Closer to Home: A Structural Estimate-Then-Optimize Approach to Improve Access to Healthcare Services

• 成果类型: Article; Early Access
• 署名作者: Bravo, Fernanda; Gandhi, Ashvin; Hu, Jingyuan; Long, Elisa F.
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Irvine
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2024.06274
• 发表日期: 2025
• 关键词: Structural estimation; BLP; choice model; Facility Location; healthcare access

摘要

Geographic inequalities in healthcare access extend beyond rural-urban divides include socioeconomic, racial, and other disparities. Proximity to hospitals, clinics, healthcare providers, and pharmacies varies widely, posing a challenge in deciding where strategically locate such facilities. Demand for each service depends on local population health, individual preferences, provider capacity, and other factors. This study introduces a novel structural estimate-then-optimize (SETO) framework, combining structural demand estimation using a modified Berry-Levinsohn-Pakes approach that accounts for provider capacity with a choice-based optimal facility location model to maximize health service utilization. Our methodology is illustrated with a case study on the Federal Retail Pharmacy Program in California, a public-private partnership that administered millions of COVID-19 vaccinations. Demand estimates indicate that residents of socioeconomically vulnerable communities are more sensitive to travel distances to pharmacy-based vaccination sites. Strategically adding 500 retail stores serving lower-income communities increases predicted vaccinations by 2.9% overall (770,000 additional vaccinations statewide) and by 5.3% in the least healthy neighborhoods. Our integrative SETO approach outperforms heuristics that allocate resources based on current vaccination rates, existing service gaps, population density, or predicted demand. The case study demonstrates the importance of (1) accounting for heterogeneity in estimating demand and (2) selecting partnerships to complement existing networks with spatially heterogeneous supply and efficiently fill service gaps. Our study provides a systematic approach to optimize healthcare delivery networks, using publicly available aggregate data while accounting for individuals' preferences, highlighting the value of combining a structural demand model with prescriptive analytics.