  Nursing homes are critical facilities for caring frail older adults with
round-the-clock formal care and personal assistance. To ensure quality care for
nursing home residents, adequate staffing level is of great importance. Current
nursing home staffing practice is mainly based on experience and regulation.
The objective of this paper is to investigate the viability of experience-based
and regulation-based strategies, as well as alternative staffing strategies to
minimize labor costs subject to heterogeneous service demand of nursing home
residents under various scenarios of census. We propose a data-driven analysis
framework to model heterogeneous service demand of nursing home residents and
further identify appropriate staffing strategies by combing survival model and
computer simulation techniques as well as domain knowledge. Specifically, in
the analysis, we develop an agent-based simulation tool consisting of four main
modules, namely individual length of stay predictor, individual daily staff
time generator, facility level staffing strategy evaluator, and graphical user
interface. We use real nursing home data to validate the proposed model, and
demonstrate that the identified staffing strategy significantly reduces the
total labor cost of certified nursing assistants compared to the benchmark
strategies. Additionally, the proposed length of stay predictive model that
considers multiple discharge dispositions exhibits superior accuracy and offers
better staffing decisions than those without the consideration. Further, we
construct different census scenarios of nursing home residents to demonstrate
the capability of the proposed framework in helping adjust staffing decisions
of nursing home administrators in various realistic settings.
