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A predictive and prescriptive analytical framework for scheduling language medical interpreters.

Abdulaziz Ahmed1, Elizabeth Frohn2

  • 1Business Department, University of Minnesota, Crookston, MN, USA. aaahmed@umn.edu.

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|February 25, 2021
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Summary
This summary is machine-generated.

This study introduces a new framework to optimize hospital interpreter scheduling for limited English proficiency (LEP) patients. The model aims to reduce costs and improve service quality by predicting demand and efficiently assigning interpreters.

Keywords:
Healthcare serviceInterpreting serviceMulti-objective integer programmingOperations researchPredictive modelingPrescriptive modelingScheduling

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Area of Science:

  • Healthcare Management
  • Operations Research
  • Medical Informatics

Background:

  • Hospitals primarily use English, yet a significant U.S. population has limited English proficiency (LEP).
  • Hospital interpreting departments face challenges in selecting quality part-time interpreters and predicting emergency patient arrivals.
  • Inefficient scheduling leads to increased costs and potential service delays for LEP patients.

Purpose of the Study:

  • To propose a novel framework for optimizing the scheduling of full-time and part-time medical interpreters.
  • To address the challenges of interpreter selection, cost minimization, and demand forecasting for LEP patients.
  • To improve the overall quality and efficiency of interpreting services in medical centers.

Main Methods:

  • Developed a prediction model to forecast the demand for interpreters in emergency departments.
  • Constructed a multi-objective integer programming (MOIP) model for assigning interpreters to inpatient, outpatient, and emergency LEP patients.
  • Evaluated the framework through experiments, comparing generated schedules with those from a partner hospital's interpreting department.

Main Results:

  • The proposed prediction model effectively forecasts demand for limited English proficiency (LEP) patients in emergency settings.
  • The multi-objective integer programming (MOIP) model successfully minimized total interpreting costs.
  • The framework maximized interpreting service quality and the utilization of full-time interpreters, outperforming current hospital scheduling practices.

Conclusions:

  • The developed framework offers a robust and practical solution for medical interpreter scheduling.
  • The model demonstrates superior performance in cost reduction, service quality enhancement, and resource optimization compared to traditional methods.
  • This approach can significantly improve healthcare accessibility and efficiency for limited English proficiency (LEP) patient populations.