12 Residential Care

Nadia Lahrichi1, Louis‐Martin Rousseau1, and Willem‐Jan van Hoeve2

1Polytechnique Montréal

2Carnegie Mellon University

In this chapter, we provide a perspective on the use of analytics in the context of home care delivery. In particular, we concentrate on operational questions arising from nurse‐to‐patient assignments and employee scheduling and routing considerations. These questions are highly relevant at the operational level, but tactical and strategic decision‐makers can also benefit from quantitative models to provide insight into the trade‐offs that exist in healthcare organizations.

One of the most powerful analytical tools for formally representing (modeling) and solving the operational situations listed above is mathematical optimization. As the cornerstone of operations research, optimization‐based decision support tools have been widely applied in various industries, including healthcare. One of our goals in this chapter is to provide an overview of the state of the art in optimization technology, and describe what models would be most suitable (and scalable) to home care decision‐making.

The chapter will conclude by outlining new perspectives for analytics in home care delivery, made possible by the emergence of mobile technology, based on massive and real‐time data collection. The availability of such data, combined with efficient use of machine learning models and algorithms, opens the door to data‐driven decision support systems that ...

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