Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on sleep in brain health

LIVE: Thursday 2nd May 2024, 18:00-19:30 (CEST)

Quality sleep is essential for health. But what happens to our brains when sleep patterns are disturbed? Join our experts to explore the interplay between sleep disruption and neurological diseases, and the questions that you need to be asking your patients to help you prevent the harmful effects of sleep deprivation.
ADVANCED SEARCH
Log in
Top
BMC Health Services Research
Published in: BMC Health Services Research 1/2019

Open Access 01-12-2019 | Research article

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

Authors: Donna Lykke Wolff, Frans Boch Waldorff, Christian von Plessen, Christian Backer Mogensen, Thomas Lund Sørensen, Kim Christian Houlind, Søren Bie Bogh, Katrine Hass Rubin

Published in: BMC Health Services Research | Issue 1/2019

Login to get access

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).
Appendix
Available only for authorised users
Literature
1.
Hwang AS, Atlas SJ, Cronin P, Ashburner JM, Shah SJ, He W, et al. Appointment “no-shows” are an independent predictor of subsequent quality of care and resource utilization outcomes. J Gen Intern Med. 2015;30(10):1426–33.CrossRef
2.
Nuti LA, Lawley M, Turkcan A, Tian Z, Zhang L, Chank K, et al. No-shows to primary care appointments: subsequent acute care utilization among diabetic patients. BMC Health Serv Res. 2012;12:304.
3.
Rhee MK, Slocum W, Ziemer DC, Culler SD, Cook CB, El-Kebbi IM, et al. Patient adherence improves glycemic control. Diabetes Educ. 2005;31(2):240–50.CrossRef
4.
Karter AJ, Parker MM, Moffet HH, Ahmed AT, Ferrara A, Liu JY, et al. Missed appointments and poor glycemic control: an opportunity to identify high-risk diabetic patients. Med Care. 2004;42(2):110–5.CrossRef
5.
Dantas LF, Fleck JL, Cyrino Oliveira FL, Hamacher S. No-shows in appointment scheduling - a systematic literature review. Health Policy. 2018;122(4):412–21.CrossRef
6.
Blaehr EE, Sogaard R, Kristensen T, Vaeggemose U. Observational study identifies non‐attendance characteristics in two hospital out‐patient clinics. Dan Med J. 2016;63:A5283.
7.
8.
Guedes R, Leite I, Baptista A. Dermatology missed appointments: an analysis of outpatient non-attendance in a general hospital’s population. Int J Dermatol. 2014;53(1):39–42.CrossRef
9.
Sola-vera J, Saez J, Laveda R, Girona E, Garcia-Sepulcre MF, Cuesta A, et al. Factors associated with non-attendance at outpatient endoscopy. Scand J Gastroenterol. 2008;43(2):202–6.CrossRef
10.
Dyer PH, Lloyd CE, Lancashire RJ, Bain SC, Barnett AH. Factors associated with clinic non-attendance in adults with type 1 diabetes mellitus. Diabet Med. 1998;15(4):339–43.CrossRef
11.
Chang JT, Sewell JL, Day LW. Prevalence and predictors of patient no-shows to outpatient endoscopic procedures scheduled with anesthesia. BMC Gastroenterol. 2015;15:123.CrossRef
12.
Parikh A, Gupta K, Wilson AC, Fields K, Cosgrove NM, Kostis JB. The effectiveness of outpatient appointment reminder systems in reducing no-show rates. Am J Med. 2010;123(6):542–8.CrossRef
13.
Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43.CrossRef
14.
Ward BW, Schiller JS. Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey. Prev Chronic Dis. 2010;2013(10):E65.
15.
Orueta JF, García-Álvarez A, García-Goñi M, Paolucci F, Nuño-Solinís R. Prevalence and costs of multimorbidity by deprivation levels in the Basque Country: a population based study using health administrative databases. PLoS One. 2014;9(2):e89787.CrossRef
16.
van Oostrom SH, Gijsen R, Stirbu I, Korevaar JC, Schellevis FG, Picavet HS, et al. Time trends in prevalence of chronic diseases and multimorbidity not only due to aging: data from general practices and health surveys. PLoS One. 2016;11(8):e0160264.CrossRef
17.
18.
Kingston A, Robinson L, Booth H, Knapp M, Jagger C, MODEM project. Projections of multi-morbidity in the older population in England to 2035: estimates from the population ageing and care simulation (PACSim) model. Age Ageing. 2018;47(3):374–80.
19.
Rijken M, Struckmann V, Dyakova M, Melchiorre MG, Rissanen S, van Ginneken E. ICARE4EU: improving care for people with multiple chronic conditions in Europe Eurohealth incorporating Euro Observer. 2013;19(3):29–31.
20.
Pruitt SD, Epping-Jordan JE. Preparing the 21st century global healthcare workforce. BMJ. 2005;330(7492):637–9.CrossRef
21.
22.
23.
The Danish health authority. Treatment programs for chronic diseases - the generic model [Forløbsprogrammer for kronisk sygdom - den generiske model]. 2012 [in Danish].
24.
25.
Daggy J, Lawley M, Willis D, Thayer D, Suelzer C, DeLaurentis PC, et al. Using no-show modeling to improve clinic performance. Health Informatics J. 2010;16(4):246–59.CrossRef
26.
National Institute of Public Health, Eight public health diseases - prevalence and development [Otte Folkesygdomme - forekomst og udvikling]. 2005 [in Danish].
27.
28.
Wolff DL, von Plessen C, Waldorff FB, Sorensen TL, Bogh SB, Rubin KH, et al. Time trends in patients managed simultaneously in multiple hospital outpatient specialty clinics for chronic diseases: A register-based cross-sectional study. J Comorb. 2019;9:2235042X19831907.CrossRef
29.
30.
31.
Schmidt M, Schmidt SA, Sandegaard JL, Ehrenstein V, Pedersen L, Sorensen HT. The Danish National Patient Registry: a review of content, data quality, and research potential. Clin Epidemiol. 2015;7:449–90.
32.
Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scand J Public Health. 2011;39(7 Suppl):30–3.CrossRef
33.
Pedersen CB. The Danish civil registration system. Scand J Public Health. 2011;39(7 Suppl):22–5.CrossRef
34.
Jensen VM, Rasmussen AW. Danish education registers. Scand J Public Health. 2011;39(7 Suppl):91–4.CrossRef
35.
Baadsgaard M, Quitzau J. Danish registers on personal income and transfer payments. Scand J Public Health. 2011;39(7 Suppl):103–5.CrossRef
36.
37.
Stubbs ND, Geraci SA, Stephenson PL, Jones DB, Sanders S. Methods to reduce outpatient non-attendance. Am J Med Sci. 2012;344(3):211–9.CrossRef
Metadata
Title
Rate and predictors for non-attendance of patients undergoing hospital outpatient treatment for chronic diseases: a register-based cohort study
Authors
Donna Lykke Wolff
Frans Boch Waldorff
Christian von Plessen
Christian Backer Mogensen
Thomas Lund Sørensen
Kim Christian Houlind
Søren Bie Bogh
Katrine Hass Rubin
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2019
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-019-4208-9

Other articles of this Issue 1/2019

BMC Health Services Research 1/2019 Go to the issue

Research article

The influence of hospital accreditation: a longitudinal assessment of organisational culture

Research article

Promoting and delivering antenatal care in rural Jimma Zone, Ethiopia: a qualitative analysis of midwives’ perceptions

Research article

Bottom-up development of national obstetric guidelines in middle-income country Suriname

Research article

Characteristics and determinants of community physiotherapy utilization and supply

Research article

Use of primary healthcare services prior to suicide in Norway: a descriptive comparison of immigrants and the majority population

Research article

How well is the electronic health record supporting the clinical tasks of hospital physicians? A survey of physicians at three Norwegian hospitals