c. Efforts to enhance glycemic outcomes should include emphasis on these simple but critically important aspects of patient adherence.
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              The effectiveness of outpatient appointment reminder systems in reducing no-show rates.

              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. 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). 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. 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.
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                Author and article information

                Contributors
                0045-29796589 , donna.wolff@rsyd.dk
                fwaldorff@health.sdu.dk
                Christian.von-plessen@vd.ch
                Christian.Backer.Mogensen@rsyd.dk
                THLS@SST.DK
                Kim.Christian.Houlind@rsyd.dk
                Soren.Bie.Bogh@rsyd.dk
                Krubin@health.sdu.dk
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                14 June 2019
                14 June 2019
                2019
                : 19
                : 386
                Affiliations
                [1 ]Hospital of Southern Denmark, DK-6200 Aabenraa, Denmark
                [2 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Department of Regional Health Research, , University of Southern Denmark, ; Winsløwparken 19, DK-5000 Odense C, Denmark
                [3 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Research Unit of General Practice, Department of Public Health, , University of Southern Denmark, ; Odense, Denmark
                [4 ]Direction Général de la Santé and Unisanté, Lausanne, Switzerland
                [5 ]ISNI 0000 0001 0728 0170, GRID grid.10825.3e, Department of Clinical Research, , University of Southern Denmark, ; Odense, Denmark
                [6 ]The Danish Patient Safety Authority, Kolding, Denmark
                [7 ]ISNI 0000 0004 0631 5249, GRID grid.415434.3, Department of Vascular Surgery, , Kolding Hospital, Part of Hospital Lillebaelt, ; Kolding, Denmark
                [8 ]GRID grid.425874.8, OPEN—Open Patient data Explorative Network— Department of Clinical Research and Odense University Hospital, Region of Southern Denmark, ; Odense, Denmark
                Author information
                http://orcid.org/0000-0002-9131-8991
                Article
                4208
                10.1186/s12913-019-4208-9
                6570866
                31200720
                d76cb8c2-347b-488c-9ab0-1e40b02b5702
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 October 2018
                : 31 May 2019
                Funding
                Funded by: Knud og Edith Eriksens Mindefond
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Health & Social care
                non-attendance,no-show,attendance rate,chronic patients,hospital outpatient clinic,appointments,predictors

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