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Community health pathways modeling and scheduling under uncertainty.

Jiangyue Gong¹, Lewis Ntaimo¹

¹Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA.

Health Informatics Journal

|February 5, 2024

Summary

Optimally scheduling community health pathways (CHPs) is challenging. A stochastic programming approach enhances resource allocation under uncertainty, providing equitable and realistic client schedules compared to deterministic methods.

Keywords:

community health community health pathways pathways community HUB scheduling stochastic programming

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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.