Observational studyJournal of general internal medicine2015
Predicting Non-Adherence with Outpatient Colonoscopy Using a Novel Electronic Tool that Measures Prior Non-Adherence.
Observational study in Journal of general internal medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Precision Patient Navigation to Improve Rates of Follow-up Colonoscopy, An Individual Randomized Effectiveness Trial.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Trial
- Estimated no-show rate from the electronic record strongly correlates with endoscopy no-show.BMC gastroenterology · 2026Article
- Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study.Ophthalmology science · 2026Article
- Development and Validation of a Predictive Model of Prostate Screening Compliance: A Nationwide Population-Based Study.The Prostate · 2025Observational
- Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.BMC health services research · 2024Article
- Associations between Missed Colonoscopy Appointments and Multiple Prior Adherence Behaviors in an Integrated Healthcare System: An Observational Study.Journal of general internal medicine · 2024Observational
- Colorectal cancer screening barriers and facilitators among Jordanians: A cross-sectional study.Preventive medicine reports · 2023Article
- Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review.Clinical and translational science · 2023Article
- Article
- Evaluation of Patient No-Shows in a Tertiary Hospital: Focusing on Modes of Appointment-Making and Type of Appointment.International journal of environmental research and public health · 2021Article
- Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.Digestive diseases and sciences · 2021Review
- Patient No-Show Prediction: A Systematic Literature Review.Entropy (Basel, Switzerland) · 2020Review
- Colonoscopy education delivered via the patient portal does not improve adherence to scheduled first-time screening colonoscopy.Endoscopy international open · 2020Article
- Patient randomized trial of a targeted navigation program to improve rates of follow-up colonoscopy in community health centers.Contemporary clinical trials · 2020Article
- A survey on colonoscopy shows poor understanding of its protective value and widespread misconceptions across Europe.PloS one · 2020Article
- Cloud-based multi-media systems for patient education and adherence: a pilot study to explore patient compliance with colonoscopy procedure preparation.Health systems (Basingstoke, England) · 2019Article
- Predicting scheduled hospital attendance with artificial intelligence.NPJ digital medicine · 2019Article
- Social Disadvantage, Healthcare Utilization, and Colorectal Cancer Screening: Leveraging Longitudinal Patient Address and Health Records Data.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurately predicting the risk of no-show for a scheduled colonoscopy can help target interventions to improve compliance with colonoscopy, and thereby reduce the disease burden of colorectal cancer and enhance the utilization of resources within endoscopy units.
objectivesWe aimed to utilize information available in an electronic medical record (EMR) and endoscopy scheduling system to create a predictive model for no-show risk, and to simultaneously evaluate the role for natural language processing (NLP) in developing such a model.
designThis was a retrospective observational study using discovery and validation phases to design a colonoscopy non-adherence prediction model. An NLP-derived variable called the Non-Adherence Ratio ("NAR") was developed, validated, and included in the model.
participantsPatients scheduled for outpatient colonoscopy at an Academic Medical Center (AMC) that is part of a multi-hospital health system, 2009 to 2011, were included in the study. MAIN MEASURES: Odds ratios for non-adherence were calculated for all variables in the discovery cohort, and an Area Under the Receiver Operating Curve (AUC) was calculated for the final non-adherence prediction model. KEY
resultsThe non-adherence model included six variables: 1) gender; 2) history of psychiatric illness, 3) NAR; 4) wait time in months; 5) number of prior missed endoscopies; and 6) education level. The model achieved discrimination in the validation cohort (AUC= =70.2 %). At a threshold non-adherence score of 0.46, the model's sensitivity and specificity were 33 % and 92 %, respectively. Removing the NAR from the model significantly reduced its predictive power (AUC = 64.3 %, difference = 5.9 %, p < 0.001).
conclusionsA six-variable model using readily available clinical and demographic information demonstrated accuracy for predicting colonoscopy non-adherence. The NAR, a novel variable developed using NLP technology, significantly strengthened this model's predictive power.
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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.