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Updated: Sep 2, 2025

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Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning

Amir Rastpour1, Carolyn McGregor1,2

  • 1Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON, Canada.

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|August 9, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict mental health outpatient wait times, even with deidentified data. Incorporating system knowledge improved predictions and identified the priority system as a factor contributing to long wait times.

Keywords:
machine learningmental health careoutpatient clinicsrandom forestsystem’s knowledgewait time prediction

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Area of Science:

  • Healthcare analytics
  • Machine learning in mental health
  • Predictive modeling for patient wait times

Background:

  • Patient wait times significantly affect satisfaction, treatment effectiveness, and care efficiency.
  • Predicting mental health wait times is complex due to variable session needs, no-show rates, and group therapy options.
  • Data deidentification reduces utility, further challenging wait time analysis.

Purpose of the Study:

  • Develop machine learning models for predicting psychiatric outpatient wait times using real-time data.
  • Enhance predictive model performance by integrating system knowledge with highly deidentified data.
  • Identify key factors contributing to long wait times and create practical, implementable models for providers.

Main Methods:

  • Analysis of retrospective, highly deidentified administrative data from 8 mental health outpatient clinics.
  • Application of 6 machine learning methods to predict first appointment wait times for new outpatients.
  • Utilized system knowledge to overcome low data utility and improve model performance, analyzing data from 4187 patients and 30,342 appointments.

Main Results:

  • Average wait times exceeded 3 months in over half of the clinics, with significant variation in scheduled appointments and no-show rates.
  • The random forest method demonstrated superior performance, achieving minimum or second-minimum root mean square error across 8 clinics.
  • Integrating system knowledge enhanced data utility and improved the predictive accuracy of the machine learning models.

Conclusions:

  • The random forest method, augmented with system knowledge, reliably predicts outpatient wait times despite data limitations and clinic variations.
  • The study identified the priority system as a driver of extended wait times.
  • A fast-track system was proposed as a potential solution to mitigate long wait times.