ORAHS2024
Abstract Submission

182. Benchmarking data-driven policies to cope with absenteeism in nurse rostering using simulated predictions

Contributed abstract in session FC-4: Policies and Networks, stream Regular talks.

Friday, 14:00-15:30
Room: Room S3

Authors (first author is the speaker)

1. Martina Doneda
DEIB, Politecnico di Milano
2. Pieter Smet
Computer Science, KU Leuven
3. Giuliana Carello
Elettronica, Informazione e Bioingegneria, Politecnico di Milano
4. Ettore Lanzarone
DIGIP, University of Bergamo
5. Greet Vanden Berghe
Computer Science, KU Leuven

Abstract

Healthcare is experiencing a surge of interest in the use of Machine Learning (ML) for decision-making. However, employing ML as a silver bullet is a rather naive suggestion. In this work, we make a case for this argument using an example from nurse rostering and rerostering considering absenteeism, referring to instances derived from the Second International Nurse Rostering Competition.

On any given day, each nurse can be absent with a certain probability. When such a disruption occurs, the roster needs to be repaired. We assume that we can incorporate some robustness in the roster using reserve shifts. If the reserve shifts can not repair the disruptions on their own, we assume that ad interim nurses need to be called in, at an high expense.
Suppose having a ML model capable of predicting how many nurses will be absent on a given day. How to determine whether using it improves system performance more than using a non-data-driven policy?

When using ML to help decision-making, usually i) the ML model is trained and tested, and ii) deployed in several simulated scenarios of the problem to evaluate its effectiveness. We purposely subverted this sequence, simulating the use of a nondescript ML model (an “oracle”) at various levels of predictive performance, deploying it directly in several simulated scenarios. This inversion allows us to evaluate whether it is worth it to invest resources to implement a ML approach, and, be it the case, to determine what its mini

Keywords

Status: accepted

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