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Healthcare capacity shortage contributes to poor access in many countries. Moreover, rapid urbanization often occurring in these countries has exacerbated the imbalance between healthcare capacity and need. One way to address the above challenge is expanding the total capacity and redistributing the capacity spatially. In this research, we studied the problem of locating new hospitals in a two-tier outpatient care system comprising multiple central and district hospitals, and upgrading existing district hospitals to central hospitals. We formulated the problem with a discrete location optimization model. To parameterize the optimization model, we used a multinomial logit model to characterize individual patients’ diverse hospital choice and to quantify the patient arrival rates at each hospital accordingly. To solve the hard nonlinear combinatorial optimization problem, we developed a queueing network model to approximate the impact of hospital locations on patient flows. We then proposed a multi-fidelity optimization approach, which involves both the aforementioned queuing network model as a surrogate and a self-developed stochastic simulation as the high-fidelity model. With a real-world case study of Shanghai, we demonstrated the changes in the care network and examined the impacts on the network design by population center emergence, governmental budget change and considering patients with different age groups or income levels. Note to Practitioners —Our work focuses on improving system-wide care access in a two-tier care network. We believe that our work can lead to effective development of a location analytics tool for city-wide healthcare system planners. We also think the importance of this study is further strengthened by the case studies based on real-world hospital choice experimental data from Shanghai, China, a region suffering from the imbalance between healthcare capacity and need. Our case studies are expected to make recommendations on care facility expansion and dispersion to better align with the spatial distribution of residential communities and patient hospital choice behavior.
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Stable Teamwork Marriages in Healthcare: Applying Machine Learning to Surgeon-Nurse-Patient Matching
Hospitals are plagued with a multitude of logistical challenges amplified by a time-sensitive and high intensity environment. These conditions have resulted in burnout among both doctors and nurses as they work tirelessly to provide critical care to patients in need. We propose a new machine-learning-powered matching mechanism that manages the surgeon-nurse-patient assignment process in an efficient way that saves time and energy for hospitals, enabling them to focus almost entirely on delivering effective care. Through this design, we show how incorporating artificial intelligence into management systems enables teams of all sizes to meaningfully coordinate in highly chaotic and complex environments.
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- Award ID(s):
- 1654054
- PAR ID:
- 10345525
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 62
- Issue:
- 1
- ISSN:
- 2169-5067
- Page Range / eLocation ID:
- 1202 to 1206
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1541931218621276
