How can we use data to help healthcare systems survive and recover from the pandemic?

The COVID-19 Pandemic has been a stress test that has stretched the boundaries of the U.S. Healthcare system and revealed large gaps in both data collection and healthcare delivery. This shock to the healthcare system sparked conversations about the value of healthcare resilience, from the Well Being Trust’s recently launched Thriving Together initiative, which acts as a springboard for healthcare equity and resilience, to renewed conversations in the private sector about planning, healthcare survival, and recovery.

What is health system resilience and how can we measure it?

COVID-19 is one of a number of external challenges such as antimicrobial resistance, financial burdens, extreme climate events, and larger disease outbreaks that have put pressure on the healthcare system. The term “health system resilience” has been used to understand the capacity in which health systems across the United States and in global settings respond to COVID-19 surges or effectively share information that would help combat the pandemic. For example, the United States Agency for International Development (USAID) aims to promote health resilience in international settings by distributing commodities to areas in need, redeploying key human and financial resources for vulnerable populations, and working across government sectors to engage community leaders and other key stakeholders.

The Office of the Assistant Secretary for Health (OASH) is currently gathering information to help understand how key stakeholders have defined resilience through “their use of data, analytic approaches, and proven indicators.” MIT professor David Simchi-Levi has noted that, like other industries, health system resilience may be reflected in two major metrics:

  • The Time to Survive: This metric seeks to better understand how long an enterprise can survive when there is a shortage of some critical good. How long can a clinic, hospital, or healthcare network survive without access to ICU beds, PPE, or ventilators when intaking patients? 
  • The Time to Recover: This metric seeks to understand how long it will take for a system to properly restore a shortage of some critical good.

Health system resilience indicates the ability of a health system to respond to extreme changes or shocks without the possibility of collapse or lack of function. An NIH paper points out that the overall definition of resilience is “the capacity of an individual, population or system to absorb a shock, while still retaining the fundamental functions or characteristics of the original state.” This definition, however, has been critiqued for not incorporating possible changes in capacity or the ability to adapt to a new state. Other definitions of resilience, especially for healthcare, have claimed that resilience should incorporate adaptive and transformative capabilities that adjust capacity to anticipate future shocks.

How can data be used to measure resilience?

The NIH have adopted key resilience metrics from the World Health Organization’s (WHO) framework that summarize the health system’s six major functions: leadership and governance, information, health workforce, financing, medical products, and service delivery. Practitioners have sometimes incorporated specific dimensions and metrics for these six attributes.

Information may be one of the most important attributes of healthcare resilience. A variety of studies that have sought to understand healthcare resilience have emphasized the need for timely surveillance data in order to enact relevant and effective mitigation measures and policies. Moreover, the WHO framework highlights service delivery as a critical factor that is dependent on the other five functions.

Researchers have been attempting to develop models for some time to understand how healthcare systems are able to respond to major crises. In the current COVID-19 pandemic, researchers and policymakers must consider a wide array of measures, from personnel and hospital staff levels to the volume of equipment a hospital may have to respond to the crisis.

Some researchers have sought to understand and use hospital capacity and demand to model resilience. For example, a model developed by researchers at Colorado State University aimed to predict resilience in the event of an earthquake. Their model incorporated a number of key factors such as the number of staffed beds, hospital staff availability, housing functionality, patient waiting time for treatment, and the probability of patient X going to healthcare facility Y. They also tried to factor in some of the environmental and physical conditions that could impact a healthcare system from electric power to the strength of their telecommunications system.  

In the private sector, Facebook AI has partnered with New York University’s Courant Institute of Mathematical Sciences to create localized forecasting models of the spread of COVID-19. The researchers used testing data published by the State of New Jersey and State of New York, and applied sophisticated analytic models to account for relationships among counties. To build hospital-level COVID-19 forecasts for medical resource allocation, Facebook is also collaborating with NYU Langone Health’s Predictive Analytics Unit and Department of Radiology to develop models that can learn from de-identified clinical data, and then share open-source predictive algorithms so that hospitals can train models on their own data. Facebook’s models are helpful since they make local predictions on a county and hospital level. The detailed AI algorithms have not yet been made public, as the research team continues evaluating other sources of data, such as Mobility Data Network Map from Facebook’s Data for Good team, to see whether they help improve the model’s performance.

While these and other models used to predict health system resilience are promising, they need to be fed with reliable data. Some data is in short supply currently, especially without widespread US testing for the novel coronavirus that causes COVID-19. For example, it is unknown how many people have been infected without symptoms. Other inputs, such as incubation periods and death rates, change by the day as we learn more about the virus. Human factors also make the modeling challenging. Individual behaviors, health care infrastructure and political response can all affect the outcome of an epidemic.

What SDOH factors can measure resilience?

Apart from the basic medical components of healthcare systems, SDOH factors like transportation, access to food, and economic stability all impact healthcare system resilience. Models of healthcare resilience show that SDOH factors are critical to understanding how healthcare systems can survive and recover from pandemics. Researchers have noted that planners should incorporate key socioeconomic data into disease surveillance systems to measure how certain communities will be affected both by COVID-19 and by potential future pandemics and disease outbreaks. Some key factors include the following:

Transportation and National Infrastructure. Transportation ensures that HHS and other federal actors can rapidly distribute PPEs, vaccines, and other equipment to hospitals and healthcare institutions around the country. Factoring in transportation systems also can pinpoint how different communities will respond to a shock like COVID-19, from urban transportation systems that might function as vectors of transmission to rural communities that may have little access to transportation in the event they need to visit a healthcare facility. 

Climate Data and the Built Environment. A recent report from the Natural Resources Defense Council noted that climate data and planning can support healthcare resilience planning in two respects. First, climate scientists have long attempted to model how a variety of institutions and supply chains would respond to a sudden climate event such as a natural disaster. These models may provide guidance to healthcare planners. Secondly, there is a growing connection between climate events and how they impact public health, from heat waves that could impact COVID-19 transmission to how air pollution has functioned as a potential comorbid factor for COVID-19. The CDC currently has a Climate-Ready States and Cities Initiative that provides “public health expertise to help state and city health departments prepare for and respond to the health effects that a changing climate may bring to their communities.”

Access to Food and Food Distribution. The food distribution system of the United States is an important part of the infrastructure needed to ensure the ongoing health of communities. During the pandemic, it is also essential to distribute food to communities that are impacted by virus mitigation measures.