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

Articles in Press

Document Type : Research Paper

Authors

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.

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References

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Journal of Industrial and Systems Engineering

Articles in Press, Accepted Manuscript
Available Online from 16 February 2024

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  • Receive Date: 16 February 2024
  • Accept Date: 16 February 2024

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