As the third anniversary of the WHO’s global pandemic declaration rapidly approaches, access to medical care and health resources is a top concern around the globe, with health care inequities continuing to obstruct trade and travel. New research led by two NYU Shanghai professors -- Assistant Professor of Urban Science and Policy Guan Chenghe and Assistant Professor of Practice in Urban Studies Li Ying – aims to help China’s urban planners make essential care more accessible with an innovative hospital access model utilizing big data from real Shanghai residents.
The study, published this month in the American Association of Geographers (AAG)’s flagship journal The Professional Geographer, combines techniques from opposing approaches in geographic modeling with sophisticated simulations to find optimal sites for new healthcare facilities and to improve access to existing ones.
Guan, Li, and their coauthors – including NYU Shanghai undergraduate student Shi Yiling ’22 and professors from the University of Oxford, East China Normal University, and Southeast University in Nanjing – explain that most existing studies overlook key dimensions in real accessibility by focusing solely on either macro or micro analysis of public hospital locations. Micro-scale approaches examining information such as patient health condition, health care worker allocation, and diagnostic tool availability cannot offer effective models for planning new facility sites. In contrast, the typical macro-scale approach utilizing enhanced gravity models predicts better facility locations with low geographic precision – too low for the high density seen in most Chinese urban areas – while also failing to consider micro-scale factors.
So Guan and Li’s team integrated macro and micro approaches to take an in-depth look at how allocation of public hospitals across each district of Shanghai affects real residents’ access to health care resources. They adopted a dot density tool to improve on enhanced gravity models, then combined the resulting spatial model with analysis of factors such as the quality of health services available and the ratio of hospital beds to total population in the service area. The team then used real-time traffic data drawn from Baidu Map API to run simulations predicting real transit times between area homes and hospitals. The resulting model more closely simulates Shanghai residents’ real experience of trying to access health care, improving significantly upon access time estimates that can be crucial to saving lives.
The research team proposed four key solutions to improve overall access to health care across Shanghai. First, they identified the need to allocate more public hospitals to places such as the central areas in Jinshan and Jiading District, where population density is high while the number of large hospitals is low. Second, they recommended cooperation between district governments and tertiary public hospitals to build sub-branches of first-class hospitals in the suburban districts of Minhang, Jiading and Pudong New District, where residents currently do not have easy access to top-tier public hospitals. Third, the team advised that city planners develop health care access plans tailored to each district, since access to facilities varied widely across districts and subdistricts. Finally, their findings showed that expanding the services and improving the care level at existing community health centers across the city can greatly improve accessibility, removing the “last hurdle” to access of public medical resources.
The research team’s integrated approach draws upon the strengths of the newly established Laboratory of Urban Design and Science (LOUD) at NYU Shanghai, co-directed by Guan and Li, which is dedicated to applying cutting-edge technologies and data tools to support the development of digital smart cities. According to Li, the study gives city administrators the ability to understand and find remedies for the imbalances in public health resources caused or exacerbated by the pandemic. “With our integrated approach, we can provide solutions to many problems such as how to use hospital access frequency to gauge the efficiency of and patient satisfaction with care, how to lower the number of peak hour visits to better balance care demand against supply, and how to improve access to cross-district medical treatment to save public resources,” Li said.
In LOUD’s first annual conference on February 18, 2022, "Focusing on Urban Design and Science, Promoting Smart Digital Transformation," Li and fellow researchers discussed the current study’s contributions to a long-term research project, “Healthy City, Public Sentiment, and Urban Morphology.” Future directions for this project include establishing patient-centered experience evaluation indicators, developing health-related data center technology, and leveraging digitalization and key data indicators to track the full long-term effects of care access and treatment. The application of this project can also provide guidance for policy intervention combating COVID-19 pandemic.
“Our study is just one of the first steps in understanding the spatio-temporal distribution of health care resources in the post-pandemic era and against the backdrop of the digital transformation of urban infrastructure, ” Guan said. “Leveraging the potential of urban data to better allocate all urban public resources, including public hospitals, is the most important goal for urban planners and administrators who want to build healthy communities and create a healthy urban life. These state-of-the-art urban studies tools will also lay the foundation for future work on social equity, rational resource allocation, and efficient and timely policy adjustments across the urban planning field.”