Trial reportJournal of general internal medicine2023
Reducing Disparities in No Show Rates Using Predictive Model-Driven Live Appointment Reminders for At-Risk Patients: a Randomized Controlled Quality Improvement Initiative.
Trial report in Journal of general internal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06767423 (Comparative Effectiveness of Targeted Outreach With an Automated Caller for Increasing Appointment Completion), which is not on this 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.
Comparative Effectiveness of Targeted Outreach With an Automated Caller for Increasing Appointment Completion: A Randomized Quality Improvement Initiative
Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Predictors of Specialty Outpatient Palliative Care Utilization Among Persons With Serious Illness.Journal of pain and symptom management · 2024Trial
- Barriers to Accessing Urogynecologic Care in a Federally Qualified Health Center.Urogynecology (Philadelphia, Pa.) · 2026Article
- Behavioral and Engagement Predictors of Arthroplasty Clinic No-Shows: A Calibrated Machine Learning Analysis.Arthroplasty today · 2026Article
- Novel Strategies to Reduce Patient No-Show Rates: Single Institutional Study at Jane H. Booker Family Health Center.Cureus · 2026Article
- Article
- Social vulnerability and reliability to attend clinic appointments in an urban oculoplastic surgery clinic.Orbit (Amsterdam, Netherlands) · 2025Article
- Predicting Missed Appointments in Primary Care: A Personalized Machine Learning Approach.Annals of family medicine · 2025Article
- Patient "No-Show" Prior to Elective Primary Total Hip Arthroplasty Increases Risk of Postoperative Anemia.Arthroplasty today · 2025Article
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic.Journal of student-run clinics · 2024Article
- Vision 2030 healthcare efficiency: Radiology no-shows at King Fahd Hospital of the University - Financial and operational burden.Journal of family & community 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
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAppointment no shows are prevalent in safety-net healthcare systems. The efficacy and equitability of using predictive algorithms to selectively add resource-intensive live telephone outreach to standard automated reminders in such a setting is not known.
objectiveTo determine if adding risk-driven telephone outreach to standard automated reminders can improve in-person primary care internal medicine clinic no show rates without worsening racial and ethnic show-rate disparities.
designRandomized controlled quality improvement initiative.
participantsAdult patients with an in-person appointment at a primary care internal medicine clinic in a safety-net healthcare system from 1/1/2022 to 8/24/2022.
interventionsA random forest model that leveraged electronic health record data to predict appointment no show risk was internally trained and validated to ensure fair performance. Schedulers leveraged the model to place reminder calls to patients in the augmented care arm who had a predicted no show rate of 15% or higher. MAINE MEASURES: The primary outcome was no show rate stratified by race and ethnicity. KEY
resultsThere were 5840 appointments with a predicted no show rate of 15% or higher. A total of 2858 had been randomized to the augmented care group and 2982 randomized to standard care. The augmented care group had a significantly lower no show rate than the standard care group (33% vs 36%, p < 0.01). There was a significant reduction in no show rates for Black patients (36% vs 42% respectively, p < 0.001) not reflected in white, non-Hispanic patients.
conclusionsIn this randomized controlled quality improvement initiative, adding model-driven telephone outreach to standard automated reminders was associated with a significant reduction of in-person no show rates in a diverse primary care clinic. The initiative reduced no show disparities by predominantly improving access for Black patients.
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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.