# Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $327,250

## Abstract

PROJECT SUMMARY
Physicians often report feeling pressured to see more patients to maintain revenue, while having less available
time for patient care. Systematic data-driven methods for efficiently scheduling patients are important as
physicians are pressured to see more and more patients. We propose that real time prediction models of
patient visit lengths, the likelihood of missing appointments, and of patient wait times will help schedule
patients more efficiently. Clinics will be able to safely overbook to avoid empty slots from missed appointments,
have guidance for scheduling urgent add-on patients, and provide wait time estimates for patients when there
are delays. We will develop methodologies for accessing data needed for these predictions in real time and
propose that the integration of these models into workflows will improve scheduling accuracy, patient wait time,
and patient satisfaction, while also increasing clinic volumes.

## Key facts

- **NIH application ID:** 10227120
- **Project number:** 5R01LM013426-02
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Michelle Hribar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $327,250
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10227120

## Citation

> US National Institutes of Health, RePORTER application 10227120, Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency (5R01LM013426-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10227120. Licensed CC0.

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