ArticleInformatics in primary care2014
Can we predict a national profile of non-attendance paediatric urology patients: a multi-institutional electronic health record study.
Article in Informatics in primary care, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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
11 citing papers in PubMed.
- Predictors of Subspecialty Appointment Scheduling and Completion for Patients Referred From a Pediatric Primary Care Clinic.Clinical pediatrics · 2024Article
- Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.Journal of medical Internet research · 2023Article
- Causes of Patient Nonattendance at Medical Appointments: Protocol for a Mixed Methods Study.JMIR research protocols · 2023Article
- Predicting no-show appointments in a pediatric hospital in Chile using machine learning.Health care management science · 2023Article
- Predicting and explaining absenteeism risk in hospital patients before and during COVID-19.Socio-economic planning sciences · 2023Article
- Evaluating the reasons for nonattendance to outpatient consultations: is waiting time an important factor?BMC health services research · 2022Article
- Predicting non-attendance in hospital outpatient appointments using deep learning approach.Health systems (Basingstoke, England) · 2022Article
- Predicting no-shows in paediatric orthopaedic clinics.BMJ health & care informatics · 2020Article
- "Minimally invasive research?" Use of the electronic health record to facilitate research in pediatric urology.Journal of pediatric urology · 2018Review
- Big Data Analytics in urology: the story so far and the road ahead.Therapeutic advances in urologyReview
- Telehealth Visits have Lower Non-attendance than Traditional Orthopaedic Clinic Visits.Missouri medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundNon-attendance at paediatric urology outpatient appointments results in the patient's failure to receive medical care and wastes health care resources.
objectiveTo determine the utility of using routinely collected electronic health record (EHR) data for multi-centre analysis of variables predictive of patient noshows (NS) to identify areas for future intervention.
methodsData were obtained from Children's Hospital Colorado, Rady Children's Hospital San Diego and University of Virginia Hospital paediatric urology practices, which use the Epic® EHR system. Data were extracted for all urology outpatient appointments scheduled from 1 October 2010 to 30 September 2011 using automated electronic data extraction techniques. Data included appointment type; date; provider type and days from scheduling to appointment. All data were de-identified prior to analysis. Predictor variables identified using χ(2) and analysis of variance were modelled using multivariate logistic regression.
resultsA total of 2994 NS patients were identified within a population of 28,715, with a mean NS rate of 10.4%. Multivariate logistic regression determined that an appointment with mid-level provider (odds ratio (OR) 1.70 95% CI (1.56, 1.85)) and an increased number of days between scheduling and appointment (15-28 days OR 1.24 (1.09, 1.41); 29+ days OR 1.70 (1.53, 1.89)) were significantly associated with NS appointments.
conclusionWe demonstrated sufficient interoperability among institutions to obtain data rapidly and efficiently for use in 1) interventions; 2) further study and 3) more complex analysis. Demographic and potentially modifiable clinic characteristics were associated with NS to the outpatient clinic. The analysis also demonstrated that available data are dependent on the clinical data collection systems and practices.
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Registered trials
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