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Updated: Jun 12, 2026
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A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
Predicting Outpatient Cardiac Rehabilitation Enrollment: CAN STAFF MEMBERS ACCURATELY ASSESS PATIENTS FUTURE
Emma Busenkell1, Kamryn Jebb1, Madeline Broccoli1
1University of Massachusetts Chan School of Medicine, Worcester, Massachusetts (Ms Busenkell, Dr Jebb, Dr Broccoli, and Mr McAnally).
Journal of Cardiopulmonary Rehabilitation and Prevention
|June 11, 2026
Summary
Cardiac rehabilitation (CR) staff can somewhat predict patient attendance, but their predictions are not reliable enough for routine use. All patients require intensive intervention to promote CR enrollment, and staff performance needs monitoring.
Area of Science:
- Cardiology
- Rehabilitation Medicine
- Health Services Research
Background:
- Hospital-based interventions improve cardiac rehabilitation (CR) enrollment.
- Optimizing enrollment requires efficient staff resource allocation.
- Focusing on undecided patients may enhance intervention effectiveness.
Purpose of the Study:
- To evaluate the accuracy of CR staff predictions of patient attendance.
- To determine if staff can reliably identify patients likely to attend CR.
Main Methods:
- Staff members estimated patient likelihood to attend outpatient CR on a 0-10 scale.
- Logistic regression analyzed attendance at ≥1 CR session.
- Model discrimination was assessed using the C-statistic.
Main Results:
- Higher staff likelihood scores correlated with increased CR attendance odds.
- Initial staff predictions showed poor discrimination (C-statistic 0.58).
- Adding patient and facility variables improved discrimination (C-statistic 0.67).
Conclusions:
- CR staff subjective predictions of attendance are not sufficiently reliable for routine use.
- Staff performance significantly impacts CR attendance rates.
- Until predictive models improve, intensive interventions for all patients and staff performance monitoring are recommended.
