Hospital Capacity Management: How to Prepare for COVID-19 Patient Surges

Health systems use capacity planning methods to balance the availability of beds, staff, and other resources with patient demand. Planning for annual surges in volume (e.g., flu season) is difficult, given uncertainty about timing and duration, but the unprecedented scale of the COVID-19 pandemic has far exceeded the worst surges. COVID-19 spread and severity have proven variable over time and place, leading to demand spikes and empty beds.

A variety of factors, including population demographics, governmental policies, local culture, and the physical environment, have shaped COVID-19 demand for healthcare resources. As regions around the world have been struggling to obtain sufficient beds, staff, and equipment during the pandemic, advanced analytics-driven capacity planning has emerged as a healthcare must-have.

容量预测为了解何时会出现患者激增以及需求是否/何时会耗尽资源提供了关键的见解。This foresight allows planners to design and implement mitigation strategies that can prevent disastrous pandemic resource strain, like those inItalyandNew York Cityin the spring of 2020 andFlorida在2020年夏天，德克萨斯州和亚利桑那州。与此同时，过高估计需求可能会对这些提供护理的组织的财务可行性造成危险。虽然有国家和区域爆发预测，但医院领导需要结合当地动态、他们的专业知识和风险承受能力进行预测。

The Value of a Reliable Capacity Planning Model

In choosing a capacity planning model, organizations can’t afford to waste valuable discussion time debating model methodology, which is likely with black box or proprietary models. Using a transparent capacity planning tool from an established analytics partner, such as Health Catalyst, promises more efficient, accurate outcomes. Health Catalyst initially built its COVID-19 Capacity Planning Tool on thewell-regarded, open-sourceCOVID-19 Hospital Impact Model for Epidemics(CHIME) application fromPenn Medicine.

Known COVID-19 Case Data Boosts Accuracy

At the beginning of the pandemic, health systems forecasted the need for patient beds, staff, and supplies using the standardSIR model for infectious disease. Users could adjust the model at a single point in time to account for the impact of mitigation policies, such as social distancing. But the required assumptions for accurate adjustment were often unknowable or more dynamic. Even the most sophisticated models predicted patient volumes and deaths that disease spreadrapidly proved wrong.

As moredatabecame available from sources such as theNew York Timesand theU.S. Census, Health Catalyst added the ability to leverage empirical forecasting with well-documented methods from the fields ofmicrobiologyandepidemiologyand implemented the open-source statistical computing packageR. As a result, Health Catalyst makes increasing data and model sophistication available through a simple, transparent interface healthcare planners can easily leverage. These design decisions have led to increased credibility in the broader healthcare community and kept conversations focused on decisions and action.

COVID-19 New Case Forecasting Over Time

COVID-19 spread rates have progressed differently over place and time. In the five sample regions in the animation below, black points indicate data used in generating the forecast, grey points indicate actual counts held out from the forecast, and the yellow rectangle shows the next 30 days. The colored lines show the forecast using the measures of spread (reproduction number versus doubling time) and forecast techniques (e.g., linear versus spline) that worked best for each region.

Access to Multiple Scenarios Guides Hospital Capacity Management

迅速变化的条件限制了社会互动、旅行和商业运作，这可能会影响疫情预测的准确性。产能规划工具旨在为领导层争取行动的时间。鉴于民众对病毒的反应存在高度不确定性，各组织应调整模型输入，以反映不同的大流行情景。例如，迅速采取旅行限制和社交距离政策最初减缓了病毒的传播，并避免了一些地区的患者激增。然而，随着限制的不同执行或取消，传播不断变化。

An accurate capacity planning tool supports timely action with its ability to manage, use, and save scenarios. The ability to easily compare a variety of situations gives a better picture of uncertainty around resource availability and provides more support to mitigation efforts.

Preparing for Fundamental Changes in Hospital Utilization Patterns

容量规划通常在区域一级根据医院的市场份额和利用模式进行。考虑到地区之间人口密度的差异和不断变化的社会距离限制，适当的缓解策略将因医院而异。

Some organizations have been surprised to find they cannot reliably estimate market share and thus alter this parameter during scenario modeling. Cultural factors can shape market share, such as when a patient feels more comfortable presenting at an academic medical center over a community hospital (even if they lack objective evidence to support this preference).

此外，医院利用模式也发生了变化。例如暂停非紧急服务，将分娩和分娩服务转移到门诊手术中心。一些医院正在与当地和更远的技术熟练的护理机构合作，作为COVID-19患者脱离重症监护后康复的地方。

Celebrating Executive Adoption of Data-Driven Decision Making and the Ongoing Need

COVID-19危机带来的一个意想不到的希望是，医疗保健高管文化愿意接受数据科学来支持其决策。许多数据科学应用程序存在于护理或服务点，以支持个人提供者和患者，但让卫生系统领导参与数据科学一直是更具挑战性的。部署用于能力规划的高级分析是一个难得的机会，可以直接影响高管和战略规划人员以同样的数据驱动方式工作。

While governmental policies initially slowed the spread of COVID-19 to prevent dramatic patient surges in many areas, as regions have reopened during the summer months, infection rates have again risen. Hospital leadership will need to monitor forecasts for some time, particularly as fall and winter approach. An effective capacity planning tool must leverage robust data and advanced analytics in the face of shifting circumstances. Such a solution will provide valuable insights into several possible futures and gift healthcare leaders and managers with the time to anticipate the necessary resources to keep communities healthy.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. How Data Transforms the Hospital Command Center to Pandemic Proportions
2. What Health Systems Need to Know About COVID-19 Relief Funding
3. Cross-State Nurse Licensing: One Way to Improve Care During COVID-19
4. How to Scale Telehealth Solutions to Increase Patient Access During COVID-19
5. Activity-Based Costing in Healthcare During COVID-19: Meeting Four Critical Needs

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