ArticleHealthcare (Basel, Switzerland)2016
Large-Scale No-Show Patterns and Distributions for Clinic Operational Research.
Article in Healthcare (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- An Evidence Map of the Women Veterans' Health Literature, 2016 to 2023: A Systematic Review.JAMA network open · 2025Pooled it
- Using Nudges to Reduce Missed Appointments in Primary Care and Mental Health: a Pragmatic Trial.Journal of general internal medicine · 2023Trial
- Enhancing Usability of Appointment Reminders: Qualitative Interviews of Patients Receiving Care in the Veterans Health Administration.Journal of general internal medicine · 2021Trial
- Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.Arthroplasty today · 2026Article
- Clinic visit no-shows and last-minute cancellations are common among teens and emerging adults with type 1 diabetes.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Novel Strategies to Reduce Patient No-Show Rates: Single Institutional Study at Jane H. Booker Family Health Center.Cureus · 2026Article
- At-Home Urea Breath Testing Demonstrates Increased Patient Uptake, High Satisfaction Rates, and Reduction in Carbon Emission Due to Eliminated Hospital Attendances, While Maintaining Diagnostic Accuracy forJournal of clinical medicine · 2025Article
- Neighborhood Poverty and Clinic Attendance in the Michigan Screening and Intervention for Glaucoma and Eye Health Through Telemedicine Program.Journal of glaucoma · 2025Article
- A Solution to Reduce the Impact of Patients' No-Show Behavior on Hospital Operating Costs: Artificial Intelligence-Based Appointment System.Healthcare (Basel, Switzerland) · 2024Article
- Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study.JAMIA open · 2024Article
- Factors Affecting Patients' Atttendance for Periodontal Follow-up Visits after Crown Lengthening Surgery.Oral health & preventive dentistry · 2024Article
- Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.Health science reports · 2024Article
- Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study.JMIR medical informatics · 2024Article
- Telepsychiatry implementation in Qatar: A three-year analysis of no-show rates and its impact on mental health service delivery.Qatar medical journal · 2024Article
- Health care overbooking cost minimization model.Heliyon · 2023Article
- Sociodemographic and Clinical Factors Associated with Nonattendance at the Hepatology Clinic.Digestive diseases and sciences · 2023Article
- Predictors of Patient-Related Cancellations and No-Shows for Pediatric Orthopaedics and Sports Medicine Appointments Are Condition Specific.Journal of the Pediatric Orthopaedic Society of North America · 2023Article
- Clinical implications of gender and race in patients admitted with autoimmune hepatitis: updated analysis of US hospitals.Frontline gastroenterology · 2023Article
- Article
- Characterizing No-shows in the Omani Primary Healthcare Setting.Oman medical journal · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75-79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.