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Predicting Anticipated Telehealth Use: Development of the CONTEST Score and Machine Learning Models Using a National

Richard C Wang1, Usha Sambamoorthi2

  • 1St. Mark's School of Texas, 10600 Preston Rd., Dallas, TX 75230, USA.

Healthcare (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

A new tool, CONTEST, helps identify patients likely to stop using telehealth. Key factors include convenience, technical issues, perceived quality, and willingness to recommend. This aids in maintaining telehealth use in hybrid care models.

Keywords:
HINTSfairnessmachine learningtelehealth

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Area of Science:

  • Health Services Research
  • Digital Health
  • Health Informatics

Background:

  • Anticipated telehealth use is crucial for integrating telehealth into hybrid care models.
  • Limited tools exist to predict patient discontinuation of telehealth services.
  • Identifying at-risk patients is essential for sustained telehealth adoption.

Purpose of the Study:

  • Identify factors influencing patients' intention to continue telehealth use.
  • Develop and validate a risk stratification tool (CONTEST) for predicting telehealth discontinuation.
  • Compare the performance and fairness of CONTEST against machine learning (ML) models.

Main Methods:

  • Retrospective analysis of the 2024 Health Information National Trends Survey 7 (HINTS 7) data.
  • Survey-weighted logistic regression to develop the CONTEST point score.
  • Machine learning models (XGBoost, random forest, logistic regression) were trained and evaluated using AUROC, precision, and recall.
  • Fairness assessment using group and individual counterfactual metrics across sex and race/ethnicity.

Main Results:

  • Nearly 10% of telehealth users expressed unwillingness to continue future use.
  • Lower perceived convenience, technical problems, lower perceived quality, and unwillingness to recommend were key factors.
  • CONTEST achieved strong discrimination (AUROC 0.876); XGBoost performed best among ML models (AUROC 0.902).
  • ML-informed scores and models showed comparable performance to CONTEST.
  • Disparities in fairness metrics were observed across sex and race/ethnicity, with low individual counterfactual rates.

Conclusions:

  • The CONTEST score and ML models effectively stratify risk for lower anticipated telehealth use.
  • Ensuring convenience, technical reliability, and perceived quality are vital for sustained telehealth engagement.
  • Implementation requires integrating predictive tools with operational support and ongoing fairness monitoring to address disparities.