Community health pathways modeling and scheduling under uncertainty

Area of Science:

• Operations Research
• Health Systems Management
• Public Health

Background:

• Coordinating constrained resources in community healthcare settings, particularly at centralized Pathways Community HUBs, presents significant challenges due to resource limitations and dynamic operational factors.
• Effective scheduling is crucial for timely patient access to care but is often hindered by uncertainties in resource availability and fluctuating client demand.

Purpose of the Study:

• To introduce and evaluate a stochastic programming (SP) approach for optimizing the scheduling of community health pathways (CHPs) within a centralized community health setting.
• To address the uncertainty in resource availability and its impact on client access times and schedule equity.

Main Methods:

• Developed a stochastic programming (SP) model to optimally schedule community health pathways (CHPs) considering uncertain resource availability.
• Applied the SP methodology to real-world data from a U.S. county's Pathways Community HUB, incorporating various CHPs, healthcare workers, and other resources.

Main Results:

• Client access times are significantly influenced by the uncertain future availability of HUB resources and the level of client demand.
• High client demand correlates with longer client access times, highlighting the impact of demand fluctuations on service delivery.
• Stochastic programming models yield more realistic schedules than deterministic approaches, which can be overly optimistic when resource availability is assumed to be known.

Conclusions:

• The stochastic programming model provides equitable client schedules across similar community health worker roles.
• This approach offers valuable managerial insights for improving resource allocation and scheduling efficiency in community health settings.
• Addressing resource uncertainty is critical for accurate access time predictions and equitable service distribution.