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Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming.

Michelle Alvarado1, Lewis Ntaimo2

  • 1Texas A&M University College Station College Station, Texas, USA. michelle.alvarado@tamu.edu.

Health Care Management Science
|September 18, 2016
PubMed
Summary
This summary is machine-generated.

Optimizing chemotherapy scheduling is crucial for oncology clinics facing resource limitations. A new risk-averse model (SIP-CHEMO) significantly reduces patient wait times and nurse overtime, improving clinic operations.

Keywords:
Chemotherapy schedulingHealth careMean-risk stochastic programmingOncology clinicsPatient service

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Oncology

Background:

  • Oncology clinics face significant challenges in scheduling chemotherapy patients due to limited resources (nurses, chairs) and the critical timing of treatments.
  • Patient appointment durations, acuity levels, and nurse availability present stochastic parameters that complicate efficient clinic operations.
  • Increasing demand and rising treatment costs necessitate improved scheduling and operational efficiencies in outpatient oncology.

Purpose of the Study:

  • To develop and evaluate stochastic integer programming (SIP) models for optimizing individual chemotherapy patient appointment scheduling.
  • To address the complexities of uncertain appointment durations, varying patient acuity, and fluctuating nurse availability.
  • To enhance the efficiency of oncology clinic operations and resource allocation through advanced scheduling methodologies.

Main Methods:

  • Development of three mean-risk stochastic integer programming (SIP) models, termed SIP-CHEMO, for chemotherapy patient scheduling.
  • Design of an algorithm to enhance the computational speed of the proposed SIP models.
  • Validation of the models using a simulation model to compare performance against deterministic scheduling algorithms.

Main Results:

  • The risk-averse SIP-CHEMO model, utilizing the expected excess mean-risk measure, demonstrated a 42% decrease in patient waiting times.
  • The same model achieved a 27% reduction in nurse overtime compared to traditional deterministic scheduling methods.
  • Computational results confirmed the effectiveness of the SIP-CHEMO models in improving oncology clinic operational efficiency.

Conclusions:

  • Stochastic integer programming models, specifically the risk-averse SIP-CHEMO approach, offer a superior method for scheduling chemotherapy appointments.
  • Implementing these advanced scheduling models can lead to substantial improvements in patient flow and resource utilization within oncology clinics.
  • The findings highlight the potential for significant operational gains and cost reductions by adopting risk-aware scheduling in demanding healthcare environments.