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Designing hierarchical networks to improve patient access to care: a case study in veteran eye care

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  • Adam VanDeusen
  • Amy Cohn
  • April Maa

Abstract

Across the United States, many patients, including veteran patients, face barriers in accessing appropriate, timely, and affordable healthcare. When developing or modifying healthcare systems to improve patient access, we can consider strategies (e.g., telehealth) in which lower-cost, more abundant resources are used for services often performed with more constrained and/or more expensive, specialized resources. We propose a framework in which hierarchical care networks allow patients to receive frontline care from lower cost, more accessible alternatives, while specialized resources are reserved for more complex care. We use operations research tools, including mixed-integer programming and facility location models, to design and analyze these networks. We present a case study applying these methods to improve veterans’ access to eye care screenings. The case study results demonstrate that using different providers in locations throughout the system can increase the number of patients screened by nearly four times the number currently screened while increasing the budget by only 2.5%. When designing healthcare networks to improve access, decision makers must consider several trade-offs, including how resources are distributed. Operations research tools are effective methods for understanding, designing, and evaluating these trade-offs to best understand the wide-ranging impacts of resource (re)distribution.

Suggested Citation

  • Adam VanDeusen & Amy Cohn & April Maa, 2022. "Designing hierarchical networks to improve patient access to care: a case study in veteran eye care," The Journal of Defense Modeling and Simulation, , vol. 19(4), pages 679-689, October.
  • Handle: RePEc:sae:joudef:v:19:y:2022:i:4:p:679-689
    DOI: 10.1177/15485129211021167
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    References listed on IDEAS

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    1. George C. Moore & Charles ReVelle, 1982. "The Hierarchical Service Location Problem," Management Science, INFORMS, vol. 28(7), pages 775-780, July.
    2. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    3. repec:mpr:mprres:7725 is not listed on IDEAS
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