Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

A new data-driven decision-making method for therapist patient allocation and scheduling

Document Type : Research Paper

Authors
Naser Abdali 1
Mohammad Alipour Vaezi 2
Masoud Rabani 1
Amir Aghsami 1
1 School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall, 1145 Perry Street, Blacksburg, VA 24060, United States of America
Abstract
One of the constant problems that people with mental health conditions are faced with now is that they cannot establish a good relationship with their therapist, or the client's disease type is not in the therapist's specialty. These clients may not receive adequate treatment and stop the therapy before feeling well. Therefore, the classification of mental patients based on their disorder types and allocating a therapist with the same expertise to them could lead to better treatment and improve the quality of the therapy sessions. This paper will compare several machine learning (ML) algorithms to classify patients with mental conditions. Moreover, benefiting from the best ML algorithm, patients will be categorized into different classes based on their disorder types. Finally, a mathematical model will be developed to determine the allocation policy of therapists to each group of patients to maximize the summation of the utilization between therapists and patients. To explore the implementation of the proposed method, we have conducted a real-life case study to assess the validation of the model.
Keywords
Mental health
data-driven decision-making
scheduling
mathematical modeling
machine learning
patient allocation
Subjects

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  • Receive Date 16 February 2024
  • Revise Date 28 March 2024
  • Accept Date 29 March 2024