A predictive and prescriptive analytical framework for scheduling language medical interpreters

Abdulaziz Ahmed () and Elizabeth Frohn
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Abdulaziz Ahmed: University of Minnesota
Elizabeth Frohn: Cloudbreak Healthath

Health Care Management Science, 2021, vol. 24, issue 3, No 6, 550 pages

Abstract: Abstract Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives.

Keywords: Interpreting service; Multi-objective integer programming; Predictive modeling; Prescriptive modeling; Healthcare service; Scheduling; Operations research (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10729-020-09536-y

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