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Related Experiment Video

Updated: Aug 5, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

No-show Prediction Model Performance Among People With HIV: External Validation Study.

Joseph A Mason¹, Eleanor E Friedman¹, Juan C Rojas²

¹The Chicago Center for HIV Elimination, Department of Medicine, University of Chicago, Chicago, IL, United States.

Journal of Medical Internet Research

|March 29, 2023

Summary

A new model improved predicting HIV patient no-shows by including demographic and clinical data, outperforming the standard Epic model. This enhances appointment attendance for people with HIV.

Keywords:

Epic systems HIV care electronic medical record external validation human immunodeficiency virus model no-show patient people with HIV prediction model technology

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Last Updated: Aug 5, 2025

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Published on: December 1, 2011

Area of Science:

Health Informatics
Epidemiology
Clinical Medicine

Background:

Regular medical care is crucial for individuals living with HIV (Human Immunodeficiency Virus).
Predictive models for no-shows can improve care by enabling proactive patient engagement.
Epic's no-show model lacks external validation in people with HIV.

Purpose of the Study:

To evaluate the performance of Epic's no-show predictive model in people with HIV.
To assess if adding demographic and HIV clinical data improves model accuracy.
To compare Epic's model with a custom-built model for HIV care appointments.

Main Methods:

Retrospective analysis of encounter data for people with HIV from January to March 2022.
Comparison of Epic model's predicted no-show probability against actual outcomes.
Development and evaluation of an alternate random forest model using specific features.

Main Results:

The Epic model showed an AUC of 0.65 for all outpatient appointments and 0.63 for HIV care appointments.
An alternate model developed for HIV care appointments achieved a significantly higher AUC of 0.78.
Key predictors in the alternate model included lead time, appointment length, viral load, CD4 count, and sex.

Conclusions:

Epic's no-show model performance was lower in people with HIV than previously reported.
Incorporating demographic and HIV-specific clinical data significantly enhances no-show prediction accuracy.
CD4 count and viral load are critical features for improving appointment attendance prediction in this population.