- The current and future occupancy of the hospitals in the state you selected.
- If more beds are needed and how many.
- The best strategy for transferring patients between hospitals.
COVID-19 Hospital Capacity Management using Mathematical Models
This website shows how to transfer patients to optimally use the available capacity of hospitals in the US. You can find out how many additional beds are needed per hospital or region.
State-level Hospital Occupancy and Optimal Transfers
Dashboard Page
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You can choose 1) state, 2) patient type, and 3) displayed hospitals to see:
You can choose 1) state, 2) patient type, and 3) displayed hospitals to see:
Hospital Occupancy Near You
Nearby Hospitals Page
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Enter an place to see how hospitals in that area are ranked based on their potential to accept new patients. The more stressed or further away the hospital, the lower its rank will be.
Customizable Hospital Occupancy and Optimal Transfers
Customize Results Page
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This page is almost fully customizable. You can choose any US hospital system, Hospital Service Area (HSA), Hospital Referral Region (HRR), or state. For instance, select Johns Hopkins Health System, NYC Health and Hospitals, or Houston, TX HRR from the dropdown menus and run the models in real time to see the results.
Other customizable parameters include:
- The maximum number of allowed daily patient transfers per hospital
- The time period (start and end date)
- Strategic decision preferences, e.g., whether to evenly balance load or just minimize the required additional capacity
With the ongoing COVID-19 pandemic, many hospitals across the US are at capacity or are quickly approaching it. Hospitals are coping with the COVID surge through a variety of methods, including opening up new beds, but these are often costly and can hurt the level of care that patients receive. This burden on hospitals can be reduced if we optimally transfer patients from over-capacity hospitals to nearby hospitals that have available space.
To find the optimal number of patients to transfer and where to transfer them to, we use mathematical optimization models developed by the Johns Hopkins Center for Systems Science and Engineering (JHU CSSE), hospitalization data from the US Department of Health and Human Services (HHS), and COVID forecasts from the US Centers for Disease Control (CDC). To learn more, visit the About tab.
Optimal Hospital Capacity Management for COVID-19
Introduction. The COVID-19 pandemic has put an unprecedented strain on healthcare capacity around the globe. To accommodate the surge in patients, healthcare facilities have responded by both reducing demand, through canceling elective procedures, and by increasing capacity, through opening up new COVID-19-suitable beds and calling in additional personnel. While these measures have proven effective, increased hospital load and delays in elective care may result in adverse effects for patients. Additionally, increasing COVID-19 capacity is costly, slow, and may not be feasible for all healthcare centers. We aim to develop data-driven strategies to handle the COVID-19 patient surge more effectively by using coordination between hospitals.
Capacity Management. We use mathematical optimization to determine optimal strategies to reduce the burden of COVID-19 on hospitals. In particular, we focus on patient transfers. In practice, hospitals, even those in the same region or system, experience very different COVID patient loads, meaning that some may have unused capacity even while COVID overwhelms other hospitals. Patient transfers can balance these uneven loads and ensure that all capacity to care for patients is efficiently used. Patient transfers were used by some hospital systems that were hit particularly hard during the first wave of the pandemic, but these transfers were generally made reactively as hospitals ran out of capacity rather than planned. Optimal coordinated transfers have potential to significantly help hospitals cope with the burden of COVID and ensure that all patients receive the best possible level of care. We aim to demonstrate the benefits of this approach on this website using real-world data.
Dashboard. This website is a platform to explore hospital capacity management during COVID-19 and investigate the impacts that optimal patient transfers can have. The Dashboard page describes our hospital capacity management insights with interactive figures, including the map below, based on precomputed results. The Customize Results page allows for more control over the model and hospitals selected, and runs our model in real-time. The Nearby Hospitals page shows hospitals near you and scores them according to their distance and current occupancy.
Data. This website uses data on hospital occupancy and COVID-19 hospitalizations published by the US Department of Health and Human Services (HHS) for past COVID-19 hospital occupancy. To project this data into the future we use the US Center for Disease Control’s (CDC’s) county-level forecasts of COVID-19 cases, which is an ensemble of models from many forecasting teams, and deaggregate these forecasts to the hospital-level.
Patient Transfer Optimization. Our goal is to transfer patients from areas that are (or soon will be) overloaded to areas that are (and expected to remain) under capacity. These transfers must be done with care so that locations receiving patients do not later run out of capacity by accepting too many patients. The operational constraints and overheads of transferring patients between two facilities also need to be considered for each pair of hospitals. Our models use linear and mixed-integer programming to recommend the optimal number of patients to be transferred between each pair of hospitals (each day) to best utilize the available capacity. The results also provide insight into the minimum required capacity at each hospital and enable an early warning if extra beds are needed.
About us. We are a team from the Johns Hopkins Center for Systems Science and Engineering and Malone Center for Engineering in Healthcare. For more information, or to contact us, please visit the About Us tab.
Additional information. For more information about our approach, see our paper for details on the technical aspects of our methodology or our GitHub repository which implements our models and data processing.
About Us
We provide models and an interactive tool to find the optimal patient transfers for hospitals that are experiencing a large number of COVID-19 patients. To read more about the problem, please visit the home page.
Team: Felix Parker, Fardin Ganjkhanloo, Farzin Ahmadi, and Professor Kimia Ghobadi
Our team is affiliated with:
The Center for Systems Science and Engineering
The Malone Center for Engineering in Healthcare
The Center for Data Science in Emergency Medicine
Department of Civil and Systems Engineering
Johns Hopkins University
Links
Our paper (preprint)
GitHub repository for this website
GitHub repository for the paper
Contact Information: For further information, please contact us at fparker9 at jhu.edu or kghobad1 at jhu.edu.